what is data analysis techniques in research

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Data Analysis in Research: Types & Methods

What is data analysis in research?

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Why analyze data in research?

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Types of data in research

Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data : It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Finding patterns in the qualitative data

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

Methods used for data analysis in qualitative research

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

Choosing the right software can be tough. Whether you’re a researcher, business leader, or marketer, check out the top 10  qualitative data analysis software  for analyzing qualitative data.

Data analysis in quantitative research

Preparing data for analysis.

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

Methods used for data analysis in quantitative research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.

Considerations in research data analysis

• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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What Is Data Analysis? Types, Methods, and Tools for Research

• Couchbase Product Marketing December 17, 2023

Data analysis is the process of cleaning, transforming, and interpreting data to uncover insights, patterns, and trends. It plays a crucial role in decision making, problem solving, and driving innovation across various domains. 

This blog post will discuss common data analysis techniques, delve into the distinction between quantitative and qualitative data, explore popular data analysis tools, explain the importance of data analysis across organizations, and discuss exactly how to analyze data.

By the end, you should have a deeper understanding of data analysis and its applications, empowering you to harness the power of data to make informed decisions and gain actionable insights.

Why Is Data Analysis Important?

Data analysis is important across various domains and industries. It helps with:

• Decision Making : Data analysis provides valuable insights that support informed decision making, enabling organizations to make data-driven choices for better outcomes.
• Problem Solving : Data analysis helps identify and solve problems by uncovering root causes, detecting anomalies, and optimizing processes for increased efficiency.
• Performance Evaluation : Data analysis allows organizations to evaluate performance, track progress, and measure success by analyzing key performance indicators (KPIs) and other relevant metrics.
• Gathering Insights : Data analysis uncovers valuable insights that drive innovation, enabling businesses to develop new products, services, and strategies aligned with customer needs and market demand.
• Risk Management : Data analysis helps mitigate risks by identifying risk factors and enabling proactive measures to minimize potential negative impacts.

By leveraging data analysis, organizations can gain a competitive advantage, improve operational efficiency, and make smarter decisions that positively impact the bottom line.

Quantitative vs. Qualitative Data

In data analysis, you’ll commonly encounter two types of data: quantitative and qualitative. Understanding the differences between these two types of data is essential for selecting appropriate analysis methods and drawing meaningful insights. Here’s an overview of quantitative and qualitative data:

Quantitative Data

Quantitative data is numerical and represents quantities or measurements. It’s typically collected through surveys, experiments, and direct measurements. This type of data is characterized by its ability to be counted, measured, and subjected to mathematical calculations. Examples of quantitative data include age, height, sales figures, test scores, and the number of website users.

Quantitative data has the following characteristics:

• Numerical : Quantitative data is expressed in numerical values that can be analyzed and manipulated mathematically.
• Objective : Quantitative data is objective and can be measured and verified independently of individual interpretations.
• Statistical Analysis : Quantitative data lends itself well to statistical analysis. It allows for applying various statistical techniques, such as descriptive statistics, correlation analysis, regression analysis, and hypothesis testing.
• Generalizability : Quantitative data often aims to generalize findings to a larger population. It allows for making predictions, estimating probabilities, and drawing statistical inferences.

Qualitative Data

Qualitative data, on the other hand, is non-numerical and is collected through interviews, observations, and open-ended survey questions. It focuses on capturing rich, descriptive, and subjective information to gain insights into people’s opinions, attitudes, experiences, and behaviors. Examples of qualitative data include interview transcripts, field notes, survey responses, and customer feedback.

Qualitative data has the following characteristics:

• Descriptive : Qualitative data provides detailed descriptions, narratives, or interpretations of phenomena, often capturing context, emotions, and nuances.
• Subjective : Qualitative data is subjective and influenced by the individuals’ perspectives, experiences, and interpretations.
• Interpretive Analysis : Qualitative data requires interpretive techniques, such as thematic analysis, content analysis, and discourse analysis, to uncover themes, patterns, and underlying meanings.
• Contextual Understanding : Qualitative data emphasizes understanding the social, cultural, and contextual factors that shape individuals’ experiences and behaviors.
• Rich Insights : Qualitative data enables researchers to gain in-depth insights into complex phenomena and explore research questions in greater depth.

In summary, quantitative data represents numerical quantities and lends itself well to statistical analysis, while qualitative data provides rich, descriptive insights into subjective experiences and requires interpretive analysis techniques. Understanding the differences between quantitative and qualitative data is crucial for selecting appropriate analysis methods and drawing meaningful conclusions in research and data analysis.

Types of Data Analysis

Different types of data analysis techniques serve different purposes. In this section, we’ll explore four types of data analysis: descriptive, diagnostic, predictive, and prescriptive, and go over how you can use them.

Descriptive Analysis

Descriptive analysis involves summarizing and describing the main characteristics of a dataset. It focuses on gaining a comprehensive understanding of the data through measures such as central tendency (mean, median, mode), dispersion (variance, standard deviation), and graphical representations (histograms, bar charts). For example, in a retail business, descriptive analysis may involve analyzing sales data to identify average monthly sales, popular products, or sales distribution across different regions.

Diagnostic Analysis

Diagnostic analysis aims to understand the causes or factors influencing specific outcomes or events. It involves investigating relationships between variables and identifying patterns or anomalies in the data. Diagnostic analysis often uses regression analysis, correlation analysis, and hypothesis testing to uncover the underlying reasons behind observed phenomena. For example, in healthcare, diagnostic analysis could help determine factors contributing to patient readmissions and identify potential improvements in the care process.

Predictive Analysis

Predictive analysis focuses on making predictions or forecasts about future outcomes based on historical data. It utilizes statistical models, machine learning algorithms, and time series analysis to identify patterns and trends in the data. By applying predictive analysis, businesses can anticipate customer behavior, market trends, or demand for products and services. For example, an e-commerce company might use predictive analysis to forecast customer churn and take proactive measures to retain customers.

Prescriptive Analysis

Prescriptive analysis takes predictive analysis a step further by providing recommendations or optimal solutions based on the predicted outcomes. It combines historical and real-time data with optimization techniques, simulation models, and decision-making algorithms to suggest the best course of action. Prescriptive analysis helps organizations make data-driven decisions and optimize their strategies. For example, a logistics company can use prescriptive analysis to determine the most efficient delivery routes, considering factors like traffic conditions, fuel costs, and customer preferences.

In summary, data analysis plays a vital role in extracting insights and enabling informed decision making. Descriptive analysis helps understand the data, diagnostic analysis uncovers the underlying causes, predictive analysis forecasts future outcomes, and prescriptive analysis provides recommendations for optimal actions. These different data analysis techniques are valuable tools for businesses and organizations across various industries.

Data Analysis Methods

In addition to the data analysis types discussed earlier, you can use various methods to analyze data effectively. These methods provide a structured approach to extract insights, detect patterns, and derive meaningful conclusions from the available data. Here are some commonly used data analysis methods:

Statistical Analysis 

Statistical analysis involves applying statistical techniques to data to uncover patterns, relationships, and trends. It includes methods such as hypothesis testing, regression analysis, analysis of variance (ANOVA), and chi-square tests. Statistical analysis helps organizations understand the significance of relationships between variables and make inferences about the population based on sample data. For example, a market research company could conduct a survey to analyze the relationship between customer satisfaction and product price. They can use regression analysis to determine whether there is a significant correlation between these variables.

Data Mining

Data mining refers to the process of discovering patterns and relationships in large datasets using techniques such as clustering, classification, association analysis, and anomaly detection. It involves exploring data to identify hidden patterns and gain valuable insights. For example, a telecommunications company could analyze customer call records to identify calling patterns and segment customers into groups based on their calling behavior. 

Text Mining

Text mining involves analyzing unstructured data , such as customer reviews, social media posts, or emails, to extract valuable information and insights. It utilizes techniques like natural language processing (NLP), sentiment analysis, and topic modeling to analyze and understand textual data. For example, consider how a hotel chain might analyze customer reviews from various online platforms to identify common themes and sentiment patterns to improve customer satisfaction.

Time Series Analysis

Time series analysis focuses on analyzing data collected over time to identify trends, seasonality, and patterns. It involves techniques such as forecasting, decomposition, and autocorrelation analysis to make predictions and understand the underlying patterns in the data.

For example, an energy company could analyze historical electricity consumption data to forecast future demand and optimize energy generation and distribution.

Data Visualization

Data visualization is the graphical representation of data to communicate patterns, trends, and insights visually. It uses charts, graphs, maps, and other visual elements to present data in a visually appealing and easily understandable format. For example, a sales team might use a line chart to visualize monthly sales trends and identify seasonal patterns in their sales data.

These are just a few examples of the data analysis methods you can use. Your choice should depend on the nature of the data, the research question or problem, and the desired outcome.

How to Analyze Data

Analyzing data involves following a systematic approach to extract insights and derive meaningful conclusions. Here are some steps to guide you through the process of analyzing data effectively:

Define the Objective : Clearly define the purpose and objective of your data analysis. Identify the specific question or problem you want to address through analysis.

Prepare and Explore the Data : Gather the relevant data and ensure its quality. Clean and preprocess the data by handling missing values, duplicates, and formatting issues. Explore the data using descriptive statistics and visualizations to identify patterns, outliers, and relationships.

Apply Analysis Techniques : Choose the appropriate analysis techniques based on your data and research question. Apply statistical methods, machine learning algorithms, and other analytical tools to derive insights and answer your research question.

Interpret the Results : Analyze the output of your analysis and interpret the findings in the context of your objective. Identify significant patterns, trends, and relationships in the data. Consider the implications and practical relevance of the results.

Communicate and Take Action : Communicate your findings effectively to stakeholders or intended audiences. Present the results clearly and concisely, using visualizations and reports. Use the insights from the analysis to inform decision making.

Remember, data analysis is an iterative process, and you may need to revisit and refine your analysis as you progress. These steps provide a general framework to guide you through the data analysis process and help you derive meaningful insights from your data.

Data Analysis Tools

Data analysis tools are software applications and platforms designed to facilitate the process of analyzing and interpreting data . These tools provide a range of functionalities to handle data manipulation, visualization, statistical analysis, and machine learning. Here are some commonly used data analysis tools:

Spreadsheet Software

Tools like Microsoft Excel, Google Sheets, and Apple Numbers are used for basic data analysis tasks. They offer features for data entry, manipulation, basic statistical functions, and simple visualizations.

Business Intelligence (BI) Platforms

BI platforms like Microsoft Power BI, Tableau, and Looker integrate data from multiple sources, providing comprehensive views of business performance through interactive dashboards, reports, and ad hoc queries.

Programming Languages and Libraries

Programming languages like R and Python, along with their associated libraries (e.g., NumPy, SciPy, scikit-learn), offer extensive capabilities for data analysis. These tools provide flexibility, customizability, and access to a wide range of statistical and machine-learning algorithms.

Cloud-Based Analytics Platforms

Cloud-based platforms like Google Cloud Platform (BigQuery, Data Studio), Microsoft Azure (Azure Analytics, Power BI), and Amazon Web Services (AWS Analytics, QuickSight) provide scalable and collaborative environments for data storage, processing, and analysis. They have a wide range of analytical capabilities for handling large datasets.

Data Mining and Machine Learning Tools

Data analysis tools like RapidMiner, KNIME, and Weka automate the process of data preprocessing, feature selection, model training, and evaluation. They’re designed to extract insights and build predictive models from complex datasets.

Text Analytics Tools

Text analytics tools, such as Natural Language Processing (NLP) libraries in Python (NLTK, spaCy) or platforms like RapidMiner Text Mining Extension, enable the analysis of unstructured text data . They help extract information, sentiment, and themes from sources like customer reviews or social media.

Choosing the right data analysis tool depends on analysis complexity, dataset size, required functionalities, and user expertise. You might need to use a combination of tools to leverage their combined strengths and address specific analysis needs. 

By understanding the importance of data analysis, you can leverage its power to make informed decisions, identify opportunities for improvement, and drive innovation within your organization. Whether you’re working with quantitative data for statistical analysis or qualitative data for in-depth insights, it’s important to select the right analysis techniques and tools for your objectives.

To continue learning about data analysis, review the following resources:

• What is Big Data Analytics?
• Operational Analytics
• JSON Analytics + Real-Time Insights
• Database vs. Data Warehouse: Differences, Use Cases, Examples
• Couchbase Capella Columnar Product Blog

• Posted in: Analytics , Application Design , Best Practices and Tutorials
• Tagged in: data analytics , data visualization , time series

Posted by Couchbase Product Marketing

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Data & Finance for Work & Life

Data Analysis: Types, Methods & Techniques (a Complete List)

( Updated Version )

While the term sounds intimidating, “data analysis” is nothing more than making sense of information in a table. It consists of filtering, sorting, grouping, and manipulating data tables with basic algebra and statistics.

In fact, you don’t need experience to understand the basics. You have already worked with data extensively in your life, and “analysis” is nothing more than a fancy word for good sense and basic logic.

Over time, people have intuitively categorized the best logical practices for treating data. These categories are what we call today types , methods , and techniques .

This article provides a comprehensive list of types, methods, and techniques, and explains the difference between them.

For a practical intro to data analysis (including types, methods, & techniques), check out our Intro to Data Analysis eBook for free.

Descriptive, Diagnostic, Predictive, & Prescriptive Analysis

If you Google “types of data analysis,” the first few results will explore descriptive , diagnostic , predictive , and prescriptive analysis. Why? Because these names are easy to understand and are used a lot in “the real world.”

Descriptive analysis is an informational method, diagnostic analysis explains “why” a phenomenon occurs, predictive analysis seeks to forecast the result of an action, and prescriptive analysis identifies solutions to a specific problem.

That said, these are only four branches of a larger analytical tree.

Good data analysts know how to position these four types within other analytical methods and tactics, allowing them to leverage strengths and weaknesses in each to uproot the most valuable insights.

Let’s explore the full analytical tree to understand how to appropriately assess and apply these four traditional types.

Tree diagram of Data Analysis Types, Methods, and Techniques

Here’s a picture to visualize the structure and hierarchy of data analysis types, methods, and techniques.

If it’s too small you can view the picture in a new tab . Open it to follow along!

Note: basic descriptive statistics such as mean , median , and mode , as well as standard deviation , are not shown because most people are already familiar with them. In the diagram, they would fall under the “descriptive” analysis type.

Tree Diagram Explained

The highest-level classification of data analysis is quantitative vs qualitative . Quantitative implies numbers while qualitative implies information other than numbers.

Quantitative data analysis then splits into mathematical analysis and artificial intelligence (AI) analysis . Mathematical types then branch into descriptive , diagnostic , predictive , and prescriptive .

Methods falling under mathematical analysis include clustering , classification , forecasting , and optimization . Qualitative data analysis methods include content analysis , narrative analysis , discourse analysis , framework analysis , and/or grounded theory .

Moreover, mathematical techniques include regression , Nïave Bayes , Simple Exponential Smoothing , cohorts , factors , linear discriminants , and more, whereas techniques falling under the AI type include artificial neural networks , decision trees , evolutionary programming , and fuzzy logic . Techniques under qualitative analysis include text analysis , coding , idea pattern analysis , and word frequency .

It’s a lot to remember! Don’t worry, once you understand the relationship and motive behind all these terms, it’ll be like riding a bike.

We’ll move down the list from top to bottom and I encourage you to open the tree diagram above in a new tab so you can follow along .

But first, let’s just address the elephant in the room: what’s the difference between methods and techniques anyway?

Difference between methods and techniques

Though often used interchangeably, methods ands techniques are not the same. By definition, methods are the process by which techniques are applied, and techniques are the practical application of those methods.

For example, consider driving. Methods include staying in your lane, stopping at a red light, and parking in a spot. Techniques include turning the steering wheel, braking, and pushing the gas pedal.

Data sets: observations and fields

It’s important to understand the basic structure of data tables to comprehend the rest of the article. A data set consists of one far-left column containing observations, then a series of columns containing the fields (aka “traits” or “characteristics”) that describe each observations. For example, imagine we want a data table for fruit. It might look like this:

Now let’s turn to types, methods, and techniques. Each heading below consists of a description, relative importance, the nature of data it explores, and the motivation for using it.

Quantitative Analysis

• It accounts for more than 50% of all data analysis and is by far the most widespread and well-known type of data analysis.
• As you have seen, it holds descriptive, diagnostic, predictive, and prescriptive methods, which in turn hold some of the most important techniques available today, such as clustering and forecasting.
• It can be broken down into mathematical and AI analysis.
• Importance : Very high . Quantitative analysis is a must for anyone interesting in becoming or improving as a data analyst.
• Nature of Data: data treated under quantitative analysis is, quite simply, quantitative. It encompasses all numeric data.
• Motive: to extract insights. (Note: we’re at the top of the pyramid, this gets more insightful as we move down.)

Qualitative Analysis

• It accounts for less than 30% of all data analysis and is common in social sciences .
• It can refer to the simple recognition of qualitative elements, which is not analytic in any way, but most often refers to methods that assign numeric values to non-numeric data for analysis.
• Because of this, some argue that it’s ultimately a quantitative type.
• Importance: Medium. In general, knowing qualitative data analysis is not common or even necessary for corporate roles. However, for researchers working in social sciences, its importance is very high .
• Nature of Data: data treated under qualitative analysis is non-numeric. However, as part of the analysis, analysts turn non-numeric data into numbers, at which point many argue it is no longer qualitative analysis.
• Motive: to extract insights. (This will be more important as we move down the pyramid.)

Mathematical Analysis

• Description: mathematical data analysis is a subtype of qualitative data analysis that designates methods and techniques based on statistics, algebra, and logical reasoning to extract insights. It stands in opposition to artificial intelligence analysis.
• Importance: Very High. The most widespread methods and techniques fall under mathematical analysis. In fact, it’s so common that many people use “quantitative” and “mathematical” analysis interchangeably.
• Nature of Data: numeric. By definition, all data under mathematical analysis are numbers.
• Motive: to extract measurable insights that can be used to act upon.

Artificial Intelligence & Machine Learning Analysis

• Description: artificial intelligence and machine learning analyses designate techniques based on the titular skills. They are not traditionally mathematical, but they are quantitative since they use numbers. Applications of AI & ML analysis techniques are developing, but they’re not yet mainstream enough to show promise across the field.
• Importance: Medium . As of today (September 2020), you don’t need to be fluent in AI & ML data analysis to be a great analyst. BUT, if it’s a field that interests you, learn it. Many believe that in 10 year’s time its importance will be very high .
• Nature of Data: numeric.
• Motive: to create calculations that build on themselves in order and extract insights without direct input from a human.

Descriptive Analysis

• Description: descriptive analysis is a subtype of mathematical data analysis that uses methods and techniques to provide information about the size, dispersion, groupings, and behavior of data sets. This may sounds complicated, but just think about mean, median, and mode: all three are types of descriptive analysis. They provide information about the data set. We’ll look at specific techniques below.
• Importance: Very high. Descriptive analysis is among the most commonly used data analyses in both corporations and research today.
• Nature of Data: the nature of data under descriptive statistics is sets. A set is simply a collection of numbers that behaves in predictable ways. Data reflects real life, and there are patterns everywhere to be found. Descriptive analysis describes those patterns.
• Motive: the motive behind descriptive analysis is to understand how numbers in a set group together, how far apart they are from each other, and how often they occur. As with most statistical analysis, the more data points there are, the easier it is to describe the set.

Diagnostic Analysis

• Description: diagnostic analysis answers the question “why did it happen?” It is an advanced type of mathematical data analysis that manipulates multiple techniques, but does not own any single one. Analysts engage in diagnostic analysis when they try to explain why.
• Importance: Very high. Diagnostics are probably the most important type of data analysis for people who don’t do analysis because they’re valuable to anyone who’s curious. They’re most common in corporations, as managers often only want to know the “why.”
• Nature of Data : data under diagnostic analysis are data sets. These sets in themselves are not enough under diagnostic analysis. Instead, the analyst must know what’s behind the numbers in order to explain “why.” That’s what makes diagnostics so challenging yet so valuable.
• Motive: the motive behind diagnostics is to diagnose — to understand why.

Predictive Analysis

• Description: predictive analysis uses past data to project future data. It’s very often one of the first kinds of analysis new researchers and corporate analysts use because it is intuitive. It is a subtype of the mathematical type of data analysis, and its three notable techniques are regression, moving average, and exponential smoothing.
• Importance: Very high. Predictive analysis is critical for any data analyst working in a corporate environment. Companies always want to know what the future will hold — especially for their revenue.
• Nature of Data: Because past and future imply time, predictive data always includes an element of time. Whether it’s minutes, hours, days, months, or years, we call this time series data . In fact, this data is so important that I’ll mention it twice so you don’t forget: predictive analysis uses time series data .
• Motive: the motive for investigating time series data with predictive analysis is to predict the future in the most analytical way possible.

Prescriptive Analysis

• Description: prescriptive analysis is a subtype of mathematical analysis that answers the question “what will happen if we do X?” It’s largely underestimated in the data analysis world because it requires diagnostic and descriptive analyses to be done before it even starts. More than simple predictive analysis, prescriptive analysis builds entire data models to show how a simple change could impact the ensemble.
• Importance: High. Prescriptive analysis is most common under the finance function in many companies. Financial analysts use it to build a financial model of the financial statements that show how that data will change given alternative inputs.
• Nature of Data: the nature of data in prescriptive analysis is data sets. These data sets contain patterns that respond differently to various inputs. Data that is useful for prescriptive analysis contains correlations between different variables. It’s through these correlations that we establish patterns and prescribe action on this basis. This analysis cannot be performed on data that exists in a vacuum — it must be viewed on the backdrop of the tangibles behind it.
• Motive: the motive for prescriptive analysis is to establish, with an acceptable degree of certainty, what results we can expect given a certain action. As you might expect, this necessitates that the analyst or researcher be aware of the world behind the data, not just the data itself.

Clustering Method

• Description: the clustering method groups data points together based on their relativeness closeness to further explore and treat them based on these groupings. There are two ways to group clusters: intuitively and statistically (or K-means).
• Importance: Very high. Though most corporate roles group clusters intuitively based on management criteria, a solid understanding of how to group them mathematically is an excellent descriptive and diagnostic approach to allow for prescriptive analysis thereafter.
• Nature of Data : the nature of data useful for clustering is sets with 1 or more data fields. While most people are used to looking at only two dimensions (x and y), clustering becomes more accurate the more fields there are.
• Motive: the motive for clustering is to understand how data sets group and to explore them further based on those groups.
• Here’s an example set:

Classification Method

• Description: the classification method aims to separate and group data points based on common characteristics . This can be done intuitively or statistically.
• Importance: High. While simple on the surface, classification can become quite complex. It’s very valuable in corporate and research environments, but can feel like its not worth the work. A good analyst can execute it quickly to deliver results.
• Nature of Data: the nature of data useful for classification is data sets. As we will see, it can be used on qualitative data as well as quantitative. This method requires knowledge of the substance behind the data, not just the numbers themselves.
• Motive: the motive for classification is group data not based on mathematical relationships (which would be clustering), but by predetermined outputs. This is why it’s less useful for diagnostic analysis, and more useful for prescriptive analysis.

Forecasting Method

• Description: the forecasting method uses time past series data to forecast the future.
• Importance: Very high. Forecasting falls under predictive analysis and is arguably the most common and most important method in the corporate world. It is less useful in research, which prefers to understand the known rather than speculate about the future.
• Nature of Data: data useful for forecasting is time series data, which, as we’ve noted, always includes a variable of time.
• Motive: the motive for the forecasting method is the same as that of prescriptive analysis: the confidently estimate future values.

Optimization Method

• Description: the optimization method maximized or minimizes values in a set given a set of criteria. It is arguably most common in prescriptive analysis. In mathematical terms, it is maximizing or minimizing a function given certain constraints.
• Importance: Very high. The idea of optimization applies to more analysis types than any other method. In fact, some argue that it is the fundamental driver behind data analysis. You would use it everywhere in research and in a corporation.
• Nature of Data: the nature of optimizable data is a data set of at least two points.
• Motive: the motive behind optimization is to achieve the best result possible given certain conditions.

Content Analysis Method

• Description: content analysis is a method of qualitative analysis that quantifies textual data to track themes across a document. It’s most common in academic fields and in social sciences, where written content is the subject of inquiry.
• Importance: High. In a corporate setting, content analysis as such is less common. If anything Nïave Bayes (a technique we’ll look at below) is the closest corporations come to text. However, it is of the utmost importance for researchers. If you’re a researcher, check out this article on content analysis .
• Nature of Data: data useful for content analysis is textual data.
• Motive: the motive behind content analysis is to understand themes expressed in a large text

Narrative Analysis Method

• Description: narrative analysis is a method of qualitative analysis that quantifies stories to trace themes in them. It’s differs from content analysis because it focuses on stories rather than research documents, and the techniques used are slightly different from those in content analysis (very nuances and outside the scope of this article).
• Importance: Low. Unless you are highly specialized in working with stories, narrative analysis rare.
• Nature of Data: the nature of the data useful for the narrative analysis method is narrative text.
• Motive: the motive for narrative analysis is to uncover hidden patterns in narrative text.

Discourse Analysis Method

• Description: the discourse analysis method falls under qualitative analysis and uses thematic coding to trace patterns in real-life discourse. That said, real-life discourse is oral, so it must first be transcribed into text.
• Importance: Low. Unless you are focused on understand real-world idea sharing in a research setting, this kind of analysis is less common than the others on this list.
• Nature of Data: the nature of data useful in discourse analysis is first audio files, then transcriptions of those audio files.
• Motive: the motive behind discourse analysis is to trace patterns of real-world discussions. (As a spooky sidenote, have you ever felt like your phone microphone was listening to you and making reading suggestions? If it was, the method was discourse analysis.)

Framework Analysis Method

• Description: the framework analysis method falls under qualitative analysis and uses similar thematic coding techniques to content analysis. However, where content analysis aims to discover themes, framework analysis starts with a framework and only considers elements that fall in its purview.
• Importance: Low. As with the other textual analysis methods, framework analysis is less common in corporate settings. Even in the world of research, only some use it. Strangely, it’s very common for legislative and political research.
• Nature of Data: the nature of data useful for framework analysis is textual.
• Motive: the motive behind framework analysis is to understand what themes and parts of a text match your search criteria.

Grounded Theory Method

• Description: the grounded theory method falls under qualitative analysis and uses thematic coding to build theories around those themes.
• Importance: Low. Like other qualitative analysis techniques, grounded theory is less common in the corporate world. Even among researchers, you would be hard pressed to find many using it. Though powerful, it’s simply too rare to spend time learning.
• Nature of Data: the nature of data useful in the grounded theory method is textual.
• Motive: the motive of grounded theory method is to establish a series of theories based on themes uncovered from a text.

Clustering Technique: K-Means

• Description: k-means is a clustering technique in which data points are grouped in clusters that have the closest means. Though not considered AI or ML, it inherently requires the use of supervised learning to reevaluate clusters as data points are added. Clustering techniques can be used in diagnostic, descriptive, & prescriptive data analyses.
• Importance: Very important. If you only take 3 things from this article, k-means clustering should be part of it. It is useful in any situation where n observations have multiple characteristics and we want to put them in groups.
• Nature of Data: the nature of data is at least one characteristic per observation, but the more the merrier.
• Motive: the motive for clustering techniques such as k-means is to group observations together and either understand or react to them.

Regression Technique

• Description: simple and multivariable regressions use either one independent variable or combination of multiple independent variables to calculate a correlation to a single dependent variable using constants. Regressions are almost synonymous with correlation today.
• Importance: Very high. Along with clustering, if you only take 3 things from this article, regression techniques should be part of it. They’re everywhere in corporate and research fields alike.
• Nature of Data: the nature of data used is regressions is data sets with “n” number of observations and as many variables as are reasonable. It’s important, however, to distinguish between time series data and regression data. You cannot use regressions or time series data without accounting for time. The easier way is to use techniques under the forecasting method.
• Motive: The motive behind regression techniques is to understand correlations between independent variable(s) and a dependent one.

Nïave Bayes Technique

• Description: Nïave Bayes is a classification technique that uses simple probability to classify items based previous classifications. In plain English, the formula would be “the chance that thing with trait x belongs to class c depends on (=) the overall chance of trait x belonging to class c, multiplied by the overall chance of class c, divided by the overall chance of getting trait x.” As a formula, it’s P(c|x) = P(x|c) * P(c) / P(x).
• Importance: High. Nïave Bayes is a very common, simplistic classification techniques because it’s effective with large data sets and it can be applied to any instant in which there is a class. Google, for example, might use it to group webpages into groups for certain search engine queries.
• Nature of Data: the nature of data for Nïave Bayes is at least one class and at least two traits in a data set.
• Motive: the motive behind Nïave Bayes is to classify observations based on previous data. It’s thus considered part of predictive analysis.

Cohorts Technique

• Description: cohorts technique is a type of clustering method used in behavioral sciences to separate users by common traits. As with clustering, it can be done intuitively or mathematically, the latter of which would simply be k-means.
• Importance: Very high. With regard to resembles k-means, the cohort technique is more of a high-level counterpart. In fact, most people are familiar with it as a part of Google Analytics. It’s most common in marketing departments in corporations, rather than in research.
• Nature of Data: the nature of cohort data is data sets in which users are the observation and other fields are used as defining traits for each cohort.
• Motive: the motive for cohort analysis techniques is to group similar users and analyze how you retain them and how the churn.

Factor Technique

• Description: the factor analysis technique is a way of grouping many traits into a single factor to expedite analysis. For example, factors can be used as traits for Nïave Bayes classifications instead of more general fields.
• Importance: High. While not commonly employed in corporations, factor analysis is hugely valuable. Good data analysts use it to simplify their projects and communicate them more clearly.
• Nature of Data: the nature of data useful in factor analysis techniques is data sets with a large number of fields on its observations.
• Motive: the motive for using factor analysis techniques is to reduce the number of fields in order to more quickly analyze and communicate findings.

Linear Discriminants Technique

• Description: linear discriminant analysis techniques are similar to regressions in that they use one or more independent variable to determine a dependent variable; however, the linear discriminant technique falls under a classifier method since it uses traits as independent variables and class as a dependent variable. In this way, it becomes a classifying method AND a predictive method.
• Importance: High. Though the analyst world speaks of and uses linear discriminants less commonly, it’s a highly valuable technique to keep in mind as you progress in data analysis.
• Nature of Data: the nature of data useful for the linear discriminant technique is data sets with many fields.
• Motive: the motive for using linear discriminants is to classify observations that would be otherwise too complex for simple techniques like Nïave Bayes.

Exponential Smoothing Technique

• Description: exponential smoothing is a technique falling under the forecasting method that uses a smoothing factor on prior data in order to predict future values. It can be linear or adjusted for seasonality. The basic principle behind exponential smoothing is to use a percent weight (value between 0 and 1 called alpha) on more recent values in a series and a smaller percent weight on less recent values. The formula is f(x) = current period value * alpha + previous period value * 1-alpha.
• Importance: High. Most analysts still use the moving average technique (covered next) for forecasting, though it is less efficient than exponential moving, because it’s easy to understand. However, good analysts will have exponential smoothing techniques in their pocket to increase the value of their forecasts.
• Nature of Data: the nature of data useful for exponential smoothing is time series data . Time series data has time as part of its fields .
• Motive: the motive for exponential smoothing is to forecast future values with a smoothing variable.

Moving Average Technique

• Description: the moving average technique falls under the forecasting method and uses an average of recent values to predict future ones. For example, to predict rainfall in April, you would take the average of rainfall from January to March. It’s simple, yet highly effective.
• Importance: Very high. While I’m personally not a huge fan of moving averages due to their simplistic nature and lack of consideration for seasonality, they’re the most common forecasting technique and therefore very important.
• Nature of Data: the nature of data useful for moving averages is time series data .
• Motive: the motive for moving averages is to predict future values is a simple, easy-to-communicate way.

Neural Networks Technique

• Description: neural networks are a highly complex artificial intelligence technique that replicate a human’s neural analysis through a series of hyper-rapid computations and comparisons that evolve in real time. This technique is so complex that an analyst must use computer programs to perform it.
• Importance: Medium. While the potential for neural networks is theoretically unlimited, it’s still little understood and therefore uncommon. You do not need to know it by any means in order to be a data analyst.
• Nature of Data: the nature of data useful for neural networks is data sets of astronomical size, meaning with 100s of 1000s of fields and the same number of row at a minimum .
• Motive: the motive for neural networks is to understand wildly complex phenomenon and data to thereafter act on it.

Decision Tree Technique

• Description: the decision tree technique uses artificial intelligence algorithms to rapidly calculate possible decision pathways and their outcomes on a real-time basis. It’s so complex that computer programs are needed to perform it.
• Importance: Medium. As with neural networks, decision trees with AI are too little understood and are therefore uncommon in corporate and research settings alike.
• Nature of Data: the nature of data useful for the decision tree technique is hierarchical data sets that show multiple optional fields for each preceding field.
• Motive: the motive for decision tree techniques is to compute the optimal choices to make in order to achieve a desired result.

Evolutionary Programming Technique

• Description: the evolutionary programming technique uses a series of neural networks, sees how well each one fits a desired outcome, and selects only the best to test and retest. It’s called evolutionary because is resembles the process of natural selection by weeding out weaker options.
• Importance: Medium. As with the other AI techniques, evolutionary programming just isn’t well-understood enough to be usable in many cases. It’s complexity also makes it hard to explain in corporate settings and difficult to defend in research settings.
• Nature of Data: the nature of data in evolutionary programming is data sets of neural networks, or data sets of data sets.
• Motive: the motive for using evolutionary programming is similar to decision trees: understanding the best possible option from complex data.
• Video example :

Fuzzy Logic Technique

• Description: fuzzy logic is a type of computing based on “approximate truths” rather than simple truths such as “true” and “false.” It is essentially two tiers of classification. For example, to say whether “Apples are good,” you need to first classify that “Good is x, y, z.” Only then can you say apples are good. Another way to see it helping a computer see truth like humans do: “definitely true, probably true, maybe true, probably false, definitely false.”
• Importance: Medium. Like the other AI techniques, fuzzy logic is uncommon in both research and corporate settings, which means it’s less important in today’s world.
• Nature of Data: the nature of fuzzy logic data is huge data tables that include other huge data tables with a hierarchy including multiple subfields for each preceding field.
• Motive: the motive of fuzzy logic to replicate human truth valuations in a computer is to model human decisions based on past data. The obvious possible application is marketing.

Text Analysis Technique

• Description: text analysis techniques fall under the qualitative data analysis type and use text to extract insights.
• Importance: Medium. Text analysis techniques, like all the qualitative analysis type, are most valuable for researchers.
• Nature of Data: the nature of data useful in text analysis is words.
• Motive: the motive for text analysis is to trace themes in a text across sets of very long documents, such as books.

Coding Technique

• Description: the coding technique is used in textual analysis to turn ideas into uniform phrases and analyze the number of times and the ways in which those ideas appear. For this reason, some consider it a quantitative technique as well. You can learn more about coding and the other qualitative techniques here .
• Importance: Very high. If you’re a researcher working in social sciences, coding is THE analysis techniques, and for good reason. It’s a great way to add rigor to analysis. That said, it’s less common in corporate settings.
• Nature of Data: the nature of data useful for coding is long text documents.
• Motive: the motive for coding is to make tracing ideas on paper more than an exercise of the mind by quantifying it and understanding is through descriptive methods.

Idea Pattern Technique

• Description: the idea pattern analysis technique fits into coding as the second step of the process. Once themes and ideas are coded, simple descriptive analysis tests may be run. Some people even cluster the ideas!
• Importance: Very high. If you’re a researcher, idea pattern analysis is as important as the coding itself.
• Nature of Data: the nature of data useful for idea pattern analysis is already coded themes.
• Motive: the motive for the idea pattern technique is to trace ideas in otherwise unmanageably-large documents.

Word Frequency Technique

• Description: word frequency is a qualitative technique that stands in opposition to coding and uses an inductive approach to locate specific words in a document in order to understand its relevance. Word frequency is essentially the descriptive analysis of qualitative data because it uses stats like mean, median, and mode to gather insights.
• Importance: High. As with the other qualitative approaches, word frequency is very important in social science research, but less so in corporate settings.
• Nature of Data: the nature of data useful for word frequency is long, informative documents.
• Motive: the motive for word frequency is to locate target words to determine the relevance of a document in question.

Types of data analysis in research

Types of data analysis in research methodology include every item discussed in this article. As a list, they are:

• Quantitative
• Qualitative
• Mathematical
• Machine Learning and AI
• Descriptive
• Prescriptive
• Classification
• Forecasting
• Optimization
• Grounded theory
• Artificial Neural Networks
• Decision Trees
• Evolutionary Programming
• Fuzzy Logic
• Text analysis
• Idea Pattern Analysis
• Word Frequency Analysis
• Nïave Bayes
• Exponential smoothing
• Moving average
• Linear discriminant

Types of data analysis in qualitative research

As a list, the types of data analysis in qualitative research are the following methods:

Types of data analysis in quantitative research

As a list, the types of data analysis in quantitative research are:

Data analysis methods

As a list, data analysis methods are:

• Content (qualitative)
• Narrative (qualitative)
• Discourse (qualitative)
• Framework (qualitative)
• Grounded theory (qualitative)

Quantitative data analysis methods

As a list, quantitative data analysis methods are:

Tabular View of Data Analysis Types, Methods, and Techniques

About the author.

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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Data Analysis

• Introduction to Data Analysis
• Quantitative Analysis Tools
• Qualitative Analysis Tools
• Mixed Methods Analysis
• Geospatial Analysis
• Further Reading

What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" ( Responsible Conduct in Data Management ). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible  data management , and maintaining "honest and accurate analysis" ( Responsible Conduct in Data Management ). 

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" ( OMB Circular 110 ). This broad definition can include information in many formats. 

Some examples of types of data are as follows:

• Photographs 
• Hand-written notes from field observation
• Machine learning training data sets
• Ethnographic interview transcripts
• Sheet music
• Scripts for plays and musicals 
• Observations from laboratory experiments ( CMU Data 101 )

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis. 

Data analysis falls within the larger research data lifecycle, as seen below. 

( University of Virginia )

Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data. 

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods. 

Quantitative research typically involves numbers and "close-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures ( Creswell & Creswell, 2018 , p. 4). Quantitative analysis usually uses deductive reasoning. 

Qualitative  research typically involves words and "open-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" ( 2018 , p. 4). Thus, qualitative analysis usually invokes inductive reasoning. 

Mixed methods  research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" ( Creswell & Creswell, 2018 , p. 4). 

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• Last Updated: Sep 30, 2024 4:47 PM
• URL: https://guides.library.georgetown.edu/data-analysis

The 7 Most Useful Data Analysis Methods and Techniques

Data analytics is the process of analyzing raw data to draw out meaningful insights. These insights are then used to determine the best course of action.

When is the best time to roll out that marketing campaign? Is the current team structure as effective as it could be? Which customer segments are most likely to purchase your new product?

Ultimately, data analytics is a crucial driver of any successful business strategy. But how do data analysts actually turn raw data into something useful? There are a range of methods and techniques that data analysts use depending on the type of data in question and the kinds of insights they want to uncover.

You can get a hands-on introduction to data analytics in this free short course .

In this post, we’ll explore some of the most useful data analysis techniques. By the end, you’ll have a much clearer idea of how you can transform meaningless data into business intelligence. We’ll cover:

• What is data analysis and why is it important?
• What is the difference between qualitative and quantitative data?
• Regression analysis
• Monte Carlo simulation
• Factor analysis
• Cohort analysis
• Cluster analysis
• Time series analysis
• Sentiment analysis
• The data analysis process
• The best tools for data analysis
•  Key takeaways

The first six methods listed are used for quantitative data , while the last technique applies to qualitative data. We briefly explain the difference between quantitative and qualitative data in section two, but if you want to skip straight to a particular analysis technique, just use the clickable menu.

1. What is data analysis and why is it important?

Data analysis is, put simply, the process of discovering useful information by evaluating data. This is done through a process of inspecting, cleaning, transforming, and modeling data using analytical and statistical tools, which we will explore in detail further along in this article.

Why is data analysis important? Analyzing data effectively helps organizations make business decisions. Nowadays, data is collected by businesses constantly: through surveys, online tracking, online marketing analytics, collected subscription and registration data (think newsletters), social media monitoring, among other methods.

These data will appear as different structures, including—but not limited to—the following:

The concept of big data —data that is so large, fast, or complex, that it is difficult or impossible to process using traditional methods—gained momentum in the early 2000s. Then, Doug Laney, an industry analyst, articulated what is now known as the mainstream definition of big data as the three Vs: volume, velocity, and variety. 

• Volume: As mentioned earlier, organizations are collecting data constantly. In the not-too-distant past it would have been a real issue to store, but nowadays storage is cheap and takes up little space.
• Velocity: Received data needs to be handled in a timely manner. With the growth of the Internet of Things, this can mean these data are coming in constantly, and at an unprecedented speed.
• Variety: The data being collected and stored by organizations comes in many forms, ranging from structured data—that is, more traditional, numerical data—to unstructured data—think emails, videos, audio, and so on. We’ll cover structured and unstructured data a little further on.

This is a form of data that provides information about other data, such as an image. In everyday life you’ll find this by, for example, right-clicking on a file in a folder and selecting “Get Info”, which will show you information such as file size and kind, date of creation, and so on.

Real-time data

This is data that is presented as soon as it is acquired. A good example of this is a stock market ticket, which provides information on the most-active stocks in real time.

Machine data

This is data that is produced wholly by machines, without human instruction. An example of this could be call logs automatically generated by your smartphone.

Quantitative and qualitative data

Quantitative data—otherwise known as structured data— may appear as a “traditional” database—that is, with rows and columns. Qualitative data—otherwise known as unstructured data—are the other types of data that don’t fit into rows and columns, which can include text, images, videos and more. We’ll discuss this further in the next section.

2. What is the difference between quantitative and qualitative data?

How you analyze your data depends on the type of data you’re dealing with— quantitative or qualitative . So what’s the difference?

Quantitative data is anything measurable , comprising specific quantities and numbers. Some examples of quantitative data include sales figures, email click-through rates, number of website visitors, and percentage revenue increase. Quantitative data analysis techniques focus on the statistical, mathematical, or numerical analysis of (usually large) datasets. This includes the manipulation of statistical data using computational techniques and algorithms. Quantitative analysis techniques are often used to explain certain phenomena or to make predictions.

Qualitative data cannot be measured objectively , and is therefore open to more subjective interpretation. Some examples of qualitative data include comments left in response to a survey question, things people have said during interviews, tweets and other social media posts, and the text included in product reviews. With qualitative data analysis, the focus is on making sense of unstructured data (such as written text, or transcripts of spoken conversations). Often, qualitative analysis will organize the data into themes—a process which, fortunately, can be automated.

Data analysts work with both quantitative and qualitative data , so it’s important to be familiar with a variety of analysis methods. Let’s take a look at some of the most useful techniques now.

3. Data analysis techniques

Now we’re familiar with some of the different types of data, let’s focus on the topic at hand: different methods for analyzing data. 

a. Regression analysis

Regression analysis is used to estimate the relationship between a set of variables. When conducting any type of regression analysis , you’re looking to see if there’s a correlation between a dependent variable (that’s the variable or outcome you want to measure or predict) and any number of independent variables (factors which may have an impact on the dependent variable). The aim of regression analysis is to estimate how one or more variables might impact the dependent variable, in order to identify trends and patterns. This is especially useful for making predictions and forecasting future trends.

Let’s imagine you work for an ecommerce company and you want to examine the relationship between: (a) how much money is spent on social media marketing, and (b) sales revenue. In this case, sales revenue is your dependent variable—it’s the factor you’re most interested in predicting and boosting. Social media spend is your independent variable; you want to determine whether or not it has an impact on sales and, ultimately, whether it’s worth increasing, decreasing, or keeping the same. Using regression analysis, you’d be able to see if there’s a relationship between the two variables. A positive correlation would imply that the more you spend on social media marketing, the more sales revenue you make. No correlation at all might suggest that social media marketing has no bearing on your sales. Understanding the relationship between these two variables would help you to make informed decisions about the social media budget going forward. However: It’s important to note that, on their own, regressions can only be used to determine whether or not there is a relationship between a set of variables—they don’t tell you anything about cause and effect. So, while a positive correlation between social media spend and sales revenue may suggest that one impacts the other, it’s impossible to draw definitive conclusions based on this analysis alone.

There are many different types of regression analysis, and the model you use depends on the type of data you have for the dependent variable. For example, your dependent variable might be continuous (i.e. something that can be measured on a continuous scale, such as sales revenue in USD), in which case you’d use a different type of regression analysis than if your dependent variable was categorical in nature (i.e. comprising values that can be categorised into a number of distinct groups based on a certain characteristic, such as customer location by continent). You can learn more about different types of dependent variables and how to choose the right regression analysis in this guide .

Regression analysis in action: Investigating the relationship between clothing brand Benetton’s advertising expenditure and sales

b. Monte Carlo simulation

When making decisions or taking certain actions, there are a range of different possible outcomes. If you take the bus, you might get stuck in traffic. If you walk, you might get caught in the rain or bump into your chatty neighbor, potentially delaying your journey. In everyday life, we tend to briefly weigh up the pros and cons before deciding which action to take; however, when the stakes are high, it’s essential to calculate, as thoroughly and accurately as possible, all the potential risks and rewards.

Monte Carlo simulation, otherwise known as the Monte Carlo method, is a computerized technique used to generate models of possible outcomes and their probability distributions. It essentially considers a range of possible outcomes and then calculates how likely it is that each particular outcome will be realized. The Monte Carlo method is used by data analysts to conduct advanced risk analysis, allowing them to better forecast what might happen in the future and make decisions accordingly.

So how does Monte Carlo simulation work, and what can it tell us? To run a Monte Carlo simulation, you’ll start with a mathematical model of your data—such as a spreadsheet. Within your spreadsheet, you’ll have one or several outputs that you’re interested in; profit, for example, or number of sales. You’ll also have a number of inputs; these are variables that may impact your output variable. If you’re looking at profit, relevant inputs might include the number of sales, total marketing spend, and employee salaries. If you knew the exact, definitive values of all your input variables, you’d quite easily be able to calculate what profit you’d be left with at the end. However, when these values are uncertain, a Monte Carlo simulation enables you to calculate all the possible options and their probabilities. What will your profit be if you make 100,000 sales and hire five new employees on a salary of $50,000 each? What is the likelihood of this outcome? What will your profit be if you only make 12,000 sales and hire five new employees? And so on. It does this by replacing all uncertain values with functions which generate random samples from distributions determined by you, and then running a series of calculations and recalculations to produce models of all the possible outcomes and their probability distributions. The Monte Carlo method is one of the most popular techniques for calculating the effect of unpredictable variables on a specific output variable, making it ideal for risk analysis.

Monte Carlo simulation in action: A case study using Monte Carlo simulation for risk analysis

 c. Factor analysis

Factor analysis is a technique used to reduce a large number of variables to a smaller number of factors. It works on the basis that multiple separate, observable variables correlate with each other because they are all associated with an underlying construct. This is useful not only because it condenses large datasets into smaller, more manageable samples, but also because it helps to uncover hidden patterns. This allows you to explore concepts that cannot be easily measured or observed—such as wealth, happiness, fitness, or, for a more business-relevant example, customer loyalty and satisfaction.

Let’s imagine you want to get to know your customers better, so you send out a rather long survey comprising one hundred questions. Some of the questions relate to how they feel about your company and product; for example, “Would you recommend us to a friend?” and “How would you rate the overall customer experience?” Other questions ask things like “What is your yearly household income?” and “How much are you willing to spend on skincare each month?”

Once your survey has been sent out and completed by lots of customers, you end up with a large dataset that essentially tells you one hundred different things about each customer (assuming each customer gives one hundred responses). Instead of looking at each of these responses (or variables) individually, you can use factor analysis to group them into factors that belong together—in other words, to relate them to a single underlying construct. In this example, factor analysis works by finding survey items that are strongly correlated. This is known as covariance . So, if there’s a strong positive correlation between household income and how much they’re willing to spend on skincare each month (i.e. as one increases, so does the other), these items may be grouped together. Together with other variables (survey responses), you may find that they can be reduced to a single factor such as “consumer purchasing power”. Likewise, if a customer experience rating of 10/10 correlates strongly with “yes” responses regarding how likely they are to recommend your product to a friend, these items may be reduced to a single factor such as “customer satisfaction”.

In the end, you have a smaller number of factors rather than hundreds of individual variables. These factors are then taken forward for further analysis, allowing you to learn more about your customers (or any other area you’re interested in exploring).

Factor analysis in action: Using factor analysis to explore customer behavior patterns in Tehran

d. Cohort analysis

Cohort analysis is a data analytics technique that groups users based on a shared characteristic , such as the date they signed up for a service or the product they purchased. Once users are grouped into cohorts, analysts can track their behavior over time to identify trends and patterns.

So what does this mean and why is it useful? Let’s break down the above definition further. A cohort is a group of people who share a common characteristic (or action) during a given time period. Students who enrolled at university in 2020 may be referred to as the 2020 cohort. Customers who purchased something from your online store via the app in the month of December may also be considered a cohort.

With cohort analysis, you’re dividing your customers or users into groups and looking at how these groups behave over time. So, rather than looking at a single, isolated snapshot of all your customers at a given moment in time (with each customer at a different point in their journey), you’re examining your customers’ behavior in the context of the customer lifecycle. As a result, you can start to identify patterns of behavior at various points in the customer journey—say, from their first ever visit to your website, through to email newsletter sign-up, to their first purchase, and so on. As such, cohort analysis is dynamic, allowing you to uncover valuable insights about the customer lifecycle.

This is useful because it allows companies to tailor their service to specific customer segments (or cohorts). Let’s imagine you run a 50% discount campaign in order to attract potential new customers to your website. Once you’ve attracted a group of new customers (a cohort), you’ll want to track whether they actually buy anything and, if they do, whether or not (and how frequently) they make a repeat purchase. With these insights, you’ll start to gain a much better understanding of when this particular cohort might benefit from another discount offer or retargeting ads on social media, for example. Ultimately, cohort analysis allows companies to optimize their service offerings (and marketing) to provide a more targeted, personalized experience. You can learn more about how to run cohort analysis using Google Analytics .

Cohort analysis in action: How Ticketmaster used cohort analysis to boost revenue

e. Cluster analysis

Cluster analysis is an exploratory technique that seeks to identify structures within a dataset. The goal of cluster analysis is to sort different data points into groups (or clusters) that are internally homogeneous and externally heterogeneous. This means that data points within a cluster are similar to each other, and dissimilar to data points in another cluster. Clustering is used to gain insight into how data is distributed in a given dataset, or as a preprocessing step for other algorithms.

There are many real-world applications of cluster analysis. In marketing, cluster analysis is commonly used to group a large customer base into distinct segments, allowing for a more targeted approach to advertising and communication. Insurance firms might use cluster analysis to investigate why certain locations are associated with a high number of insurance claims. Another common application is in geology, where experts will use cluster analysis to evaluate which cities are at greatest risk of earthquakes (and thus try to mitigate the risk with protective measures).

It’s important to note that, while cluster analysis may reveal structures within your data, it won’t explain why those structures exist. With that in mind, cluster analysis is a useful starting point for understanding your data and informing further analysis. Clustering algorithms are also used in machine learning—you can learn more about clustering in machine learning in our guide .

Cluster analysis in action: Using cluster analysis for customer segmentation—a telecoms case study example

f. Time series analysis

Time series analysis is a statistical technique used to identify trends and cycles over time. Time series data is a sequence of data points which measure the same variable at different points in time (for example, weekly sales figures or monthly email sign-ups). By looking at time-related trends, analysts are able to forecast how the variable of interest may fluctuate in the future.

When conducting time series analysis, the main patterns you’ll be looking out for in your data are:

• Trends: Stable, linear increases or decreases over an extended time period.
• Seasonality: Predictable fluctuations in the data due to seasonal factors over a short period of time. For example, you might see a peak in swimwear sales in summer around the same time every year.
• Cyclic patterns: Unpredictable cycles where the data fluctuates. Cyclical trends are not due to seasonality, but rather, may occur as a result of economic or industry-related conditions.

As you can imagine, the ability to make informed predictions about the future has immense value for business. Time series analysis and forecasting is used across a variety of industries, most commonly for stock market analysis, economic forecasting, and sales forecasting. There are different types of time series models depending on the data you’re using and the outcomes you want to predict. These models are typically classified into three broad types: the autoregressive (AR) models, the integrated (I) models, and the moving average (MA) models. For an in-depth look at time series analysis, refer to our guide .

Time series analysis in action: Developing a time series model to predict jute yarn demand in Bangladesh

g. Sentiment analysis

When you think of data, your mind probably automatically goes to numbers and spreadsheets.

Many companies overlook the value of qualitative data, but in reality, there are untold insights to be gained from what people (especially customers) write and say about you. So how do you go about analyzing textual data?

One highly useful qualitative technique is sentiment analysis , a technique which belongs to the broader category of text analysis —the (usually automated) process of sorting and understanding textual data.

With sentiment analysis, the goal is to interpret and classify the emotions conveyed within textual data. From a business perspective, this allows you to ascertain how your customers feel about various aspects of your brand, product, or service.

There are several different types of sentiment analysis models, each with a slightly different focus. The three main types include:

Fine-grained sentiment analysis

If you want to focus on opinion polarity (i.e. positive, neutral, or negative) in depth, fine-grained sentiment analysis will allow you to do so.

For example, if you wanted to interpret star ratings given by customers, you might use fine-grained sentiment analysis to categorize the various ratings along a scale ranging from very positive to very negative.

Emotion detection

This model often uses complex machine learning algorithms to pick out various emotions from your textual data.

You might use an emotion detection model to identify words associated with happiness, anger, frustration, and excitement, giving you insight into how your customers feel when writing about you or your product on, say, a product review site.

Aspect-based sentiment analysis

This type of analysis allows you to identify what specific aspects the emotions or opinions relate to, such as a certain product feature or a new ad campaign.

If a customer writes that they “find the new Instagram advert so annoying”, your model should detect not only a negative sentiment, but also the object towards which it’s directed.

In a nutshell, sentiment analysis uses various Natural Language Processing (NLP) algorithms and systems which are trained to associate certain inputs (for example, certain words) with certain outputs.

For example, the input “annoying” would be recognized and tagged as “negative”. Sentiment analysis is crucial to understanding how your customers feel about you and your products, for identifying areas for improvement, and even for averting PR disasters in real-time!

Sentiment analysis in action: 5 Real-world sentiment analysis case studies

4. The data analysis process

In order to gain meaningful insights from data, data analysts will perform a rigorous step-by-step process. We go over this in detail in our step by step guide to the data analysis process —but, to briefly summarize, the data analysis process generally consists of the following phases:

Defining the question

The first step for any data analyst will be to define the objective of the analysis, sometimes called a ‘problem statement’. Essentially, you’re asking a question with regards to a business problem you’re trying to solve. Once you’ve defined this, you’ll then need to determine which data sources will help you answer this question.

Collecting the data

Now that you’ve defined your objective, the next step will be to set up a strategy for collecting and aggregating the appropriate data. Will you be using quantitative (numeric) or qualitative (descriptive) data? Do these data fit into first-party, second-party, or third-party data?

Learn more: Quantitative vs. Qualitative Data: What’s the Difference? 

Cleaning the data

Unfortunately, your collected data isn’t automatically ready for analysis—you’ll have to clean it first. As a data analyst, this phase of the process will take up the most time. During the data cleaning process, you will likely be:

• Removing major errors, duplicates, and outliers
• Removing unwanted data points
• Structuring the data—that is, fixing typos, layout issues, etc.
• Filling in major gaps in data

Analyzing the data

Now that we’ve finished cleaning the data, it’s time to analyze it! Many analysis methods have already been described in this article, and it’s up to you to decide which one will best suit the assigned objective. It may fall under one of the following categories:

• Descriptive analysis , which identifies what has already happened
• Diagnostic analysis , which focuses on understanding why something has happened
• Predictive analysis , which identifies future trends based on historical data
• Prescriptive analysis , which allows you to make recommendations for the future

Visualizing and sharing your findings

We’re almost at the end of the road! Analyses have been made, insights have been gleaned—all that remains to be done is to share this information with others. This is usually done with a data visualization tool, such as Google Charts, or Tableau.

Learn more: 13 of the Most Common Types of Data Visualization

To sum up the process, Will’s explained it all excellently in the following video:

5. The best tools for data analysis

As you can imagine, every phase of the data analysis process requires the data analyst to have a variety of tools under their belt that assist in gaining valuable insights from data. We cover these tools in greater detail in this article , but, in summary, here’s our best-of-the-best list, with links to each product:

The top 9 tools for data analysts

• Microsoft Excel
• Jupyter Notebook
• Apache Spark
• Microsoft Power BI

6. Key takeaways and further reading

As you can see, there are many different data analysis techniques at your disposal. In order to turn your raw data into actionable insights, it’s important to consider what kind of data you have (is it qualitative or quantitative?) as well as the kinds of insights that will be useful within the given context. In this post, we’ve introduced seven of the most useful data analysis techniques—but there are many more out there to be discovered!

So what now? If you haven’t already, we recommend reading the case studies for each analysis technique discussed in this post (you’ll find a link at the end of each section). For a more hands-on introduction to the kinds of methods and techniques that data analysts use, try out this free introductory data analytics short course. In the meantime, you might also want to read the following:

• The Best Online Data Analytics Courses for 2024
• What Is Time Series Data and How Is It Analyzed?
• What is Spatial Analysis?

8 Types of Data Analysis

The different types of data analysis include descriptive, diagnostic, exploratory, inferential, predictive, causal, mechanistic and prescriptive. Here’s what you need to know about each one.

Data analysis is an aspect of data science and  data analytics that is all about analyzing data for different kinds of purposes. The data analysis process involves inspecting, cleaning, transforming and  modeling data to draw useful insights from it.

Types of Data Analysis

• Descriptive analysis
• Diagnostic analysis
• Exploratory analysis
• Inferential analysis
• Predictive analysis
• Causal analysis
• Mechanistic analysis
• Prescriptive analysis

With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including energy, healthcare and marketing, among others. As businesses thrive under the influence of technological advancements in data analytics, data analysis plays a huge role in decision-making , providing a better, faster and more effective system that minimizes risks and reduces human biases .

That said, there are different kinds of data analysis with different goals. We’ll examine each one below.

Two Camps of Data Analysis

Data analysis can be divided into two camps, according to the book R for Data Science :

• Hypothesis Generation: This involves looking deeply at the data and combining your domain knowledge to generate  hypotheses about why the data behaves the way it does.
• Hypothesis Confirmation: This involves using a precise mathematical model to generate falsifiable predictions with statistical sophistication to confirm your prior hypotheses.

More on Data Analysis: Data Analyst vs. Data Scientist: Similarities and Differences Explained

Data analysis can be separated and organized into types, arranged in an increasing order of complexity.  

1. Descriptive Analysis

The goal of descriptive analysis is to describe or summarize a set of data . Here’s what you need to know:

• Descriptive analysis is the very first analysis performed in the data analysis process.
• It generates simple summaries of samples and measurements.
• It involves common, descriptive statistics like measures of central tendency, variability, frequency and position.

Descriptive Analysis Example

Take the Covid-19 statistics page on Google, for example. The line graph is a pure summary of the cases/deaths, a presentation and description of the population of a particular country infected by the virus.

Descriptive analysis is the first step in analysis where you summarize and describe the data you have using descriptive statistics, and the result is a simple presentation of your data.

2. Diagnostic Analysis  

Diagnostic analysis seeks to answer the question “Why did this happen?” by taking a more in-depth look at data to uncover subtle patterns. Here’s what you need to know:

• Diagnostic analysis typically comes after descriptive analysis, taking initial findings and investigating why certain patterns in data happen. 
• Diagnostic analysis may involve analyzing other related data sources, including past data, to reveal more insights into current data trends.  
• Diagnostic analysis is ideal for further exploring patterns in data to explain anomalies .  

Diagnostic Analysis Example

A footwear store wants to review its  website traffic levels over the previous 12 months. Upon compiling and assessing the data, the company’s marketing team finds that June experienced above-average levels of traffic while July and August witnessed slightly lower levels of traffic. 

To find out why this difference occurred, the marketing team takes a deeper look. Team members break down the data to focus on specific categories of footwear. In the month of June, they discovered that pages featuring sandals and other beach-related footwear received a high number of views while these numbers dropped in July and August. 

Marketers may also review other factors like seasonal changes and company sales events to see if other variables could have contributed to this trend.    

3. Exploratory Analysis (EDA)

Exploratory analysis involves examining or  exploring data and finding relationships between variables that were previously unknown. Here’s what you need to know:

• EDA helps you discover relationships between measures in your data, which are not evidence for the existence of the correlation, as denoted by the phrase, “ Correlation doesn’t imply causation .”
• It’s useful for discovering new connections and forming hypotheses. It drives design planning and data collection .

Exploratory Analysis Example

Climate change is an increasingly important topic as the global temperature has gradually risen over the years. One example of an exploratory data analysis on climate change involves taking the rise in temperature over the years from 1950 to 2020 and the increase of human activities and industrialization to find relationships from the data. For example, you may increase the number of factories, cars on the road and airplane flights to see how that correlates with the rise in temperature.

Exploratory analysis explores data to find relationships between measures without identifying the cause. It’s most useful when formulating hypotheses. 

4. Inferential Analysis

Inferential analysis involves using a small sample of data to infer information about a larger population of data.

The goal of statistical modeling itself is all about using a small amount of information to extrapolate and generalize information to a larger group. Here’s what you need to know:

• Inferential analysis involves using estimated data that is representative of a population and gives a measure of uncertainty or  standard deviation to your estimation.
• The accuracy of inference depends heavily on your sampling scheme. If the sample isn’t representative of the population, the generalization will be inaccurate. This is known as the central limit theorem .

Inferential Analysis Example

A psychological study on the benefits of sleep might have a total of 500 people involved. When they followed up with the candidates, the candidates reported to have better overall attention spans and well-being with seven to nine hours of sleep, while those with less sleep and more sleep than the given range suffered from reduced attention spans and energy. This study drawn from 500 people was just a tiny portion of the 7 billion people in the world, and is thus an inference of the larger population.

Inferential analysis extrapolates and generalizes the information of the larger group with a smaller sample to generate analysis and predictions. 

5. Predictive Analysis

Predictive analysis involves using historical or current data to find patterns and make predictions about the future. Here’s what you need to know:

• The accuracy of the predictions depends on the input variables.
• Accuracy also depends on the types of models. A linear model might work well in some cases, and in other cases it might not.
• Using a variable to predict another one doesn’t denote a causal relationship.

Predictive Analysis Example

The 2020 United States election is a popular topic and many prediction models are built to predict the winning candidate. FiveThirtyEight did this to forecast the 2016 and 2020 elections. Prediction analysis for an election would require input variables such as historical polling data, trends and current polling data in order to return a good prediction. Something as large as an election wouldn’t just be using a linear model, but a complex model with certain tunings to best serve its purpose.

6. Causal Analysis

Causal analysis looks at the cause and effect of relationships between variables and is focused on finding the cause of a correlation. This way, researchers can examine how a change in one variable affects another. Here’s what you need to know:

• To find the cause, you have to question whether the observed correlations driving your conclusion are valid. Just looking at the surface data won’t help you discover the hidden mechanisms underlying the correlations.
• Causal analysis is applied in randomized studies focused on identifying causation.
• Causal analysis is the gold standard in data analysis and scientific studies where the cause of a phenomenon is to be extracted and singled out, like separating wheat from chaff.
• Good data is hard to find and requires expensive research and studies. These studies are analyzed in aggregate (multiple groups), and the observed relationships are just average effects (mean) of the whole population. This means the results might not apply to everyone.

Causal Analysis Example  

Say you want to test out whether a new drug improves human strength and focus. To do that, you perform randomized control trials for the drug to test its effect. You compare the sample of candidates for your new drug against the candidates receiving a mock control drug through a few tests focused on strength and overall focus and attention. This will allow you to observe how the drug affects the outcome. 

7. Mechanistic Analysis

Mechanistic analysis is used to understand exact changes in variables that lead to other changes in other variables . In some ways, it is a predictive analysis, but it’s modified to tackle studies that require high precision and meticulous methodologies for physical or engineering science. Here’s what you need to know:

• It’s applied in physical or engineering sciences, situations that require high  precision and little room for error, only noise in data is measurement error.
• It’s designed to understand a biological or behavioral process, the pathophysiology of a disease or the mechanism of action of an intervention. 

Mechanistic Analysis Example

Say an experiment is done to simulate safe and effective nuclear fusion to power the world. A mechanistic analysis of the study would entail a precise balance of controlling and manipulating variables with highly accurate measures of both variables and the desired outcomes. It’s this intricate and meticulous modus operandi toward these big topics that allows for scientific breakthroughs and advancement of society.

8. Prescriptive Analysis  

Prescriptive analysis compiles insights from other previous data analyses and determines actions that teams or companies can take to prepare for predicted trends. Here’s what you need to know: 

• Prescriptive analysis may come right after predictive analysis, but it may involve combining many different data analyses. 
• Companies need advanced technology and plenty of resources to conduct prescriptive analysis. Artificial intelligence systems that process data and adjust automated tasks are an example of the technology required to perform prescriptive analysis.  

Prescriptive Analysis Example

Prescriptive analysis is pervasive in everyday life, driving the curated content users consume on social media. On platforms like TikTok and Instagram,  algorithms can apply prescriptive analysis to review past content a user has engaged with and the kinds of behaviors they exhibited with specific posts. Based on these factors, an  algorithm seeks out similar content that is likely to elicit the same response and  recommends it on a user’s personal feed. 

More on Data Explaining the Empirical Rule for Normal Distribution

When to Use the Different Types of Data Analysis  

• Descriptive analysis summarizes the data at hand and presents your data in a comprehensible way.
• Diagnostic analysis takes a more detailed look at data to reveal why certain patterns occur, making it a good method for explaining anomalies. 
• Exploratory data analysis helps you discover correlations and relationships between variables in your data.
• Inferential analysis is for generalizing the larger population with a smaller sample size of data.
• Predictive analysis helps you make predictions about the future with data.
• Causal analysis emphasizes finding the cause of a correlation between variables.
• Mechanistic analysis is for measuring the exact changes in variables that lead to other changes in other variables.
• Prescriptive analysis combines insights from different data analyses to develop a course of action teams and companies can take to capitalize on predicted outcomes. 

A few important tips to remember about data analysis include:

• Correlation doesn’t imply causation.
• EDA helps discover new connections and form hypotheses.
• Accuracy of inference depends on the sampling scheme.
• A good prediction depends on the right input variables.
• A simple linear model with enough data usually does the trick.
• Using a variable to predict another doesn’t denote causal relationships.
• Good data is hard to find, and to produce it requires expensive research.
• Results from studies are done in aggregate and are average effects and might not apply to everyone.​

Frequently Asked Questions

What is an example of data analysis.

A marketing team reviews a company’s web traffic over the past 12 months. To understand why sales rise and fall during certain months, the team breaks down the data to look at shoe type, seasonal patterns and sales events. Based on this in-depth analysis, the team can determine variables that influenced web traffic and make adjustments as needed.

How do you know which data analysis method to use?

Selecting a data analysis method depends on the goals of the analysis and the complexity of the task, among other factors. It’s best to assess the circumstances and consider the pros and cons of each type of data analysis before moving forward with a particular method.

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• Data, Analysis, & AI

What is Data Analysis? Methods, Techniques & Tools

• What Is Data Analysis?

The systematic application of statistical and logical techniques to describe the data scope, modularize the data structure, condense the data representation, illustrate via images, tables, and graphs, and evaluate statistical inclinations, probability data, and derive meaningful conclusions known as Data Analysis. These analytical procedures enable us to induce the underlying inference from data by eliminating the unnecessary chaos created by its rest. Data generation is a continual process; this makes data analysis a continuous, iterative process where the collection and performing data analysis simultaneously. Ensuring data integrity is one of the essential components of data analysis. 

There are various examples where data analysis is used, ranging from transportation, risk and fraud detection, customer interaction, city planning healthcare, web search, digital advertisement, and more. 

Considering the example of healthcare, as we have noticed recently that with the outbreak of the pandemic, Coronavirus hospitals are facing the challenge of coping up with the pressure in treating as many patients as possible, considering data analysis allows to monitor machine and data usage in such scenarios to achieve efficiency gain. 

Before diving any more in-depth, make the following pre-requisites for proper Data Analysis: 

• Ensure availability of the necessary analytical skills
• Ensure appropriate implementation of data collection methods and analysis.
• Determine the statistical significance
• Check for inappropriate analysis
• Ensure the presence of legitimate and unbiased inference
• Ensure the reliability and validity of data, data sources, data analysis methods, and inferences derived.
• Account for the extent of analysis 
• Data Analysis Methods

There are two main methods of Data Analysis: 

1. Qualitative Analysis

This approach mainly answers questions such as ‘why,’ ‘what’ or ‘how.’ Each of these questions is addressed via quantitative techniques such as questionnaires, attitude scaling, standard outcomes, and more. Such analysis is usually in the form of texts and narratives, which might also include audio and video representations.

2. Quantitative Analysis

Generally, this analysis is measured in terms of numbers. The data here present themselves in terms of measurement scales and extend themselves for more statistical manipulation.  

The other techniques include: 

3. Text analysis

Text analysis is a technique to analyze texts to extract machine-readable facts. It aims to create structured data out of free and unstructured content. The process consists of slicing and dicing heaps of unstructured, heterogeneous files into easy-to-read, manage and interpret data pieces. It is also known as text mining, text analytics, and information extraction.

The ambiguity of human languages is the biggest challenge of text analysis. For example, humans know that “Red Sox Tames Bull” refers to a baseball match. Still, if this text is fed to a computer without background knowledge, it would generate several linguistically valid interpretations. Sometimes people who are not interested in baseball might have trouble understanding it too.

4. Statistical analysis

Statistics involves data collection, interpretation, and validation. Statistical analysis is the technique of performing several statistical operations to quantify the data and apply statistical analysis. Quantitative data involves descriptive data like surveys and observational data. It is also called a descriptive analysis. It includes various tools to perform statistical data analysis such as SAS (Statistical Analysis System), SPSS (Statistical Package for the Social Sciences), Stat soft, and more.

5. Diagnostic analysis

The diagnostic analysis is a step further to statistical analysis to provide a more in-depth analysis to answer the questions. It is also referred to as root cause analysis as it includes processes like data discovery, mining, and drill down and drill through.

The functions of diagnostic analytics fall into three categories:

• Identify anomalies: After performing statistical analysis, analysts are required to identify areas requiring further study as such data raise questions that cannot be answered by looking at the data.
• Drill into the Analytics (discovery): Identification of the data sources helps analysts explain the anomalies. This step often requires analysts to look for patterns outside the existing data sets. It requires pulling in data from external sources, thus identifying correlations and determining if they are causal in nature.
• Determine Causal Relationships: Hidden relationships are uncovered by looking at events that might have resulted in the identified anomalies. Probability theory, regression analysis, filtering, and time-series data analytics can all be useful for uncovering hidden stories in the data.

6. Predictive analysis

Predictive analysis uses historical data and feds it into the machine learning model to find critical patterns and trends. The model is applied to the current data to predict what would happen next. Many organizations prefer it because of its various advantages like volume and type of data, faster and cheaper computers, easy-to-use software, tighter economic conditions, and a need for competitive differentiation.

The following are the common uses of predictive analysis:

• Fraud Detection: Multiple analytics methods improves pattern detection and prevents criminal behavior.
• Optimizing Marketing Campaigns: Predictive models help businesses attract, retain, and grow their most profitable customers. It also helps in determining customer responses or purchases, promoting cross-sell opportunities.
• Improving Operations: The use of predictive models also involves forecasting inventory and managing resources. For example, airlines use predictive models to set ticket prices.
• Reducing Risk:  The credit score used to assess a buyer’s likelihood of default for purchases is generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.

7. Prescriptive Analysis

Prescriptive analytics suggests various courses of action and outlines the potential implications that could be reached after predictive analysis. Prescriptive analysis generating automated decisions or recommendations requires specific and unique algorithmic and clear direction from those utilizing the analytical techniques.

Data Analysis Masterclass (4 courses in 1)

• Data Analysis Process

Once you set out to collect data for analysis, you are overwhelmed by the amount of information you find to make a clear, concise decision. With so much data to handle, you need to identify relevant data for your analysis to derive an accurate conclusion and make informed decisions. The following simple steps help you identify and sort out your data for analysis.

1. Data Requirement Specification - define your scope:

• Define short and straightforward questions, the answers to which you finally need to make a decision.
• Define measurement parameters
• Define which parameter you take into account and which one you are willing to negotiate.
• Define your unit of measurement. Ex – Time, Currency, Salary, and more.

2. Data Collection

• Gather your data based on your measurement parameters. 
• Collect data from databases, websites, and many other sources. This data may not be structured or uniform, which takes us to the next step.

3. Data Processing

• Organize your data and make sure to add side notes, if any.
• Cross-check data with reliable sources.
• Convert the data as per the scale of measurement you have defined earlier.
• Exclude irrelevant data.

4. Data Analysis

• Once you have collected your data, perform sorting, plotting, and identifying correlations.  
• As you manipulate and organize your data, you may need to traverse your steps again from the beginning. You may need to modify your question, redefine parameters, and reorganize your data. 
• Make use of the different tools available for data analysis.

5. Infer and Interpret Results

• Review if the result answers your initial questions
• Review if you have considered all parameters for making the decision
• Review if there is any hindering factor for implementing the decision.
• Choose data visualization techniques to communicate the message better. These visualization techniques may be charts, graphs, color coding, and more.

Once you have an inference, always remember it is only a hypothesis. Real-life scenarios may always interfere with your results. In Data Analysis, there are a few related terminologies that identity with different phases of the process. 

1. Data Mining

This process involves methods in finding patterns in the data sample. 

2. Data Modelling

This refers to how an organization organizes and manages its data. 

Data Analysis Techniques 

There are different techniques for Data Analysis depending upon the question at hand, the type of data, and the amount of data gathered. Each focuses on taking onto the new data, mining insights, and drilling down into the information to transform facts and figures into decision-making parameters. Accordingly, the different techniques of data analysis can be categorized as follows:

1. Techniques based on Mathematics and Statistics

• Descriptive Analysis : Descriptive Analysis considers the historical data, Key Performance Indicators and describes the performance based on a chosen benchmark. It takes into account past trends and how they might influence future performance.
• Dispersion Analysis : Dispersion in the area onto which a data set is spread. This technique allows data analysts to determine the variability of the factors under study.
• Regression Analysis : This technique works by modeling the relationship between a dependent variable and one or more independent variables. A regression model can be linear, multiple, logistic, ridge, non-linear, life data, and more.
• Factor Analysis : This technique helps to determine if there exists any relationship between a set of variables. This process reveals other factors or variables that describe the patterns in the relationship among the original variables. Factor Analysis leaps forward into useful clustering and classification procedures.
• Discriminant Analysis : It is a classification technique in data mining. It identifies the different points on different groups based on variable measurements. In simple terms, it identifies what makes two groups different from one another; this helps to identify new items.
• Time Series Analysis : In this kind of analysis, measurements are spanned across time, which gives us a collection of organized data known as time series.

2. Techniques based on Artificial Intelligence and Machine Learning

• Artificial Neural Networks: a Neural network is a biologically-inspired programming paradigm that presents a brain metaphor for processing information. An Artificial Neural Network is a system that changes its structure based on information that flows through the network. ANN can accept noisy data and are highly accurate. They can be considered highly dependable in business classification and forecasting applications.
• Decision Trees : As the name stands, it is a tree-shaped model representing a classification or regression model. It divides a data set into smaller subsets, simultaneously developing into a related decision tree.
• Evolutionary Programming : This technique combines the different types of data analysis using evolutionary algorithms. It is a domain-independent technique, which can explore ample search space and manages attribute interaction very efficiently.
• Fuzzy Logic : It is a data analysis technique based on the probability that helps handle the uncertainties in data mining techniques. 

3. Techniques based on Visualization and Graphs

• Column Chart, Bar Chart : Both these charts are used to present numerical differences between categories. The column chart takes to the height of the columns to reflect the differences. Axes interchange in the case of the bar chart.
• Line Chart : This chart represents the change of data over a continuous interval of time. 
• Area Chart : This concept is based on the line chart. It also fills the area between the polyline and the axis with color, representing better trend information.
• Pie Chart : It is used to represent the proportion of different classifications. It is only suitable for only one series of data. However, it can be made multi-layered to represent the proportion of data in different categories.
• Funnel Chart : This chart represents the proportion of each stage and reflects the size of each module. It helps in comparing rankings.
• Word Cloud Chart: It is a visual representation of text data. It requires a large amount of data, and the degree of discrimination needs to be high for users to perceive the most prominent one. It is not a very accurate analytical technique.
• Gantt Chart : It shows the actual timing and the progress of the activity compared to the requirements.
• Radar Chart : It is used to compare multiple quantized charts. It represents which variables in the data have higher values and which have lower values. A radar chart is used for comparing classification and series along with proportional representation.
• Scatter Plot : It shows the distribution of variables in points over a rectangular coordinate system. The distribution in the data points can reveal the correlation between the variables.
• Bubble Chart : It is a variation of the scatter plot. Here, in addition to the x and y coordinates, the bubble area represents the 3rd value.
• Gauge: It is a kind of materialized chart. Here the scale represents the metric, and the pointer represents the dimension. It is a suitable technique to represent interval comparisons.
• Frame Diagram : It is a visual representation of a hierarchy in an inverted tree structure.
• Rectangular Tree Diagram : This technique is used to represent hierarchical relationships but at the same level. It makes efficient use of space and represents the proportion represented by each rectangular area.
• Regional Map: It uses color to represent value distribution over a map partition.
• Point Map: It represents the geographical distribution of data in points on a geographical background. When the points are the same in size, it becomes meaningless for single data, but if the points are as a bubble, it also represents the size of the data in each region.
• Flow Map: It represents the relationship between an inflow area and an outflow area. It represents a line connecting the geometric centers of gravity of the spatial elements. The use of dynamic flow lines helps reduce visual clutter.
• Heat Map : This represents the weight of each point in a geographic area. The color here represents the density.

Let us now read about a few tools used in data analysis in research. 

• Data Analysis Tools

There are several data analysis tools  available in the market, each with its own set of functions. The selection of tools should always be based on the type of analysis performed and the type of data worked. Here is a list of a few compelling tools for Data Analysis. 

It has various compelling features, and with additional plugins installed, it can handle a massive amount of data. So, if you have data that does not come near the significant data margin, Excel can be a versatile tool for data analysis.

Looking to learn Excel? Data Analysis with Excel Pivot Tables course is the highest-rated Excel course on udemy.

It falls under the BI Tool category, made for the sole purpose of data analysis. The essence of Tableau is the Pivot Table and Pivot Chart and works towards representing data in the most user-friendly way. It additionally has a data cleaning feature along with brilliant analytical functions.

If you want to learn Tableau, udemy's online course Hands-On Tableau Training For Data Science can be a great asset for you.

3. Power BI

It initially started as a plugin for Excel, but later on, detached from it to develop in one of the most data analytics tools. It comes in three versions: Free, Pro, and Premium. Its PowerPivot and DAX language can implement sophisticated advanced analytics similar to writing Excel formulas.

4. Fine Report

Fine Report comes with a straightforward drag and drops operation, which helps design various reports and build a data decision analysis system. It can directly connect to all kinds of databases, and its format is similar to that of Excel. Additionally, it also provides a variety of dashboard templates and several self-developed visual plug-in libraries.

5. R & Python

These are programming languages that are very powerful and flexible. R is best at statistical analysis, such as normal distribution, cluster classification algorithms, and regression analysis. It also performs individual predictive analyses like customer behavior, spending, items preferred by him based on his browsing history, and more. It also involves concepts of machine learning and artificial intelligence.

It is a programming language for data analytics and data manipulation, which can easily access data from any source. SAS has introduced a broad set of customer profiling products for web, social media, and marketing analytics. It can predict their behaviors, manage, and optimize communications.

This is our complete beginner's guide on "What is Data Analysis". If you want to learn more about data analysis, Complete Introduction to Business Data Analysis is a great introductory course.

Data Analysis is the key to any business, whether starting up a new venture, making marketing decisions, continuing with a particular course of action, or going for a complete shut-down. The inferences and the statistical probabilities calculated from data analysis help base the most critical decisions by ruling out all human bias. Different analytical tools have overlapping functions and different limitations, but they are also complementary tools. Before choosing a data analytical tool, it is essential to consider the scope of work, infrastructure limitations, economic feasibility, and the final report to be prepared.

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Simran works at Hackr as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her.

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In this article

• Data Analysis Techniques 
• 30+ Top Data Analyst Interview Questions and Answers in 2024 Data Analytics Career Development Interview Questions
• Best Data Analysis Software in 2024 Data Analytics
• 7 Top Data Analytics Tools to Use in 2024 Data Analytics

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Data Analysis: Techniques, Tools, and Processes

Big or small, companies now expect their decisions to be data-driven. The world is growing and relying more on data. There is a greater need for professionals who know data analysis techniques.

Data analysis is a valuable skill that empowers you to make better decisions. This skill serves as a powerful catalyst in your professional and personal life. From personal budgeting to analyzing customer experiences , data analysis is the stepping stone to your career advancement.

So, whether you’re looking to upskill at work or kickstart a career in data analytics, this article is for you. We will discuss the best data analysis techniques in detail. To put all that into perspective, we’ll also discuss the step-by-step data analysis process. 

Let’s begin.

What is Data Analysis?

Data analysis is collecting, cleansing, analyzing, presenting, and interpreting data to derive insights. This process aids decision-making by providing helpful insights and statistics. 

The history of data analysis dates back to the 1640s. John Grant, a hatmaker, started collecting the number of deaths in London. He was the first person to use data analysis to solve a problem. Also, Florence Nightingale, best known as a nurse from 1854, made significant contributions to medicine through data analysis, particularly in public health and sanitation.

This simple practice of data analysis has evolved and broadened over time. “ Data analytics ” is the bigger picture. It employs data, tools, and techniques (covered later in this article) to discover new insights and make predictions.  

Why is Data Analysis so Important Now?

How do businesses make better decisions, analyze trends, or invent better products and services ?

The simple answer: Data Analysis. The distinct methods of analysis reveal insights that would otherwise get lost in the mass of information. Big data analytics is getting even more prominent owing to the below reasons.

1. Informed Decision-making

The modern business world relies on facts rather than intuition. Data analysis serves as the foundation of informed decision-making. 

Consider the role of data analysis in UX design , specifically when dealing with non-numerical, subjective information. Qualitative research delves into the 'why' and 'how' behind user behavior , revealing nuanced insights. It provides a foundation for making well-informed decisions regarding color , layout, and typography . Applying these insights allows you to create visuals that deeply resonate with your target audience.

2. Better Customer Targeting and Predictive Capabilities

Data has become the lifeblood of successful marketing . Organizations rely on data science techniques to create targeted strategies and marketing campaigns. 

Big data analytics helps uncover deep insights about consumer behavior. For instance, Google collects and analyzes many different data types. It examines search history, geography, and trending topics to deduce what consumers want.

3. Improved Operational Efficiencies and Reduced Costs

Data analytics also brings the advantage of streamlining operations and reducing organizational costs. It makes it easier for businesses to identify bottlenecks and improvement opportunities. This enables them to optimize resource allocation and ultimately reduce costs.

Procter & Gamble (P&G) , a leading company, uses data analytics to optimize their supply chain and inventory management. Data analytics helps the company reduce excess inventory and stockouts, achieving cost savings.  

4. Better Customer Satisfaction and Retention

Customer behavior patterns enable you to understand how they feel about your products, services, and brand. Also, different data analysis models help uncover future trends. These trends allow you to personalize the customer experience and improve satisfaction.

The eCommerce giant Amazon learns from what each customer wants and likes. It then recommends the same or similar products when they return to the shopping app. Data analysis helps create personalized experiences for Amazon customers and improves user experience . 

Enhance your knowledge by understanding “when” and “why” to use data analytics.

• Transcript loading…

Types of Data Analysis Methods

“We are surrounded by data, but starved for insights.” — Jay Baer, Customer Experience Expert & Speaker

The above quote summarizes that strategic analysis must support data to produce meaningful insights. 

Before discussing the top data analytics techniques , let’s first understand the two types of data analysis methods.

1. Quantitative Data Analysis

As the name suggests, quantitative analysis involves looking at the complex data, the actual numbers, or the rows and columns. Let’s understand this with the help of a scenario.

Your e-commerce company wants to assess the sales team’s performance. You gather quantitative data on various key performance indicators (KPIs). These KPIs include

The number of units sold.

Sales revenue.

Conversion rates .

Customer acquisition costs.

By analyzing these numeric data points, the company can calculate:

Monthly sales growth.

Average order value.

Return on investment (ROI) for each sales representative.

How does it help?

The quantitative analysis can help you identify:

Top-performing sales reps

Best-selling products. 

Most cost-effective customer acquisition channels.

The above metrics help the company make data-driven decisions and improve its sales strategy.

2. Qualitative Data Analysis

There are situations where numbers in rows and columns are impossible to fit. This is where qualitative research can help you understand the data’s underlying factors, patterns, and meanings via non-numerical means. Let’s take an example to understand this.

Imagine you’re a product manager for an online shopping app. You want to improve the app’s user experience and boost user engagement. You have quantitative data that tells you what's going on but not why . Here’s what to do:

Collect customer feedback through interviews, open-ended questions, and online reviews. 

Conduct in-depth interviews to explore their experiences. 

Watch this instructional video to elevate your interview preparation to a more professional level.

By reading and summarizing the comments, you can identify issues, sentiments, and areas that need improvement. This qualitative insight can guide you to identify and work on areas of frustration or confusion. 

Learn more about quantitative and qualitative user research in this video.

10 Best Data Analysis and Modeling Techniques

We generate over 120 zettabytes daily. That’s about 120 billion copies of the entire Internet in 2020, daily . Without the best data analysis techniques, businesses of all sizes will never be able to collect, analyze, and interpret data into real, actionable insights .  

Now that you have an overarching picture of data analysis , let’s move on to the nitty-gritty: top data analysis methods .

© Interaction Design Foundation, CC BY-SA 4.0

Quantitative Methods

1. cluster analysis.

Also called segmentation or taxonomy analysis, this method identifies structures within a dataset. It’s like sorting objects into different boxes (clusters) based on their similarities. The data points within a similar group are similar to each other (homogeneous). Likewise, they’re dissimilar to data points in another cluster(heterogeneous).  

Cluster analysis aims to find hidden patterns in the data. It can be your go-to approach if you require additional context to a trend or dataset.

Let’s say you own a retail store. You want to understand your customers better to tailor your marketing strategies. You collect customer data, including their shopping behavior and preferences. 

Here, cluster analysis can help you group customers with similar behaviors and preferences. Customers who visit your store frequently and shop a lot may form one cluster. Customers who shop infrequently and spend less may form another cluster.

With the help of cluster analysis, you can target your marketing efforts more efficiently.

2. Regression Analysis

Regression analysis is a powerful data analysis technique. It is quite popular in economics, biology, biology, and psychology. This technique helps you understand how one thing (or more) influences another. 

Suppose you’re a manager trying to predict next month’s sales. Many factors, like the weather, promotions, or the buzz about a better product, can affect these figures.

In addition, some people in your organization might have their own theory on what might impact sales the most. For instance, one colleague might confidently say, “When winter starts, our sales go up.” And another insists, “Sales will spike two weeks after we launch a promotion.”

All the above factors are “variables.” Now, the “dependent variable” will always be the factor being measured. In our example—the monthly sales. 

Next, you have your independent variables. These are the factors that might impact your dependent variable.

Regression analysis can mathematically sort out which variables have an impact. This statistical analysis identifies trends and patterns to make predictions and forecast possible future directions. 

There are many types of regression analysis, including linear regression, non-linear regression, binary logistic regression, and more. The model you choose will highly depend upon the type of data you have

3. Monte Carlo Simulation

This mathematical technique is an excellent way to estimate an uncertain event’s possible outcomes. Interestingly, the method derives its name from the Monte Carlo Casino in Monaco. The casino is famous for its games of chance. 

Let’s say you want to know how much money you might make from your investments in the stock market. So, you make thousands of guesses instead of one guess. Then, you consider several scenarios . The scenarios can be a growing economy or an unprecedented catastrophe like Covid-19. 

The idea is to test many random situations to estimate the potential outcomes.

4. Time Series Analysis

The time series method analyzes data collected over time. You can identify trends and cycles over time with this technique. Here, one data set recorded at different intervals helps understand patterns and make forecasts. 

Industries like finance, retail, and economics leverage time-series analysis to predict trends. It is so because they deal with ever-changing currency exchange rates and sales data. 

Using time series analysis in the stock market is an excellent example of this technique in action. Many stocks exhibit recurring patterns in their underlying businesses due to seasonality or cyclicality. Time series analysis can uncover these patterns. Hence, investors can take advantage of seasonal trading opportunities or adjust their portfolios accordingly.

Time series analysis is part of predictive analytics . It can show likely changes in the data to provide a better understanding of data variables and better forecasting. 

5. Cohort Analysis

Cohort analysis also involves breaking down datasets into relative groups (or cohorts), like cluster analysis. However, in this method, you focus on studying the behavior of specific groups over time. This aims to understand different groups’ performance within a larger population.

This technique is popular amongst marketing, product development, and user experience research teams. 

Let’s say you’re an app developer and want to understand user engagement over time. Using this method, you define cohorts based on a familiar identifier. This identifier can be the demographics, app download date, or users making an in-app purchase. In this way, your cohort represents a group of users who had a similar starting point. 

With the data in hand, you analyze how each cohort behaves over time. Do users from the US use your app more frequently than people in the UK? Are there any in-app purchases from a specific cohort?

This iterative approach can reveal insights to refine your marketing strategies and improve user engagement. 

Qualitative Methods

6. content analysis.

When you think of “data” or “analysis,” do you think of text, audio, video, or images? Probably not, but these forms of communication are an excellent way to uncover patterns, themes, and insights. 

Widely used in marketing, content analysis can reveal public sentiment about a product or brand. For instance, analyzing customer reviews and social media mentions can help brands discover hidden insights. 

There are two further categories in this method:

Conceptual analysis: It focuses on explicit data. For example, the number of times a word repeats in a content. 

Relational analysis: It examines the relationship between different concepts or words and how they connect. It's not about counting but about understanding how things fit together. A user experience technique called card sorting can help with this.

This technique involves counting and measuring the frequency of categorical data. It also studies the meaning and context of the content. This is why content analysis can be both quantitative and qualitative. 

7. Sentiment Analysis

Also known as opinion mining, this technique is a valuable business intelligence tool. It can assist you to enhance your products and services. The modern business landscape has substantial textual data, including emails, social media comments, website chats, and reviews. You often need to know whether this text data conveys a positive, negative, or neutral sentiment.

Sentiment Analysis tools help scan this text to determine the emotional tone of the message automatically. The insights from sentiment analysis are highly helpful in improving customer service and elevating brand reputation.

8. Thematic Analysis

Whether you’re an entrepreneur, a UX researcher , or a customer relationship manager— thematic analysis can help you better understand user behaviors and needs. 

The thematic technique analyzes large chunks of text data such as transcripts or interviews. It then groups them into themes or categories that come up frequently within the text. While this may sound similar to content analysis, it’s worth noting that the thematic method purely uses qualitative data. 

Moreover, it is a very subjective technique since it depends upon the researcher’s experience to derive insights. 

9. Grounded Theory Analysis

Think of grounded theory as something you, as a researcher, might do. Instead of starting with a hypothesis and trying to prove or disprove it, you gather information and construct a theory as you go along.

It's like a continuous loop. You collect and examine data and then create a theory based on your discovery. You keep repeating this process until you've squeezed out all the insights possible from the data. This method allows theories to emerge naturally from the information, making it a flexible and open way to explore new ideas.

Grounded theory is the basis of a popular user-experience research technique called contextual enquiry .

10. Discourse Analysis

Discourse analysis is popular in linguistics, sociology, and communication studies. It aims to understand the meaning behind written texts, spoken conversations, or visual and multimedia communication. It seeks to uncover:

How individuals structure a specific language

What lies behind it; and 

How social and cultural practices influence it

For instance, as a social media manager, if you analyze social media posts, you go beyond the text itself. You would consider the emojis, hashtags, and even the timing of the posts. You might find that a particular hashtag is used to mobilize a social movement. 

The Data Analysis Process: Step-by-Step Guide

You must follow a step-by-step data analytics process to derive meaningful conclusions from your data. Here is a rundown of five main data analysis steps :

1. Problem Identification

The first step in the data analysis process is “identification.” What problem are you trying to solve? In other words, what research question do you want to address with your data analysis?

Let’s say you’re an analyst working for an e-commerce company. There has been a recent decline in sales. Now, the company wants to understand why this is happening. Our problem statement is to find the reason for the decline in sales. 

2. Data Collection

The next step is to collect data. You can do this through various internal and external sources. For example, surveys , questionnaires, focus groups , interviews , etc.

Delve deeper into the intricacies of data collection with Ann Blandford in this video:

The key here is to collect and aggregate the appropriate statistical data. By “appropriate,” we mean the data that could help you understand the problem and build a forecasting model. The data can be quantitative (sales figures) or qualitative (customer reviews). 

All types of data can fit into one of three categories:

First-party data : Data that you, or your company, can collect directly from customers.

Second-party data : The first-party data of other organizations. For instance, sales figures of your competition company. 

Third-party data : Data that a third-party organization can collect and aggregate from numerous sources. For instance, government portals or open data repositories. 

3. Data Cleaning

Now that you have acquired the necessary data, the next step is to prepare it for analysis. That means you must clean or scrub it. This is essential since acquired data can be in different formats. Cleaning ensures you’re not dealing with bad data and your results are dependable. 

Here are some critical data-cleaning steps:

Remove white spaces, duplicates, and formatting errors.

Delete unwanted data points.

Bring structure to your data.

For survey data, you also need to do consistency analysis. Some of this relies on good questionnaire design, but you also need to ensure that:

Respondents are not “straight-lining” (all answers in a single column).

Similar questions are answered consistently.

Open-ended questions contain plausible responses.

4. Data Analysis

This is the stage where you’d be ready to leverage any one or more of the data analysis and research techniques mentioned above. The choice of technique depends upon the data you’re dealing with and the desired results. 

All types of data analysis fit into the following four categories:

A. Descriptive Analysis

Descriptive analysis focuses on what happened. It is the starting point for any research before proceeding with deeper explorations. As the first step, it involves breaking down data and summarizing its key characteristics.   

B. Diagnostic Analysis

This analysis focuses on why something has happened. Just as a doctor uses a patient’s diagnosis to uncover a disease, you can use diagnostic analysis to understand the underlying cause of the problem. 

C. Predictive Analysis

This type of analysis allows you to identify future trends based on historical data. It generally uses the results from the above analysis, machine learning (ML), and artificial intelligence (AI) to forecast future growth. 

D. Prescriptive Analysis

Now you know what to do, you must also understand how you’ll do it. The prescriptive analysis aims to determine your research’s best course of action.  

5. Data Interpretation

The step is like connecting the dots in a puzzle. This is where you start making sense of all the data and analysis done in the previous steps. You dig deeper into your data analysis findings and visualize the data to present insights in meaningful and understandable ways. 

Explore this comprehensive video resource to understand the complete user research data analysis process:

The Best Tools and Resources to Use for Data Analysis in 2023

You’ve got data in hand, mastered the process, and understood all the ways to analyze data . So, what comes next?

Well, parsing large amounts of data inputs can make it increasingly challenging to uncover hidden insights. Data analysis tools can track and analyze data through various algorithms, allowing you to create actionable reports and dashboards.

We’ve compiled a handy list of the best tools for you with their pros and cons. 

1. Microsoft Excel

The world’s best and most user-friendly spreadsheet software features calculations and graphing functions. It is ideal for non-techies to perform basic data analysis and create charts and reports.

No coding is required.

User-friendly interface.

Runs slow with complex data analysis.

Less automation compared to specialized tools.

2. Google Sheets

Similar to Microsoft Excel, Google Sheets stands out as a remarkable and cost-effective tool for fundamental data analysis. It handles everyday data analysis tasks, including sorting, filtering, and simple calculations. Besides, it is known for its seamless collaboration capabilities. 

Easily accessible .

Compatible with Microsoft Excel.

Seamless integration with other Google Workspace tools.

Lacks advanced features such as in Microsoft Excel.

May not be able to handle large datasets.

3. Google Analytics

Widely used by digital marketers and web analysts, this tool helps businesses understand how people interact with their websites and apps. It provides insights into website traffic, user behavior, and performance to make data-driven business decisions .

Free version available.

Integrates with Google services.

Limited customization for specific business needs.

May not support non-web data sources.

4. RapidMiner

RapidMiner is ideal for data mining and model development. This platform offers remarkable machine learning and predictive analytics capabilities. It allows professionals to work with data at many stages, including preparation, information visualization , and analysis.

Excellent support for machine learning.

Large library of pre-built models.

Can be expensive for advanced features.

Limited data integration capabilities.

Being one of the best commercial data analysis tools, Tableau is famous for its interactive dashboards and data exploration capabilities. Data teams can create visually appealing and interactive data representations through its easy-to-use interface and powerful capabilities. 

Intuitive drag-and-drop interface.

Interactive and dynamic data visualization.

Backed by Salesforce.

Expensive than competition.

Steeper learning curve for advanced features.

6. Power BI

This is an excellent choice for creating insightful business dashboards. It boasts incredible data integration features and interactive reporting, making it ideal for enterprises. 

Short for Konstanz Information Miner, KNIME is an outstanding tool for data mining. Its user-friendly graphical interface makes it accessible even to non-technical users, enabling them to create data workflows easily. Additionally, KNIME is a cost-effective choice. Hence, it is ideal for small businesses operating on a limited budget. 

Visual workflow for data blending and automation.

Active community and user support.

Complex for beginners.

Limited real-time data processing.

8. Zoho Analytics

Fueled by artificial intelligence and machine learning, Zoho Analytics is a robust data analysis platform. Its data integration capabilities empower you to seamlessly connect and import data from diverse sources while offering an extensive array of analytical functions.

Affordable pricing options.

User-friendly interface

Limited scalability for very large datasets.

Not as widely adopted as some other tools.

9. Qlik Sense

Qlik Sense offers a wide range of augmented capabilities. It has everything from AI-generated analysis and insights to automated creation and data prep, machine learning, and predictive analytics. 

Impressive data exploration and visualization features.

Can handle large datasets.

Steep learning curve for new users.

How to Pick the Right Tool?

Consider the below factors to find the perfect data analysis tool for your organization:

Your organization’s business needs.

Who needs to use the data analysis tools?

The tool’s data modeling capabilities.

The tool’s pricing. 

Besides the above tools, additional resources like a Service Design certification can empower you to provide sustainable solutions and optimal customer experiences. 

How to Become a Data Analyst? 

Data analysts are in high demand owing to the soaring data boom across various sectors. As per the US Bureau of Labor Statistics , the demand for data analytics jobs will grow by 23% between 2021 and 2031. What’s more, roles offer excellent salaries and career progression. As you gain experience and climb the ranks, your pay scales up, making it one of the most competitive fields in the job market. 

Learning data analytics methodology can help you give an all-new boost to your career. Here are some tips to become a data analyst:

1. Take an Online Course

You do not necessarily need a degree to become a data analyst. A degree can give you solid foundational knowledge in relevant quantitative skills. But so can certificate programs or university courses. 

2. Gain the Necessary Technical Skills

Having a set of specific technical skills will help you deepen your analytical capabilities. You must explore and understand the data analysis tools to deal with large datasets and comprehend the analysis. 

3. Gain Practical Knowledge

You can work on data analysis projects to showcase your skills. Then, create a portfolio highlighting your ability to handle real-world data and provide insights. You can also seek internship opportunities that provide valuable exposure and networking opportunities. 

4. Keep Up to Date with the Trends

Since data analysis is rapidly evolving, keep pace with cutting-edge analytics tools, methods, and trends. You can do this through exploration, networking, and continuous learning.

5. Search for the Ideal Job

The job titles and responsibilities continue to change and expand in data analytics. Beyond “Data Analyst,” explore titles like Business Analyst, Data Scientist, Data Engineer, Data Architect, and Marketing Analyst. Your knowledge, education, and experience can guide your path to the right data job. 

The Take Away

Whether you’re eager to delve into a personal area of interest or upgrade your skills to advance your data career, we’ve covered all the relevant aspects in this article. 

Now that you have a clear understanding of what data analysis is, and a grasp of the best data analysis techniques , it’s time to roll up your sleeves and put your knowledge into practice.

We have designed The IxDF courses and certifications to align with your intellectual and professional objectives. If you haven’t already, take the initial step toward enriching your data analytics skills by signing up today. Your journey to expertise in data analysis awaits.

Where to Learn More

1. Learn the most sought-after tool, Microsoft Excel, from basic to advanced in this LinkedIn Microsoft Excel Online Training Course .

2. Ensure all the touchpoints of your service are perfect through this certification in Service Design .

3. Learn more about the analytics data types we encounter daily in this video.

Author: Stewart Cheifet. Appearance time: 0:22 - 0:24. Copyright license and terms: CC / Fair Use. Modified: Yes. Link: https://archive.org/details/CC1218 greatestgames

4. Read this free eBook, The Elements of Statistical Learning , to boost your statistical analysis skills.

5. Check out Python for Data Analysis to learn how to solve statistical problems with Python. 

6. Join this beginner-level course and launch your career in data analytics. Data-Driven Design: Quantitative UX Research Course

Data-Driven Design: Quantitative Research for UX

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Quantitative Data Analysis 101

The Lingo, Methods and Techniques – Explained Simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Overview: Quantitative Data Analysis 101

• What (exactly) is quantitative data analysis?
• When to use quantitative analysis
• How quantitative analysis works

The two “branches” of quantitative analysis

• Descriptive statistics 101
• Inferential statistics 101
• How to choose the right quantitative methods
• Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

• Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
• Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
• And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

• Mean – this is simply the mathematical average of a range of numbers.
• Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
• Mode – this is simply the most commonly occurring number in the data set.
• In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
• Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
• Skewness . As the name suggests, skewness indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones. On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones. But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

• Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
• Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
• And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

• Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
• And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post . What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other. Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

• The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
• Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

• Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
• The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
• Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
• Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
• To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

Learn More About Quantitative:

Triangulation: The Ultimate Credibility Enhancer

Triangulation is one of the best ways to enhance the credibility of your research. Learn about the different options here.

Inferential Statistics 101: Simple Explainer (With Examples)

Learn about the key concepts and tests within inferential statistics, including t-tests, ANOVA, chi-square, correlation and regression.

Descriptive Statistics 101: Simple Explainer (With Examples)

Learn about the key concepts and measures within descriptive statistics, including measures of central tendency and dispersion.

Validity & Reliability: Explained Simply

Validity & Reliability In Research A Plain-Language Explanation (With Examples)By:...

Research Design 101: Qualitative & Quantitative

Learn about research design for both qualitative and quantitative studies. Includes plain-language explanations and examples.

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78 Comments

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Do female employees report higher job satisfaction than male employees with similar job descriptions across the South African telecommunications sector? – I though that maybe Chi Square could be used here. – Is there a gender difference in talented employees’ actual turnover decisions across the South African telecommunications sector? T-tests or Correlation in this one. – Is there a gender difference in the cost of actual turnover decisions across the South African telecommunications sector? T-tests or Correlation in this one. – What practical recommendations can be made to the management of South African telecommunications companies on leveraging gender to mitigate employee turnover decisions?

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Table of Contents

What is data analysis, data analysis process, why is data analysis important, types of data analysis, 24 data analysis methods with examples, applications of data analysis, key data analysis techniques, tools for data analysis, future trends in data analysis, choose the right program, what is data analysis: examples, types, & applications.

Analysis involves breaking down a whole into its parts for detailed study. Data analysis is the practice of transforming raw data into actionable insights for informed decision-making. It involves collecting and examining data to answer questions, validate hypotheses, or refute theories.

In the contemporary business landscape, gaining a competitive edge is imperative, given the challenges such as rapidly evolving markets, economic unpredictability, fluctuating political environments, capricious consumer sentiments, and even global health crises. These challenges have reduced the room for error in business operations. For companies striving not only to survive but also to thrive in this demanding environment, the key lies in embracing the concept of data analysis. This involves strategically accumulating valuable, actionable information, which is leveraged to enhance decision-making processes.

If you're interested in forging a career in data analysis and wish to discover the top data analysis courses in 2024, we invite you to explore our informative video. It will provide insights into the opportunities to develop your expertise in this crucial field.

Data analysis inspects, cleans, transforms, and models data to extract insights and support decision-making. As a data analyst , your role involves dissecting vast datasets, unearthing hidden patterns, and translating numbers into actionable information.

Organizations may use data analysis to make better decisions, increase efficiency, and forecast future consequences. Data analysis is widely utilized in many industries, including business, healthcare, marketing, finance, and scientific research, to gain insights and solve problems.

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The data analysis is a structured sequence of steps that lead from raw data to actionable insights. Here are the answers to what is data analysis:

• Data Collection: Gather relevant data from various sources, ensuring data quality and integrity.
• Data Cleaning: Identify and rectify errors, missing values, and inconsistencies in the dataset. Clean data is crucial for accurate analysis.
• Exploratory Data Analysis (EDA): Conduct preliminary analysis to understand the data's characteristics, distributions, and relationships. Visualization techniques are often used here.
• Data Transformation: Prepare the data for analysis by encoding categorical variables, scaling features, and handling outliers, if necessary.
• Model Building: Depending on the objectives, apply appropriate data analysis methods, such as regression, clustering, or deep learning.
• Model Evaluation: Depending on the problem type, assess the models' performance using metrics like Mean Absolute Error, Root Mean Squared Error, etc.
• Interpretation and Visualization: Translate the model's results into actionable insights. Visualizations, tables, and summary statistics help in conveying findings effectively.
• Deployment: Implement the insights into real-world solutions or strategies, ensuring that the data-driven recommendations are implemented.

Data analysis plays a pivotal role in today's data-driven world. It helps organizations harness the power of data, enabling them to make decisions, optimize processes, and gain a competitive edge. By turning raw data into meaningful insights, data analysis empowers businesses to identify opportunities, mitigate risks , and enhance their overall performance.

1. Informed Decision-Making

Data analysis is the compass that guides decision-makers through a sea of information. It enables organizations to base their choices on concrete evidence rather than intuition or guesswork. In business, this means making decisions more likely to lead to success, whether choosing the right marketing strategy, optimizing supply chains, or launching new products. By analyzing data, decision-makers can assess various options' potential risks and rewards, leading to better choices.

2. Improved Understanding

Data analysis provides a deeper understanding of processes, behaviors, and trends. It allows organizations to gain insights into customer preferences, market dynamics, and operational efficiency.

3. Competitive Advantage

Organizations can identify opportunities and threats by analyzing market trends, consumer behavior, and competitor performance. They can pivot their strategies to respond effectively, staying one step ahead of the competition. This ability to adapt and innovate based on data insights can lead to a significant competitive advantage.

4. Risk Mitigation

Data analysis is a valuable tool for risk assessment and management. By analyzing historical data, organizations can assess potential issues and take preventive measures. For instance, data analysis detects fraudulent activities in the finance industry by identifying unusual transaction patterns. This helps minimize financial losses and safeguards customers' reputations and trust.

5. Efficient Resource Allocation

Data analysis helps organizations optimize resource allocation. Whether it's allocating budgets, human resources, or manufacturing capacities, data-driven insights can ensure that resources are utilized efficiently. For example, data analysis can help hospitals allocate staff and resources to the areas with the highest patient demand, ensuring that patient care remains efficient and effective.

6. Continuous Improvement

Data analysis is a catalyst for continuous improvement . It allows organizations to monitor performance metrics, track progress, and identify areas for enhancement. This iterative process of analyzing data, implementing changes, and analyzing again leads to ongoing refinement and excellence in processes and products.

Data analysis comes in different forms, each serving its own purpose. Here’s a clear breakdown of the main types:

Descriptive Analysis

This type shows you what has already happened. It’s all about summarizing raw data into something easy to understand. For instance, a business might use it to see how much each employee sold and what the average sales look like. It’s like asking: What happened?

Diagnostic Analysis

Once you know what happened, diagnostic analysis helps explain why. Say a hospital notices more patients than usual. By looking deeper into the data, you might find that many of them had the same symptoms, helping you figure out the cause. This analysis answers: Why did it happen?

Predictive Analysis

Now we move into the future. Predictive analysis looks at trends from the past to help you guess what might come next. For example, if a store knows that sales usually go up in certain months, it can predict the same for the next year. The question here is: What might happen?

Prescriptive Analysis

This type gives you advice based on all the data you’ve gathered. If you know when sales are high, prescriptive analysis suggests how to boost them even more or improve slower months. It answers: What should we do next?

In this section, we will talk about data analysis methods along with real-time examples.

1. Descriptive Analysis

Descriptive analysis involves summarizing and organizing data to describe the current situation. It uses measures like mean, median, mode, and standard deviation to describe the main features of a data set.

Example: A company analyzes sales data to determine the monthly average sales over the past year. They calculate the mean sales figures and use charts to visualize the sales trends.

2. Diagnostic Analysis

Diagnostic analysis goes beyond descriptive statistics to understand why something happened. It looks at data to find the causes of events.

Example: After noticing a drop in sales, a retailer uses diagnostic analysis to investigate the reasons. They examine marketing efforts, economic conditions, and competitor actions to identify the cause.

3. Predictive Analysis

Predictive analysis uses historical data and statistical techniques to forecast future outcomes. It often involves machine learning algorithms.

Example: An insurance company uses predictive analysis to assess the risk of claims by analyzing historical data on customer demographics, driving history, and claim history.

4. Prescriptive Analysis

Prescriptive analysis recommends actions based on data analysis. It combines insights from descriptive, diagnostic, and predictive analyses to suggest decision options.

Example: An online retailer uses prescriptive analysis to optimize its inventory management . The system recommends the best products to stock based on demand forecasts and supplier lead times.

5. Quantitative Analysis

Quantitative analysis involves using mathematical and statistical techniques to analyze numerical data.

Example: A financial analyst uses quantitative analysis to evaluate a stock's performance by calculating various financial ratios and performing statistical tests.

6. Qualitative Research

Qualitative research focuses on understanding concepts, thoughts, or experiences through non-numerical data like interviews, observations, and texts.

Example: A researcher interviews customers to understand their feelings and experiences with a new product, analyzing the interview transcripts to identify common themes.

7. Time Series Analysis

Time series analysis involves analyzing data points collected or recorded at specific intervals to identify trends, cycles, and seasonal variations.

Example: A climatologist studies temperature changes over several decades using time series analysis to identify patterns in climate change.

8. Regression Analysis

Regression analysis assesses the relationship between a dependent variable and one or more independent variables.

Example: An economist uses regression analysis to examine the impact of interest, inflation, and employment rates on economic growth.

9. Cluster Analysis

Cluster analysis groups data points into clusters based on their similarities.

Example: A marketing team uses cluster analysis to segment customers into distinct groups based on purchasing behavior, demographics, and interests for targeted marketing campaigns.

10. Sentiment Analysis

Sentiment analysis identifies and categorizes opinions expressed in the text to determine the sentiment behind it (positive, negative, or neutral).

Example: A social media manager uses sentiment analysis to gauge public reaction to a new product launch by analyzing tweets and comments.

11. Factor Analysis

Factor analysis reduces data dimensions by identifying underlying factors that explain the patterns observed in the data.

Example: A psychologist uses factor analysis to identify underlying personality traits from a large set of behavioral variables.

12. Statistics

Statistics involves the collection, analysis, interpretation, and presentation of data.

Example: A researcher uses statistics to analyze survey data, calculate the average responses, and test hypotheses about population behavior.

13. Content Analysis

Content analysis systematically examines text, images, or media to quantify and analyze the presence of certain words, themes, or concepts.

Example: A political scientist uses content analysis to study election speeches and identify common themes and rhetoric from candidates.

14. Monte Carlo Simulation

Monte Carlo simulation uses random sampling and statistical modeling to estimate mathematical functions and mimic the operation of complex systems.

Example: A financial analyst uses Monte Carlo simulation to assess a portfolio's risk by simulating various market scenarios and their impact on asset prices.

15. Cohort Analysis

Cohort analysis studies groups of people who share a common characteristic or experience within a defined period to understand their behavior over time.

Example: An e-commerce company conducts cohort analysis to track the purchasing behavior of customers who signed up in the same month to identify retention rates and revenue trends.

16. Grounded Theory

Grounded theory involves generating theories based on systematically gathered and analyzed data through the research process.

Example: A sociologist uses grounded theory to develop a theory about social interactions in online communities by analyzing participant observations and interviews.

17. Text Analysis

Text analysis involves extracting meaningful information from text through techniques like natural language processing (NLP).

Example: A customer service team uses text analysis to automatically categorize and prioritize customer support emails based on the content of the messages.

18. Data Mining

Data mining involves exploring large datasets to discover patterns, associations, or trends that can provide actionable insights.

Example: A retail company uses data mining to identify purchasing patterns and recommend products to customers based on their previous purchases.

19. Decision-Making

Decision-making involves choosing the best action from available options based on data analysis and evaluation.

Example: A manager uses data-driven decision-making to allocate resources efficiently by analyzing performance metrics and cost-benefit analyses.

20. Neural Network

A neural network is a computational model inspired by the human brain used in machine learning to recognize patterns and make predictions.

Example: A tech company uses neural networks to develop a facial recognition system that accurately identifies individuals from images.

21. Data Cleansing

Data cleansing involves identifying and correcting inaccuracies and inconsistencies in data to improve its quality.

Example: A data analyst cleans a customer database by removing duplicates, correcting typos, and filling in missing values.

22. Narrative Analysis

Narrative analysis examines stories or accounts to understand how people make sense of events and experiences.

Example: A researcher uses narrative analysis to study patients' stories about their experiences with healthcare to identify common themes and insights into patient care.

23. Data Collection

Data collection is the process of gathering information from various sources for analysis.

Example: A market researcher collects data through surveys, interviews, and observations to study consumer preferences.

24. Data Interpretation

Data interpretation involves making sense of data by analyzing and drawing conclusions from it.

Example: After analyzing sales data, a manager interprets the results to understand the effectiveness of a recent marketing campaign and plans future strategies based on these insights.

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Data analysis is a versatile and indispensable tool that finds applications across various industries and domains. Its ability to extract actionable insights from data has made it a fundamental component of decision-making and problem-solving. Let's explore some of the critical applications of data analysis:

1. Business and Marketing

• Market Research: Data analysis helps businesses understand market trends, consumer preferences, and competitive landscapes. It aids in identifying opportunities for product development, pricing strategies, and market expansion.
• Sales Forecasting: Data analysis models can predict future sales based on historical data, seasonality, and external factors. This helps businesses optimize inventory management and resource allocation.

2. Healthcare and Life Sciences

• Disease Diagnosis: Data analysis is vital in medical diagnostics, from interpreting medical images (e.g., MRI, X-rays) to analyzing patient records. Machine learning models can assist in early disease detection.
• Drug Discovery: Pharmaceutical companies use data analysis to identify potential drug candidates, predict their efficacy, and optimize clinical trials.
• Genomics and Personalized Medicine: Genomic data analysis enables personalized treatment plans by identifying genetic markers influencing disease susceptibility and therapy response.
• Risk Management: Financial institutions use data analysis to assess credit risk, detect fraudulent activities, and model market risks.
• Algorithmic Trading: Data analysis is integral to developing trading algorithms that analyze market data and execute trades automatically based on predefined strategies.
• Fraud Detection: Credit card companies and banks employ data analysis to identify unusual transaction patterns and detect fraudulent activities in real time.

4. Manufacturing and Supply Chain

• Quality Control: Data analysis monitors and controls product quality on manufacturing lines. It helps detect defects and ensure consistency in production processes.
• Inventory Optimization: By analyzing demand patterns and supply chain data, businesses can optimize inventory levels, reduce carrying costs, and ensure timely deliveries.

5. Social Sciences and Academia

• Social Research: Researchers in social sciences analyze survey data, interviews, and textual data to study human behavior, attitudes, and trends. It helps in policy development and understanding societal issues.
• Academic Research: Data analysis is crucial to scientific physics, biology, and environmental science research. It assists in interpreting experimental results and drawing conclusions.

6. Internet and Technology

• Search Engines : Google uses complex data analysis algorithms to retrieve and rank search results based on user behavior and relevance.
• Recommendation Systems: Services like Netflix and Amazon leverage data analysis to recommend content and products to users based on their past preferences and behaviors.

7. Environmental Science

• Climate Modeling: Data analysis is essential in climate science. It analyzes temperature, precipitation, and other environmental data. It helps in understanding climate patterns and predicting future trends.
• Environmental Monitoring: Remote sensing data analysis monitors ecological changes, including deforestation, water quality, and air pollution.

1. Descriptive Statistics

Descriptive statistics provide a snapshot of a dataset's central tendencies and variability. These techniques help summarize and understand the data's basic characteristics.

2. Inferential Statistics

Inferential statistics involve making predictions or inferences based on a sample of data. Techniques include hypothesis testing, confidence intervals, and regression analysis. These methods are crucial for drawing conclusions from data and assessing the significance of findings.

3. Regression Analysis

It explores the relationship between one or more independent variables and a dependent variable. It is widely used for prediction and understanding causal links. Linear, logistic, and multiple regression are common in various fields.

4. Clustering Analysis

It is an unsupervised learning method that groups similar data points. K-means clustering and hierarchical clustering are examples. This technique is used for customer segmentation, anomaly detection, and pattern recognition.

5. Classification Analysis

Classification analysis assigns data points to predefined categories or classes. It's often used in applications like spam email detection, image recognition, and sentiment analysis. Popular algorithms include decision trees, support vector machines, and neural networks.

6. Time Series Analysis

Time series analysis deals with data collected over time, making it suitable for forecasting and trend analysis. Techniques like moving averages, autoregressive integrated moving averages (ARIMA), and exponential smoothing are applied in fields like finance, economics, and weather forecasting.

7. Text Analysis (Natural Language Processing - NLP)

Text analysis techniques, part of NLP, enable extracting insights from textual data. These methods include sentiment analysis, topic modeling, and named entity recognition. Text analysis is widely used for analyzing customer reviews, social media content, and news articles.

8. Principal Component Analysis

It is a dimensionality reduction technique that simplifies complex datasets while retaining important information. It transforms correlated variables into a set of linearly uncorrelated variables, making it easier to analyze and visualize high-dimensional data.

9. Anomaly Detection

Anomaly detection identifies unusual patterns or outliers in data. It's critical in fraud detection, network security, and quality control. Techniques like statistical methods, clustering-based approaches, and machine learning algorithms are employed for anomaly detection.

10. Data Mining

Data mining involves the automated discovery of patterns, associations, and relationships within large datasets. Techniques like association rule mining, frequent pattern analysis, and decision tree mining extract valuable knowledge from data.

11. Machine Learning and Deep Learning

ML and deep learning algorithms are applied for predictive modeling, classification, and regression tasks. Techniques like random forests, support vector machines, and convolutional neural networks (CNNs) have revolutionized various industries, including healthcare, finance, and image recognition.

12. Geographic Information Systems (GIS) Analysis

GIS analysis combines geographical data with spatial analysis techniques to solve location-based problems. It's widely used in urban planning, environmental management, and disaster response.

There are several tools that can help you work with data, no matter your experience. Some popular options include:

RapidMiner is a flexible tool for handling tasks like data prep and visualization. It’s handy if you have existing data and need to analyze it further, working well across industries like healthcare or manufacturing.

Orange is perfect for beginners, with its user-friendly interface and color-coded tools. It’s great for visualizing and analyzing data and comes with add-ons for things like bioinformatics and text mining .

KNIME is open-source and easy to use, even for newcomers. It helps with cleaning and analyzing data and integrates with programming languages like Python and SQL . Many companies use it to teach data processing.

Tableau is all about creating easy-to-understand visuals from your data. It’s widely used in business and can connect to multiple data sources, making it a popular choice for diving into analytics.

Google Charts

This free tool helps you create interactive charts that you can easily embed on websites. It’s user-friendly and works well for creating web-based visualizations.

Datawrapper

Datawrapper is great for making charts and maps online. It’s simple to use, though you’ll need to enter data manually, which can be time-consuming but worth it if you need online visuals.

Microsoft Excel & Power BI

Excel is already familiar to most and offers plenty of options for creating charts and graphs. If you need more power, Power BI is a great next step for larger-scale data analysis.

Qlik helps businesses turn their data into insights. It’s a powerful tool for real-time data integration, helping with everything from customer behavior analysis to identifying new opportunities.

Google Analytics

Google Analytics tracks how people interact with your website. It organizes this data into useful reports so businesses can see trends and understand user behavior better.

Spotfire is easy to use and helps turn data into insights. It’s great for decision-makers who need to analyze both past and current data for real-time decision-making quickly.

Relevant Read: Top 20+ Data Analysis Tools

Data analysis is an ever-evolving field driven by technological advancements. The future of data analysis promises exciting developments that will reshape how data is collected, processed, and utilized. Here are some of the key trends of data analysis:

1. Artificial Intelligence and Machine Learning Integration

Artificial intelligence (AI) and machine learning (ML) are expected to play a central role in data analysis. These technologies can automate complex data processing tasks, identify patterns at scale, and make highly accurate predictions. AI-driven analytics tools will become more accessible, enabling organizations to harness the power of ML without requiring extensive expertise.

2. Augmented Analytics

Augmented analytics combines AI and natural language processing (NLP) to assist data analysts in finding insights. These tools can automatically generate narratives, suggest visualizations, and highlight important trends within data. They enhance the speed and efficiency of data analysis, making it more accessible to a broader audience.

3. Data Privacy and Ethical Considerations

As data collection becomes more pervasive, privacy concerns and ethical considerations will gain prominence. Future data analysis trends will prioritize responsible data handling, transparency, and compliance with regulations like GDPR . Differential privacy techniques and data anonymization will be crucial in balancing data utility with privacy protection.

4. Real-time and Streaming Data Analysis

The demand for real-time insights will drive the adoption of real-time and streaming data analysis. Organizations will leverage technologies like Apache Kafka and Apache Flink to process and analyze data as it is generated. This trend is essential for fraud detection, IoT analytics, and monitoring systems.

5. Quantum Computing

It can potentially revolutionize data analysis by solving complex problems exponentially faster than classical computers. Although quantum computing is in its infancy, its impact on optimization, cryptography , and simulations will be significant once practical quantum computers become available.

6. Edge Analytics

With the proliferation of edge devices in the Internet of Things (IoT), data analysis is moving closer to the data source. Edge analytics allows for real-time processing and decision-making at the network's edge, reducing latency and bandwidth requirements.

7. Explainable AI (XAI)

Interpretable and explainable AI models will become crucial, especially in applications where trust and transparency are paramount. XAI techniques aim to make AI decisions more understandable and accountable, which is critical in healthcare and finance.

8. Data Democratization

The future of data analysis will see more democratization of data access and analysis tools. Non-technical users will have easier access to data and analytics through intuitive interfaces and self-service BI tools , reducing the reliance on data specialists.

9. Advanced Data Visualization

Data visualization tools will continue to evolve, offering more interactivity, 3D visualization, and augmented reality (AR) capabilities. Advanced visualizations will help users explore data in new and immersive ways.

10. Ethnographic Data Analysis

Ethnographic data analysis will gain importance as organizations seek to understand human behavior, cultural dynamics, and social trends. This qualitative data analysis approach and quantitative methods will provide a holistic understanding of complex issues.

11. Data Analytics Ethics and Bias Mitigation

Ethical considerations in data analysis will remain a key trend. Efforts to identify and mitigate bias in algorithms and models will become standard practice, ensuring fair and equitable outcomes.

Our Data Analytics courses have been meticulously crafted to equip you with the necessary skills and knowledge to thrive in this swiftly expanding industry. Our instructors will lead you through immersive, hands-on projects, real-world simulations, and illuminating case studies, ensuring you gain the practical expertise necessary for success. Through our courses, you will acquire the ability to dissect data, craft enlightening reports, and make data-driven choices that have the potential to steer businesses toward prosperity.

Having addressed the data analysis question, if you're considering a career in data analytics, it's advisable to begin by researching the prerequisites for becoming a data analyst. You may also want to explore the Post Graduate Program in Data Analytics offered in collaboration with Purdue University. This program offers a practical learning experience through real-world case studies and projects aligned with industry needs. It provides comprehensive exposure to the essential technologies and skills currently employed in data analytics.

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1. What is the difference between data analysis and data science? 

Data analysis primarily involves extracting meaningful insights from existing data using statistical techniques and visualization tools. Whereas, data science encompasses a broader spectrum, incorporating data analysis as a subset while involving machine learning, deep learning, and predictive modeling to build data-driven solutions and algorithms.

2. What are the common mistakes to avoid in data analysis?

Common mistakes to avoid in data analysis include neglecting data quality issues, failing to define clear objectives, overcomplicating visualizations, not considering algorithmic biases, and disregarding the importance of proper data preprocessing and cleaning. Additionally, avoiding making unwarranted assumptions and misinterpreting correlation as causation in your analysis is crucial.

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

Recommended Reads

Big Data Career Guide: A Comprehensive Playbook to Becoming a Big Data Engineer

Why Python Is Essential for Data Analysis and Data Science?

What Is Exploratory Data Analysis? Steps and Market Analysis

The Rise of the Data-Driven Professional: 6 Non-Data Roles That Need Data Analytics Skills

Exploratory Data Analysis [EDA]: Techniques, Best Practices and Popular Applications

The Best Spotify Data Analysis Project You Need to Know

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

On This Page:

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography .

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

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What Is Data Analytics? An Overview of Methods and Practical Uses

Michael Chen | Senior Writer | September 25, 2024

In This Article

What Is Data Analytics?

Four main types of data analytics, benefits of data analytics, data analytics challenges, data analytics strategies and solutions, how automation and ai transform data analytics, data analytics faqs.

For years, businesses have struggled to collect and make sense of the data generated by what seems like a constantly expanding variety of sources. Without a comprehensive—and scalable—data analytics strategy, decision-makers will miss out on valuable insights that could help them improve operations, increase revenue, and stay ahead of the competition.

A plan is key. Using today’s analytics tools and techniques, businesses can dig into data sets to uncover industry and customer trends, patterns, and correlations that marketing, sales, and other departments can use to their advantage.

Data analytics is the process of collecting information for the purpose of studying it to generate insights. High-level analysis is primarily performed by data scientists, but the latest data analytics platforms have tools, such as queries based on natural language processing and automated insights, that allow business users to dig into datasets.

Data analytics as a practice is focused on using tools and techniques to explore and analyze data in real-time or near-real-time to uncover hidden patterns, correlations, and trends. The goal is predictive and prescriptive analysis, using advanced techniques to make accurate, dynamic, and forward-looking forecasts and recommendations. Related business intelligence (BI) capabilities allow you to collect up-to-date data from your organization, present it in easy-to-understand formats such as tables and graphs, and disseminate resulting insights in a timely fashion.

Data Analytics vs Big Data and Data Science

Data analytics often overlaps with big data and data science disciplines, though the three are different. Data analytics uses big data as a key element to succeed while falling under the umbrella of data science as an area of focus. Additional differences are as follows:

Big data refers to generating, collecting, and processing heavy volumes of data. With data coming from databases, Internet of Things devices, social media and emails, and other diverse sources, data analytics systems can work best when integrated into big data stores. The greater the volume, the more context and data points feed into data analytics. In essence, big data is the fuel for the data analytics engine.

Going on with that analogy, a data scientist tunes the data analytics engine using training in data science. Data science is the study of how to use data to derive meaning and insight. A data scientist must possess a cross-section of math, statistics, programming, and other related skills to be able to build queries and models for data analytics projects.

Key Takeaways

• Data analytics is the practice of using analytics tools to derive insights from datasets to inform decisions.
• With data analytics, organizations can improve decision-making, streamline operations, and increase revenue.
• Still, data analytics projects can be resource-intensive from both a technology and skills perspective.
• Data analytics is different from business intelligence, big data, and data science, though connected to all three.

Before launching a data analytic effort, companies need to decide what they want to achieve: Do you have historical data to mine, to understand trends and patterns? Are you looking to make predictions, maybe even recommend actions to achieve desired results? Each type of data analytics serves a purpose and requires specific tools and techniques to succeed.

1. Predictive data analytics

Predictive analytics may be the most used type of data analytics. Businesses use predictive analytics to identify trends, correlations, and causation. The category can be further broken down into predictive modeling and statistical modeling , which go hand in hand.

For example, an advertising campaign for clever tee shirts on Facebook could apply predictive analytics to determine how closely conversion rates correlate with a target audience’s geographic area, income bracket, and interests. From there, predictive modeling could be used to analyze the statistics for two, or more, target audiences and provide possible revenue values for each demographic.

2. Prescriptive data analytics

Prescriptive analytics is where artificial intelligence and big data combine to help predict outcomes and identify what actions to take. This category of analytics can be further broken down into optimization and random testing . Using advancements in machine learning (ML), prescriptive analytics can help answer questions such as “What if we try this slogan?” and “What is the best shirt color for an older demographic?” You can test variables and even suggest new options that offer a higher chance of generating a positive outcome.

3. Diagnostic data analytics

While not as exciting as predicting the future, analyzing data from the past can help guide your business. Diagnostic data analytics is the process of examining data to understand cause and effect. Techniques such as drill down, data discovery, data mining, and correlations are often employed.

Diagnostic data analytics help answer why something occurred. Like the other categories, it too is broken down into two more specific categories: discover and alerts and query and drill downs . Query and drill downs are used to get more detail from a report. For example, say a sales rep closed significantly fewer deals one month. A drill down could show fewer workdays due to a two-week vacation.

Discover and alerts notify of a potential issue before it occurs; for example, an alert about a lower number of staff hours could warn of a decrease in closed deals. You could also use diagnostic data analytics to “discover” information, such as the most-qualified candidate for a new position at your company.

4. Descriptive data analytics

Descriptive analytics are the backbone of reporting—it’s impossible to have BI tools and dashboards without it. It addresses basic questions of “how many, when, where, and what.”

Once again, descriptive analytics can be further separated into two categories: ad hoc reporting and canned reports . A canned report is one that has been designed previously and contains information around a given subject. An example is a monthly report sent by your ad agency that details performance metrics on your latest tee shirt marketing efforts.

Ad hoc reports, on the other hand, are designed and run on the fly. They are generated when there is a need to answer a specific business question. These reports are useful for obtaining more in-depth information about a specific query. An ad hoc report could focus on your corporate social media profile, examining the types of people who’ve liked your page and other industry pages, as well as other engagement and demographic information. An ad-hoc report’s hyperspecificity gives a very complete picture of, say, your social media audience in a particular city at a certain time of day.

Data is generated by nearly everything these days, from smartphones to vehicles to industrial machinery. Individually, that data provides status updates from each source, but collectively it can offer insights on a level unheard of just a decade ago.

Common benefits of data analytics include the following:

• Informed decision-making : Better use of data can revolutionize an organization’s decision-making process. An executive’s hunch can now be verified through data, incorporating historical context while considering other direct and indirect variables for a clear view of how to proceed. Data integration is a key driver of this benefit. By pulling together data from across an organization as well as applicable external datasets, like publicly available data from sources such as local governments and universities, analysis can deliver much deeper context before making a final decision.
• Operational efficiency : When you have data for the end-to-end workflow of your operation, it’s then possible to break each department’s steps down and consider potential improvements. For example, analytics can tally metrics across your supply chain, highlighting areas where shipping problems, inventory loss, or unnecessarily higher prices are common. With this data in mind, supply chain management teams could theoretically adjust their approach to vendors, quality control, or inventory management.
• Enhanced customer experience : Analytics can create a better customer experience, from both operational functionality and customer individualization POVs. From an operations perspective, analytics can show what works and what creates problems in the customer workflow, highlighting where to invest in process fixes to maximize customer satisfaction. On the personalization side, analytics can build individual profiles, which then allow for stronger engagement via customized marketing, such as specific discounts or timed reminders.
• Revenue growth and competitive advantage : The more data an organization uses, the clearer its markets become—segmentation, seasonality, competitor trends, and other factors can come together to highlight areas primed for capitalization. With this type of analysis, more actionable options come into focus, from ways to leapfrog competitors to finding underserved markets to phasing out failing products and services. All of these actions feed into greater profitability, either by increasing incoming revenue or reducing expenses.
• Risk management and forecasting : An organization’s operational risks come in many forms, from fraudulent financial activity to cybersecurity to faulty processes. With data analytics, departments can translate risk identification into actual numbers that highlight the weakest or most anomalous findings. By expanding the scope and depth of identifying risk, organizations can reduce both direct and indirect financial impacts.

Data analytics is loaded with benefits for organizations, but thorough and effective implementation comes with some hurdles. The following are the most common challenges when it comes to data analytics:

• Data quality and accuracy : Data analytics efforts only work if the underlying data is accurate, clean, and relevant. Thus, identifying quality data sources is one of the key early steps in any analytics project. To facilitate high-quality data, organizations must weigh key elements including relevance and accuracy of data sources, possible data format conflicts, and necessary data preparation/cleansing steps.
• Data integration and silos : Once data sources are identified, be they from internal or external sources, related datasets must be collected in a unified repository to realize the full benefit of data analytics. Getting those datasets into a repository requires a solid data integration strategy . IT teams must enable networks have an underlying infrastructure able to support integration and any required transformation/cleansing. Depending on how organizations are structured, this may require some negotiations with departments not accustomed to sharing data.
• Scalability issues : Data analytics projects can be resource-intensive. It can be beneficial for IT teams to inventory the individual components in the data pipeline and list tasks ranging from data integration to transformation and consolidation to repository connections to the analytics application itself. This is a bigger-picture process requiring IT teams to consider the impact of the project on the network. Depending on configuration, a resource-heavy setup may create significant difficulties scaling up as demand grows.
• Data privacy and security : On its own, a data analytics application does not present a significant security challenge. However, when all the pieces are connected, the analytics process can introduce vulnerabilities. Every time data transfers between environments, that presents risk. On a user level, role-based access is necessary to enable that sensitive data is not exposed. Privacy demands of regional regulations, such as GDPR , present compliance challenges. For the entire organization, basic usage protocols and guidelines are critical to enable general understanding of how to deal with data on that level. These points and more are constantly evolving, which means that IT teams must stay up to date with the latest risks and tools on each topic.
• Skills gap : Data science is a rapidly growing and evolving field. As demand for data analytics increases, so does demand for talent, meaning top candidates are often snatched up immediately and at top dollar. That skills shortage requires companies to get strategic when building out a data science team. Consider training up current employees, where feasible, and purchasing tools that empower users with self-service functionality.

If you want to build a more insights-driven organization, there are plenty of data analytics products on the market today. Ultimately, the ideal solution offers modern analytics tools that are predictive, intuitive, self-learning, and adaptive.

To support all the ways that your organization will use data, here are a few things to keep in mind:

• Opt for a platform that integrates analytics and data management capabilities. Such a solution is easier to provision and delivers business value faster while avoiding the compatibility and access issues of a legacy system that has separate solutions for reporting, discovery, analysis, and recommendations.
• A platform that resides in the cloud but can access data in on-premises and/or hybrid environments is key. Fast, easy access to data as well as the analytics system enables everyone across the organization to gain insights and make informed decisions.

In addition, practices we consider include the following:

• An end-to-end analytics solution : Look for a solution that supports the entire analytics process—from gathering data to providing insights and prescriptive actions—with security, flexibility, reliability, and speed.
• Leverage all data : Choose a solution that accesses and analyzes all available datasets—of any size and in any location—from applications, devices including Internet of Things sensors, departments, and third-parties, whether structured or unstructured, onsite or in the cloud. This complete view unlocks the full value of your data by uncovering patterns and relevant insights to help users make informed, data-driven decisions.
• Improve productivity and data integration : The ideal data analytics solution optimizes all the steps in your data workflows. That makes data and analytics processes faster. Advanced built-in capabilities, such as machine learning, accelerate model building. Ideally, efficiency will be enhanced everywhere in the process, including data gathering, discovering insights, and improving decision-making.
• Benefit from a single source of truth : For trustworthy analytics, insights, and results, data should be consolidated into a single source. Doing so allows for consistency and accuracy with a unified view of data, metrics, and insights.
• Accelerate data insights : Look for a solution with augmented analytics—such as embedded AI and machine learning —which can simplify, accelerate, and automate tasks, giving your decision-makers the power to dig deeper and faster. Ideally the system will automatically collect and consolidate data from multiple sources and recommend new datasets for analysis.
• Visualize data : Analytics has the potential to give you a detailed snapshot of your business landscape. To help make the most of that potential, you want a smart solution that can automatically transform data into visual presentations. This can enable you to see and understand patterns, relationships, and trends that might be missed with a spreadsheet of raw numbers. It also lets you create data mash-ups to get new, unique insights. Your employees can do that without specialized training, thanks to smart technology.

Millions of manually prepared spreadsheets are used across diverse industries, including finance, healthcare, and economics. Yet, according to ZDNet, 90% of all spreadsheets have errors that affect their results. Cut-and-paste issues, hidden cells, and other mistakes have cost businesses millions of dollars.

Traditional analytics solutions and processes can also cause delays in providing businesses with the insights needed to make timely decisions. Often, data is collected from multiple applications and platforms, requiring a corporate department to create the extract, transform, and load (ETL), connections, and interfaces; transfer data from one database to another; evaluate the data quality; and enter the data into spreadsheets.

All of these tasks consume precious time and resources.

In addition, with traditional solutions and processes, you usually need to be an expert in IT or analytics to conduct the analysis. It is not a self-service experience for the busy executive who requires end-of-month analytics. And that means waiting for the IT or analytics expert to provide what’s needed.

Automating analytics processes and putting the processes in the cloud can be a game changer for businesses of all sizes and in all industries. For example, a modern analytics solution with embedded AI and ML and an integrated autonomous data warehouse that runs in a self-securing, self-patching, self-tuning autonomous cloud can revolutionize decision-making.

When you’re working with a modern analytics solution, everything can be automated: Identify a few parameters of what you want examined, which model to apply, and which column you want to predict, and the tool will take over. Data can be ingested from multiple applications, platforms, and clouds. It can be gathered, cleaned, prepared, transformed, and analyzed for predictions—all automatically, accelerating processing and reducing the chance of human-created errors.

Choose Oracle Analytics and you’ll get a single, integrated platform that combines Oracle Analytics and Oracle Autonomous Database. It’s a simple, repeatable solution with the best elements of analytics and powerful autonomous data services. That means obstacles are removed, data is brought together into a single source of truth, and highly actionable insights are unlocked—fast—which makes it an ideal data analytics solution to guide strategic business decisions.

But remember: Companies that realize the full benefit of data analytics don’t stop at tools. They also work to develop a data-driven culture within the organization, where decisions are based on facts rather than intuition. The result is better growth, profitability, and customer satisfaction.

Need a driver for a data analytics process update? Look no further than AI, as these real-world use cases show.

What are the main types of data analytics?

The main types of data analytics are as follows:

• Predictive data analytics, which identify trends, correlation, and causation.
• Prescriptive data analytics, which predict outcomes and provide suggestions on course of action.
• Diagnostic data analytics, which reviews historical data to quantify why something happened.
• Descriptive data analytics, which reviews historical data to show a comprehensive review over a past event that covers all key facts.

Why is data analytics important?

As data is constantly generated from devices and databases in nearly all facets of both business and everyday life, data analytics presents a way to turn those heavy volumes into something meaningful. Thus, data analytics is important because it provides quantifiable evidence to drive decisions while also uncovering insights that can inform further strategy.

How can data analytics improve business decisions?

Before data analytics, business decisions were executed with limited context. For example, a marketing decision might be based on campaign data, but it would have been impossible to fully factor in sales data, competitive data, seasonal factors, and other types of contextual data because of the time and effort involved. With data analytics connected to a comprehensive repository of quality data, all of this can be synthesized into a clear view of a specific situation—and in addition to justifying decisions, data analytics can produce new insights by finding patterns buried deep within datasets.

What is the difference between big data and data analytics?

Big data refers to the generation, collection, and processing of heavy volumes of data from a wide range of sources. Data analytics is the study of data to derive insights. While analytics can be performed on a single, contained dataset, it works best with heavy volumes of data—in fact, the more data, the better.

What is the best type of data analytics?

The best type of data analytics for an organization depends on its stage of development. Most companies are likely already using some sort of analytics, but it may afford insights to make only reactive, not proactive, business decisions.

More and more, businesses are adopting sophisticated data analytics solutions with machine learning capabilities to make better business decisions and help tease out market trends and opportunities. Organizations that do not start to use data analytics with proactive, future-casting capabilities may find business performance suffers because they lack the ability to uncover hidden patterns and gain unexpected insights.

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What is Data Analysis?

Data analysis is an essential aspect of modern decision-making processes across various sectors, including business, healthcare, finance, and academia. As organizations generate massive amounts of data daily, understanding how to extract meaningful insights from this data becomes crucial. In this article, we will explore the fundamental concepts of data analysis, its types, significance, methods, and the tools used for effective analysis. We will also address common queries related to data analysis, providing clarity on its definition and applications in various fields.

Table of Content

What Do You Mean by Data Analysis?

Data analysis definition, data analysis in data science, data analysis in dbms, why data analysis is important, the process of data analysis, analyzing data: techniques and methods.

In today’s data-driven world, organizations rely on data analysis to uncover patterns, trends, and relationships within their data. Whether it’s for optimizing operations, improving customer satisfaction, or forecasting future trends, effective data analysis helps stakeholders make informed decisions. The term data analysis refers to the systematic application of statistical and logical techniques to describe, summarize, and evaluate data. This process can involve transforming raw data into a more understandable format, identifying significant patterns, and drawing conclusions based on the findings.

When we ask, “ What do you mean by data analysis? ” it essentially refers to the practice of examining datasets to draw conclusions about the information they contain. The process can be broken down into several steps, including:

• Data Collection : Gathering relevant data from various sources, which could be databases, surveys, sensors, or web scraping.
• Data Cleaning : Identifying and correcting inaccuracies or inconsistencies in the data to ensure its quality and reliability.
• Data Transformation : Modifying data into a suitable format for analysis, which may involve normalization, aggregation, or creating new variables.
• Data Analysis : Applying statistical methods and algorithms to explore the data, identify trends, and extract meaningful insights.
• Data Interpretation : Translating the findings into actionable recommendations or conclusions that inform decision-making.

By employing these steps, organizations can transform raw data into a valuable asset that guides strategic planning and enhances operational efficiency.

To solidify our understanding, let’s define data analysis with an example . Imagine a retail company looking to improve its sales performance. The company collects data on customer purchases, demographics, and seasonal trends.

By conducting a data analysis , the company may discover that:

• Customers aged 18-25 are more likely to purchase specific products during holiday seasons.
• There is a significant increase in sales when promotional discounts are offered.

Based on these insights, the company can tailor its marketing strategies to target younger customers with specific promotions during peak seasons, ultimately leading to increased sales and customer satisfaction.

To further clarify the concept, let’s define data analysis in a more structured manner. Data analysis can be defined as:

“The process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.”

This definition emphasizes the systematic approach taken in analyzing data, highlighting the importance of not only obtaining insights but also ensuring the integrity and quality of the data used.

The field of data science relies heavily on data analysis to derive insights from large datasets. Data analysis in data science refers to the methods and processes used to manipulate data, identify trends, and generate predictive models that aid in decision-making.

Data scientists employ various analytical techniques, such as:

• Statistical Analysis : Applying statistical tests to validate hypotheses or understand relationships between variables.
• Machine Learning : Using algorithms to enable systems to learn from data patterns and make predictions.
• Data Visualization : Creating graphical representations of data to facilitate understanding and communication of insights.

These techniques play a vital role in enabling organizations to leverage their data effectively, ensuring they remain competitive and responsive to market changes.

Another area where data analysis plays a crucial role is within Database Management Systems ( DBMS ). Data analysis in DBMS involves querying and manipulating data stored in databases to extract meaningful insights. Analysts utilize SQL (Structured Query Language) to perform operations such as:

• Data Retrieval : Extracting specific data from large datasets using queries.
• Aggregation : Summarizing data to provide insights at a higher level.
• Filtering : Narrowing down data to focus on specific criteria.

Understanding how to perform effective data analysis in DBMS is essential for professionals who work with databases regularly, as it allows them to derive insights that can influence business strategies.

Data analysis is crucial for informed decision-making, revealing patterns, trends, and insights within datasets. It enhances strategic planning, identifies opportunities and challenges, improves efficiency, and fosters a deeper understanding of complex phenomena across various industries and fields.

• Informed Decision-Making: A nalysis of data provides a basis for informed decision-making by offering insights into past performance, current trends, and potential future outcomes.
• Business Intelligence: Analyzed data helps organizations gain a competitive edge by identifying market trends, customer preferences, and areas for improvement.
• Problem Solving: It aids in identifying and solving problems within a system or process by revealing patterns or anomalies that require attention.
• Performance Evaluation: Analysis of data enables the assessment of performance metrics, allowing organizations to measure success, identify areas for improvement, and set realistic goals.
• Risk Management: Understanding patterns in data helps in predicting and managing risks, allowing organizations to mitigate potential challenges.
• Optimizing Processes: Data analysis identifies inefficiencies in processes, allowing for optimization and cost reduction.

A Data analysis has the ability to transform raw available data into meaningful insights for your business and your decision-making. While there are several different ways of collecting and interpreting this data, most data-analysis processes follow the same six general steps.

• Define Objectives and Questions: Clearly define the goals of the analysis and the specific questions you aim to answer. Establish a clear understanding of what insights or decisions the analyzed data should inform.
• Data Collection : Gather relevant data from various sources. Ensure data integrity, quality, and completeness. Organize the data in a format suitable for analysis. There are two types of data: qualititative and quantitative data .
• Data Cleaning and Preprocessing: Address missing values, handle outliers, and transform the data into a usable format. Cleaning and preprocessing steps are crucial for ensuring the accuracy and reliability of the analysis.
• Exploratory Data Analysis (EDA) : Conduct exploratory analysis to understand the characteristics of the data. Visualize distributions, identify patterns, and calculate summary statistics . EDA helps in formulating hypotheses and refining the analysis approach.
• Statistical Analysis or Modeling: Apply appropriate statistical methods or modeling techniques to answer the defined questions. This step involves testing hypotheses, building predictive models, or performing any analysis required to derive meaningful insights from the data.
• Interpretation and Communication: Interpret the results in the context of the original objectives. Communicate findings through reports, visualizations, or presentations. Clearly articulate insights, conclusions, and recommendations based on the analysis to support informed decision-making.

When discussing analyzing data , several methods can be employed depending on the nature of the data and the questions being addressed. These methods can be broadly categorized into three types:

There are various data analysis methods, each tailored to specific goals and types of data. The major Data Analysis methods are:

1. Descriptive Analysis

A Descriptive Analysis is foundational as it provides the necessary insights into past performance. Understanding what has happened is crucial for making informed decisions in data analysis . For instance, data analysis in data science often begins with descriptive techniques to summarize and visualize data trends.

2. Diagnostic Analysis

Diagnostic analysis works hand in hand with Descriptive Analysis . As descriptive Analysis finds out what happened in the past, diagnostic Analysis, on the other hand, finds out why did that happen or what measures were taken at that time, or how frequently it has happened. By analyzing data thoroughly, businesses can address the question, “what do you mean by data analysis?” They can assess what factors contributed to specific outcomes, providing a clearer picture of their operational efficiency and effectiveness.

3. Predictive Analysis

By forecasting future trends based on historical data, Predictive analysis predictive analysis enables organizations to prepare for upcoming opportunities and challenges. This analysis type answers the inquiry of what is data science analysis by leveraging data trends to predict future behaviors and trends. This capability is vital for strategic planning and risk management in business operations. 

4. Prescriptive Analysis

Prescriptive Analysis is an advanced method that takes Predictive Analysis insights and offers actionable recommendations, guiding decision-makers toward the best course of action. It extends beyond merely analyzing data to suggesting optimal solutions based on potential future scenarios, thus addressing the need for a structured approach to decision-making.

5. Statistical Analysis

Statistical Analysis is essential for summarizing data, helping in identifying key characteristics and understanding relationships within datasets. This analysis can reveal significant patterns that inform broader strategies and policies, thereby allowing analysts to provide a robust review of data analytics practices within an organization.

6. Regression Analysis

Regression analysis is a statistical method extensively used in data analysis to model the relationship between a dependent variable and one or more independent variables. This method is particularly useful in establishing the relationship between variables, making it vital for forecasting and strategic planning, as analysts often define data analysis with examples that utilize regression techniques to illustrate these concepts.

7. Cohort Analysis

By examining specific groups over time, cohort analysis aids in understanding customer behavior and improving retention strategies. This approach allows businesses to tailor their services to different segments, thereby effectively utilizing data storage and analysis in big data to enhance customer engagement and satisfaction.

8. Time Series Analysis

Time series analysis is crucial for any domain where data points are collected over time, allowing for trend identification and forecasting. Businesses can utilize this method to analyze seasonal trends and predict future sales, addressing the question of what do you understand by data analysis in the context of temporal data.

9. Factor Analysis

Factor analysis is a statistical method that explores underlying relationships among a set of observed variables. It identifies latent factors that contribute to observed patterns, simplifying complex data structures. This technique is invaluable in reducing dimensionality, revealing hidden patterns, and aiding in the interpretation of large datasets.

10. Text Analysis

Text analysis involves extracting valuable information from unstructured textual data. Utilizing natural language processing and machine learning techniques, it enables the extraction of sentiments, key themes, and patterns within large volumes of text. analyze customer feedback, social media sentiment, and more, showcasing the practical applications of analyzing data in real-world scenarios.

Tools for Data Analysis

Several tools are available to facilitate effective data analysis . These tools can range from simple spreadsheet applications to complex statistical software. Some popular tools include:

• SAS : SAS was a programming language developed by the SAS Institute for performed advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics. , SAS was developed for very specific uses and powerful tools are not added every day to the extensive already existing collection thus making it less scalable for certain applications.
• Microsoft Excel : It is an important spreadsheet application that can be useful for recording expenses, charting data, and performing easy manipulation and lookup and or generating pivot tables to provide the desired summarized reports of large datasets that contain significant data findings. It is written in C# , C++ , and .NET Framework , and its stable version was released in 2016.
• R  :It is one of the leading programming languages for performing complex statistical computations and graphics. It is a free and open-source language that can be run on various UNIX platforms, Windows, and macOS . It also has a command-line interface that is easy to use. However, it is tough to learn especially for people who do not have prior knowledge about programming.
• Python : It is a powerful high-level programming language that is used for general-purpose programming. Python supports both structured and functional programming methods. Its extensive collection of libraries make it very useful in data analysis. Knowledge of Tensorflow, Theano, Keras, Matplotlib, Scikit-learn, and Keras can get you a lot closer to your dream of becoming a machine learning engineer.
• Tableau Public : Tableau Public is free software developed by the public company “ Tableau Software ” that allows users to connect to any spreadsheet or file and create interactive data visualizations. It can also be used to create maps, dashboards along with real-time updation for easy presentation on the web. The results can be shared through social media sites or directly with the client making it very convenient to use.
• Knime  :Knime, the Konstanz Information Miner is a free and open-source data analytics software. It is also used as a reporting and integration platform. It involves the integration of various components for Machine Learning and data mining through the modular data-pipe lining. It is written in Java and developed by KNIME.com AG . It can be operated in various operating systems such as Linux, OS X, and Windows.
• Power BI : A business analytics service that provides interactive visualizations and business intelligence capabilities with a simple interface.

In conclusion, data analysis is a vital process that involves examining, cleaning, transforming, and modeling data to extract meaningful insights that drive decision-making. With the vast amounts of data generated daily, organizations must harness the power of data analysis to remain competitive and responsive to market trends.

Understanding the different types of data analysis, the tools available, and the methods employed in this field is essential for professionals aiming to leverage data effectively. As we move further into the digital age, the significance of data analysis will continue to grow, shaping the future of industries and influencing strategic decisions across the globe.

Data Analysis- FAQs

What is the definition of data analysis in data science.

The define data analysis in data science refers to the methodology of collecting, processing, and analyzing data to generate insights and support data-driven decisions within the field of data science.

What is Data Analysis Examples?

To define data analysis with an example , consider a retail company analyzing sales data to identify trends in customer purchasing behavior. This can involve descriptive analysis to summarize past sales and predictive analysis to forecast future trends based on historical data.

How to do data analysis in Excel?

Import data into Excel, use functions for summarizing and visualizing data. Utilize PivotTables, charts, and Excel’s built-in analysis tools for insights and trends.

How does data storage and analysis work in big data?

Data storage and analysis in big data involves utilizing technologies that manage and analyze vast amounts of structured and unstructured data. This enables organizations to derive meaningful insights from large datasets, driving strategic decision-making.

What is computer data analysis?

Computer data analysis refers to the use of computer software and algorithms to perform data analysis. This method streamlines the process, allowing for efficient handling of large datasets and complex analyses.

Where can I find a review of data analytics?

A review of data analytics can be found on various platforms, including academic journals, industry reports, and websites like Geeks for Geeks that provide comprehensive insights into data analytics practices and technologies.

What are the benefits of data analysis?

The benefits of data analysis include improved decision-making, enhanced operational efficiency, better customer insights, and the ability to identify market trends. Organizations that leverage data analysis gain a competitive advantage by making informed choices.

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Quantitative Methods
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

Need Help Locating Statistics?

Resources for locating data and statistics can be found here:

Statistics & Data Research Guide

Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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Market Research: A How-To Guide and Template

Discover the different types of market research, how to conduct your own market research, and use a free template to help you along the way.

MARKET RESEARCH KIT

5 Research and Planning Templates + a Free Guide on How to Use Them in Your Market Research

Updated: 02/21/24

Published: 03/30/16

Today's consumers have a lot of power. As a business, you must have a deep understanding of who your buyers are and what influences their purchase decisions.

Enter: Market Research.

Whether you're new to market research or not, I created this guide to help you conduct a thorough study of your market, target audience, competition, and more. Let’s dive in.

Table of Contents

What is market research?

Primary vs. secondary research, types of market research, how to do market research, market research report template, market research examples.

Market research is the process of gathering information about your target market and customers to verify the success of a new product, help your team iterate on an existing product, or understand brand perception to ensure your team is effectively communicating your company's value effectively.

Market research can answer various questions about the state of an industry. But if you ask me, it's hardly a crystal ball that marketers can rely on for insights on their customers.

Market researchers investigate several areas of the market, and it can take weeks or even months to paint an accurate picture of the business landscape.

However, researching just one of those areas can make you more intuitive to who your buyers are and how to deliver value that no other business is offering them right now.

How? Consider these two things:

• Your competitors also have experienced individuals in the industry and a customer base. It‘s very possible that your immediate resources are, in many ways, equal to those of your competition’s immediate resources. Seeking a larger sample size for answers can provide a better edge.
• Your customers don't represent the attitudes of an entire market. They represent the attitudes of the part of the market that is already drawn to your brand.

The market research services market is growing rapidly, which signifies a strong interest in market research as we enter 2024. The market is expected to grow from roughly $75 billion in 2021 to $90.79 billion in 2025 .

Free Market Research Kit

• SWOT Analysis Template
• Survey Template
• Focus Group Template

Download Free

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You're all set!

Click this link to access this resource at any time.

Why do market research?

Market research allows you to meet your buyer where they are.

As our world becomes louder and demands more of our attention, this proves invaluable.

By understanding your buyer's problems, pain points, and desired solutions, you can aptly craft your product or service to naturally appeal to them.

Market research also provides insight into the following:

• Where your target audience and current customers conduct their product or service research
• Which of your competitors your target audience looks to for information, options, or purchases
• What's trending in your industry and in the eyes of your buyer
• Who makes up your market and what their challenges are
• What influences purchases and conversions among your target audience
• Consumer attitudes about a particular topic, pain, product, or brand
• Whether there‘s demand for the business initiatives you’re investing in
• Unaddressed or underserved customer needs that can be flipped into selling opportunity
• Attitudes about pricing for a particular product or service

Ultimately, market research allows you to get information from a larger sample size of your target audience, eliminating bias and assumptions so that you can get to the heart of consumer attitudes.

As a result, you can make better business decisions.

To give you an idea of how extensive market research can get , consider that it can either be qualitative or quantitative in nature — depending on the studies you conduct and what you're trying to learn about your industry.

Qualitative research is concerned with public opinion, and explores how the market feels about the products currently available in that market.

Quantitative research is concerned with data, and looks for relevant trends in the information that's gathered from public records.

That said, there are two main types of market research that your business can conduct to collect actionable information on your products: primary research and secondary research.

Primary Research

Primary research is the pursuit of first-hand information about your market and the customers within your market.

It's useful when segmenting your market and establishing your buyer personas.

Primary market research tends to fall into one of two buckets:

• Exploratory Primary Research: This kind of primary market research normally takes place as a first step — before any specific research has been performed — and may involve open-ended interviews or surveys with small numbers of people.
• Specific Primary Research: This type of research often follows exploratory research. In specific research, you take a smaller or more precise segment of your audience and ask questions aimed at solving a suspected problem.

Secondary Research

Secondary research is all the data and public records you have at your disposal to draw conclusions from (e.g. trend reports, market statistics, industry content, and sales data you already have on your business).

Secondary research is particularly useful for analyzing your competitors . The main buckets your secondary market research will fall into include:

• Public Sources: These sources are your first and most-accessible layer of material when conducting secondary market research. They're often free to find and review — like government statistics (e.g., from the U.S. Census Bureau ).
• Commercial Sources: These sources often come in the form of pay-to-access market reports, consisting of industry insight compiled by a research agency like Pew , Gartner , or Forrester .
• Internal Sources: This is the market data your organization already has like average revenue per sale, customer retention rates, and other historical data that can help you draw conclusions on buyer needs.
• Focus Groups
• Product/ Service Use Research
• Observation-Based Research
• Buyer Persona Research
• Market Segmentation Research
• Pricing Research
• Competitive Analysis Research
• Customer Satisfaction and Loyalty Research
• Brand Awareness Research
• Campaign Research

1. Interviews

Interviews allow for face-to-face discussions so you can allow for a natural flow of conversation. Your interviewees can answer questions about themselves to help you design your buyer personas and shape your entire marketing strategy.

2. Focus Groups

Focus groups provide you with a handful of carefully-selected people that can test out your product and provide feedback. This type of market research can give you ideas for product differentiation.

3. Product/Service Use Research

Product or service use research offers insight into how and why your audience uses your product or service. This type of market research also gives you an idea of the product or service's usability for your target audience.

4. Observation-Based Research

Observation-based research allows you to sit back and watch the ways in which your target audience members go about using your product or service, what works well in terms of UX , and which aspects of it could be improved.

5. Buyer Persona Research

Buyer persona research gives you a realistic look at who makes up your target audience, what their challenges are, why they want your product or service, and what they need from your business or brand.

6. Market Segmentation Research

Market segmentation research allows you to categorize your target audience into different groups (or segments) based on specific and defining characteristics. This way, you can determine effective ways to meet their needs.

7. Pricing Research

Pricing research helps you define your pricing strategy . It gives you an idea of what similar products or services in your market sell for and what your target audience is willing to pay.

8. Competitive Analysis

Competitive analyses give you a deep understanding of the competition in your market and industry. You can learn about what's doing well in your industry and how you can separate yourself from the competition .

9. Customer Satisfaction and Loyalty Research

Customer satisfaction and loyalty research gives you a look into how you can get current customers to return for more business and what will motivate them to do so (e.g., loyalty programs , rewards, remarkable customer service).

10. Brand Awareness Research

Brand awareness research tells you what your target audience knows about and recognizes from your brand. It tells you about the associations people make when they think about your business.

11. Campaign Research

Campaign research entails looking into your past campaigns and analyzing their success among your target audience and current customers. The goal is to use these learnings to inform future campaigns.

• Define your buyer persona.
• Identify a persona group to engage.
• Prepare research questions for your market research participants.
• List your primary competitors.
• Summarize your findings.

1. Define your buyer persona.

You have to understand who your customers are and how customers in your industry make buying decisions.

This is where your buyer personas come in handy. Buyer personas — sometimes referred to as marketing personas — are fictional, generalized representations of your ideal customers.

Use a free tool to create a buyer persona that your entire company can use to market, sell, and serve better.

10 Free Competitive Analysis Templates

Track and analyze your competitors with these ten free planning templates.

• SWOT Analysis
• Battle Cards
• Feature Comparison
• Strategic Overview

Identifying Content Competitors

Search engines are your best friends in this area of secondary market research.

To find the online publications with which you compete, take the overarching industry term you identified in the section above, and come up with a handful of more specific industry terms your company identifies with.

A catering business, for example, might generally be a “food service” company, but also consider itself a vendor in “event catering,” “cake catering,” or “baked goods.” Once you have this list, do the following:

• Google it. Don't underestimate the value in seeing which websites come up when you run a search on Google for the industry terms that describe your company. You might find a mix of product developers, blogs, magazines, and more.
• Compare your search results against your buyer persona. If the content the website publishes seems like the stuff your buyer persona would want to see, it's a potential competitor, and should be added to your list of competitors.

5. Summarize your findings.

Feeling overwhelmed by the notes you took? We suggest looking for common themes that will help you tell a story and create a list of action items.

To make the process easier, try using your favorite presentation software to make a report, as it will make it easy to add in quotes, diagrams, or call clips.

Feel free to add your own flair, but the following outline should help you craft a clear summary:

• Background: Your goals and why you conducted this study.
• Participants: Who you talked to. A table works well so you can break groups down by persona and customer/prospect.
• Executive Summary : What were the most interesting things you learned? What do you plan to do about it?
• Awareness: Describe the common triggers that lead someone to enter into an evaluation. (Quotes can be very powerful.)
• Consideration: Provide the main themes you uncovered, as well as the detailed sources buyers use when conducting their evaluation.
• Decision: Paint the picture of how a decision is really made by including the people at the center of influence and any product features or information that can make or break a deal.
• Action Plan: Your analysis probably uncovered a few campaigns you can run to get your brand in front of buyers earlier and/or more effectively. Provide your list of priorities, a timeline, and the impact it will have on your business.

Within a market research kit, there are a number of critical pieces of information for your business‘s success. Let’s take a look at these elements.

Pro Tip: Upon downloading HubSpot's free Market Research Kit , you'll receive editable templates for each of the given parts of the kit, instructions on how to use the kit, and a mock presentation that you can edit and customize.

What Is a Competitive Analysis — and How Do You Conduct One?

The Beginner's Guide to the Competitive Matrix [+ Templates]

What is a Competitive Analysis — and How Do You Conduct One?

9 Best Marketing Research Methods to Know Your Buyer Better [+ Examples]

SWOT Analysis: How To Do One [With Template & Examples]

28 Tools & Resources for Conducting Market Research

TAM, SAM & SOM: What Do They Mean & How Do You Calculate Them?

How to Run a Competitor Analysis [Free Guide]

5 Challenges Marketers Face in Understanding Audiences [New Data + Market Researcher Tips]

Causal Research: The Complete Guide

Free Guide & Templates to Help Your Market Research

Marketing software that helps you drive revenue, save time and resources, and measure and optimize your investments — all on one easy-to-use platform

Understanding data analysis: A beginner's guide

Before data can be used to tell a story, it must go through a process that makes it usable. Explore the role of data analysis in decision-making.

What is data analysis?

Data analysis is the process of gathering, cleaning, and modeling data to reveal meaningful insights. This data is then crafted into reports that support the strategic decision-making process.

Types of data analysis

There are many different types of data analysis. Each type can be used to answer a different question.

Descriptive analytics

Descriptive analytics refers to the process of analyzing historical data to understand trends and patterns. For example, success or failure to achieve key performance indicators like return on investment.

An example of descriptive analytics is generating reports to provide an overview of an organization's sales and financial data, offering valuable insights into past activities and outcomes.

Predictive analytics

Predictive analytics uses historical data to help predict what might happen in the future, such as identifying past trends in data to determine if they’re likely to recur.

Methods include a range of statistical and machine learning techniques, including neural networks, decision trees, and regression analysis.

Diagnostic analytics

Diagnostic analytics helps answer questions about what caused certain events by looking at performance indicators. Diagnostic analytics techniques supplement basic descriptive analysis.

Generally, diagnostic analytics involves spotting anomalies in data (like an unexpected shift in a metric), gathering data related to these anomalies, and using statistical techniques to identify potential explanations.

Cognitive analytics

Cognitive analytics is a sophisticated form of data analysis that goes beyond traditional methods. This method uses machine learning and natural language processing to understand, reason, and learn from data in a way that resembles human thought processes.

The goal of cognitive analytics is to simulate human-like thinking to provide deeper insights, recognize patterns, and make predictions.

Prescriptive analytics

Prescriptive analytics helps answer questions about what needs to happen next to achieve a certain goal or target. By using insights from prescriptive analytics, organizations can make data-driven decisions in the face of uncertainty.

Data analysts performing prescriptive analysis often rely on machine learning to find patterns in large semantic models and estimate the likelihood of various outcomes.

analyticsText analytics

Text analytics is a way to teach computers to understand human language. It involves using algorithms and other techniques to extract information from large amounts of text data, such as social media posts or customer previews.

Text analytics helps data analysts make sense of what people are saying, find patterns, and gain insights that can be used to make better decisions in fields like business, marketing, and research.

The data analysis process

Compiling and interpreting data so it can be used in decision making is a detailed process and requires a systematic approach. Here are the steps that data analysts follow:

1. Define your objectives.

Clearly define the purpose of your analysis. What specific question are you trying to answer? What problem do you want to solve? Identify your core objectives. This will guide the entire process.

2. Collect and consolidate your data.

Gather your data from all relevant sources using  data analysis software . Ensure that the data is representative and actually covers the variables you want to analyze.

3. Select your analytical methods.

Investigate the various data analysis methods and select the technique that best aligns with your objectives. Many free data analysis software solutions offer built-in algorithms and methods to facilitate this selection process.

4. Clean your data.

Scrutinize your data for errors, missing values, or inconsistencies using the cleansing features already built into your data analysis software. Cleaning the data ensures accuracy and reliability in your analysis and is an important part of data analytics.

5. Uncover valuable insights.

Delve into your data to uncover patterns, trends, and relationships. Use statistical methods, machine learning algorithms, or other analytical techniques that are aligned with your goals. This step transforms raw data into valuable insights.

6. Interpret and visualize the results.

Examine the results of your analyses to understand their implications. Connect these findings with your initial objectives. Then, leverage the visualization tools within free data analysis software to present your insights in a more digestible format.

7. Make an informed decision.

Use the insights gained from your analysis to inform your next steps. Think about how these findings can be utilized to enhance processes, optimize strategies, or improve overall performance.

By following these steps, analysts can systematically approach large sets of data, breaking down the complexities and ensuring the results are actionable for decision makers.

The importance of data analysis

Data analysis is critical because it helps business decision makers make sense of the information they collect in our increasingly data-driven world. Imagine you have a massive pile of puzzle pieces (data), and you want to see the bigger picture (insights). Data analysis is like putting those puzzle pieces together—turning that data into knowledge—to reveal what’s important.

Whether you’re a business decision maker trying to make sense of customer preferences or a scientist studying trends, data analysis is an important tool that helps us understand the world and make informed choices.

Primary data analysis methods

Quantitative analysis

Quantitative analysis deals with numbers and measurements (for example, looking at survey results captured through ratings). When performing quantitative analysis, you’ll use mathematical and statistical methods exclusively and answer questions like ‘how much’ or ‘how many.’ 

Qualitative analysis

Qualitative analysis is about understanding the subjective meaning behind non-numerical data. For example, analyzing interview responses or looking at pictures to understand emotions. Qualitative analysis looks for patterns, themes, or insights, and is mainly concerned with depth and detail.

Data analysis solutions and resources

Turn your data into actionable insights and visualize the results with ease.

Microsoft 365

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