business analytics essay

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4 Examples of Business Analytics in Action

• 15 Jan 2019

Data is a valuable resource in today’s ever-changing marketplace. For business professionals, knowing how to interpret and communicate data is an indispensable skill that can inform sound decision-making.

“The ability to bring data-driven insights into decision-making is extremely powerful—all the more so given all the companies that can’t hire enough people who have these capabilities,” says Harvard Business School Professor Jan Hammond , who teaches the online course Business Analytics . “It’s the way the world is going.”

Before taking a look at how some companies are harnessing the power of data, it’s important to have a baseline understanding of what the term “business analytics” means.

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What Is Business Analytics?

Business analytics is the use of math and statistics to collect, analyze, and interpret data to make better business decisions.

There are four key types of business analytics: descriptive, predictive, diagnostic, and prescriptive. Descriptive analytics is the interpretation of historical data to identify trends and patterns, while predictive analytics centers on taking that information and using it to forecast future outcomes. Diagnostic analytics can be used to identify the root cause of a problem. In the case of prescriptive analytics , testing and other techniques are employed to determine which outcome will yield the best result in a given scenario.

Related : 4 Types of Data Analytics to Improve Decision-Making

Across industries, these data-driven approaches have been employed by professionals to make informed business decisions and attain organizational success.

Check out the video below to learn more about business analytics, and subscribe to our YouTube channel for more explainer content!

Business Analytics vs. Data Science

It’s important to highlight the difference between business analytics and data science . While both processes use big data to solve business problems they’re separate fields.

The main goal of business analytics is to extract meaningful insights from data to guide organizational decisions, while data science is focused on turning raw data into meaningful conclusions through using algorithms and statistical models. Business analysts participate in tasks such as budgeting, forecasting, and product development, while data scientists focus on data wrangling , programming, and statistical modeling.

While they consist of different functions and processes, business analytics and data science are both vital to today’s organizations. Here are four examples of how organizations are using business analytics to their benefit.

Business Analytics Examples

According to a recent survey by McKinsey , an increasing share of organizations report using analytics to generate growth. Here’s a look at how four companies are aligning with that trend and applying data insights to their decision-making processes.

1. Improving Productivity and Collaboration at Microsoft

At technology giant Microsoft , collaboration is key to a productive, innovative work environment. Following a 2015 move of its engineering group's offices, the company sought to understand how fostering face-to-face interactions among staff could boost employee performance and save money.

Microsoft’s Workplace Analytics team hypothesized that moving the 1,200-person group from five buildings to four could improve collaboration by increasing the number of employees per building and reducing the distance that staff needed to travel for meetings. This assumption was partially based on an earlier study by Microsoft , which found that people are more likely to collaborate when they’re more closely located to one another.

In an article for the Harvard Business Review , the company’s analytics team shared the outcomes they observed as a result of the relocation. Through looking at metadata attached to employee calendars, the team found that the move resulted in a 46 percent decrease in meeting travel time. This translated into a combined 100 hours saved per week across all relocated staff members and an estimated savings of $520,000 per year in employee time.

The results also showed that teams were meeting more often due to being in closer proximity, with the average number of weekly meetings per person increasing from 14 to 18. In addition, the average duration of meetings slightly declined, from 0.85 hours to 0.77 hours. These findings signaled that the relocation both improved collaboration among employees and increased operational efficiency.

For Microsoft, the insights gleaned from this analysis underscored the importance of in-person interactions and helped the company understand how thoughtful planning of employee workspaces could lead to significant time and cost savings.

2. Enhancing Customer Support at Uber

Ensuring a quality user experience is a top priority for ride-hailing company Uber. To streamline its customer service capabilities, the company developed a Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve their speed and accuracy when responding to support tickets.

COTA’s implementation delivered positive results. The tool reduced ticket resolution time by 10 percent, and its success prompted the Uber Engineering team to explore how it could be improved.

For the second iteration of the product, COTA v2, the team focused on integrating a deep learning architecture that could scale as the company grew. Before rolling out the update, Uber turned to A/B testing —a method of comparing the outcomes of two different choices (in this case, COTA v1 and COTA v2)—to validate the upgraded tool’s performance.

Preceding the A/B test was an A/A test, during which both a control group and a treatment group used the first version of COTA for one week. The treatment group was then given access to COTA v2 to kick off the A/B testing phase, which lasted for one month.

At the conclusion of testing, it was found that there was a nearly seven percent relative reduction in average handle time per ticket for the treatment group during the A/B phase, indicating that the use of COTA v2 led to faster service and more accurate resolution recommendations. The results also showed that customer satisfaction scores slightly improved as a result of using COTA v2.

With the use of A/B testing, Uber determined that implementing COTA v2 would not only improve customer service, but save millions of dollars by streamlining its ticket resolution process.

Related : How to Analyze a Dataset: 6 Steps

3. Forecasting Orders and Recipes at Blue Apron

For meal kit delivery service Blue Apron, understanding customer behavior and preferences is vitally important to its success. Each week, the company presents subscribers with a fixed menu of meals available for purchase and employs predictive analytics to forecast demand , with the aim of using data to avoid product spoilage and fulfill orders.

To arrive at these predictions, Blue Apron uses algorithms that take several variables into account, which typically fall into three categories: customer-related features, recipe-related features, and seasonality features. Customer-related features describe historical data that depicts a given user’s order frequency, while recipe-related features focus on a subscriber’s past recipe preferences, allowing the company to infer which upcoming meals they’re likely to order. In the case of seasonality features, purchasing patterns are examined to determine when order rates may be higher or lower, depending on the time of year.

Through regression analysis—a statistical method used to examine the relationship between variables—Blue Apron’s engineering team has successfully measured the precision of its forecasting models. The team reports that, overall, the root-mean-square error—the difference between predicted and observed values—of their projection of future orders is consistently less than six percent, indicating a high level of forecasting accuracy.

By employing predictive analytics to better understand customers, Blue Apron has improved its user experience, identified how subscriber tastes change over time, and recognized how shifting preferences are impacted by recipe offerings.

Related : 5 Business Analytics Skills for Professionals

4. Targeting Consumers at PepsiCo

Consumers are crucial to the success of multinational food and beverage company PepsiCo. The company supplies retailers in more than 200 countries worldwide , serving a billion customers every day. To ensure the right quantities and types of products are available to consumers in certain locations, PepsiCo uses big data and predictive analytics.

PepsiCo created a cloud-based data and analytics platform called Pep Worx to make more informed decisions regarding product merchandising. With Pep Worx, the company identifies shoppers in the United States who are likely to be highly interested in a specific PepsiCo brand or product.

For example, Pep Worx enabled PepsiCo to distinguish 24 million households from its dataset of 110 million US households that would be most likely to be interested in Quaker Overnight Oats. The company then identified specific retailers that these households might shop at and targeted their unique audiences. Ultimately, these customers drove 80 percent of the product’s sales growth in its first 12 months after launch.

PepsiCo’s analysis of consumer data is a prime example of how data-driven decision-making can help today’s organizations maximize profits.

Developing a Data Mindset

As these companies illustrate, analytics can be a powerful tool for organizations seeking to grow and improve their services and operations. At the individual level, a deep understanding of data can not only lead to better decision-making, but career advancement and recognition in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” Hammond says . “If you’re able to go into a meeting, and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Do you want to leverage the power of data within your organization? Explore Business Analytics —one of our online business essentials courses —to learn how to use data analysis to solve business problems.

This post was updated on March 24, 2023. It was originally published on January 15, 2019.

About the Author

How to Write a Business Analysis Report [Examples and Templates]

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Business analysis reports are a lot like preparing a delicious meal.

Sometimes, the recipe is simple enough that you only need to use the basic ingredients. Other times, you will have to follow specific instructions to ensure those tasty delicacies turn out just right.

Want to make sure your business report never turns out like a chewy piece of meat? You’ve come to the right place.

Stay tuned until the end of this blog post, and we promise you won’t be hungry… for business knowledge!

What Is a Business Analysis Report?

Why is analytical reporting important, what should be included in a business analysis report, how do you write a business analysis report, business data analysis report examples and templates.

• Improve Business Reporting with Databox

A business analysis report provides information about the current situation of your company. This report is usually created by the management to help in the decision-making process and is usually used by other departments within a company.

Business analysis reports can either focus your research on the effectiveness of an existing business process or a proposed new process. Besides, an effective business analysis report should also assess the results to determine if the process changes had a positive or negative effect on the company’s goals. In fact, according to Databox’s State of business reporting , an overwhelming majority of companies said that reporting improved their performance.

Analytical reports are the bridge that connects your company to an effective, data-driven business intelligence strategy . By leveraging analytical reports , you can make informed decisions about your organization’s most critical issues. You will no longer need to rely on gut instinct or anecdotal evidence when assessing risks, threats, and opportunities. Instead, you will have access to a wealth of reliable data to inform your decisions.

Here are some essential benefits of analytical reporting:

• Improve communication and foster collaboration – The most obvious benefit of business analysis report writing is an improvement in communication between all stakeholders involved in the project. Also, analytical business reports can help you to generate more trust and foster better collaboration among your employees and colleagues. By using data analytics reporting tools , you will be able to monitor your employees’ performance on a day-to-day basis. This will allow you to hold them accountable for their actions and give them greater freedom within the business as they know that their superiors have faith in their decision-making capabilities.
• Increase productivity – Without this level of shared insight, businesses struggle to stay on top of their most important tasks and can become less efficient. An effective analytical business report provides the information needed for more efficient internal processes and helps you find more time for strategic activities such as improving your business strategy or working on long-term goals .
• Innovation – In today’s digital age, the pressure to innovate was never greater. When consumers basically have everything they want at their fingertips, stepping up to the plate with a new and improved product or service has never been more important. With an accessible dashboard in place, you will be able to create data-driven narratives for each of your business’ critical functions. For example, if you are a software company, you can use the insights gained from report analysis done with your dashboard software to tailor your product development efforts to the actual needs of your customers. By doing so, you will be able to develop products that are better tailored to specific customer groups. You can also use the same information for developing new marketing strategies and campaigns.
• Continuous business evolution – When it comes to digital businesses, data is everything. No model lasts forever, so having access to a business dashboard software that allows you to constantly keep tabs on your business’ performance will help you refine it as time goes on. If there are any glitches in your business model, or if something isn’t panning out as expected, the insight offered by a business analysis report can help you improve upon what works while scrapping what doesn’t.

A business analysis report has several components that need to be included to give a thorough description of the topic at hand. The structure and length of business analysis reports can vary depending on the needs of the project or task.

They can be broken down into different sections that include an:

• Executive summary
• Study introduction
• Methodology
• Review of statistics

Reports of this nature may also include case studies or examples in their discussion section.

A report can be written in a formal or informal tone, depending on the audience and purpose of the document. While a formal tone is best for executives , an informal tone is more appropriate for technical audiences . It is also a good idea to use something like an executive summary template to report on the results repeatedly with ease.

A good business analysis report is detailed and provides recommendations in the form of actionable steps. Here we have listed some simple steps that you need to follow to write a good business analysis report. Report writing is a major part of the business analysis process. In this section, you will learn how to write a report for your company:

Preparation

Presentation.

Obtain an overview of what you want to analyze in the business report . For example, if you are writing a business analysis report on how to improve customer service at an insurance company, you will want to look through all the customer service processes to determine where the problems lie. The more prepared you are when starting a project, the easier it will be to get results. Here is what your preparation should look like:

Set your goals

The first step in writing this document is to set your goals . What do you hope to accomplish with this paper? Do you need to assess the company’s finances? Are you looking for ways to make improvements? Or do you have outside investors who want to know if they should buy into the company? Once you know what your goal is, then you can begin setting up your project.

PRO TIP: How Well Are Your Marketing KPIs Performing?

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:

• Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
• New Contacts from Sessions . How well is your campaign driving new contacts and customers?
• Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
• Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
• Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?

Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot and Google Analytics 4 accounts with Databox. 

Step 3: Watch your dashboard populate in seconds.

Assess the Company’s Mission

It’s almost impossible to write a business analysis report without access to the company’s mission statement. Even if you don’t plan on using the mission statement as part of your business analysis summary, it can help you understand the company’s culture and goals. Mission statements are typically short and easy to read, but they may not include every area of focus that you want to include in your report.

Thus, it is important to use other sources when possible. For example, if you are writing a business analysis report for a small start-up company that is just beginning to market its product or service, review the company website or talk directly with management to learn what they believe will be most crucial in growing the company from the ground up.

Stakeholder Analysis

Who is your audience? Create the reader’s persona and tailor all information to their perspective. Create a stakeholder map that identifies all the groups, departments, functions, and individuals involved in this project (and any other projects related to this one). Your stakeholder map should include a description of each group’s role.

Review Financial Performance

Review the financing of the business and determine whether there are any potential threats to the company’s ability to meet its future financial obligations. This includes reviewing debt payments and ownership equity compared with other types of financing such as accounts receivable, cash reserves, and working capital. Determine whether there have been any changes in the funding over time, such as an increase in long-term debt or a decrease in owners’ equity.

Apart from reviewing your debt payments and ownership equity with other types of financing, wouldn’t it be great if you could compare your financial performance to companies that are exactly like yours? With Databox, this can be done in less than 3 minutes.

For example, by  joining this benchmark group , you can better understand your gross profit margin performance and see how metrics like income, gross profit, net income, net operating increase, etc compare against businesses like yours.

One piece of data that you would be able to discover is the average gross profit a month for B2B, B2C, SaaS and eCommerce. Knowing that you perform better than the median may help you evaluate your current business strategy and identify the neccessary steps towards improving it.

Instantly and Anonymously Benchmark Your Company’s Performance Against Others Just Like You

If you ever asked yourself:

• How does our marketing stack up against our competitors?
• Are our salespeople as productive as reps from similar companies?
• Are our profit margins as high as our peers?

Databox Benchmark Groups can finally help you answer these questions and discover how your company measures up against similar companies based on your KPIs.

When you join Benchmark Groups, you will:

• Get instant, up-to-date data on how your company stacks up against similar companies based on the metrics most important to you. Explore benchmarks for dozens of metrics, built on anonymized data from thousands of companies and get a full 360° view of your company’s KPIs across sales, marketing, finance, and more.
• Understand where your business excels and where you may be falling behind so you can shift to what will make the biggest impact. Leverage industry insights to set more effective, competitive business strategies. Explore where exactly you have room for growth within your business based on objective market data.
• Keep your clients happy by using data to back up your expertise. Show your clients where you’re helping them overperform against similar companies. Use the data to show prospects where they really are… and the potential of where they could be.
• Get a valuable asset for improving yearly and quarterly planning . Get valuable insights into areas that need more work. Gain more context for strategic planning.

The best part?

• Benchmark Groups are free to access.
• The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.

When it comes to showing you how your performance compares to others, here is what it might look like for the metric Average Session Duration:

And here is an example of an open group you could join:

And this is just a fraction of what you’ll get. With Databox Benchmarks, you will need only one spot to see how all of your teams stack up — marketing, sales, customer service, product development, finance, and more. 

• Choose criteria so that the Benchmark is calculated using only companies like yours
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Sounds like something you want to try out? Join a Databox Benchmark Group today!

Examine the “Four P’s”

“Four P’s” — product , price , place, and promotion . Here’s how they work:

• Product — What is the product? How does it compare with those of competitors? Is it in a position to gain market share?
• Price — What is the price of the product? Is it what customers perceive as a good value?
• Place — Where will the product be sold? Will existing distribution channels suffice or should new channels be considered?
• Promotion — Are there marketing communications efforts already in place or needed to support the product launch or existing products?

Evaluate the Company Structure

A business analysis report examines the structure of a company, including its management, staff, departments, divisions, and supply chain. It also evaluates how well-managed the company is and how efficient its supply chain is. In order to develop a strong strategy, you need to be able to analyze your business structure.

When writing a business analysis report, it’s important to make sure you structure your work properly. You want to impress your readers with a clear and logical layout, so they will be able to see the strengths of your recommendations for improving certain areas of the business. A badly written report can completely ruin an impression, so follow these steps to ensure you get it right the first time.

A typical business analysis report is formatted as a cover page , an executive summary , information sections, and a summary .

• A cover page contains the title and author of the report, the date, a contact person, and reference numbers.
• The information section is backed up by data from the work you’ve done to support your findings, including charts and tables. Also, includes all the information that will help you make decisions about your project. Experience has shown that the use of reputable study materials, such as  StuDocu  and others, might serve you as a great assistant in your findings and project tasks.
• A summary is a short overview of the main points that you’ve made in the report. It should be written so someone who hasn’t read your entire document can understand exactly what you’re saying. Use it to highlight your main recommendations for how to change your project or organization in order to achieve its goals.
• The last section of a business analysis report is a short list of references that include any websites or documents that you used in your research. Be sure to note if you created or modified any of these documents — it’s important to give credit where credit is due.

The Process of Investigation

Explain the problem – Clearly identify the issue and determine who is affected by it. You should include a detailed description of the problem you are analyzing, as well as an in-depth analysis of its components and effects. If you’re analyzing a small issue on a local scale, make sure that your report reflects this scale. That way, if someone else reads your work who had no idea about its context or scope, they would still be able to understand it.

Explain research methods – There are two ways to do this. Firstly, you can list the methods you’ve used in the report to determine your actions’ success and failure. Secondly, you should add one or two new methods to try instead. Always tell readers how you came up with your answer or what data you used for your report. If you simply tell them that the company needs to improve customer service training then they won’t know what kind of data led you to that conclusion. Also, if there were several ways of addressing a problem, discuss each one and why it might not work or why it may not be appropriate for the company at this time.

Analyze data – Analyzing data is an integral part of any business decision, whether it’s related to the costs of manufacturing a product or predicting consumer behavior. Business analysis reports typically focus on one aspect of an organization and break down that aspect into several parts — all of which must be analyzed in order to come to a conclusion about the original topic.

The Outcome of Each Investigation Stage

The recommendations and actions will usually follow from the business objectives not being met. For example, if one of your goals was to decrease costs then your recommendations would include optimization strategies for cost reduction . If you have more than one suggestion you should make a list of the pros and cons of each one. You can make several recommendations in one report if they are related. In addition, make sure that every recommendation has supporting arguments to back them up.

Report Summary

Every business analysis report should start with a summary. It’s the first thing people see and it needs to capture their attention and interest. The report summary can be created in two ways, depending on the nature of the report:

• If the report is a brief one, that simply gives a summary of the findings, then it can be created as part of the executive summary.
• But if it’s a long report, it could be too wordy to summarise. In this case, you can create a more detailed overview that covers all the main aspects of the project from both an internal and external point of view.

Everything comes down to this section. A presentation is designed to inform, persuade and influence decision-makers to take the next action steps.

Sometimes a slide or two can make them change their mind or open new horizons. These days, digital dashboards are becoming increasingly popular when it comes to presenting data in business reports. Dashboards combine different visualizations into one place, allowing users to get an overview of the information they need at a glance rather than searching through a bunch of documents or spreadsheets trying.

Databox offers dynamic and accessible digital dashboards that will help you to convert raw data into a meaningful story. And the best part is that you can do it with a ‘blink of an eye’ even if you don’t have any coding or designs skills. There is also an option of individual report customization so that you can tailor any dashboard to your own needs.

Pre-made dashboard templates can be extremely useful when creating your own business analysis report. While examples serve as inspiration, templates allow you to create reports quickly and easily without having to spend time (and money) developing the underlying data models.

Databox dashboard templates come with some of the most common pre-built metrics and KPIs different types of businesses track across different departments. In order to create powerful business insights within minutes, all you need to do is download any of our free templates and connect your data source — the metrics will populate automatically.

Business Report Examples and Templates

Databox business dashboard examples are simple and powerful tools for tracking your business KPIs and performance. These dashboards can be used by executive teams and managers as well as by senior management, marketing, sales, customer support, IT, accounting, and other departments. If you are new to this kind of reporting, you may not know how to set up a dashboard or what metrics should be displayed on it. This is where a premade template for business dashboards comes in handy.

For example, this Google Ads Report Template is designed to give you a simple way to keep track of your campaigns’ performance over time, and it’s a great resource for anyone who uses Google’s advertising platform, regardless of whether they’re an SMB, an SME or an enterprise.

KPI Report Examples and Templates

KPIs are the foundation of any business analysis, and they can come in a multitude of forms. While we’ve defined KPIs as metrics or measurements that allow you to assess the effectiveness of a given process, department, or team, there are a number of ways to evaluate your KPIs. Through the use of color-coding, user-friendly graphs and charts, and an intuitive layout, your KPIs should be easy for anyone to understand. A good way to do this is by having a dedicated business analyst on your team who can take on the task of gathering data, analyzing it, and presenting it in a way that will drive actionable insights. However, if you don’t have a dedicated analyst or don’t want to spend money on one, you can still create KPI reporting dashboards using free KPI Databox templates and examples .

For example, this Sales Overview template is a great resource for managers who want to get an overview of their sales team’s performance and KPIs. It’s perfect for getting started with business analysis, as it is relatively easy to understand and put together.

Performance Report Examples and Templates

All businesses, regardless of size or industry, need to know how well they are performing in order to make the best decisions for their company and improve overall ROI. A performance dashboard is a strategic tool used to track key metrics across different departments and provide insight into the health of a business. Databox has a collection of 50+ Performance Dashboard Examples and Templates which are available for free download.

For example, if your business is investing a lot into customer support, we recommend tracking your customer service performance with this Helpscout Mailbox Dashboard which will give you insights into conversations, your team’s productivity, customer happiness score, and more.

Executive Report Examples and Templates

An executive dashboard is a visual representation of the current state of a business. The main purpose of an executive dashboard is to enable business leaders to quickly identify opportunities, identify areas for improvement, pinpoint issues, and make data-informed decisions for driving sales growth, new product launches, and overall business growth. When an executive dashboard is fully developed, as one of these 50+ Databox Free Executive Examples and Templates , it offers a single view of the most important metrics for a business at a glance.

For example, you probably have more than one set of financial data tracked using an executive dashboard software : invoices, revenue reports (for accounting), income statements, to mention a few. If you want to view all this data in one convenient place, or even create a custom report that gives you a better picture of your business’s financial health, this Stripe Dashboard Template is a perfect solution for you.

Metrics Report Examples and Templates

Choosing the right metrics for your business dashboard can be crucial to helping you meet your business objectives, evaluate your performance, and get insights into how your business is operating. Metrics dashboards are used by senior management to measure the performance of their company on a day-to-day basis. They are also used by mid-level managers to determine how their teams are performing against individual goals and objectives. Databox provides 50+ Free Metrics Dashboard Examples and Templates that you can use to create your company’s own dashboards. Each is unique and will depend on your business needs.

For example, if you are looking for ways to track the performance of your DevOps team, and get the latest updates on projects quickly – from commits, and repository status, to top contributors to your software development projects, this GitHub Overview Dashboard is for you.

Small Business Report Examples and Templates

A lot of small business owners don’t realize how important it is to have a proper dashboard in place until they actually use one. A dashboard can help you track and compare different metrics, benchmark your performance against industry averages, evaluate the effectiveness of your marketing and sales strategies, track financials, and much more. So if you’re looking for a tool to help you measure and manage your small business’ performance, try some of these 50+ Free Small Business Dashboard Examples and Templates .

For example, this Quickbooks Dashboard template can help you get a clear understanding of your business’s financial performance, ultimately allowing you to make better-informed decisions that will drive growth and profitability.

Agency Report Examples and Templates

Agency dashboards are not a new concept. They have been around for years and are used by companies all over the world. Agency dashboards can be powerful tools for improving your marketing performance, increasing client loyalty, and landing new clients. There is no single correct way to create an agency dashboard. Everyone has their own goals and objectives, which will ultimately determine which data points you choose to include or track using a client dashboard software , but with these Databox 100+ Free Agency Dashboard Examples and Templates you have plenty of options to start with.

For example, you can use this Harvest Clients Time Report to easily see how much time your employees spend working on projects for a particular client, including billable hours and billable amount split by projects.

Better Business Reporting with Databox

Business analysis is all about finding smart ways to evaluate your organization’s performance and future potential. And that’s where Databox comes in.

Databox can be a helpful tool for business leaders who are required to analyze data, hold frequent meetings, and generate change in their organizations. From improving the quality and accessibility of your reporting to tracking critical performance metrics in one place, and sharing performance metrics with your peers and team members in a cohesive, presentable way, allow Databox to be your personal assistant in these processes, minimize the burdens of reporting and ensure you always stay on top of your metrics game.

Sign up today for free to start streamlining your business reporting process.

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Business analytics refers to the statistical methods and computing technologies for processing, mining and visualizing data to uncover patterns, relationships and insights that enable better business decision-making.

Business analytics involves companies that use data created by their operations or publicly available data to solve business problems, monitor their business fundamentals, identify new growth opportunities, and better serve their customers.

Business analytics uses data exploration, data visualization, integrated dashboards, and more to provide users with access to actionable data and business insights.

This IBM ebook uncovers the value of integrating a business analytics solution that turns insights into action.

Read the guide for data leaders

Business intelligence (BI) enables better business decisions that are based on a foundation of business data. Business analytics (BA) is a subset of business intelligence, with business analytics providing the analysis, while the umbrella business intelligence infrastructure includes the tools for the identification and storage of the data that will be used for decision-making. Business intelligence collects, manages and uses both the raw input data and also the resulting knowledge and actionable insights generated by business analytics. The ongoing purpose of business analytics is to develop new knowledge and insights to increase a company’s total business intelligence.

Business analytics can be used to answer questions about what happened in the past, make predictions and forecast business results. 1 An organization can gain a more complete picture of its business, enabling it to understand user behavior more effectively.

Data scientists and advanced data analysts use business analytics to provide advanced statistical analysis. Some examples of statistical analysis include regression analysis which uses previous sales data to estimate customer lifetime value, and cluster analysis for analyzing and segmenting high-usage and low-usage users in a particular area.

Business analytics solutions provide benefits for all departments, including finance , human resources , supply chain , marketing , sales  or information technology , plus all industries, including healthcare , financial services and consumer goods .

Business analytics uses analytics—the action of deriving insights from data—to drive increases in business performance. 4 types of valuable analytics are often used:

As the name implies, this type of analytics describes the data it contains. An example would be a pie chart that breaks down the demographics of a company’s customers.

Diagnostic analytics helps pinpoint the root cause of an event. It can help answer questions such as: What are the series of events that influenced the business outcomes?  Where do the true correlation and causality lie within a given historical time frame? What are the drivers behind the findings? For example, manufacturers can analyze a failed component on an assembly line and determine the reason behind its failure.

Predictive analytics mines existing data, identifies patterns and helps companies predict what might happen in the future based on that data. It uses predictive models that make hypotheses about future behaviors or outcomes. For example, an organization could make predictions about the change in coat sales if the upcoming winter season is projected to have warmer temperatures. Predictive modeling 2 also helps organizations avoid issues before they occur, such as knowing when a vehicle or tool will break and intervening before it occurs, or knowing when changing demographics or psychographics will positively or negatively impact their product lines. 

These analytics help organizations make decisions about the future based on existing information and resources. Every business can use prescriptive analytics by reviewing their existing data to make a guess about what will happen next. For example, marketing and sales organizations can analyze the lead success rates of recent content to determine what types of content they should prioritize in the future. Financial services firms use it for fraud detection by analyzing existing data to make real-time decisions on whether any purchase is potentially fraudulent.

Business analytics practices involve several tools that help companies make sense of the data they are collecting and use it to turn that data into insights. Here are some of the most common tools, disciplines and approaches:

• Data management: Data management is the practice of ingesting, processing, securing and storing an organization’s data. It is then used for strategic decision-making to improve business outcomes. The data management discipline has become an increasing priority as expanding data stores has created significant challenges, such as data silos, security risks and general bottlenecks to decision-making.
• Data mining or KDD : Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets and is a significant component of big data analytics. The growing importance of big data makes data mining a critical component of any modern business by assisting companies in transforming their raw data into useful knowledge.
• Data warehousing : A data warehouse, or enterprise data warehouse (EDW), is a system that aggregates data from different sources, including apps, Internet of Things (IoT) devices, social media and spreadsheets into a single, central, consistent data store to support data analysis, data mining, artificial intelligence (AI) and machine learning (ML). A data warehouse system enables an organization to run powerful analytics on large amounts of data (petabytes and petabytes) in ways that a standard database cannot.
• Data visualization : The representation of data by using graphics such as charts, plots, infographics and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easier to understand, being especially helpful for nontechnical staff to understand analytics concepts, and helping show patterns in multiple data points. Data visualization can also help with idea generation, idea illustration or visual discovery.
• Forecasting : This tool takes historical data and current market conditions and then makes predictions as to how much revenue an organization can expect to bring in over the next few months or years. Forecasts are adjusted as new information becomes available. When companies embrace data and analytics with well-established planning and forecasting best practices, they enhance strategic decision-making and can be rewarded with more accurate plans and more timely forecasts.
• Machine learning algorithms : A machine learning algorithm is a set of rules or processes used by an AI system to conduct tasks, most often to discover new data insights and patterns, or to predict output values from a given set of input variables. Machine learning algorithms enable machine learning to learn, delivering the power to analyze data, identify trends and predict issues before they occur.
• Reporting : Business analytics runs on the fuel of data to help organizations make informed decisions. Enterprise-grade reporting software can extract information from various applications used by an enterprise, analyze the data and generate reports.
• Statistical analysis : Statistical analysis enables an organization to extract actionable insights from its data. Advanced statistical analysis procedures help ensure high accuracy and quality decision-making. The analytics lifecycle includes data preparation and management to analysis and reporting.
• Text analysis : Identifies textual patterns and trends within unstructured data by using machine learning, statistics and linguistics. By transforming the data into a more structured format through text mining and text analysis , more quantitative insights can be found.

Modern organizations need to be able to make quick decisions to compete in a rapidly changing world, where new competitors spring up frequently and customers’ habits are always changing. Organizations that prioritize business analytics have several advantages over competitors who do not.

Faster and better-informed decisions: Having a flexible and expansive view of all the data an organization possesses can eliminate uncertainty, prompt an organization to take action faster, and improve business processes. If an organization’s data suggests that sales of a particular product line are declining precipitously, it might decide to discontinue that line. If climate risk impacts the harvesting of a raw material another organization depends on, it might need to source a new material from somewhere else. It’s especially helpful when considering pricing strategies.

How a company prices its goods or services is based on thousands of data points, many of which do not remain static over time. Whether a company has a fixed or dynamic pricing strategy, being able to access real-time data to make smarter short- and long-term pricing data is critical. For organizations that want to incorporate dynamic pricing, business analytics enables them to use thousands of data points to react to external events and trends to identify the most profitable price point as frequently as necessary.

Single-window view of information: Increased collaboration between departments and line-of-business users means that everyone has the same data and is talking from the same playbook. Having that single pane of glass shows more unseen patterns, enabling different departments to understand the company’s holistic approach and increase an organization’s ability to respond to changes in the marketplace.

Enhanced customer service: By knowing what customers want, when and how they want it, organizations encourage happier customers and build greater loyalty. In addition to an improved customer experience , by being able to make smarter decisions on resource allocation or manufacturing, organizations are likely able to offer those goods or services at a more affordable price.

Companies looking to harness business data will likely need to upskill existing employees or hire new employees, potentially creating new job descriptions. Data-driven organizations need employees with excellent hands-on analytical and communication skills.

Here are some of the employees that they need to take advantage of the full potential of robust business analytics strategies:

Data scientists: These people are responsible for managing the algorithms and models that power the business analytics programs. Organizational data scientists  either use open source libraries, such as the natural language toolkit (NTLK) for algorithms or build their own to analyze data. They excel at problem-solving and usually need to know several programming languages, such as Python, which helps access out-of-the-box machine learning algorithms and structured query language (SQL) , which helps extract data from databases to feed into a model.

In recent years, an increasing number of schools offer Master of Science or Bachelor’s degrees in data science where students engage in degree program coursework that teaches them computer science, statistical modeling and other mathematical applications.

Data engineers: They create and maintain information systems that collect data from different places that are cleaned and sorted, and placed into a master database. They are often responsible for helping to ensure that data can be easily collected and accessed by stakeholders to provide organizations with a unified view of their data operations.

Data analysts: They play a pivotal role in communicating insights to external and internal stakeholders. Depending on the size of the organization, they might collect and analyze the data sets and build the data visualizations, or they might take the work created by other data scientists and focus on building strong storytelling for the key takeaways.

To maximize the benefits of an organization’s business analytics, it needs to clean and connect its data, create data visualizations and provide insights on where the business is today while helping predict what will happen tomorrow. This usually involves these steps:

First, organizations must identify all the data they have on hand and what external data they want to incorporate to understand what opportunities for business analytics they have.

Unfortunately, much of a company's data remains uncleaned, rendering it useless for accurate analysis until addressed.

Here are some reasons why an organization’s data might need cleaning:

• Incorrect data fields: Due to manual entry or incorrect data transfers, an organization might have bad data mixed in with accurate data. If it has any bad data in the system, this has the potential to render the entire set meaningless.
• Outdated data values: Certain data sets, including customer information, might need editing due to customers leaving, product lines being discontinued or other historical data that is no longer relevant.
• Missing data: Companies might have changed how they collect data or the data they collect, which means historic entries might be missing data that is crucial to future business analysis. Companies in this situation might need to invest in either manual data entry or identify ways to use algorithms  or machine learning  to predict what the correct data should be.
• Data silos: If an organization’s existing data is in multiple spreadsheets or other types of databases, it might need to merge the data so it’s all in one place. While the foundation of any business analytics approach is first-party data (data the company has collected from stakeholders and owns), they might want to append third-party data (data they’ve purchased or gleaned from other organizations) to match their data with external insights.

Companies can now query and quickly parse gigabytes or terabytes of data rapidly with more cloud computing . Data scientists can analyze data more effectively by using machine learning, algorithms, artificial intelligence (AI ) and other technologies. Doing so can produce actionable insights based on an organization’s key performance indicators (KPIs) .

Business analytics programs can now quickly take huge amounts of that analyzed data to create dashboards, visualizations and panels where the data can be stored, viewed, sorted, manipulated and sent to stakeholders.

Data visualization best practices include understanding which visual best fits the data an organization is using and the key points it hopes to make, keeping the visual as clean and simple as possible, and providing the right explanations and content to help ensure that the audience understands what they’re viewing.

Ongoing data management is conducted in tandem with what was mentioned earlier. An organization that embraces business analytics must create a comprehensive strategy for maintaining its cleaned data, especially as it incorporates new data sources.

Business analytics are useful for every type of business unit as a way to make sense of the data it has and help it generate specific insights that drive smarter decision-making.

• Financial and operational planning: Business analytics provides valuable insights to help organizations align their financial planning and operations more seamlessly. It does this by setting rules for supply chain management , integrating data across functions, and improving supply chain analytics and demand forecasting.
• Planning analytics: An integrated business planning approach that combines spreadsheets and database technologies to make effective business decisions about topics such as demand and lead generation, optimization of operating costs, and technology requirements based on solid metrics. Many organizations have historically used tools including Microsoft Excel for business planning, but some are transitioning to tools such as IBM Planning Analytics .
• Integrated sales and marketing planning: Most organizations have historical data about their lead generation, sales conversions and customer retention success rates. Organizations looking to create more accurate revenue plans and forecasts and gain deeper visibility into their marketing and sales data are using business analytics to allocate resources based on performance or changing demand to meet business objectives.
• Integrated workforce performance planning: As organizations undergo digital transformation and otherwise react to changing landscapes, they might need to ensure they have the right workforce with the right analytical skills. This is especially true in a world where employees are more likely to leave a company for a new job. Workforce performance planning helps organizations understand their workforce requirements, identify and address skill gaps, and better recruit and retain talent to meet the organization's needs today and in the future.

The flexibility of spreadsheets. Control of a database. The power of integrated business planning. Now available as a Service on AWS.

AI-powered automation and insights in Cognos Analytics enable everyone in your organization to unlock the full potential of your data. 

Detects application and business risks affecting the customer experience, enabling users to correlate application service level objectives with underlying infrastructure resourcing.

Learn more about business analytics by reading these blogs and articles. 

IBM Planning Analytics has helped support organizations across not only the office of finance but all departments in their organization.

A growing number of forward-looking companies are successfully navigating complexities using IBM Planning Analytics, a technology capable of supporting secure collaboration, fast automated data acquisition, and more.

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning.

Scale AI workloads for all your data, anywhere, with IBM watsonx.data, a fit-for-purpose data store built on an open data lakehouse architecture.

1 Business intelligence versus business analytics  (link resides outside ibm.com), Harvard Business School. 2  How predictive analytics can boost product development  (link resides outside ibm.com), McKinsey, August 16, 2018.

Why Business Analytics is so important for Success?

Table of Contents

Importance of Business Analytics

Data-driven decision-making, improved operational efficiency, better customer insights, competitive advantage, predictive modelling, descriptive analytics , predictive analytics, prescriptive analytics, does business analytics make a good career choice.

What if you could analyze your past business performances and results, and use that information to prepare for the future? That is essentially what business analytics is all about.

Business analytics entails the analysis of data to create predictive models, as well as the application of optimization techniques, and communicating the results to employees and customers. It utilizes a data-driven methodology to the business environment, and as such relying on statistics and data modeling to create insights for the business.

In today’s business environment, every organization is looking for a way to make their decision making more efficient and business analytics gives them that advantage. What makes business analytics standout is the fact that it can be applied in several areas.

Business Analytics plays a vital role in transforming raw data into valuable insights that can inform decision-making. By using Business Analytics tools, organisations can gain a deeper understanding of the primary and secondary data emerging from their activities, enabling them to refine their processes and improve productivity. To maintain a competitive edge, businesses need to stay ahead of their peers and leverage the latest tools to improve efficiency and generate more profits.

• Organizations employ Business analytics so they can make data-driven decisions. Business analytics gives business an excellent overview and insight on how companies can become more efficient, and these insights will enable such business optimize and automate their processes.
• Business analytics also offers adequate support and coverage for businesses who are looking to make the right proactive decisions. Business analytics also allows organizations to automate their entire decision-making process, so as to deliver real-time responses when needed.
• One of the apparent importance of business analytics is the fact that it helps to gain essential business insights. It does this by presenting the right data to work it. This goes a long way in making decision making more efficient, but also easy.
• Efficiency is one area of business analytics helps any organization to achieve immediately. Since its inception, business analytics have played a key role in helping business improve their efficiency. Business analytics collates a considerable volume of data in a timely manner, and also in a way that it can easily be analyzed. This allows businesses to make the right decisions faster.
• Business analytics help organizations to reduce risks. By helping them make the right decisions based on available data such as customer preferences, trends, and so on, it can help businesses to curtail short and long-term risk.

Benefits of Business Analytics 

Business Analytics offers the following benefits:

Business Analytics uses data to provide insights and inform decision-making. This helps organisations to make informed decisions based on facts and data rather than intuition or guesswork.

Business Analytics can help businesses to identify areas of inefficiency and optimise their operations for better performance and productivity.

Business Analytics can help businesses to gain a deeper understanding of their customer’s behaviour and preferences, which can inform product development, marketing strategies, and customer engagement initiatives.

Businesses that use Business Analytics to inform their strategies and decisions are more likely to gain a competitive advantage over those that don’t.

Business Analytics can help businesses predict future trends and outcomes, aiding in proactive decision-making and risk management.

Business Analytics is an indispensable tool for businesses that want to thrive in today’s data-driven, fast-paced business environment.

Types of Business Analytics

Businesses use three types of analytics to make informed decisions: descriptive analytics, which reveal what has already happened; predictive analytics, which forecasts what could happen; and prescriptive analytics, which advise what should happen in the future. While each of these methods has its own benefits, they are most effective when used in conjunction with one another.

The most basic form of analytics, descriptive analytics, is utilised by 90% of organisations today. Analyzing historical and real-time data for insights into the future answers the question “What has happened?”. Descriptive analytics aims to determine the reasons for past successes and failures. The term “past” can refer to any point in time when an event occurred, whether it be a month or just a minute ago. Descriptive analytics accounts for the vast majority of big data analytics used by organisations.

After descriptive analytics, the next stage in data analysis is predictive analytics. By examining historical trends and patterns, businesses can accurately anticipate future possibilities. This process helps companies establish realistic goals, plan strategically, and manage expectations. Predictive analytics enables businesses to study data and predict potential outcomes based on previous patterns and trends. It is akin to peering into a crystal ball to answer the question, “What could be the future outcome?”

The subsequent phase after predictive analytics is prescriptive analytics, which involves manipulating the future. It offers recommendations on potential outcomes and suggests actions to optimise essential business metrics. Prescriptive analytics employs simulation and optimisation techniques to answer the question, “What should a business do?”

Prescriptive analytics is a sophisticated concept that relies on optimisation to achieve the best results. It also utilises stochastic optimisation to comprehend how to attain the best outcomes and recognise data uncertainties to make informed decisions.

With a promising job outlook, business analytics can be an excellent career choice. Working as a business analyst can be challenging and rewarding for those who enjoy problem-solving. Business analysts work in an environment that demands continuous evaluation and improvement, making it an ideal career for those who thrive in dynamic settings.

In addition to technical skills, business analysts also have the opportunity to hone their leadership skills and make significant contributions to an organisation’s success. They often work with a diverse range of employees across a business, allowing them to expand their professional network and learn about multiple facets of the industry.

There is no denying it that business analytics have come to change the dynamics of businesses and how they operate. Its importance cannot be overestimated, and with more and more companies relying on it for their decision-making process, it is something your business should consider incorporating if it hasn’t done so already.

Working on various projects also provides business analysts with new experiences and expertise that can be applied to future projects. Most importantly, business analysts can use their creativity to help solve business challenges, making a real difference in an organisation.

To learn more about pursuing a career in business analytics, interested individuals can explore the MBA in Business Analytics Program offered by Exeed College. 

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Regression Analysis in Business Analytics Essay

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Regression analysis is useful for business analysts because they apply this tool to identify a relationship between variables to analyze the modern state of affairs and predict the future development of some relationship. Every regression analysis has a single dependent variable and at least one explanatory variable that is used to define the dependent one. Often, the relationship between the two variables is linear, meaning that all interception points cluster around a straight line. However, the world is not perfect, and some exceptions occur, referring to outliers that stand out from the big picture. This essay will define outliers, discuss how an outlier or absence thereof can influence the slope of a regression of Y versus a single X, and explain whether an outlier always has such an impact.

To begin with, one should present the definition of an outlier. Albright and Winston (2016) argue that an outlier “is an observation that falls outside of the general pattern of the rest of the observations” (p. 426). This information allows supposing that an outlier’s presence can have a significant impact on regression analysis results. It relates to the fact that an outlier affects the slope of a regression of Y versus a single X. For example, Figure 1 by Albright and Winston (2016) demonstrates how an outlier can be represented in a scatter plot and how it affects the graph. The graph indicates that most observations cluster around a straight line, while a single outlier that is the highest point has different values. Thus, the authors explain that the outlier “tilts the regression line toward it” (Albright & Winston, 2016, p. 513). If an outlier results in such changes, it is called an influential point. Simultaneously, Figure 1 shows that the outlier’s absence will result in a significantly different regression line.

In addition to that, one should emphasize that it is not always that outliers make the slope of a regression change. Albright and Winston (2016) mention that the regression analysis results with an outlier and without it can have the same results. Since an outlier is a point that is found outside of the general pattern, it is necessary to emphasize that this position is caused by its extreme values regarding X-axis, Y-axis, or both. For example, it is reasonable to use Figure 2 by Albright and Winston (2016) to understand when these points make no difference. Initially, the graph did not have any outliers, and all the observations fell within standard values. Thus, it is reasonable to imagine that the visual has an outliner with 60 points across the Y-axis and 65 points across the X-axis. For the purpose of this paper, a black point has been added to Figure 2. Even though this outlier implies extreme values, it does not change a regression line significantly.

In conclusion, regression analysis draws significant attention to outliers and their impact. It is so because these points can impact the slope of a regression differently, and the two graphs have demonstrated it. On the one hand, an outlier can make no difference when it has extreme values that are a continuation of a regression line. On the other hand, an outlier can tilt the regression line toward it when placed outside of a regression line. Such an outlier is called an influential point, and it necessarily changes the regression analysis results.

Albright, S. C., & Winston, W. L. (2016). Business analytics: Data analysis and decision making (6 th ed.). Cengage Learning.

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Sample Personal Statement Business Analytics

by Talha Omer, M.Eng., Cornell Grad

In personal statement samples by field.

Here is the personal statement of an applicant who got admitted to top Master’s in Business Analytics programs, including CMU, and MIT. For personal statement, the programs posed several questions to applicant, which the admissions committee expects to be answered in an essay form. The universities provide these personal statement prompts to encourage students to self-reflect and then to share their insights with the program.

The following essays are an example of a compelling story and reflect the original voice and personality of the applicant. Get inspiration from them and try to incorporate their strengths into your own personal statement.

Related Personal Statements 1) Sample Personal Statement in Advanced Analytics (admitted to NCSU) 2) Sample Personal Statement in Analytics (admitted to Georgia Tech) 3) Sample Personal Statement in Data Science and Analytics (admitted to CMU and Vanderbilt) 4) Sample Personal Statement in Management and Analytics (admitted to LBS)

In this Article

Personal Statement Prompt 1

Personal statement prompt 2, personal statement prompt 3, personal statement prompt 4.

Please attach to the application a brief narrative outlining your academic interests. Include any current or long-range interests in research, teaching or other professional objectives. Please describe these if you have progressed far enough in your career to have publications or other evidence of scholarly endeavour.

I want to play the critical role of a business strategist and capitalize on the most useful of the sophisticated technologies flooding modern businesses. In the short run, I want to set up a consulting firm which provides analytics-driven marketing solutions to large firms operating to deliver a consistent and easy buying experience for their customers. I would ideally like to leverage recent shifts in the marketing industry towards data, expand my consulting services across countries, and become a global leader in revolutionizing customer experience through data analytics.

I am an ideal candidate for the Business Analytics program due to my strong academic record as I graduated in Electrical Engineering with a cum laude GPA. My undergraduate has equipped me with extensive quantitative knowledge and technical experience around different themes in engineering. I’ve focused most of my studies on parametric analysis using different software like C++, VB, MySQL, Python and MATLAB. This was further honed when I joined Facebook’s Meta as an Analyst Software Engineer and, using MySQL, predicted patterns of interpersonal behaviour to optimally pair customers with an agent. Solving demand-related issues of customers from all around the globe, I have become aware of the mismatches between marketing outreach and customers’ buying needs. Therefore, I aspire to influence global customers’ marketing experience by investigating ways in which customer needs can be well aligned with the sales strategies of firms supplying goods online.

To materialize my aspirations into a successful reality, I want to glean more information on predicting customer behaviours and matching it with the right promotion offers since it ideally dovetails with my professional expertise in using MySQL to predict patterns of interpersonal behaviour to optimally pair customers with an agent. While I developed a strong skillset in matching commercially available information to determine successful behavioural interaction patterns, I plan to extend my research pursuits by exploring the right mix of traditional and digital marketing vehicles.

Beyond the classroom, I would greatly appreciate the opportunity to learn from and collaborate with the influential faculty and the Center and Laboratory for Behavioral Operations and Economics’ endeavours to solve critical IT-related decisions that advance company performance. In particular, the techniques of Professor Gary Bolton in experimenting with real-world trading markets within laboratory settings are foundational for my current work. Moreover, they will equip me with experimenting tools through which I can excel in my future career. Moreover, my future aspirations require strong leadership qualities recognized in a data-driven world. For this purpose, I would greatly benefit from The Leadership Center’s four dynamic offerings to mould my personality into a global leader’s persona.

Lastly, I will exploit the strong placement services and gain access to leading consulting companies where I can seek pro bono consulting opportunities and enhance my problem-solving acumen. I am also confident in acquiring the necessary communication skills to present solutions to Product Managers, Sales Associates, Engineers, and Marketing Teams for effective decision-making.

Thus, to sum up, owing to my strong academic credentials and professional expertise in applying artificial intelligence, I am confident of fully utilizing vibrant opportunities at Master in Business Analytics and converting it into an ideal segue for my future career aspirations.

Introduce yourself to your future classmates in 100 words or less.  

I have a passion for entrepreneurship, and during my sophomore year, I laid the foundations of the “ pay as you play”  console gaming platform and earned $4,000 a month. I have strong interpersonal skills and have won five national debating championships and represented at various national and international competitions. I am a fighter and showed great resolve in fighting acute liver failure, which I suffered in my junior year. I am a risk taker and quit my high-paying job at Unilever to start my venture of connecting home-cooked food suppliers with potential buyers.

What professional experiences have made you determine that business school is the right next step? (150 words)  

I pitched a business idea named Homestove – a food sharing application based on the B2C model. The business idea was successful as it won a substantial seed funding. Encouraged by its traction, I spearheaded its execution of supplying home-cooked food to potential buyers. However, the main factor I overlooked was doing intensive data analytics based on the market’s demographics. The locus I initially defined was 8 km, which was far from optimal. As a result, it created a mismatch between the area from where demand was being generated and where the chef was supplying the home-cooked food. Consequently, we incurred a considerable loss and customer dissatisfaction because of extended delivery times. Therefore, to avoid such losses, I strongly need a degree based on data management, technology, and analytics to create an ideal operations model for my business.

What career do you plan to pursue after business school and why? (150 words)

In the short run, I would restructure my business model by carefully analyzing the individual-level micro-data. Ideally, I aim to optimize my operations, mitigate the mismatch of distances between chefs and potential buyers, and reclaim the currently lost market share. If successful, I would like to expand my business to other metropolitan cities of US.

In the long run, alongside my business, I would like to provide consulting services to other tech-based startups suffering from a lack of operational optimization of their business. Unfortunately, despite having a lot of potential, most tech-based startups fail because of a lack of proper training in operational research. Consequently, they are unable to sustain themselves and run out of funding. I want to provide such firms consulting based on data analytics, business insights and marketing so they can harness themselves and become profitable.

Is there anything not addressed elsewhere in the application that you would like the Admissions Committee to know about you to evaluate your candidacy? (300-word maximum)

During my senior year, I got recruited by British American Tobacco after winning their battle of mental challenges. They had devised stringent criteria based on a rigorous aptitude test. Additionally, they assigned a group-based project related to any issue in public policy. I was the team lead and submitted a basic blueprint idea for using solar panels for domestic power needs. The basic plan was that the cost of these solar panels, being the main hindrance, would be offset once the government provided low-interest loans to homeowners to power their own houses. The savings on electricity bills would then be used to pay off the loans. It was an innovative idea that was well crafted after hundreds of hours of hard work. For example, my team and I conducted a rigorous field activity and accumulated over 400 bills from different homes in Frederick, Maryland to prove that using solar panels would not cost anything extra to these houses. We won the competition based on our innovative idea, and I was later called for an interview. Later, I was selected as MT and five other candidates out of 7000 applicants. I worked there for two years and eventually moved to Unilever. However, I always wanted to create social value and impact the lives of others, so after a brief stint at Unilever, I started my venture.

Describe a situation in your life where you failed to achieve a goal. What did you learn from this experience? (Max. 400 words)

I pitched a business idea named Homestove – a food sharing application based on the B2C model. The app’s primary purpose was to create local clusters where domestic chefs and homemakers market and sell their food to potential buyers within a particular locus. The business idea was successful as it was selected at top incubators and won a substantial seed funding award. Encouraged by its traction, I spearheaded its execution  to become a pioneer in supplying home-cooked food to potential buyers . The main factor I overlooked was doing intensive market research based on the market’s demographics. Due to a lack of in-depth data analysis, the locus I initially defined was an 8km radius, which was far from optimal. As a result, it created a mismatch between the area from where demand was being generated and where the chef was supplying the home-cooked food. Consequently, we levied a considerable cost of transportation and customer dissatisfaction because of extended delivery timings. The  goal of becoming a pioneer not only failed severely , but the loss’s extent was so much that it even eroded our initial seed funding. 

Although I failed in my initial ambition, my failure taught me deep insights into this business. Therefore, I am not let down by this experience. Instead, it has given me a direction leading me to future success. I realize that I conceded this partly because I didn’t discount some factors but, more importantly, because I lacked the necessary skill set. I realized that had I had better analysis skills in synthesizing microeconomic data, I would have developed a better spatial model of the business. I also felt that I needed to hone my skills in optimizing the operations of this business so that the mismatch between consumer and food supplier localities is mitigated. 

My more profound understanding of my failure and firm resolve to stand up again only point in one direction. And that is to pursue a Master’s degree in Business Analytics, where I can bolster my quantitative research skills and get international exposure to similar business models. I am therefore confident that a Master’s degree in Business Analytics will go a long way in pursuing my career goals and ambitions.

You just entered the elevator with a member of the Applicant Review Committee for the MS in Business Analytics. You can only speak to this person on the elevator for a short time. What would you say to this individual so that they would look favorably upon your application? (Max. 250 words)

During my senior year, I was inducted as Management Trainee by British American Tobacco after aced their “Battle of Minds” challenge. I was among the top 5 candidates out of other 7,000 aspirants. I worked there for almost two years, and after a very brief stint at Unilever, one of the leading multinationals, I decided to start my business venture. I looked for seed funding programs and pitched them my idea of supplying home-cooked food to potential buyers. The business idea won a substantial seed funding award. The idea was unique and covered on the local news channels during their prime-time transmission. After initial success, I experienced a downturn in my business. I realized the need to hone my quantitative analysis skills, particularly focusing on the operational optimization of business ventures. While searching for programs, I was excited by the curriculum. The vibrant community and home to more than 400 businesses, is an ideal place to learn and build new experiences. Additionally, the business analytics research centre is a perfect platform where I can test new quantitative research methods to calibrate the optimal spatial model of my business. In short, my strong credentials and desire to learn more align with the core values of the program in Business Analytics and will be ideal for fulfilling my career ambitions. 

One of our core values is Integrity. What does academic integrity mean to you? (250 words or less)

Academic integrity to me means being honest first and foremost to myself and anyone else. For me, it’s a way of life beyond just being honest in academic writing and following rules and regulations. I have stood up for academic sanctity, even against my friends and peers and have never tolerated slight slip-ups. I clearly remember how I had to take a firm stand against my group fellows in a Finance course when they were inclined to plagiarize a business idea. Additionally, when I started my undergraduate, I had many problems with citing during my writing courses. I wanted to learn and uphold academic integrity, so I took an additional paid online course to understand and master the art of referencing and citations. I have ensured that I take academic integrity beyond my university. As an entrepreneur, I have created a platform where cooks from around the city supply food to various customers. Dealing with almost 40 such cooks, I have ensured everyone is treated equally and honestly. For this purpose, I have developed systems where my employees’ names and background details are encrypted against an identification number so that my judgment remains impartial towards them. These are just some notable instances. Still, throughout my life, I have not let my moral guard down and confidently say that my ethical code of conduct strongly resonated with the core value of integrity.

Describe your experience using Excel, Databases or Software Tools. How has this influenced your decision to pursue a career in analytics? (250 words or less)

During my undergraduate studies, I worked with advanced Excel and basic STATA for various courses involving small and medium datasets. However, I realized a massive handicap of advanced data analytics when I started my business venture. I started a business named Homestove – a food sharing application based on the B2C model. Due to a lack of in-depth data analysis, the locus I initially defined was an 8 km radius, which was far from optimal. As a result, it created a mismatch between the area from where demand was being generated and where the chef was supplying the home-cooked food. I realized that had I had better analysis skills in synthesizing microeconomic data, I would have developed a better spatial model of the business. I also felt that I needed to hone my skills in optimizing the operations of this business so that the mismatch of consumer and food suppliers’ localities is mitigated. My deeper understanding of my failure and firm resolve to stand up again only point in one direction: to pursue a Master’s degree in Business Analytics. Through this, I can bolster my quantitative research skills and get international exposure to similar business models.

What is your target industry post-graduation? What impact do you want to make in your industry using analytics? (250 words or less)

In the short run, I would like to restructure my business model by carefully analyzing the individual data. Ideally, I aim to optimize my operations and mitigate the mismatch of the distance between chefs and potential buyers and reclaim the currently lost market share. If successful, I would like to expand my business to other metropolitan cities. In the long run, alongside my business, I would like to provide consulting services to other tech-based startups suffering from a lack of operational optimization of their business. Unfortunately, despite having a lot of potential, most tech-based startups fail because of a lack of proper training in operational research. As a result, they cannot sustain themselves and run out of funding. I would like to provide such firms with the necessary training based on data analytics, business insights and marketing to harness themselves and become profitable.

Please provide any additional information you would like to bring to the admissions committee’s attention. (250 words or less)

I have a keen sense of developing businesses from simple ideas of human needs. During my sophomore year, I developed a passion for console gaming. However, I soon realized my limited options as each game cost around $50. It was my handicap that I started my first business. I formed a virtual club initially with only 17 of my friends who could play up to 17 other games by only paying an additional fee of $20 per day. By the following year, my club had 123 members across various cohorts. Just by renting the games I bought from the initial deposits; I earned as much as $4,000 per month. 

When I joined Unilever, I had many problems having lunch at the office. So, I searched for cooks nearby my office and found a lady who was ready to supply it. Her food was tasty, and soon she had five customers from our office. This gave me an idea to lay down the foundations of Homestore – a food-sharing B2C modelled application. The application’s primary purpose was to create local clusters where domestic chefs and housewives marketed and sold their food to potential buyers within a particular locus. The idea was well received as it won seed funding and got promoted on local news channels’ prime-time transmission. Currently, I am running this business, but I need a data analytics degree to smoothen operations so that I can increase the impact of my venture. 

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Business Analytics Essays

Business analytics and decision-making, business analytics and decision making, analytics in a corporation, popular essay topics.

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How To Write an Analytical Essay

If you enjoy exploring topics deeply and thinking creatively, analytical essays could be perfect for you. They involve thorough analysis and clever writing techniques to gain fresh perspectives and deepen your understanding of the subject. In this article, our expert research paper writer will explain what an analytical essay is, how to structure it effectively and provide practical examples. This guide covers all the essentials for your writing success!

What Is an Analytical Essay

An analytical essay involves analyzing something, such as a book, movie, or idea. It relies on evidence from the text to logically support arguments, avoiding emotional appeals or personal stories. Unlike persuasive essays, which argue for a specific viewpoint, a good analytical essay explores all aspects of the topic, considering different perspectives, dissecting arguments, and evaluating evidence carefully. Ultimately, you'll need to present your own stance based on your analysis, synthesize findings, and decide whether you agree with the conclusions or have your own interpretation.

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How to Structure an Analytical Essay

Crafting an excellent paper starts with clear organization and structuring of arguments. An analytical essay structure follows a simple outline: introduction, body, and conclusion.

Introduction: Begin by grabbing the reader's attention and stating the topic clearly. Provide background information, state the purpose of the paper, and hint at the arguments you'll make. The opening sentence should be engaging, such as a surprising fact or a thought-provoking question. Then, present your thesis, summarizing your stance in the essay.

Body Paragraphs: Each paragraph starts with a clear topic sentence guiding the reader and presents evidence supporting the thesis. Focus on one issue per paragraph and briefly restate the main point at the end to transition smoothly to the next one. This ensures clarity and coherence in your argument.

Conclusion: Restate the thesis, summarize key points from the body paragraphs, and offer insights on the significance of the analysis. Provide your thoughts on the topic's importance and how your analysis contributes to it, leaving a lasting impression on the reader.

Meanwhile, you might also be interested in how to write a reflection paper , so check out the article for more information!

How to Write an Analytical Essay in 6 Simple Steps

Once you've got a handle on the structure, you can make writing easier by following some steps. Preparing ahead of time can make the process smoother and improve your essay's flow. Here are some helpful tips from our experts. And if you need it, you can always request our experts to write my essay for me , and we'll handle it promptly.

Step 1: Decide on Your Stance

Before diving into writing, it's crucial to establish your stance on the topic. Let's say you're going to write an analytical essay example about the benefits and drawbacks of remote work. Before you start writing, you need to decide what your opinion or viewpoint is on this topic.

• Do you think remote work offers flexibility and improved work-life balance for employees?
• Or maybe you believe it can lead to feelings of isolation and decreased productivity?

Once you've determined your stance on remote work, it's essential to consider the evidence and arguments supporting your position. Are there statistics or studies that back up your viewpoint? For example, if you believe remote work improves productivity, you might cite research showing increased output among remote workers. On the other hand, if you think it leads to isolation, you could reference surveys or testimonials highlighting the challenges of remote collaboration. Your opinion will shape how you write your essay, so take some time to think about what you believe about remote work before you start writing.

Step 2: Write Your Thesis Statement

Once you've figured out what you think about the topic, it's time to write your thesis statement. This statement is like the main idea or argument of your essay.

If you believe that remote work offers significant benefits, your thesis statement might be: 'Remote work presents an opportunity for increased flexibility and work-life balance, benefiting employees and employers alike in today's interconnected world.'

Alternatively, if you believe that remote work has notable drawbacks, your thesis statement might be: 'While remote work offers flexibility, it can also lead to feelings of isolation and challenges in collaboration, necessitating a balanced approach to its implementation.'

Your thesis statement guides the rest of your analytical essay, so make sure it clearly expresses your viewpoint on the benefits and drawbacks of remote work.

Step 3: Write Topic Sentences

After you have your thesis statement about the benefits and drawbacks of remote work, you need to come up with topic sentences for each paragraph while writing an analytical essay. These sentences introduce the main point of each paragraph and help to structure your essay.

Let's say your first paragraph is about the benefits of remote work. Your topic sentence might be: 'Remote work offers employees increased flexibility and autonomy, enabling them to better manage their work-life balance.'

For the next paragraph discussing the drawbacks of remote work, your topic sentence could be: 'However, remote work can also lead to feelings of isolation and difficulties in communication and collaboration with colleagues.'

And for the paragraph about potential solutions to the challenges of remote work, your topic sentence might be: 'To mitigate the drawbacks of remote work, companies can implement strategies such as regular check-ins, virtual team-building activities, and flexible work arrangements.'

Each topic sentence should relate back to your thesis statement about the benefits and drawbacks of remote work and provide a clear focus for the paragraph that follows.

Step 4: Create an Outline

Now that you have your thesis statement and topic sentences, it's time to create an analytical essay outline to ensure your essay flows logically. Here's an outline prepared by our analytical essay writer based on the example of discussing the benefits and drawbacks of remote work:

Step 5: Write Your First Draft

Now that you have your outline, it's time to start writing your first draft. Begin by expanding upon each point in your outline, making sure to connect your ideas smoothly and logically. Don't worry too much about perfection at this stage; the goal is to get your ideas down on paper. You can always revise and polish your draft later.

As you write, keep referring back to your thesis statement to ensure that your arguments align with your main argument. Additionally, make sure each paragraph flows naturally into the next, maintaining coherence throughout your essay.

Once you've completed your first draft, take a break and then come back to review and revise it. Look for areas where you can strengthen your arguments, clarify your points, and improve the overall structure and flow of your essay.

Remember, writing is a process, and it's okay to go through multiple drafts before you're satisfied with the final result. Take your time and be patient with yourself as you work towards creating a well-crafted essay on the benefits and drawbacks of remote work.

Step 6: Revise and Proofread

Once you've completed your first draft, it's essential to revise and proofread your essay to ensure clarity, coherence, and correctness. Here's how to approach this step:

• Check if your ideas make sense and if they support your main point.
• Make sure your writing style stays the same and your format follows the rules.
• Double-check your facts and make sure you've covered everything important.
• Cut out any extra words and make your sentences clear and short.
• Look for mistakes in spelling and grammar.
• Ask someone to read your essay and give you feedback.

What is the Purpose of an Analytical Essay?

Analytical essays aim to analyze texts or topics, presenting a clear argument. They deepen understanding by evaluating evidence and uncovering underlying meanings. These essays promote critical thinking, challenging readers to consider different viewpoints.

They're also great for improving critical thinking skills. By breaking down complex ideas and presenting them clearly, they encourage readers to think for themselves and reach their own conclusions.

This type of essay also adds to academic discussions by offering fresh insights. By analyzing existing research and literature, they bring new perspectives or shine a light on overlooked parts of a topic. This keeps academic conversations lively and encourages more exploration in the field.

Analytical Essay Examples

Check out our essay samples to see theory in action. Crafted by our dissertation services , they show how analytical thinking applies to real situations, helping you understand concepts better.

With our tips on how to write an analytical essay, you're ready to boost your writing skills and craft essays that captivate your audience. With practice, you'll become a pro at analytical writing, ready to tackle any topic with confidence. And, if you need help to buy essay online , just drop us a line saying ' do my homework for me ' and we'll jump right in!

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How to Write an Analytical Essay?

What is an analytical essay.

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is a seasoned educational writer focusing on scholarship guidance, research papers, and various forms of academic essays including reflective and narrative essays. His expertise also extends to detailed case studies. A scholar with a background in English Literature and Education, Daniel’s work on EssayPro blog aims to support students in achieving academic excellence and securing scholarships. His hobbies include reading classic literature and participating in academic forums.

is an expert in nursing and healthcare, with a strong background in history, law, and literature. Holding advanced degrees in nursing and public health, his analytical approach and comprehensive knowledge help students navigate complex topics. On EssayPro blog, Adam provides insightful articles on everything from historical analysis to the intricacies of healthcare policies. In his downtime, he enjoys historical documentaries and volunteering at local clinics.

MS Business Analytics (MSBA)

Master of science in business analytics.

The Master of Science in Business Analytics (MSBA) is a full-time, top-ranked STEM program built on three fundamental pillars of Computer Science, Statistics & Optimization, and Business Strategy. Real-world projects and ever-evolving elective options prepare students to be comfortable both tackling unstructured analytics problems and effectively communicating results to key stakeholders. It is one of the longest-running MSBA programs in the nation and has consistently been ranked among the world’s top programs.

• APPLY TODAY Next Deadline: November 1st
• ADMISSIONS INFO
• ADMISSIONS EVENTS
• REQUEST INFO

PROGRAM PROFILE

Pacing:  Full-time

Duration:  16 months

Format:  In-Person

Class Schedule:  Weekdays on campus.

Student Profile:  Early-career professionals

STEM Designation:  For international applicants, this program is eligible for the OPT-STEM extension

BUSINESS ANALYTICS PROGRAM BENEFITS

Transforming students to business professionals.

Transitioning one’s mindset from an undergraduate student to a data professional is an important journey. When done well, as much as half of the learning process in a top graduate program happens outside the classroom and beyond the coursework. It is through workshops, industry events, social events, and other weekly opportunities that students build additional skills in the MSBA program. At Marshall, the journey each day is to prepare for greater career success.

REAL-WORLD EXPERIENCE THROUGH CASE COMPETITIONS

Our Marshall Case Competition Program ensures students have opportunities to be market ready with real-world project experience. Students combine what they are learning in the classroom with their industry interests and collaborate with diverse, cross-functional teams, making the case competition experience as valuable as an “internship in a week.” Real-world unstructured problems, real teams, real impact. Our opportunities are extensive and inclusive of all MS Business Analytics students, with a case team of over 200 data professionals.

The MSBA Marshall Global Case Competition program is founded on partnerships with professionals and mentorships for students. Why wait for summer to build a project portfolio? We bring market leading opportunities to you at Marshall.

UNIQUE, FUN & COLLABORATIVE CULTURE

The Business Analytics program fosters a culture where students work closely together and support each other. As the criteria of today's data science professionals is the ability to work in teams, the MSBA program provides students the ability to fine tune that skill set. Students are provided the tools and environment to build a strong team and collaborate on outcomes, allowing them to enhance their leadership skills and create the optimal career path.

BUSINESS ANALYTICS CURRICULUM

Academic overview.

The Master of Science in Business Analytics (MSBA) degree requires 33 units, taken in 3 semesters (with the flexibility of extending to 4 semesters): Fall, Spring, and Fall. There are 21 units of required coursework, including an internship opportunity, plus 12 units of coursework chosen from a list of electives.

USC MSBA's Three Pillars: Computer Science; Statistics, Machine Learning, Optimization; and Business Strategy.

Sample Schedule

Actual schedules may differ between students for various reasons and will be determined with the guidance of the Academic Advisor.

First Semester (Fall) GSBA 542: Communication for Management (1.5 units) GSBA 545: Data Driven Decision Making (1.5 units) DSO 545: Statistical Computing and Data Visualization (3 units) DSO 576: Algorithmic Thinking with Python (3 units) DSO 552: SQL Databases for Business Analysts (1.5 units) DSO 555: Advanced Databases for Business Analysts (1.5 units)

Second Semester (Spring) DSO 510: Business Analytics (1.5 units) DSO 530: Applied Modern Statistical Learning Methods (3 units) Elective (3 units) Elective (1.5 units)

Summer DSO 595: Internship in Data Sciences or Operations (0-1.5 units) Third Semester (Fall) DSO 573*: Data Analytics Driven Dynamic Strategy & Execution (3 units) Elective (3 units) Elective (3 units) Elective (1.5-3 units)

*DSO 573 can be replaced with DSO 574: Getting Ready for Big Data or DSO 556: Business Models for Digital Platforms

Programming:

• Data Cleaning
• Data Wrangling
• Data Visualization
• Web Scraping
• Database Management
• Python (Main Language of Study), R, SQL, NoSQL

Statistics + Machine Learning

• Probability Distributions
• Inferential Statistics
• Supervised Learning Methods (linear regression, logistic regression, lasso, ridge regression, decision trees, LDA, QDA, KNN, PCR, neural networks, random forests, SVM)
• Unsupervised Learning Methods (k-means, hierarchical clustering)
• Deep learning (CNN, RNN, LSTM, GAN)
• Simulation Models, Linear, Non-linear, Discrete, Dynamic Optimization

Business Strategy

• Marketing Analytics, Fraud Analytics, Operations Analytics, Game Analytics, Time-Series Analytics, Healthcare Analytics, Supply Chain Analytics
• Pricing Strategies
• Text Analytics & Natural Language Processing
• Business Communication
• Dashboarding, Consultation, Collaboration, Strategy
• Problem Solving, Problem Framing

APPLYING TO MSBA

Application deadlines.

Round 1 and 2: Priority Consideration for scholarships

STUDENT LIFE

Business analytics student association.

The Business Analytics Student Association (BASA) is a student-led organization dedicated to empowering MSBA students with the necessary networking and professional skills to succeed in their careers. Additionally, we aim to enrich the student experience and establish a strong community of business analytics professionals within USC and beyond. To achieve this, we offer a diverse range of opportunities including workshops, networking events, case competitions, social gatherings, and more.

Why Marshall

Career pathways.

Marshall graduate students are actively sought by the world's leading companies Amazon, Google, Salesforce, BCG and Goldman Sachs. Benefitting from Marshall's deep industry ties, they are leaders in a range of industries from Technology, Entertainment and Interactive Gaming to Healthcare, Consulting and Financial Services.

Trojan Network

The Marshall Trojan network links students and business leaders around the globe. Over 90,000 Marshall alumni worldwide create a proactive pay-it-forward culture that drives powerful career outcomes. There is simply no other alumni network quite like it.

Real-World Learning

Every Marshall graduate student has opportunities to integrate and apply what they learn in the classroom to real challenges outside the classroom. Learning by doing trains leaders to be visionary and resilient.

Faculty Expertise

USC Marshall faculty come from a wide array of backgrounds and disciplines. They engage in collaborative and inter-disciplinary teaching to explore business challenges and opportunities from a variety of viewpoints. They incorporate case studies, current events, career panels, and industry speakers to support academic content with real-world perspectives.

Global School in the Heart of LA

Located in the heart of Los Angeles, USC Marshall is embedded in one of the most vibrant global economies and part of a large, diverse university community committed to a culture of inclusion, collegiality, cooperation, and team work. Our students, faculty and alumni play leading roles in the industries that have built and continue to drive L.A. forward - from real-estate and accounting, to marketing and finance, to entertainment and tech.

PROGRAM SHOWCASE: MSBA

Business analytics faculty, dawn porter.

• Fubon Chair in Teaching Excellence
• Academic Director, MS Business Analytics
• Professor of Clinical Data Sciences and Operations

Dawn Porter is a statistician who has published in Management Science and is the coauthor of Basic Econometrics (fifth edition). Prior to joining USC, she served on the faculty at Georgetown University. Professor Porter has consulted for KPMG Consulting, Ginnie Mae, Inc., and Toys R Us.

Mohammed Alyakoob

• Assistant Professor of Data Sciences and Operations

Mohammed's research examines the economic and societal impact of digital marketplaces, with a focus on examining and measuring the role of traditional barriers, related to market structure, race, and expertise, among others, on limiting the equality of benefits derived from these platforms.

Arif Ansari

Arif Ansari is an expert in the area of data mining, business intelligence, data warehousing, and intelligent systems and technologies, a field in which he has fifteen years of research experience. He has published in IEEE Transactions on Systems and Man and Cybernetics .

Cosimo Arnesano

• Assistant Professor of Clinical Data Sciences and Operations

Cosimo Arnesano, Ph.D., MBA is a highly educated and experienced professional with a strong background in research services and business analytics implementation. With a Ph.D. in multiple fields and an MBA from the University of Southern California, Cosimo possesses a diverse skill set that includes fluorescence microscopy, biomedical engineering, Project/Operations Management, ML/AI, and more. His current interest focuses on how AI/ML can be applied to business processes, and their implications in the short and long run.

STAY CONNECTED

University of Southern California Marshall School of Business Popovich Hall, Suite 308 Los Angeles, CA 90089-2633

Phone: 213-740-7846

Email: [email protected]

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Home — Essay Samples — Education — Academic Interests — My Motivation To Pursue A Graduate Program In Business Analytics

My Motivation to Pursue a Graduate Program in Business Analytics

• Categories: Academic Interests Motivation

About this sample

Words: 631 |

Published: Apr 2, 2020

Words: 631 | Page: 1 | 4 min read

Works Cited

• Chou, T., & Shen, Y. (2019). Big Data Analytics: A Literature Review, Framework, and Research Agenda. International Journal of Information Management, 42, 80-90.
• Davis, D. (2018). Predictive Analytics: How to Use Analytics at Work. John Wiley & Sons.
• DeMauro, E. P., Grexa, M. E., & McCormick, K. A. (2015). Understanding Business Analytics: The Importance of Business Intelligence and the Emerging Role of Predictive Analytics. Journal of Business & Economics Research, 13(2), 73-78.
• Duan, Y., Edwards, J. S., Dwivedi, Y. K., & Williams, M. D. (2018). Big Data Analytics in Supply Chain Management: A Balanced Scorecard Approach. International Journal of Production Economics, 200, 118-136.
• Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study. International Journal of Production Economics, 165, 234-246.
• Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, Adoption Barriers and Myths of Open Data and Open Government. Information Systems Management, 29(4), 258-268.
• Kiron, D., Prentice, P. K., & Ferguson, R. B. (2014). Driving Business Value through Big Data Analytics: A Technology and Decision Making Perspective. MIS Quarterly Executive, 13(2), 97-107.
• Marr, B. (2015). Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. John Wiley & Sons.
• Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly Media.
• Raghupathi, W., & Raghupathi, V. (2014). Big Data Analytics in Healthcare: Promise and Potential. Health Information Science and Systems, 2(3).

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Tips and techniques for writing effective and powerful analytical essays.

Are you struggling to craft an effective analytical essay? Do you find yourself getting lost in a sea of information or unsure of how to present your analysis in a clear and concise manner? Look no further – this step-by-step guide will provide you with valuable tips and strategies to elevate your analytical essay writing skills to the next level.

Writing an analytical essay involves carefully examining a subject or topic and providing a thorough analysis and interpretation of the information gathered. It requires critical thinking, attention to detail, and the ability to express ideas and arguments coherently. In this guide, we will explore various techniques and approaches to help you develop and refine your analytical writing skills, allowing you to tackle any analytical essay with confidence and precision.

One crucial aspect of writing an analytical essay is conducting thorough research. To ensure your essay is well-informed and robust, it’s important to delve into reputable sources, such as academic journals, books, and reliable websites. By gathering a wide range of sources related to your topic, you will acquire a solid understanding of the subject matter, which will enable you to provide a comprehensive analysis in your essay.

Another essential element of analytical essay writing is the development of a strong thesis statement. Your thesis should present a clear and concise argument or claim that you will support throughout your essay. It should be specific, debatable, and relevant to the topic at hand. By crafting a well-defined thesis statement, you provide the backbone for your entire essay, guiding your analysis and helping you maintain a focused approach.

Furthermore, an effective analytical essay requires careful organization and structure. Divide your essay into logical paragraphs, each addressing a specific point or aspect of your analysis. Use topic sentences to introduce each paragraph, guiding your reader and providing a clear roadmap of your essay’s progression. Additionally, utilize transitional phrases and words to create smooth transitions between paragraphs, ensuring a cohesive and coherent flow.

Finding a Strong Topic for Your Essay

Choosing the right topic is a crucial first step in writing an analytical essay. The topic sets the tone for the entire essay and determines its scope and focus. It is important to select a topic that is engaging, relevant, and allows for in-depth analysis. This section will provide you with helpful strategies for finding a strong topic that will captivate your readers.

Gathering and Evaluating Reliable Sources

One of the key aspects of writing an analytical essay is gathering and evaluating reliable sources. The quality of your sources can greatly impact the strength and credibility of your arguments. In this section, we will explore the importance of finding trustworthy sources and discuss strategies for evaluating their reliability.

When conducting research for your essay, it is crucial to seek out sources that are authoritative and trustworthy. Reliable sources are those that have been written by experts in the field or have been published in reputable academic journals. These sources are often backed by extensive research and provide accurate and unbiased information.

When evaluating the reliability of a source, there are several factors to consider. Firstly, check the author’s credentials and expertise in the subject matter. Look for individuals who have relevant qualifications or experience in the field. This will help establish their credibility and ensure that they are knowledgeable on the topic.

In addition to the author’s credentials, consider the publication or website where the source is found. Reputable academic journals, books from respected publishers, and reputable websites such as government or educational institutions are generally more reliable sources of information. Be cautious of sources from unknown or biased sources, as they may not provide accurate or unbiased information.

Furthermore, it is important to analyze the content of the source itself. Look for evidence-based arguments, logical reasoning, and a balanced presentation of different perspectives. A reliable source should provide well-supported claims and back them up with relevant evidence and examples.

Finally, consider the date of publication. While older sources can still provide valuable insights, it is important to have up-to-date information, especially in fields that are rapidly evolving or undergoing significant changes. Check for recent studies and publications to ensure that your information is current.

In conclusion, gathering and evaluating reliable sources is a critical step in writing an analytical essay. By seeking out trustworthy sources and analyzing their credibility, you can strengthen your arguments and provide a solid foundation for your essay. Keep in mind the importance of author expertise, publication credibility, content analysis, and the recency of the information. Through careful evaluation, you can ensure that your essay is well-informed and persuasive.

Creating an Outline for Your Essay

One of the most important steps in the essay writing process is creating an outline. An outline serves as a roadmap for your essay, helping you to organize your thoughts and ideas in a logical and coherent manner.

When creating an outline for your essay, it’s important to start by identifying the main points or arguments that you want to make. These main points will serve as the foundation of your essay and should be presented in a clear and concise manner. You can think of these main points as the “backbone” of your essay.

Once you have identified the main points, you can then begin to develop subpoints that support and expand upon each main point. These subpoints should provide specific examples, evidence, or analysis to strengthen your main arguments. In essence, they help to fill in the details and provide a deeper understanding of your main points.

Organizing your main points and subpoints can be done in various ways. One common method is to use a hierarchical structure, such as an outline with Roman numerals, capital letters, and Arabic numerals. Another method is to use bullet points or a numbered list. You can choose the method that works best for you, based on the complexity of your essay and the level of detail you want to include in your outline.

As you create your outline, it’s important to keep in mind the overall structure of your essay. Your introduction should provide a brief overview of the topic and present your thesis statement. The body paragraphs should present and develop your main points and subpoints, providing evidence and analysis to support your arguments. Finally, your conclusion should summarize your main points and restate your thesis in a concise and compelling manner.

Remember that your outline is a flexible tool that can be revised and adjusted as you work on your essay. It’s not set in stone and can be modified as needed to better reflect your evolving ideas and arguments. Don’t be afraid to make changes and reorganize your outline as you go along.

In conclusion, creating an outline for your essay is an essential step in the writing process. It helps you to organize your thoughts, develop your arguments, and maintain a logical flow throughout your essay. By investing time and effort into creating a strong outline, you’ll set yourself up for success and make the writing process much smoother.

Developing a Clear and Coherent Argument

When it comes to writing analytical essays, one of the most important aspects is developing a clear and coherent argument. It is essential to have a well-structured and logical argument in order to effectively convey your ideas and convince your readers.

First and foremost, it is crucial to have a clear understanding of the topic you are writing about. Take the time to thoroughly research and gather relevant information, as this will provide you with the necessary knowledge to build a strong argument. Additionally, make sure to identify any key terms or concepts that are essential to your argument, as this will help you stay focused and ensure coherence throughout your essay.

Once you have a solid foundation of knowledge, it is important to organize your argument in a logical manner. Start by outlining the main points or claims you want to make, and then provide supporting evidence or examples for each of these points. Be sure to clearly state your thesis statement, which should summarize the main argument you are making in your essay.

In order to maintain coherence in your argument, it is important to use logical transitions between your ideas. This can be done through the use of transitional words and phrases, such as “however,” “in addition,” or “on the other hand.” These transitions will help guide your reader through your argument and ensure that your ideas flow smoothly from one point to the next.

Lastly, it is crucial to anticipate and address counterarguments in your essay. By acknowledging opposing viewpoints and effectively refuting them, you can further strengthen your overall argument. This demonstrates that you have considered multiple perspectives and have arrived at your own well-supported conclusion.

In conclusion, developing a clear and coherent argument is essential when writing analytical essays. By thoroughly researching your topic, organizing your ideas in a logical manner, using transitions effectively, and addressing counterarguments, you can effectively convey your ideas and make a convincing argument to your readers.

Strengthening Your Essay with Relevant Evidence

In order to create a compelling and persuasive analytical essay, it is essential to back up your arguments with relevant evidence. This evidence serves to support your claims and gives your essay credibility and authority.

When selecting evidence for your essay, it is important to choose examples and facts that are directly related to your topic. This will help to establish a strong connection between your argument and the evidence you present. Additionally, using relevant evidence allows you to make a more convincing case and gives your readers confidence in the validity of your assertions.

Relevant evidence can come in various forms, such as statistics, research studies, expert opinions, and real-life examples. By incorporating a mix of different types of evidence, you can strengthen the overall impact of your essay and appeal to a wider range of readers.

To ensure that the evidence you include in your essay is relevant, it is necessary to thoroughly research your topic and gather information from reliable sources. This will help you to find the most up-to-date and accurate evidence to support your arguments.

In addition to choosing relevant evidence, it is also crucial to properly integrate it into your essay. Make sure to clearly introduce each piece of evidence and explain how it supports your main points. Use strong and persuasive language to highlight the significance of the evidence and its connection to your argument.

Remember that the purpose of using relevant evidence is not only to support your argument but also to engage your readers and help them understand your perspective. By presenting well-chosen and compelling evidence, you can make your essay more persuasive and leave a lasting impression on your audience.

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Kamala Harris has put the Democrats back in the race

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Visual Essay

New Foreign Direct Investment in the United States in 2023

August 28, 2024

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The U.S. Bureau of Economic Analysis (BEA) recently released statistics on 2023 new foreign direct investment in the United States. These statistics provide information on the acquisition and establishment of U.S. business enterprises by foreign investors and on the expansion of existing U.S. affiliates of foreign companies to establish new production facilities. These annual statistics provide information on the amount and characteristics of new investments in the United States by foreign investors and are obtained from a mandatory survey of foreign-owned U.S. businesses conducted by BEA.

Expenditures

[Click chart to expand]

• Expenditures by foreign direct investors to acquire, establish, or expand U.S. businesses totaled $148.8 billion (preliminary) in 2023.
• Expenditures decreased $57.4 billion, or 28 percent, from $206.2 billion (revised) in 2022 and were below the annual average of $265.6 billion for 2014–2022.
• As in previous years, acquisitions of existing businesses accounted for a large majority of total expenditures.
• In 2023, expenditures for acquisitions were $136.5 billion, expenditures to establish new U.S. businesses were $7.4 billion, and expenditures to expand existing foreign-owned businesses were $5.0 billion.
• Planned total expenditures, which include both first-year and planned future expenditures for investments initiated in 2023, were $175.9 billion.
• Canada was the largest source of new foreign direct investment expenditures in 2023.
• Europe was the second largest source of new foreign direct investment in 2023, after being the top source from 2019 to 2022. Expenditures from European investors decreased $67.3 billion from $117.7 billion in 2022 to $50.3 billion in 2023, the largest change in first year expenditures for any region.
• Canada contributed 35.9 percent of new investment expenditures in 2023.
• By country of UBO, the largest investing country was Canada, with expenditures of $53.4 billion in 2023.
• Japan ($14.6 billion) was the second-largest country, followed by Sweden ($8.4 billion).
• The top five investing countries accounted for 61.6 percent of new foreign direct investment expenditures in 2023.
• Expenditures for new direct investment were largest in the manufacturing sector, at $42.9 billion, accounting for 28.8 percent of total expenditures in 2023.
• Within manufacturing, expenditures were largest in chemical manufacturing ($17.8 billion) and electrical equipment, appliances, and components ($6.6 billion).
• There were also notable expenditures in professional, scientific, and technical services ($16.0 billion).
• In 2023, 47 states and the District of Columbia received new foreign direct investment. Four states had first-year expenditures of over $10 billion.
• By state, Missouri received the most investment in 2023, but its value is suppressed due to confidentiality requirements.
• California received the second-most investment ($12.8 billion), followed by New Jersey ($12.1 billion) and Texas ($10.1 billion).
• Greenfield investment expenditures—expenditures to either establish a new U.S. business or to expand an existing foreign-owned U.S. business—were $12.4 billion in 2023.
• Total planned expenditures for greenfield investment initiated in 2023, which include both first-year and future expenditures, were $39.5 billion.
• By industry, total planned expenditures for greenfield investment initiated in 2023 were highest in manufacturing ($35.1 billion), primarily electrical equipment, appliances, and components, which includes batteries.
• In 2023, employment at newly acquired, established, or expanded foreign-owned businesses in the United States was 110,000 employees.
• Current employment of acquired enterprises was 106,100.
• By region of UBO, Europe accounted for 55.7 percent of current employment in 2023.
• By country of UBO, Canada (28,000), the United Kingdom (23,500), and Germany (13,000) accounted for the largest number of employees.
• Total planned employment, which includes the current employment of acquired enterprises, the planned future employment of newly established business enterprises when fully operational, and the planned future employment associated with expansions, was 132,500.
• By industry, manufacturing accounted for the highest planned total employment (44,700) for the second year in a row.
• Planned employment for greenfield investments initiated in 2023 was highest in electrical equipment, appliances, and components manufacturing (13,200), which includes batteries.
• 38 states and the District of Columbia had planned employment from greenfield investments initiated in 2023.
• Planned total greenfield employment was largest in Georgia (7,000).

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Guest Essay

Kamala Harris and Donald Trump Are Shredding Basic Economic Good Sense

By Roger Lowenstein

Mr. Lowenstein is the author of “Ways and Means: Lincoln and His Cabinet and the Financing of the Civil War.”

In a speech on economic policy in Raleigh, N.C., this month, Kamala Harris uncorked a beautiful riff that slammed a certain policy proposal as nothing more than a “national sales tax” on the American public, one that would raise the price of “everyday products and basic necessities” and cost the typical family $3,900 a year.

For once, a presidential candidate was speaking like an economist — or at least, a practical policymaker with a realist’s grasp of supply and demand.

The proposal she knocked was that of her opponent Donald Trump — namely, a tariff of as much as 20 percent (and considerably higher on Chinese imports). Ms. Harris got it right: Tariffs are a tax, paid by American consumers. They make the country poorer. When Mr. Trump tried them, that resulted in no new net jobs, and American consumers, including employers that depend on foreign steel, were stuck with higher prices, hurting homegrown industry.

Moreover, other nations retaliated against U.S. exports. In retrospect, America produced a little bit more in areas where it is less competitive, such as washing machines, and sold less of what it does best, such as soybeans. That nonsensical result is why Mr. Trump’s proposed tariff expansion remains one of the two worst ideas to surface in the campaign.

Unfortunately, the other one was unfurled in the same speech and with equal violence to free-market principles. Ms. Harris promised to seek federal authority to control food prices. Whereas she was hard-hitting and specific in describing Mr. Trump’s failed tariff policy, she was censorious and vague in pitching price controls. She wouldn’t go after all companies, only “bad actors” that “exploit crises” by “price gouging.”

Forget that her proposal addresses a problem that no longer exists (over the past year, food prices rose a mere 1 percent ) and that supermarkets operate on notoriously thin margins. More dismaying was her seeming ignorance that price controls, almost without exception, have led to shortages, supply chain disruptions and eventually higher prices. When Ms. Harris pledged to crack down on “opportunistic” offenders and restrict “excessive corporate profits ,” she seemed unaware that exploiting opportunities for profit is exactly what private enterprise is intended to foster (Henry Ford, Steve Jobs, Warren Buffett — opportunists all).

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