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Five Outstanding Data Visualization Examples for Marketing

• Written by John Terra
• Updated on June 24, 2024

Data has become a large part of our everyday lives, insinuating itself in everything from our workplaces to our homes and everything in between. Data is how we track health and wellness from our wearables, enjoy suggested music and movies, and report on campaign performance and effectiveness, especially in marketing.

Data abundance and ubiquity make all these functions possible is the abundance of data. Yes, data is everywhere, and more of it is being generated every day. At the same time, this information proliferation certainly powers our lives in many ways everyone appreciates, it can also be overwhelming when communicating it to an audience. Call it “too much of a good thing.”

We also know that a picture is worth a thousand words, so that axiom can help us deal with the constant tidal waves of new data. In this context, we need data visualization.

This article explains data visualization and provides several outstanding examples. We’ll also define the term and explain its advantages. It also shares an online data science bootcamp that gives professionals practical training in data visualization tools and techniques.

So, what’s data visualization?

What Is Data Visualization?

Data visualization is the process of developing visual assets to represent data, making it easier to visualize and understand the information. The process typically involves using bars, charts, and graphs, with current examples incorporating graphics, icons, and infographics.

Today, data visualization in marketing is frequently used in reports, white papers, case studies, website content, social media, and e-mail marketing. The process quickly communicates facts to external audiences, internal teams, and management.

Also Read: What is Exploratory Data Analysis? Types, Tools, Importance, etc.

Five Advantages of Data Visualization in Marketing

Data visualization brings many advantages to the table. Let’s take a closer look at its benefits.

• It’s valuable for analyzing patterns. Data visualization is vital for revealing patterns in vast datasets. Fulfillment teams typically store thousands of SKUs in warehouses, tracked on one extensive, all-encompassing database. In this particular instance, it’s hard to do inventory management without employing visual aids like graphs to show stock level fluctuation by season. Now, marketers work with thousands of customers, so why shouldn’t they have access to the same techniques? With data visualization, marketers can rapidly spot relationships and trends that would otherwise be overlooked. This advantage lets them better identify areas of opportunity. Marketers can gain better insights into market trends, customer behavior, and other valuable data points they can conveniently show to management and other teams through regular reports, presentations, or real-time dashboards.
• It boosts sales. Data visualization helps marketers boost their sales by supplying valuable insights into their customers’ behavior. Through data visualization, marketers can quickly identify critical patterns and trends in customer data and then use the insights to adjust their marketing strategies in real time. For example, marketers can easily plot shipping addresses on a map to find where their most valuable customers are located. This information would show the marketers where to allocate resources to conduct more effective, targeted advertising. Marketers can also use data visualization to identify customer buying trends over time, using these insights to optimize pricing and promotional strategies based on factors such as the customer’s lifetime value or seasonal trends.
• It facilitates creative decisions. Data visualization helps marketers make better-informed creative decisions by giving them a better view of customers and what they want. By letting customer data guide the content strategy, marketers can brainstorm better ideas relevant to their target market. Marketers can easily and rapidly identify which channels are performing better or underutilized using data visualization. Marketers can use these results to justify investments in specific channels, performing tasks such as editing videos better to optimize their content’s impact on the platform.
• It yields better reports. Data visualization tools such as Power BI create unique reports with intuitive data representation, transforming marketing data into an understandable format. These tools enable marketers to draft clear, concise reports that exceed traditional charts and graphs. With data visualization, marketers can access intuitive layouts and interactive features that help readers focus on key metrics and trends. These reports include drill-down capability, dynamic filtering, and interactive maps, offering a deeper understanding of the data. Additionally, marketers can easily customize these reports, tailoring them to specific needs and helping them make better-informed decisions.
• It breaks down complex data into manageable, useful pieces. Data visualization breaks down complex data into a format that everyone can reason about. For instance, it’s a challenge figuring out how to forecast your inventory if you have several dozen variables to contend with simultaneously. These variables could include seasonal trends, current stock levels, and changing consumer behavior. Data visualization tools and practices can quickly sift through this information to find the most meaningful trends and relationships. These things might otherwise be difficult to spot simply by looking at numbers on a spreadsheet.

Also Read: What is Data Wrangling? Importance, Tools, and More

Five Inspirational Data Visualization Examples in Marketing

Whether you have storehouses of data you want to communicate or a smaller amount of complex and demanding information, you can ease the burden on your readers by generating simple, easy-to-digest visuals to represent it.

Here are five marketing data visualization examples that engage your brain and inspire you.

How Americans Eat

Infographic

KIPP Bay Area Public Schools

Annual Report

When creating their annual report, KIPP Bay Area Public Schools wanted to highlight their outreach to the school system’s children. The design inserted playful graphics to represent the audience and iconography to symbolize the services offered to that audience.

Without the benefit of visual assets, readers may be less likely to read and grasp the information. With that visual advantage, it’s easier to communicate the impact of any report.

Gender Pay Gap

Online Article

Data visualization is used for more than just official reports or business plans. The following online article found an excellent example of data visualization in marketing. The graphic displays the gender pay gap, with the circle sizes aligning with the number of people working those jobs, including men and women. It’s also a highly interactive visual asset highlighting and emphasizing information as you scroll. Check it out using the link above.

World Vision

The Global Water, Sanitation, and Hygiene (WASH) Business Plan

World Vision WASH put together a business plan showcasing its impact on a global scale. Although the data accumulated was enormous, the organization successfully used its data visualization talents to showcase the information in exciting, compelling, and engaging ways.

For example, the design incorporated icons representing water, sanitation, and hygiene facts. The article uses graphs to communicate ratios in the brand’s color palette, and symbols make another appearance to demonstrate the different sectors impacted.

Spotify Culture Next 2021

Their Web Design for Global Trend Report

Spotify created a highly interactive website design that relied on data visualization to tell the story of its global trend report. For example, in this screenshot, notice that the front level represents how listeners view music’s health factor. The various spaces of the design allow you to explore the various roles music plays in people’s lives. Plus, you can’t ignore the coolness factor of the accompanying music on this site. This design is for Spotify, after all.

Also Read: What is Spatial Data Science? Definition, Applications, Careers & More

Do You Want to Learn More About How to Incorporate Data Visualization into Marketing?

If this article has you interested in creating your own data visuals for marketing, then take the next step and enroll in this post-graduate program in data science certification program. This 44-week course offers a high-engagement learning experience that teaches the concepts of data analysis and predictive modeling, including tools like Generative AI, Prompt Engineering, ChatGPT, DALL-E, Midjourney, and other popular tools.

According to Indeed.com, digital marketers earn an annual average of $64,898. Check out this intense online certification course and bolster your digital marketing skills to meet the challenges of today’s online economy.

Q: What are some examples of data visualization?

• Line graphs
• Area charts
• Pivot tables

Q: How is data visualization used in marketing?

It aids decision-making, improves retention and engagement, Increases accessibility, identifies areas that need attention or improvement, enables predictive analysis and real-time monitoring, reveals patterns and trends, and simplifies complex data for all stakeholders.

Q: Why is visualization important in marketing?

Data visualization improves storytelling by curating data into forms that are easier to understand. Good visualization tells a story, filtering out the useless noise from data and highlighting helpful information.

You might also like to read:

Data Science and Marketing: Transforming Strategies and Enhancing Engagement

An Introduction to Natural Language Processing in Data Science

Why Use Python for Data Science?

A Beginner’s Guide to the Data Science Process

What Is Data Mining? A Beginner’s Guide

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Data visualization: What it is and how it adds value to marketing

Written by by Jacqueline Zote

Published on  April 30, 2020

Reading time  7 minutes

The abundance of data can be a bane as much as it is a boon. While marketers now have plenty of data to back up their campaigns and strategies, that also means they have to go through the tedious process of sifting through a sea of data to find what they need to measure performance and what can help them make a case for the value of their work. This could easily lead to analysis paralysis in which they get so overwhelmed with data that they end up not making any decision at all.

That’s where data visualization enters the picture. By showcasing the most critical data in a visual format, it makes the information easier to process and understand. In this post, we give you an in-depth look at data visualization in reporting , why you need it and how you can apply it in your organization.

What is marketing data visualization?

Data visualization in marketing is the process of translating large and complex datasets and summarizing them in a visual format. This not only makes the data easier to understand but also pleasant to look at, which helps you get people’s attention more effectively.

Sprout Social regularly publishes data visualization examples with accompanying infographics in our content. This allows us to highlight the most important details at a glance, while the body of the article elaborates on the findings.

You’ll also find other marketing data visualization examples through out reports in the Sprout platform itself such as this report on Facebook competitors . The comparative line graph helps you quickly visualize how your Facebook page compares to your competitor in terms of audience growth by day.

Just a few of the most common types of data visualizations include:

• Area charts
• Line charts
• Scatter plots

These can act as standalone visualizations in analysis reports, illustrate text content or even play a part in a larger data storytelling effort . It’s important to understand the best use cases for different types of data visualization so your imagery actually clarifies and highlights the takeaways for your data rather than confusing viewers further–read on for tips and best practices.

Advantages of data visualization in marketing

There are a lot of ways data visualization can fuel and strengthen your marketing efforts other than making the information easier to process. Let’s take a closer look at the advantages of data visualization so you can understand how it adds value to your organization:

1. Provide greater insight

The most obvious advantage is that it helps connect the dots between different datasets to uncover patterns and trends, thus enhancing comprehension. It adds more context and assigns meaning to your data, helping you understand its relevance in the real world and how you should apply it. Instead of just overwhelming you with information, data visualization puts together the most valuable bits in a way that makes sense for you or for the audience of your content.

Data visualization provides insights that you can’t get through traditional descriptive statistics, helping you visualize the variations between seemingly similar datasets. Anscombe’s Quartet serves as a classic example of this. This illustrates four datasets that share similar descriptive statistics such as the same numerical average or standard deviation, but when plotted in visual graphs, clearly tell four different stories.

2. Improve your decision-making process

With improved insight and better comprehension, data visualization helps improve the decision-making process. As critical decision-makers won’t have to go through the tedious process of sifting through data to uncover the insights they need, they can avoid analysis paralysis and make informed decisions much faster.

That’s exactly why you need data visualization for marketing, as it helps you develop powerful strategies and campaigns before your competitors can catch up with you.

3. Engage the audience

There’s no doubt that well-designed visuals are attractive and engaging. Data visualization combined with data storytelling can help you draw in your target audience and engage them. It can add more substance to the information you want to share and help you get your message across more effectively.

So it’s no surprise that even for publications like The Washington Post, the most-read story it ever published online is a visualization-driven story involving the coronavirus simulator . And for The New York Times, the most-read piece it published online during 2013 was a dialect map.

4. Easily repurposed

One of the best advantages of data visualization is its versatility, allowing you to repurpose it in different formats for various aspects of your business–from social media to content marketing. Since it helps translate the information into a format that’s easy to process, it improves the understanding of crucial metrics at every level. This makes it perfect for use in internal reporting and client reporting as well as content development.

The Sprout example given at the beginning showcases how data visualization serves as content for your organization. The addition of visualized data makes your content easier to consume and share, especially on social media where visual content dominates.

For example, see how the Content Marketing Institute tweets out one of the charts from its annual report and then invites followers to read the full report.

84% of B2B content marketers have use paid distribution channels in the last 12 months. To learn more about what marketers had to say about distribution channels, including which generated the best results, read our report. https://t.co/HUl0n2Zs6U pic.twitter.com/7QGKKsxCRA — Content Marketing Institute (@CMIContent) April 20, 2020

You can further communicate your data into other formats including:

• Annual reports
• Articles and blog posts
• Case studies
• Presentations
• Infographics
• Internal reports

Data visualization tips for marketers

Before you rope in your design team for data visualization, there are a few essentials to take care of. You need to make sure you’re working on a subject that would appeal to your target audience and properly source the data you want to visualize. So use the following tips to nail your data visualization efforts:

1. Get specific with your subject

For your data visualization efforts to make an impact, the first step is to tackle a subject that’s relevant and interesting to the people you’re targeting. But even the most relevant data can be difficult to process if you overwhelm the audience with too much information. And your data can go all over the place if you don’t have a clear idea of what story you want to tell.

So, define a clear purpose for your visualized data to narrow down on the main subject you want to address. This will also help you put together the information in a logical flow for powerful and effective data storytelling.

2. Collect credible data

Make sure the data you’re using is solid and credible. Since data is easy to manipulate and misrepresented to serve one’s purpose, it’s crucial that you only trust unbiased sources. You may also conduct your own study through reliable and valid research methods.

3. Use design best practices

Of course, the visual elements are just as important as the information itself. The whole point of presenting your data in a visual format is to engage the audience and make your data more readable and digestible. Design best practices are essential here, which is why it’s ideal to work with team members that specialize in design rather than taking your best guess at layout and color decisions that can significantly impact how well a viewer understands your data.

These best practices include:

• Carefully choosing the type of chart to best translate your data
• Guiding the eye by highlighting the most critical details
• Using the right color palette that’s brand-consistent and pleasant to the eyes
• Using fonts that are brand-consistent and easy to read

Of course, having a brand style guide makes it a lot easier as you don’t have to go through the whole process of brainstorming what visual guidelines to follow down to the font and color every time.

The following infographic from MediPENSE nails all of these best practices. It uses bar graphs to showcase the various ways in which caregivers dealt with their highly demanding jobs. It highlights the crucial numbers to get key messages across.

Plus, the color palette remains consistent with the brand’s blue and green colors, while the white background ensures minimal strain on the eyes. And they use two or three font styles that are easy to read.

Data visualization tools

If you don’t have a background in design, it will be difficult to pull off a successful data visualization solo. So it’s ideal to collaborate with a dedicated design team if you want your data to have the desired impact. If you are crunched for time or resources, though, there are tools that can help you automatically generate visualized data or come with data visualization templates that you can easily customize.

Here are a few tools to help with your data visualization efforts:

One of the best tools to visualize your analytics data, Tableau lets you connect to cloud databases to collect your data and turn it into visually-appealing charts and graphs. You can use bubble charts, word clouds and tree diagrams to add more context to your data and allow for easy comprehension.

2. Sprout Social

Sprout Social comes with a comprehensive reporting tool that automatically generates visual reports for your social media performance. You can easily track follower growth rate, measure post performance and compare yourself against the competition through these visual reports. It even lets you generate visuals for internal reports including task performance and team reports.

3. Venngage

Venngage can help you put together infographics to paint a bigger picture through data storytelling. It comes with many data visualization templates that you can customize with your own information, colors, charts and visuals.

Get ready for powerful data storytelling

With everything that you’ve seen and read so far, it’s clear that data visualization plays a major role in multiple aspects of your business. Not only do you need data visualization for marketing, but you also need it for better communication within the organization and faster decisions. Plus, it serves your content marketing efforts in so many ways with the versatility to adapt to various formats.

So if you’re still not using it to its full potential, it’s time to change that. Get our free social media toolkit to get a better picture of how data-fueled social strategies should look like.

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A Reader on Data Visualization

Chapter 3 case studies.

This chapter explores some interesting case studies of data visualizations. Critiquing these case studies is a valuable exercise that helps both expand our knowledge of possible visual representations of data as well as develop the type of critical thinking that improves our own visualizations. Furthermore, the examination and evaluation of case studies help show that new designs are just as usable as existing techniques, demonstrating that the field is suitable for future development.

3.1 Introduction

Visualization is like art; it speaks where words fail. The usefulness of data visualizations is not just limited to business and analytics; visualizations can explain almost anything in the world. Wars, rescue operations, social issues, etc. can be visualized to synthesize the details important details relevant to the issues. In particular, phenomena like the Syrian war, the number flights during Thanksgiving in the USA, the controversy of ‘#OscarsSoWhite,’ etc. present such complexity that we can write endless paragraphs and still fail to convince readers. Below are visualizations of some of these important and complex topics - visualizations that are much more persuasive than an essay, and with a tiny fraction of the text.

Many of the case studies mentioned below come from the following articles:

3.2 Geographic Visualizations

Geovisualization or geovisualisation (short for geographic visualization), refers to a set of tools and techniques supporting the analysis of geospatial data through the use of interactive visualization. Like the related fields of scientific visualization and information visualization geovisualization emphasizes knowledge construction over knowledge storage or information transmission.To do this, geovisualization communicates geospatial information in ways that, when combined with human understanding, allow for data exploration and decision-making processes. Source: (contributors 2019 a ) More specifically, Geovisualization is a process that alters geographic information so that we can consume it with our eyes. Its purpose is to capitalize on our affinity for visual things and convert the seemingly random collection of information available to us into a form that can be quickly understood. Many tools can be used for Geographic Visualization, such as Mapbox,Carto,ArcGIS Online and HERE Data Lens. Source: (Gloag, n.d. : Tools & Techniques)

Often, people use maps to visualize data that should not be mapped. Here are some examples of when a map visualization is a good choice.

3.2.1 Spies in the Skies

The map below is from a Buzzfeed article (Aldhous and Seife 2016 ) that shows how common it is for the government to observe people. It was filled with red and blue lines (representing FBI and DHS aircraft, respectively) which illustrate the flight paths of the planes. When planes circle an area more than once, the circles become darker. The circles change by day and time, and individual cities can be typed into a search bar to see the flight patterns over them. The visualization rather creatively looks almost like a hand-drawn map. While presenting an ordinarily uncomfortable topic, this allows individuals to check things for themselves, hopefully providing some peace of mind.

Source: (Kayla Darling 2017 )

New York Flight Patterns

3.2.2 Two Centuries of U.S. Immigration

This interactive map from (Galka 2016 ) shows the rate of immigration into the U.S. from other countries over the last 200 years in 10-year segments. Each colored dot represents 10,000 people coming from the specified country. Countries then light up when they have one of the highest rates of migration. A tracker on the left indicates what countries sent the most people to the U.S. at what times.

This is a good visualization because it is engaging and easy to read and interpret. The movement of the dots draws the reader’s attention while the brightly lit countries make it easy to pick out the highest total migrations. The bright colors and dark background help the information stand out. This map is a bit simple, but effective.

Source: (Kayla Darling 2017 ) .

US Immigration

3.2.3 Uber: Crafting Data-Driven Maps

Map visualization is essential for companies like Uber that need to track metrics using geo-space points. In this article, the designer from Uber talks about the challenges of designing such visualizations and the possible solutions (Klimczak 2016 ) .

To tackle these problems, Uber started by defining base map themes by optimizing detail, color, and typography. Based on that, data layers are added using scatter plots and hex bins, with careful color selection to help their team make decisions. To make it even better, Uber took a further step by adding trip lines (see images below), which became a signature visualization of Uber. Choropleths are also used to help visualize how metrics and values differ across geographic areas. Uber uses US postal codes as geographic boundaries and infuses various datasets to create the color variation.

The visualization in this article is a classic problem of visualizing geographic data. The detailed explanation of the problems and how they are solved can be beneficial for people or startups trying to conceptualize and make appropriate visualizations that support the decision-making process.

Uber Route Maps

Source: (Klimczak 2016 )

3.3 Demographic Comparisons

One common use of visualization is to compare different groups against each other, such as political parties or generations.

3.3.1 Young Voters, Class and Turnout: How Britain Voted in 2017

This article’s goal is to convey the change in party votes in the 2017 UK general election compared to votes in 2015 (Holder, Barr, and Kommenda 2017 ) . The change in party votes was shown with regards to three demographic factors: age, class, and ethnicity. For each factor, there are four graphs (one per political party), each illustrated in the party’s standard color. The change in the percent of votes is shown as an arrow where the arrow’s shaft is the length of the difference from 2015 to 2017 while the x-axis is the demographic factor split into different bins.

This a good visualization because it is straightforward to read and interpret. The color-coding of the arrows and party names makes it easy to pick out the different parties. The index is smartly spread across the visualization to reduce cross-referencing, and color in the graph represents the actual party colors in the campaign. The arrow lengths highlight just how significant of a change happened. For example, in the Age section, it is easy to see the pattern between the Labour party gaining many voters aged 18 to 44 and the Conservative party gaining voters aged 45 and up.

UK Party Votes by Age

Source: (Holder, Barr, and Kommenda 2017 )

3.3.2 U.S. Migration Patterns

The New York Times data team mapped out Americans’ moving patterns from 1900 to present, and the results are fascinating to interact with (Aisch, Gebeloff, and Quealy 2014 ) . We can see where people living in each state were born, and where people are moving to and from. The groupings of the destinations vary based on that state’s trends, preventing unnecessary clutter while still showing detail when vital, as can be seen by the difference between the charts for California and Pennsylvania. When generating interactive charts, one must always assume that the audience will not interact with it. The message of a chart has to be clear enough that anyone just viewing the generic chart can understand.

Overall, this type of chart can work well to visualize movement in data over time, such as with migration. However, it must be done carefully to maintain clarity. Too many categories with colors and crossing lines can make it difficult for a reader to keep track of what the data is saying and it can quickly go from a very graphic visualization to a chaotic mess of lines. The designer does a pretty good job with these visualizations by limiting the number of categories in grouping states by region (West, South, Midwest, etc.). But when introducing many dimenional variables such as Migration from Pennsylvania, the chart can quickly turn convoluted and hard to read which costs the audience. Finally, it is not completely clear why so many crossing lines are necessary for the Pennsylvania chart. The crossing lines, along with the use of the same color for different lines within the same regional categories, can introduce unnecessary complexity.

Migration from California

Migration from Pennsylvania

Source: (Aisch, Gebeloff, and Quealy 2014 )

3.3.3 The American Workday

NPR tapped into American Time Use Survey data to ascertain the share of workers in a wide range of industries who are at work at any given time (Quoctrung Bui 2014 ) . The original question of when Americans work, rather than the number of hours worked, is answered in the graph. The chart overlays the traditional 9 AM-5 PM standard workday as a reference point, helping the audience draw exciting conclusions. Below is a screenshot of the data product; the original graph is more interactive and allows the audience to explore when people are working for different occupations.

Some interesting findings include: 1. Construction workers both start and finish their workday earlier and generally do not work at lunch hours as there is a massive drop at noon.

• Servers and cooks’ schedule are the opposite of all other occupations with the peak from lunch through the evening.

This data product is an excellent example because the analytic design has been applied to contrast specific occupations to the traditional 9-5 working hours. This is easy to understand and make particular occupations stand out more manageable. The use of color for highlighting the selected occupation in the graph helps to categorize different occupations as well.

3.3.4 How People Like You Spend Their Time

This visualization from (Yau 2016 ) lists several categories such as “personal care” and “work” along one side of a graph with a line illustrating the amount of time the average person in a particular demographic spends on each subject. Entering different parameters at the top, such as changing gender or age, causes the lines to shift to feature that demographic. The simplicity of this visualization helps the information get across and avoids bogging down the statistics. Sometimes, less is more.

3.3.5 Britain’s Diet In Data

This is an excellent example about how to present a significant amount of comprehensive data - distributed across different categories and measured in different metrics - in a simple yet effective manner, while still maintaining interest and aesthetics. The data product attempts to show how the average Briton’s diet has changed over the last four decades for the better (Institute 2016 ) . It does this by displaying simple trend lines that show that more harmful and fatty foods are being consumed less while consumed more healthier and leaner foods. It further breaks down every major food category into tens of its constituent products, and in both the overview and deep-dive versions, provides further levers to massage more meaning out of the data. It also shows how the contribution of different foods to the typical diet has changed over the years. Here, we can toggle the year to see exactly how much of each food was consumed, again with another deep-dive into the constituents of every primary food group.

Such a visualization is ideal for a layman who would want to walk away with an immediate and accurate understanding of the overall dietary changes. It also provides plenty detail on demand for the more discerning viewer who might have more time and inclination to dissect and parse through the graphs. It is difficult to use the same data product to cater to both types of viewers in such an adequate capacity, which is what makes this particular data product so impressive and useful. It satisfies the principles of graphical excellence as stated by Edward Tufte : >“Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.”

Source: (Tufte 1986 )

3.3.6 Selfie City

Selfie City, a detailed multi-component visual exploration of 3,200 selfies from five major cities around the world, offers a close look at the demographics and trends of selfies (Manovich et al. 2014 ) . This project is based on a unique dataset compiled by analyzing tens of thousands of images from each city, both through automatic image analysis and human judgment. The team behind the project collected and filtered the data using Instagram and Mechanical Turk. Rich media visualizations (imageplots) assemble thousands of photos to reveal interesting patterns. It provides a demographic and regional comparison of selfies.

Estimated Age and Gender Distribution

Source: (Manovich et al. 2014 )

3.3.7 Evolving Demographics

Another frequent use is to look at how something changes over time. Time-series data can be shown many ways, and these are some examples.

3.3.7.1 Millennial Generation Diversity

CNNMoney created an interactive chart using U.S. Census Data to show the size and diversity of the millennial generation compared to baby boomers (Kurtz and Yellin 2018 ) . While the article’s main point is that the millennial generation is bigger and more diverse than the baby boomer generation, it also contains information about all of the other living generations. It turns hard numbers into an intriguing story, illustrating the racial makeup of different age groups from 1913 to present.

The author also summarized three key findings from the graph: 1| The most common age in the US is 22 years old. 2| The median age in the US is 37.6 years old. * 3| Among the youngest generation, only 50% of the population is white with the potential of dropping from the biggest race in the US.

Racial Diversity of US Generations

Source: (Kurtz and Yellin 2018 )

This is an effective graph because while it contains many data points, it makes the overall trends very clear without sacrificing much detail. You can see the drop in some white people and the increasing growth of the other racial categories.

3.3.7.2 How the Recession Reshaped the Economy, in 255 Charts

The first large graph contains 255 lines to show how the number of jobs has changed for every industry in America, using color to highlight the lines and let viewers see the specifics for each industry (Ashkenas and Parlapiano 2014 ) . By hovering over a line, viewers can get the detailed information of that industry’s job trend. Keeping this extra data hidden until needd will make it easier for readers to absorb the bigger picture from this vast data visualization. Following charts are subsets categorized by job sector and sub-industries. Readers can choose the industry or sector they are interested in and, similar to the first graph, view the more detailed information by hovering over a line.

Source: (Ashkenas and Parlapiano 2014 )

3.3.7.3 An Aging Population: Projected Number of Children and Older Adults

An aging population is always a hot topic in social economics and politics (United States Census Bureau 2018 ) . Here we explore a collection of data visualizations showing the aging population in the U.S. and the world.

Source: (United States Census Bureau 2018 )

This example includes a bar chart and a line graph to demonstrate the aging population compared with the population of children. This visualization allows easy comparison, employs color to differentiate the categories, and highlights the intersection point.

3.3.7.4 From Pyramid to Pillar: A Century of Change, Population of the U.S.

This is a population pyramid . “A population pyramid is a pair of back-to-back histograms for each sex that displays the distribution of a population in all age groups and in gender” (Bureau 2018 b ) . It is good to visualize changes in population distributions (sex, age, year). The shape of a pyramid is also used to represent other characteristics of a population. To illustrate, A pyramid with a very wide base and a narrow top section suggests a population with both high fertility and death rates. It is a useful tool to make sense of census data. (“An Aging Population,” n.d. ) offers an animated pyramid.

Comparison of aging population in US and Japan

Source: (“An Aging Population,” n.d. )

This is an animated and multiple-population pyramid. It used to compare different patterns across countries. One additional benefit for the interactive population pyramid is that it shows the shape changes by year, which is useful for time-series comparison. A similar project with R code is here .

3.3.7.5 Music Timeline

Google’s Music Timeline illustrates a variety of music genres waxing and waning in popularity from 2010 to the present day, based on how many Google Play Music users have an artist or album in their library, and other data such as album release dates (Google 2014 ) . One useful feature of this graph is the reader’s ability to explore one specific genre and its subgenres at a more detailed level, as well as view the general timeline of all music. The drill-down interaction allows for more details without cluttering the overview of the visualization. Embedding the graph with names (e.g., Rock/Pop) makes similar color lines easy to distinguish.

Source: (Google 2014 )

3.4 Visualizing Urban Data for Social Change

(Neira 2016 )

One field in which visualization can have a meaningful social impact is promoting understanding of and generating discussions around cities. With the development of a city, demographic changes, economic, environmental and social problems become important issues. Visualization plays an important role in promoting understanding of how the cities and the societies within them work, debating the problems that cities face, and engaging citizens to work toward their dream cities.

Recently, as part of Habitat III side event , LlactaLAB - Sustainable Cities Research Group, presented a project called Live Infographics. It was an interactive methodology that put citizens and experts opinions about the New Urban Agenda on one platform to help generate a ‘horizontal governance’. The different opinions were materialized with a dynamic map to visualize the generated data. The primary objective of the project is to generate citizen-led data collection and to enable governments to build a better understanding of public sentiment, and then engaging people in the process.

A great Urban Data Visualization ought to have the capacity to start “Sociological Imagination”. It should provoke individuals to consider how their individual choices, issues, struggles, and in general their daily lives, are a extension of society, and how their choices collectively influence public opinion. Another key aspect of these kinds of data visualizations is their ability to make the audience understand how their activities impacts the cities they live in and help them work towards the betterment of the cities.

The following is an example of a visualization that is trying to effect social change. It shows how different states are populated on our way to wealth at the cost of the Environment and the percentage of adults who support the cause by estimating public opinions. Source : (“We Have Poluted Our Way to the Wealth in the Expense of the Environment,” n.d. )

Urbanization and the spread of information technologies transform Cities into huge data pools, that data will play a major role in understanding how city areas have changed and are likely to change in the future. Urban Data Visualization gives us a quick view of the architectural contrast of Urban changes in Cities. (MORPHOCODE 2019 )

This Urban Data Visualization based on the NYC Department of City Planning Data set, the result is a snapshot of Brooklyn’s evolution, revealing how development has rippled across certain neighborhoods while leaving some pockets unchanged for decades, even centuries. The visualization is interactive, the reader can check every block’s name and built year. (MORPHOCODE 2019 )

As urban areas continue to develop, diverse and complex issues evolve along with them. Disparity, isolation, loss of biodiversity and environmental quality, etc. are all important but thorny issues, and finding successful solutions will require uniting strategy producers, academics, designers, and citizens. Visualization, if done right, can help jumpstart important discussions between these diverse groups of people and help solve the issues that emerge as the world becomes more urbanized.

3.5 Animated Data Visualization

Like evolving demographics, these visualizations are demographics that change over time. These, however, are self-animated instead of interactive.

3.5.1 A Day in the Life of Americans

This animated data visualization shows the time people spend on daily activities throughout the day (Nathan Yau 2015 b ) . The plot is simple and easy to interpret, but it also includes a good number of variables including time, activity type, number of people doing each activity, and the order in which activities are done.

One of the plot’s biggest strengths is that by using one dot to represent each person in the study and using animation, we can drill down to the level of an individual and follow him or her throughout the day. The accumulation of dots for each particular activity also gives us an aggregate-level view of the same data, so that we get both individual and aggregate insights.

A drawback of the plot is that it is hard for our eyes to keep track of 1000 simultaneously moving dots. The author of the post addresses this by creating subsequent plots with stationary lines at crucial times of the day. This represents people’s movements from one activity to another without overwhelming the reader.

Overall, this is an engaging, informative, relevant, and fun animated plot that tells a story.

Source: (Nathan Yau 2015 b )

3.5.2 Hans Rosling’s 200 Countries, 200 Years, 4 Minutes

Global health data expert Hans Rosling’s famous statistical documentary “The Joy of Stats” aired on BBC in 2010, but it is still turning heads. In the remarkable segment “200 Countries, 200 Years, 4 Minutes”, Rosling uses augmented reality to explore public health data in 200 countries over 200 years using 120,000 numbers, in just four minutes (Rosling, Hans 2010 ) .

Screenshot from “200 Countries, 200 Years, 4 Minutes”

Source: (Rosling, Hans 2010 )

What makes this visualization so well-known is its use of animation and narration to highlight different stories within the overall data. While the visualization could have been made as an interactive chart where the audience can select the year, instead it is a video. Rosling’s narration of how various regions have fluctuated over the last two hundred years is necessary for his argument since there is no other description or explanation.

3.6 Dust in the Wind: Visualization and Environmental Problems

Environmental issues can quickly become extremely complex. When dealing with assessments of site, environmental remediation design, monitoring, environmental litigation, the quantity of data involved can quickly become overwhelming. Maintaining and organizing that data and keep a balance is insufficient. Visualization is the only means for condensing and communicating vast quantities of data. Visualization provides an invaluable tool to communicate complex data in a form that makes it intelligible to all parties. There are many case studies on visualization of environment-related issues. Some of them are mentioned below:

3.6.1 Global Carbon Emissions

This data visualization, based on data from the World Resource Institute’s Climate Analysis Indicators Tool and the Intergovernmental Panel on Climate Change, shows how national CO₂ emissions have transformed over the last 150 years and what the future might hold. It also allows the audience to explore emissions by country for a range of different scenarios (World Resources Institute 2014 ) .

Source: (World Resources Institute 2014 )

3.6.2 What’s really warming the world?

This case study begins by clearly explaining necessary background information and the analytic questions it seeks to answer. Next, it analyzes each factor separately using both verbal explanations and dynamic graphics to compare the observed temperature movements, and then categorizes related factors into “natural factors” or “human factors.” After that, it combines all the dynamic graphics into one, which makes the results more accessible and more straightforward to compare. Lastly, the authors provide further detailed explanations of dataset sources to support their results. Overall, this case study is straightforward, easy to understand and informative (Roston and Migliozzi 2015 ) (Crooks 2017 ) .

Source: (Roston and Migliozzi 2015 )

3.6.3 Understanding Plastic pollution using visualization

Plastic pollution is the accumulation of plastic products in the environment that adversely affects wildlife, wildlife habitat, or humans. Human usage of plastic has increased manifolds in last few decades. Since plastic is inexpensive and durable, it has a wide variety of uses in our everyday life. Since the 1950’s, an estimated 6.3 billion tons of plastic has been produced, of which only about 9% is recycled (contributors 2019 b ) .

Plastic has become part of our daily life, and human dependence on plastic has increased over time. The visualization below shows some common plastic products undermining environmental health. (Grün 2016 )

With a share of 26 percent, China may be the largest plastic producer in the world; yet the largest plastic consumer is neighboring Japan. The people living in the island nation have consumption that exceeds that of Africa and the rest of Asia combined.

Donut chart is a modern version of pie-chart which looks cleaner, and embedded visual imagery makes the distribution easy to understand. (Grün 2016 )

Plastic Use: Industrial nations top the charts (Grün 2016 )

This visualization uses a simple line chart to show increasing trends. A positive aspect of this chart is the removal of the vertical grid which creates noise in the visualization when its objective is to show the trend, rather than the numbers.

“Plastic where it shouldn’t be” combines four large-scale plastic marine pollution datasets, each published in a different scientific journal over the last five years, totaling 9,490 surface net tows. It is a symbol map shows the amounts of plastic wastes distribute in oceans. Please note: just because there is no plastic displayed in a certain region does not mean that it isn’t there. The open ocean is vast and pollution research is both time- and cost-intensive. (Moret 2014 )

How long does plastic remain in the ocean? (Grün 2016 )

Overall, this visualization is useful in the following ways:

• It provides content: those plots serve one of the primary purposes of data visualization - storytelling. It naturally leads the audience to understand the effects of plastic pollution.
• Effective use of charts: the correct use of different types of plots makes the visualization both effective and exciting.
• Efficient use of color: this visualization is a good example of color playing an essential role in a data visualization by guiding the reader to grasp the relationships in the data. There is no redundant color, and no primary color is missing.

3.7 Language

3.7.1 green honey.

Language shapes the way we view the world. Different languages may have vastly different ways of describing things—including color.

Source: (Lee 2016 )

3.7.2 Linguistic Concepts

This case study is about the use of linguistic concepts; it discusses how the data is being used and how visual graphics are used to deliver the central insights. It presents an educational tool that integrates computational linguistics resources for use in non-technical undergraduate language science courses. By using the tool in conjunction with case studies, it provides opportunities for students to gain an understanding of linguistic concepts and analysis through the lens of practical problems in feasible ways. (Alm, Meyers, and Prud’hommeaux 2017 ) .

HistoBankVis is a novel visualization system designed for the interactive analysis of complex, multidimensional data to facilitate historical linguistic work (Michael Hund 2015 ) . In this paper, the visualization’s efficacy and power are illustrated utilizing a concrete case study investigating the diachronic interaction of word order and subject case in Icelandic.

Much of what computational linguists(CL) fall back upon to improve natural language processing and model language “understanding” is the structure that has, at best, only an indirect attestation in observable data. The sheer complexity of these structures and the visible patterns on which they are based, however, usually limit their accessibility, often even to the researchers creating or studying them. Traditional statistical graphs and custom-designed data illustrations fill the pages of CL papers, providing insight into linguistic and algorithmic structures, but visual ‘externalizations’ such as these are almost exclusively used in CL for presentation and explanation. There are particular statistical methods, falling under the rubric of “exploratory data analysis,” and visualization techniques just for this purpose are available. However, these are not widely used. These novel data visualization techniques offer the potential for creating new methods that reveal structure and detail in data. Visualization can provide new ways for interacting with large corpora, complex linguistic structures, and can lead to a better understanding of the states of stochastic processes.

3.7.3 State of the Union 2014 Minute by Minute on Twitter

Twitter’s data team assembled an impressive interactive data hub that depicts how Twitter users across the globe reacted to each paragraph of President Obama’s 2014 State of the Union address (Belmonte 2014 ) . You can slice and dice the data by topic hashtag (for example, #budget, #defense, or #education) and state, resulting in a powerful detailed and cluttered visualization. Since the visualization is about the topic density in a specific time frame, maybe it’s a good idea for us to use this kind of format when we encounter the expression of a poisson distribution.

Source: (Belmonte 2014 )

3.8 Political Relationships

3.8.1 connecting the dots behind the election.

This article in the New York Times lists several different candidates and creates compelling visuals that link their campaigns to previous ones (Aisch and Yourish 2015 ) (Kayla Darling 2017 ) . Each visual contains several different sized dots that represent a specific campaign, administration, or other governmental organization related to the candidate’s current campaign, which is then connected by arrows. Hovering over a specific dot highlights the connections between the groups. This visual is a great way to summarize what would otherwise require a long slog through years of information into an easily accessible and viewable format so that voters can figure out where the candidates’ experiences lie.

Clinton 2016 Campaign Staff

3.8.2 A Guide to Who is Fighting Whom in Syria

One of the charts shown in the link (Crooks 2017 ) , the visualization of ‘A Guide to Who is Fighting Whom in Syria’ is an exciting graphic to study. The visualization and its report can be seen at (Keating and Kirk 2015 ) .

Who is Fighting Whom in Syria

Source: (Keating and Kirk 2015 )

This visualization helps elucidate an extremely complicated topic like the Syrian War. It consists of 3 different emojis in three different colors, with each color and facial expression combination showing the ties and conflicts between the various groups involved in the Syrian War. When you click on each emoji, a small dialogue box pops up that explains the relationships between the various countries and rebel groups involved in the war. This is not only easy to understand but is also pleasing to the eyes.

On the other hand, the inherent complexity of relationships between different groups make it difficult to understand the complete picture. If the list of involved parties could be sorted by simplified “sides” (such as Syrian Government on one end with Syrian Rebels on the other) or ranked by how liked they are, then it may be easier for a trend to emerge at first glance. Also, the table format of the visualization means that the data is duplicated, making it appear even more complicated. Instead, one side of the diagonal divide could be greyed-out to simplify the audience’s experience with this visualization.

Green emoji shows ‘Friendly’ relationship

Red emoji shows the ‘Enemies’ relationship

Yellow emoji shows ‘Complicated’ relationship

3.9 Uncategorized

3.9.1 simpson’s paradox.

The Visualizing Urban Data Idealab (VUDlab) out of the University of California-Berkeley put together this visual representation of data that disproves the claim in a 1973 suit that charged the school with sex discrimination. Though the graduate schools had accepted 44% of male applicants but only 35% of female applicants, researchers later uncovered that if the data were properly pooled, there was a small but statistically significant bias in favor of women. This is called a Simpson’s Paradox.

By “properly pooled,” the investigators meant broken down by the department. For instance, men were more inclined towards science and women towards humanities. When compared to each other, the science departments required more specialized skills while the humanities would accept applicants with a more standard undergrad curriculum, thus creating the Simpson’s Paradox.

Simpson’s Paradox originally from vudlab.com

Source: (Lewis Lehe 2013 )

3.9.2 Every Satellite Orbiting Earth

This interactive graph, built using a database from the Union of Concerned Scientists, displays the trajectories of the 1,300 active satellites currently orbiting the Earth. Each satellite is represented by a circular icon, color-coded by country and sized according to launch mass (Yanofsky and Fernholz 2015 ) .

Low Earth Orbit Satellites

Source: (Yanofsky and Fernholz 2015 )

Interactive graph have its own specific advantages. It helps bridge the gap between programmers and non-programmers. This plot is a good example why using interactive graph is a good idea: - It provides an intuitive way for anyone to understand the data regardless of their technical knowledge. - It helps to identifying causes and trends more quickly - It tells a consistent story through data - It improves efficiency of representing data

3.9.3 Malaria

The authors of Vizwiz redesigned “The Seasonality of Confirmed Malaria Cases in Zambia Southern Province” by pointing out what works well, what could be improved, and why their new visualization will be better (Andy 2009 ) .

This chart below shows number of malaria cases reported for health facilities and community health workers and a comparison between the two over the years. From this chart we can clearly see that as summer approaches, cases of malaria increase indicating a seasonality. The colors are also distinct from each other.

The original visualization effectively shows the seasonality of malaria cases but is unclear if the two reporting categories are stacked or one behind the other and is rather garish. The creator of the redesign made the seasonality more obvious by combining the reporting categories and explaining the spikes better.

Furthermore, by adding the yearly data split by districts, we can lead to a possible actionable solution to the study of malaria cases in Zambia which is an important objective of visualization. The author has combined the data to find out what the data looks like when combined with health facilities and health workers. And the usage of the color scheme is much more effective than the previous version which makes seasonality more evident.

3.9.4 Is it Better to Rent or Buy?

There are many factors involved in deciding to rent or buy a house which has led to many calculators that are supposed to simplify this decision. This calculator includes several sloping charts, each including a factor that will affect how much you will have to pay, such as the individual cost of your home and your mortgage rates (Bostock, Carter, and Tse 2014 ) . A movable scale along the bottom of each chart allows you to enter different data, such as changing the “cost of rent per month” on the side. This can be useful for price comparison: if you can find a similar house to rent for that much per month or less, it is more cost effective just to rent the home. This visualization is incredibly thorough and a useful tool for homeowners of any age and status.

Source: (Bostock, Carter, and Tse 2014 )

3.9.5 An Interactive Visualization of NYC Street Trees

Using data from NYC Open Data, this interactive visualization shows the variety and quantity of street trees planted across the five New York City boroughs (Zapata 2014 ) . As the reader hovers over a tree or bar segment, the connected sections light up, making it easier for the reader to look at what otherwise could have been a very dense chart.

We can see what some of the familiar and uncommon trees planted in the five boroughs of New York City are. This visualization allows one to see the distribution quickly. One can make inferences based on the distribution, such as trees in the Bronx and Manhattan seem to be distributed more uniformly compared to the other three boroughs. It gives a direct comparison between the five boroughs which could be used to make a compelling decision by the audience.

NYC Street Trees

Source: (Zapata 2014 )

The interactive visualization is an advantage that enables the display, and intuitive understanding of multidimensional data provides a variety of visualization chart types and enables the audience to accomplish traditional data exploration tasks by making charts interactive. Moreover, this visualization provides a good example: it enables the audience to explore on their own and finds exciting facts about NYC street trees.

3.9.6 Adding up the White Oscars Winners

A visualization of all previous winners of the Best Actor/Actress Oscar winners can be seen in an article by Bloomberg (“Adding up the White Oscar Winners” 2016 ) . From the attributes of past Oscars winners, the authors have developed a set of attributes that they believe will continue to be prevalent in future Oscar winners. It is fascinating to see how the article shows the features of the Best Actress, Actor, movies, etc. in a simple and captivating visual.

The visualization is interactive, and we can click on each attribute like ‘Hair Color,’ ‘Eye Color,’ etc. to see the features of the actors and actresses who are likely to win the Oscars. Based on different attributes selected, the visualization changes to give you the data specific to the attributes. For each attribute selected, it gives you a fact about the selected attribute related to the Oscar Winner. For instance, when you select the race, it states “In the entire history of the Oscars all but 8 of the Best Actors and Best Actresses have been white”. Similarly, the visualization also gives information about the different aspects of movies that are more likely to win, like ‘Length,’ ‘Month,’ ‘Budget,’ etc., and also predict about the future nominees who are likely to win Oscar.

Best Actor and Best Actress

Best Picture

Source: (“How to Build an Oscar Winner” 2015 )

3.9.7 Kissmetrics blog: visualization of metrics

Kissmetrics blog is a place where people talk about analytics, marketing, and testing through narratives and visualization of metrics. Metrics are essential in the real world, especially when developing/promoting products. Visualization of metrics is also essential so that stakeholders can monitor performance, identify problems and dive deep into potential issues.

This example from the Kissmetrics blog is about Facebook’s organic reach (Patel 2018 ) . One crucial point discussed in the blog is whether the Facebook’s organic reach is decreasing drastically.

The general trend shows that there is a considerable decline in Facebook’s page organic reach.

The following graphs show that the engagement is increasing; that is, while the quantity of content is decreasing, the quantity is increasing.

Source: (Patel 2018 )

This resonates with what we have learned at class regarding how different perspectives of interpreting data can lead to different conclusions.

3.9.8 Describe Artists with Emoji

Using the data from Spotify, the author listed the ten most distinctive emoji used in the playlists related to favorite artists (Insights 2017 ) . The table being used in this visual is very straightforward to link the artist to the emojis and is very easy to compare among artists. When you hover over the emoji, further information is presented.

Source: (Insights 2017 )

3.9.9 Goldilocks Exoplanets

Using data from the Planetary Habitability Laboratory at the University of Puerto Rico, the interactive graph on Astrobiology plots planetary mass, atmospheric pressure, and temperature to determine what exoplanets might be home, or have been home at one point, to living beings (Tomanio and Gonzalez Veira 2014 ) .

One highlight of the graph is how color has been used. The red dots represent planets that are too hot, the blue dots mean too cold, and the green ones mean just the right temperature. This is very intuitive for people to understand without the necessity to read through the notes. The dots are semi-transparent so the overlapping of planets does not detract from the audience’s ability to read the graph. (VERGANO 2014 )

Additionally, the size of each dot represents the radius of each planet. At first glance, one might assume that most planets are much larger than Eath, but the visualization includes a note explaining that larger planets are easier to find. This is a good example of how much explanation to include in a visualization, not so much that the audience is distracted from the graph but enough that they have the information needed to interpret it.

Source:[Astrobiology]

3.9.10 Washington Wizards’ Shooting Stars

This detailed data visualization demonstrates D.C.’s basketball team’s shooting success during the 2013 season (Lindeman and Gamio 2014 ) . Using statistics released by the NBA, the visualization allows viewers to examine data for each of 15 players. For example, viewers can see how successful each player was at a variety of types of shots from a range of spots on the court, compared to others in the league.

Source: (Lindeman and Gamio 2014 )

Generally this is a data visualization for following reasons because it demonstrates complex infomation in a simple and topic-related format. It highlights fact numbers to tell important information. The use of colr is retrained but efficient. However, it is undefined that what is targeted audience. It can also reduce cognitive overload for lines.

3.9.11 Visualization of big data security: a case study on the KDD99 cup data set

This paper utilized a visualization algorithm together with significant data analysis to gain better insights into the KDD99 dataset:

Abstract Cybersecurity has been thrust into the limelight in the modern technological era because of an array of attacks often bypassing new intrusion detection systems (IDSs). Therefore, deciphering better methods for identifying attack types to train IDSs more effectively has become a field of great interest. Critical cyber-attack insights exist in big data; however, an efficient approach is required to determine strong attack types to train IDSs to become more active in critical areas. Despite the rising growth in IDS research, there is a lack of studies involving big data visualization, which is crucial. The KDD99 dataset has served as a reliable benchmark since 1999; therefore, this dataset was utilized in the experiment. This study utilized a hash algorithm, a weight table, and sampling method to deal with the inherent problems caused by analyzing big data: volume, variety, and velocity. By utilizing a visualization algorithm, the researchers were able to gain insights into the KDD99 dataset with precise identification of “normal” clusters and described distinct clusters of possible attacks.

To read the full paper, please follow the reference link:

(Ruan et al. 2017 )

3.9.12 The Atlas of Sustainable Development Goals 2018 - Data Visualization of World Development

(TEAM 2018 )

This is an exciting source and an excellent visual guide to data and development. It discusses trends, comparisons, and measurement issues using accessible and shareable data visualizations. As the graphs cite below, they are informative and clean:

The data draws on the World Development Indicators- the World Bank’s compilation of internationally comparable statistics about global development and the quality of people’s lives. For each of the SDGs, relevant indicators have been chosen to illustrate important ideas. The Atlas features maps and data visualizations, primarily drawn from World Development Indicators (WDI) - the World Bank’s compilation of internationally comparable statistics about global development and the quality of people’s lives.

The editors have been selected to emphasize on essential issues by experts in the World Bank’s Global Practices. The Atlas aims to reflect the breadth of the Goals themselves and presents national and regional trends and snapshots of progress towards the UN’s seventeen Sustainable Development Goals related to: poverty, hunger, health, education, gender, water, energy, jobs, infrastructure, inequalities, cities, consumption, climate, oceans, the environment, peace, institutions, and partnerships.

Contents of this publication: (Group 2018 a ) . The data is available at (Group 2018 b ) . The code used to generate the majority of figures is available at (Whitby 2018 ) .

3.9.13 Is Beauty Important?

This case study is about this article: https://www.infoworld.com/article/3048315/the-inevitability-of-data-visualization-criticism.html

Andy Cotgreave is the current Senior Technical Evangelist at Tableau. In the above article he defends the use of elaborate visualizations and argues that beauty is a quality worth pursuing when making data visualizations. One visualization that he focuses on is a heat map that shows the effect of introducing vaccines on the number of polio cases in the US made by the Wall Street Journal. This particular visualization received a great deal of attention, and was sent around the internet to demonstrate the positive effects of vaccination. After spending some time on the internet, another author named Randy Olson responded with his own article where he remade the heat map as a simple line graph. Both versions are shown below.

In his article, Cotgreave argues that the heat map was visually striking, and its novelty made him more likely to interact with it. As someone involved in visualizations, he seen hundreds, if not thousands of line graphs, and would’ve likely skipped over the line graph version. Cotgreave doubts that the line version would have won awards, or been virally shared as the heat map was. While Cotgreave acknowledges the readability of the line graph, he ultimately feels that there is a place for visualizations to be beautiful.

The takeaway then, is that the visualization you choose to present should be tailored to your situation. In other words, think of your audience. If you were presenting your visualization to the internet at large, then being beautiful and novel is important. If your visualization becomes viral, then it will advance and promote your message to exponentially more people. On the other hand, if you have a more limited audience, like a team of managers, that wants visualizations that can be read quickly, then the line chart will be more suitable.

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Data Visualization with Tableau: 4 Best Case Studies to Know

Discovering the commercial benefits with data visualization..

Visualizing the information is more convenient than delving into the complex data table collections because the human brain easily digests the graphics, unlike the Excel spreadsheets due to their lack of information overload. That is why more and more people realize how to apply Tableau data visualization in the business context and discover the commercial benefits. In this article, you will find the examples of Tableau business cases with an ample outcome because of data visualization.

Impacting the businesses

Data visualization increases the revenue for enterprises. How, though? First of all, it allows to obtain the profound insights when answering the data-related questions: instead of trying to structure the messy information, one can easily observe trends in revenue, costs, customer count, conversion rates, and the other e-commerce metrics (MAU, DAU, CPC, CPA, LTV).

By visualizing the information, it’s easy to find inefficiencies, to determine seasonality and optimize the company’s strategy through development of profitable directions. Also, visualizing the data is the way of guaranteeing the objective source of truth for all levels of leadership and providing all of the departments with the up-to-date and truthful information. An example of efficient visual representation can be found down below:

Source: Tableau Online

Tableau, a suitable solution

Tableau is a good choice for the users in need of cross-platform reports (on tablets, smartphones, or desktops). Tableau is easy to use and is suitable for sharing the data with all the members of the company. At the same time, it is convenient for processing the large sets of information, regardless of the amount of sources.

In fact, Tableau leverages an extensive set of data connectors, such as MySQL, Google Analytics, Google SpreadSheets, Excel, CSV files, and others. Thus, it provides the users with advanced analytical dashboard capabilities along with assisted formula editing, forecasting, clustering, and flexible deployment options (in-cloud, on-premises, and online).

Coca-Cola: shaping the essence of analytics

Coca-Cola , the largest beverage company in the world, looked how to replace its daily 45-minute manual data reporting process. Previously, the team spent a considerable amount of time trying to connect over 200 million lines of data from over 100 different systems into single storage to then build one usable dashboard. To boost the efficiency and carry out a real-time data, Coca-Cola adopted Tableau. Because Tableau consolidates the data from multiple sources, various teams at Coca-Cola can now actively comprehend the metrics, including the budget, delivery operations, and profitability in a matter of few clicks. Simultaneously, the sales department can now access the data from the remote locations by using the iPads, which increased overall timeliness. Finally, the executive reports automatically refresh each day at 5:45 am, unlike the previous times.

Coca-Cola Tableau case study

Lenovo: a 95% increase in efficiency across the company

Lenovo, a global technology company, aimed to optimize its analytics experience across all the departments and worldwide offices. Previously, Lenovo operated with one single sales report that was delivered to 28 different countries. When different regions or company’s divisions wanted to adopt the report to extract the most valuable data, it required a commitment of eight to ten individuals and led to a massive number of on-hold tasks for the analytics team. In turn, Lenovo decided to use Tableau to orderly structure the data all across the company. As a result, Lenovo got a flexible dashboard with all the sales that can be adapted for the ad-hoc analyses, which also led to 95% efficiency improvement across 28 countries. With the help of Tableau dashboard ideas, Lenovo gathered the engagement metric, thus crafting a better experience and collecting more revenue.

Lenovo Tableau case study

LinkedIn: empowering 90% of the Sales Team

LinkedIn, a largest professional networking website, wanted to synchronize all the data across its internal databases ( Google Analytics , Salesforce.com , third-party tools). Previously, one analyst at LinkedIn would handle daily sales request from over 500 salespersons, which created a reporting queue of up to 6 months. To fix the issue, LinkedIn decided to use Tableau to centralize the spread out data and develop a series of customer access dashboards. As a result, thousands of individuals nowadays can access the Tableau Server on a weekly basis, which constitutes 90% of the LinkedIn sales force. With the interactive real time dashboards in Tableau, one can easily predict churn and track the current performance, which eventually created more revenue through the proactive cycle of sales.

LinkedIn Tableau case study

Bookimed: Building real-time analytical dashboards

Bookimed, a Ukrainian service for searching the best medical solutions worldwide, wanted to make an x2 increase in revenue by year. To do so, the companies that use Tableau would have to make reasonable decisions based on data. We understood that previously managers had issues with evaluating hypothesis and tasks prioritization because of the manual filtering through the information. Usual data analysis required next steps:

• Making a task for IT-department to load raw data in CSV file.
• Filtering this data by hand and exploring it to Excel.
• Manually linking data from Excel, CSV files to data from Google Analytics and Google AdWords.

In order to get quick and error-free insights from data and tracking the real-time state of the business, Bookimed decided to use Tableau and Tableau Online services. The main goal was to create the customizable for every department real-time dashboard system. To fulfill these tasks, we did undergo three steps:

• Connected Google Analytics, Google Adwords and MySQL to Tableau.
• Set-up Dashboards for every department at Bookimed.
• Additionally, data root labs incorporated Amazon Redshift + Amazon Kinesis for advanced data gathering and more sophisticated data analysis.

Here are some of the first graphics of the company (numbers are random and for visualization purpose only):

Graphics and Visualizations (Tableau). Source: Bookimed.com

Now, Bookimed.com has the system that delivers all the relevant data and information to every member of the company in real-time mode. Simultaneously, Bookimed got all of the following:

• Reduced the business analysis time from 1 week to 2 hours and ensured the data-driven decision making among employees.
• Build based on data reasonable growth plan for increasing the revenue by 10% monthly .
• Acquired the data visualization infrastructure that can be easily modified, scaled, and changed according to the business needs.

Other Industries where Tableau is used

Healthcare analytics.

• BJC , a company that provides healthcare to residents of Missouri and Illinois, reduced supply chain expenses from 23.5% to 19% .
• Seattle Children’s, a pediatric hospital, saved more than 40000 clinical hours each year and $100000 with demand flow.

Education Analytics

• Des Moines Public School District received an ability to quickly detect high-school students with the likelihood to drop-out .
• The University of Notre Dame got able to perform data analysis using Tableau 10x times faster .

Government Analytics

• The city of Tallahassee measured workload sewers’ efficiency and made utility decisions that improved productivity by 30% .
• Surrey County Council reduced analysis time from days to hours to track intervention at youth clubs.

Marketing Analytics

• Allrecipes, a largest digital food brand, increased the mobile site visits from 8 percent to more than three-fourth of total .
• PepsiCo cut analysts time by 90% .

Tableau Insurance Analytics

• EY, a professional risk management organization, saved clients millions of dollars and prevented fraud .
• MA Assist, the UK-based property services firm, cut insurance claim duration by 15% and improved business efficiency by 20% .

High Technology Tableau Analytics Examples

• GoDaddy, an international web hosting firm, scaled 13TB of data governance and and optimized product experience for over 17 million customers.
• Ancestry.com , a largest online resource for searching family history, visualized billions of rows of data for strategic decision-making .

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Explore the power & impact of great data visualization & storytelling.

Data visualization case studies.

See the potential that creative design and storytelling can bring to data. These data visualization case studies span Australia, the United States, Europe, the Middle East, and Asia .

Mercedes-Benz Case Study

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The Department of Transportation asked the Datalabs Agency to design a traffic management platform for its data. The result: a suite of interface designs showing the complexity of the road system and a way forward to optimize it at a systems level. See the future of transportation design…

Hydro Tasmania Energy Portal

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Walton Family Foundation Case Study

The team at Walton Family Foundation reached out to Datalabs seeking assistance with a series of Tableau dashboards, dashboards to present a series of metrics and KPIs on the environmental program for the foundation’s leadership team.

HCF Annual Report Case Study

A health care fund, HCF, asked the Datalabs Agency to design its Year in Review report and animated data videos, a suite of designs that included a video summary of two important reports.

Dell Foundation Case Study

The fine folks at Michael and Susan Dell Foundation reached out to Datalabs with a unique Power BI design and development challenge. That was to re-design the education reporting system and migrate it from custom software to Power BI.

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A long-term client of the Datalabs Agency, Mercedes-Benz in Germany and Singapore trusted us to design and develop their Power BI dashboards, style guides, and BI framework.

UPS Online Workshop

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The Dutch bank, Rabobank, hired us to train its staff in Hong Kong. We tailored our Introduction to Data Visualization & Storytelling Workshop to include agriculture data, Power BI design, and collaborative exercises.

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Al Jazeera Infographics Workshop Case Study

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Interface Design Case Study

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University of Melbourne Map Project – A Case Study

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Department of Education & Training Case Study: Interactive Calculator

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A case study on a Tableau dashboard, infographic and data design project for the marketing team at Victoria University.

Case Study: Interactive Data Map

We built this interactive map as a use-case for interactive/explorable maps. It’s UI and easy-of-use is a case study of how data visualization can make better sense of geographical data. Certainly better than a table in a spreadsheet!

Case study: International Women’s Development Agency Map

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Case Study: Monash Health Interactive Timeline Tool

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Case Study: Medical Research Infographic

Case Study: Medical Research Infographic Who: Association of Australian Medical Research Institutes What: Summary Report Infographic When: August 2016 Why: The team at the Association of Australian Medical Research Institutes

Infographic Case Study

A large Australian and New Zealand food manufacturer engaged Datalabs to visualize a set of survey results undertaken by their human resources department and an external consultancy. The result was this visually engaging infographic.

Case Study: IWDA Annual Report Microsite

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Pillar Superannuation Tableau Dashboard Report

The aim for this project was to create an interactive dashboard, utilizing Tableau, to convey the data that had been collected over the financial year. Check out this financial firm’s reporting suite.

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17 Data Visualization Techniques All Professionals Should Know

• 17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

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What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

• Gantt Chart
• Box and Whisker Plot
• Waterfall Chart
• Scatter Plot
• Pictogram Chart
• Highlight Table
• Bullet Graph
• Choropleth Map
• Network Diagram
• Correlation Matrices

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

• Bubble clouds
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close charts
• Polar areas
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Wedge stack graphs
• Violin plots

Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

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Designing GitHub’s Octoverse: A Data Visualization Case Study

Designer Gemma Busquets shares how she created a responsive website and 20+ engaging charts and graphs for the software development platform’s annual report.

By Gemma Busquets

Gemma is a designer and creative director with over 15 years of experience in UX, data visualization, and branding. She has taught data visualization for 7+ years at the university level and has directed a master’s degree program on the subject. Gemma’s portfolio includes collaborations with GitHub, Coca-Cola, Nike, Visa, and Seat.

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Last year I collaborated with GitHub to design the 2021 State of the Octoverse report . GitHub’s Octoverse analyzes real-world data from millions of developers and repositories in order to present the year’s software development industry insights. The 2021 report covers three major trends: improving performance and well-being by developing code, creating documentation, and supporting communities in a smarter, more sustainable way.

As the project’s creative liaison, it was my job to assist the GitHub team in making the data-heavy report easy to understand. Using data visualization , I designed 20+ charts, maps, and graphs to help readers unravel the information that GitHub data scientists collected.

In this data visualization case study, I explain my design process, showcase the website I helped to create for GitHub’s Octoverse, and share key learnings from the project.

Designing Engaging Digital Experiences With Data Visualization

State of the Octoverse 2021 is a sprawling report, with data collected from over 73 million GitHub developers and more than 61 million new repositories . It’s also the first time a survey on respondent demographics has been included. Making sense of the data required an extensive design effort.

Our modest team, which included developer Jose Luis Garrido and project manager Miquel Lopez , was tasked with synthesizing this immense amount of information for readers. Despite a delayed start and other simultaneous projects, we delivered.

Kicking Off the Design Process

The first stage of my data visualization design process was discovery. GitHub’s data scientists collected and analyzed information from developers and repositories through Excel files, PowerPoint presentations , and other data sets.

With this information, along with GitHub’s initial data visualization sketches and a 60-page context document, I began to think about how best to illustrate each data set. Then, I set about designing each chart, map, and diagram for maximum user engagement and an intuitive user experience.

Choosing Your Chart

There are three key points to choosing an effective data visualization :

1. Identify the Chart’s Purpose

Data can be represented in numerous ways–bar charts, line graphs, heatmaps, waterfall charts, and more. Each chart serves a purpose, and it’s important to use the right one to ensure that a clear and accurate message is conveyed.

For example, if you want to present the difference between two quantities, use a bar chart. If you want to show a trend over time, use a line graph.

2. Consider the End User

You also need to be aware of your users’ ability to read and analyze data. Most of us are familiar with pie, bar, and line charts. We see them everywhere, and we know how to read them.

On the other hand, fewer people know how to read box plots , which are used in many research publications to summarize multiple data variables into one chart.

If you present users with unfamiliar visualizations, they’ll have a hard time interpreting the data.

3. Design With Clarity

Is the data visualization clear and concise, or is there too much noise? Bar charts can be a great way to display data, but not if there are 100 bars with individual labels. Likewise, streamgraphs are beautiful and functional, but only when there’s a clear data pattern. Sometimes less is more.

Designing Perfect Data Visualizations

Throughout the 2021 State of the Octoverse report, you’ll find a variety of data visualizations that have been carefully composed in accordance with the corresponding data insight.

The Butterfly Chart

On the Overview page, I needed to design an infographic for two sets of data—showing where respondents worked before the pandemic and after it. GitHub provided me with two pie charts that each mapped out four data points: collocated, hybrid, fully remote, and not applicable. However, pie charts are not particularly effective when comparing two sets of data.

Instead, I opted for a butterfly chart . Butterfly charts plot the data as two horizontal bars side by side, resembling butterfly wings. These charts clearly show the difference between two groups that share the same parameters, and make comparing two sets of data much easier.

The Bump Chart

Another effective data visualization is the bump chart . We used this chart to present the information on the most popular computer programming languages used by developers over the past eight years. Bump charts are great for displaying changes in rank over a period of time, and they have become a staple in the Octoverse report.

The Treemap

I needed to illustrate the different sectors to which respondents contribute code. The final decision came down to pie charts versus treemaps.

Pie charts are useful when you have three or four sectors and when the quantities are clearly different. However, our brains don’t process angles well , so when there’s a pie chart with lots of similarly sized wedges, people have a hard time deciphering which is bigger.

In contrast, treemaps allow users to easily compare segments to each other, as well as to the whole. The largest rectangles are placed in the top left, followed by progressively smaller rectangles. It’s easier to compare straight lines than it is to compare wedges or angles.

The Cartogram

Finally, I needed to illustrate the geographical distribution of organizations using GitHub in 2021 by region or country. For this, I used a population cartogram. Cartograms are maps in which the geometry is distorted to accommodate a particular economic, social, political, or environmental feature.

In this data visualization, the size of the squares indicates the population size. Additionally, the saturation of the square’s color indicates how many organizations in that area are using GitHub.

Responsive Website Design For GitHub’s Octoverse 2021

In addition to designing data visualizations, I also helped the GitHub team produce a website for Octoverse 2021. This site was a hub for users to read, explore, and interact with the report’s data insights.

To encourage user engagement, we opted for a fully responsive website that would adapt the site’s rendering to different sized viewports. GitHub asked us to pay special attention to the desktop version after finding that larger devices drove the majority of Octoverse visits.

When designing the responsive site, I followed these best practices :

• Composing text with desktop-friendly and mobile-friendly typefaces. This included choosing optimal font sizes, typefaces, and line length and height, and refining how the text looks at different breakpoints.
• Laying out the visual elements on each page to encourage scrolling .
• Designing a user-friendly top navigation bar that adapts its layout to the viewport size.

Because I designed the website with different devices in mind from the start, most charts rendered well on all screen sizes. I only needed to make minor adjustments for optimal viewability, such as to the circular dendrogram at the end of the “Sustainable communities” section.

Organizing the Information Architecture

I explored different options for the website’s information architecture . I didn’t want to overwhelm users with too much information, but I also didn’t want the site to be scattered or difficult to navigate.

With this in mind, I started by designing a long scrolling website, with all the content on the same page. When that became visually overwhelming, I tried placing each chart on a separate page. To help with navigation, I added a side navigation menu to each page with a table of contents, similar to what you might find in a book. The final design on the Octoverse website consists of separate webpages for the three main trends, plus a homepage that serves as a summary of the most important data.

After deciding on the information architecture, I moved on to designing the site’s content structure, navigation flow, images, and graphics. I created wireframes to map out the content and show paths between different pages.

Making the Website Interactive

The scroll progress indicator.

To satisfy GitHub’s request for an engaging, dynamic website, we added interactive elements. For instance, under the top navigation bar, I designed a scroll progress indicator so visitors could keep track of where they were on the site. As readers scroll down a page, the indicator bar scales incrementally, and each page has a different fill color for the bar: gray, purple, blue, or green.

Animated Headers, Images, and Data Visualization

To keep the website from looking flat, we decided to animate the section headers. I created the illustrations and our team’s developer animated them. We also animated the hero image for the homepage and each subsection, and their corresponding chapter cards at the bottom of each webpage.

We also made some of the static data visualization charts interactive. For example, as you scroll over a line in the bump chart, the line thickens to emphasize the corresponding data point. It’s a simple but effective animation that lets site visitors interact with the data and quickly compare languages.

Creating Successful Data Visualizations and Digital Designs for GitHub: Key Learnings

Data is only useful if you can make sense of it, and the process of designing data-heavy content that users can easily decipher is challenging. Nevertheless, this collaboration with GitHub broadened my knowledge in data visualization design . Here are the most important takeaways from this data visualization case study:

• Know the brand: Being familiar with a brand’s core style guidelines—such as ​​its use of type, color, and images—speeds up the design process because it frees designers to move on to the creative process. I was lucky that I knew a lot about GitHub’s brand before the collaboration, and I was able to use this knowledge to inform my designs.
• Choose the right types of data visualizations: Selecting the correct visualization to represent a data point is essential. An incorrect representation can cause confusion or convey the wrong message.
• Use color wisely: The right color combination will guide the reader’s eye and draw attention to a particular data point.
• Stay curious: When you’re trying to tell a compelling data story, you’re bound to encounter complex design problems, so it’s important to be open to uncommon solutions and continuous learning.

Understanding the basics

What is the github octoverse.

GitHub’s State of the Octoverse is a report that presents software development trends and insights. Data from millions of developers and repositories is collected and analyzed to make up the annual report. Trends include working habits, productivity, and career satisfaction.

What is data visualization, and why is it used?

Data visualization is the process of creating graphical representations of data sets, such as charts, graphs, and maps. This design technique is used to clearly communicate complex data to users.

What does GitHub do, and why is it so popular?

GitHub is an open-source code-hosting platform for version control and collaboration where developers and programmers can download, review, and evaluate each other’s work. It is the platform of choice for millions of developers.

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Gemma Busquets

Barcelona, Spain

Member since December 13, 2016

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9 Powerful Data Visualization Examples

Data visualization is an important tool in understanding the swathes of information at our fingertips. In fact, the right visuals can transform data from static numbers and stats into dynamic, digestible insights. 

And this applies to more than just business. The transformation of data into graphic elements is applicable to almost any aspect of life, and some forms of data visualization have actually changed the world. So, from the interesting to the world changing, here are a few examples of some powerful data visualizations that have captured our imagination.

1. 2024 Men’s Olympic 100m Final Visualization

When we think of data visualization, we think of elements like charts and graphs. However, data can be represented in a number of different ways, and one of my recent favourites is this visual depiction of the 2024 Men’s Olympic 100m final .

This graphic captures the essence of what is being hailed as one of the greatest races in modern Olympic history. Even more than a video replay of the race, the visualization dramatically showcases how narrow the margin of victory was, turning mere milliseconds into a compelling visual narrative that illustrates just how close the competition was. 

2. Florence Nightingale’s Rose Diagram

Florence Nightingale’s Rose Diagram, also known as the Coxcomb chart , is a landmark in the history of data visualization and public health. Developed in the 1850s during the Crimean War, Nightingale created this innovative visualization to communicate the causes of death among British soldiers. At the time, hospitals were very unsanitary, and more soldiers were dying from preventable diseases like typhus, cholera, and dysentery than from battle wounds.

Nightingale, who was trained in mathematics and statistics, meticulously collected data that identified sanitation as a critical issue. The graphic itself came about because she recognized that traditional tables of data would not be effective in persuading the government and the public to take action. So, the Rose Diagram - a circular histogram where each wedge represented a month and the area of each wedge showed the number of deaths – was born. The diagram vividly illustrated how deaths from disease far outstripped those from combat.

“Printed tables and all-in double columns, I do not think anyone will read. None but scientific men ever look in the Appendix of a Report. And this is for the vulgar public.” -Marie Curie

3. John Snow’s Cholera Map

Physician John Snow’s cholera map from the 1854 London outbreak is a truly groundbreaking example of data visualization. At a time when the miasma theory, which blamed diseases like cholera on “bad smells,” was widely accepted, Snow hypothesized that cholera was waterborne. So, he collected data on deaths in the Soho district, mapping their locations alongside public water pumps. His map clearly showed that cholera deaths were concentrated around one particular pump on Broad Street, providing a visual proof of his hypothesis.

Snow’s map was groundbreaking because it offered a clear visual representation of the outbreak’s epicentre, something that a table just wouldn’t have been able to convey. The map led to the pump handle being removed, ending the outbreak and demonstrating the practical value of data visualization. Snow’s method has become foundational in epidemiology, proving the power of visual tools in communicating complex data.

4. Napoleon's March Map

We often talk in business about how data can tell a story, and there is no greater example than Charles Minard’s map of Napoleon’s Russian campaign of 1812 . It is often cited as one of the greatest data visualizations ever created, it tells a dramatic story of loss. The width of the line on the map represents the number of troops, with the line gradually narrowing to reflect the army's losses over time. This striking visual captures the dramatic decline in troop numbers due to harsh weather, disease, and combat, providing a powerful depiction of the campaign's toll. 

What makes Minard’s work so powerful is how it conveys multiple layers of information - geography, temperature, troop size, and direction - all in a single visual. It not only shows the route they took to retreat but also shows the scale of attrition experienced by the army.

5. The Next America

"The Next America" is a data visualization project from the Pew Research Center that explores the evolving demographics of the United States. It demonstrates key trends such as an aging population, and increasing racial and ethnic diversity, in an interactive format. This approach allows users to engage with data dynamically, providing insights into how these demographic changes will impact politics, the economy, culture, and society. The project is designed to make complex data accessible and engaging, helping a wide audience grasp the implications of these trends.

That’s exactly what makes "The Next America" so innovative - its interactive presentation lets users adjust timelines and compare demographic groups to explore data in depth. This enhances engagement and also deepens understanding of demographic shifts and their real-world effects. By linking these shifts to societal changes, the graphic offers valuable insights into future American life, making it a crucial resource for policymakers, educators, and the public. 

6. The Wind Map

The Wind Map , created by Fernanda Viégas and Martin Wattenberg, is a real-time visualization of wind patterns across the United States. This interactive map pulls information from the National Digital Forecast Database to create what they call a “living portrait” of the wind landscape in the US. Viégas and Wattenberg have said they would like to expand the project to the entire world.  By showing the direction and speed of wind across various regions, the map serves a practical purpose, making complex weather data easily accessible to viewers. However, it also distinguishes itself through its striking aesthetic appeal. It’s even been featured by the Museum of Modern Art .

The Wind Map has been widely recognized for its ability to make environmental data not only understandable but also visually engaging, offering a new perspective on the natural forces that shape our world.

7. Visualizing Black America by W.E.B. Du Bois

In 1900, American sociologist, socialist, historian, and Pan-Africanist civil rights activist W.E.B. Du Bois presented a series of data visualizations at the Paris Exposition depicting the economic and social conditions of Black Americans at the time. They came about as Du Bois was looking for a way to show why the African diaspora in America was being held back in a single, simple, yet powerful format. 

The result was “Visualizing Black America”, with charts, graphs, and maps that were groundbreaking in both their design and their subject matter. At a time when data visualization was still very new, Du Bois’s work stood out for its clarity, creativity, and its powerful message against the racist narratives of the era. His visualizations were not just about presenting data—they were a form of advocacy, challenging stereotypes and offering a visual argument for social justice. These visualizations fundamentally changed the representation of Black Americans. Today, this work is recognized as an important contribution to both data visualization and civil rights.

8. Ed Hawkins’ Climate Stripes

Just stripes. That’s how climate Scientist Ed Hawkins’ innovative climate visualization is often described. No numbers or graphs, just stripes. Hawkins created the Climate Stripes to visually represent the progression of global warming from 1850 to the present day. Each stripe in the visualization represents a year, with colors ranging from blue to represent cooler years to red for warmer years. 

What makes it so powerful and impactful is the simplicity. At a glance, you can see that the world is getting warmer. The Climate Stripes have fueled discussions about climate change across the globe, appearing on everything from social media to protest banners, and have become a powerful tool in raising awareness about the urgent need for action. The graphic has also inspired similar visualizations, such as NOAA’s representation of precipitation across the US . It is a masterclass in simplicity.  

9. The London Tube Map

Subway maps in major metropolitan areas are ubiquitous and, you might not realize, that they are amazing examples of data visualizations. And it was the London Tube Map, designed by Harry Beck in 1931 , that sparked this trend, revolutionizing how we visualize complex transit data. Before Beck’s design, maps of the London Underground may have been geographically accurate but they were also difficult to read, which made them almost unusable. 

Beck’s genius was in realizing that commuters cared more about the relative positions of stations and the connections between lines than about geographical accuracy. So, instead of geographic representations, he used only horizontal, vertical and 45º lines, and each line was represented by a set of standard colours. By abstracting the map he created a design that was easy to navigate and looked really nice. The London Tube Map has since become an iconic example of information design, influencing transit maps worldwide.

Not all of these visualizations will be relevant to your team or business, but there are some core principles we can take away and apply to our own data visualization strategies:

• Simplicity is key. Not every dataset needs to be displayed in a complex graph or graphic. Remember - just stripes.
• Tell a story. Data doesn’t have to be boring, it can tell a compelling narrative if you use the right visualizations. 
• Remember your audience. Some of the most powerful visualizations have been born out of tailoring the graphic to the audience. 
• Have fun with it. Data doesn’t have to be all impact and story, it can be a fun way to show trends or just fun facts. Don’t be afraid to get creative. 

Looking for a powerful but simple way to visualize and analyze data from across your tech stack? Try Hurree. Hurree’s powerful platform consolidates your data into one visual space so you can identify trends, gain insights, and share with clarity. Start a free trial now. No credit card required. 

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Top 8 Data Science Use Cases in Marketing

In this article, we want to highlight some key data science use cases in marketing.

As far as the key aim of data science is to turn data into actionable insights the marketing sphere cannot skip the application of these insights for its benefit. Big data in marketing provides an opportunity to understand the target audiences much better.  

Data science is mostly applied in marketing areas of profiling, search engine optimization, customer engagement, responsiveness, real-time marketing campaigns. Moreover, new ways to apply data science and analytics in marketing emerge every day. Among these, the new use cases include digital advertising, micro-targeting, micro-segmentation, and many others.  

Let us concentrate on several instances that present particular interest and managed to prove their efficiency in the course of time.   

Customer segmentation

All customers are individuals. Therefore, a one-size-fits-all approach is not efficient at all.  Customer segmentation comes to the rescue of the marketers in this case. Application of the statistical analysis allows marketers to slice the data and group customers.

Customer segmentation is a process of grouping customers into segments according to the coincidences of particular criteria in their characteristics.

There are three significant segmentation types that are the most often used. These are:

segmentation based on touchpoint engagement

segmentation based on purchase patterns.

Application of micro-segmentation appears to be a rising trend in marketing. Micro-segmentation is far more advanced. It helps to segment people into more precise categories especially concerning behavioral intentions. Thus, marketing actions may be tailored to the preferences even of the least numerous customer groups.

Real-time analytics

Real-time analytics proved to bring marketing insights into campaigns immediately. These real-time marketing opportunities become possible due to the recent boost in popularity of social media and communication technologies. 

Efficient real-time analysis of data brings a considerable increase in revenues for the companies. Real-time algorithms work with two groups of data: customer data and operational data. 

Customer data provides insights into customers’ wants, preferences, and needs. Operational data reflect various transactions, actions, and decisions made by the customers. Application of real-time data analysis brings efficiency, speed and high-performance rates to marketing campaigns. 

Real-time analytics in marketing provides an opportunity to:

get more details about customers

find the efficient platforms

provide a unique customer experience

run real-time test

identify the best working practices

react and respond immediately. 

Predictive analytics

At present, the data is easily accessible and available even for middle-size companies. This is why predictive analytics is so widely applied in marketing. 

Predictive analytics is the application of statistical and machine learning algorithms to predict future with high probability. There are a lot of opportunities to apply predictive analytics in marketing. Let's consider those, which proved to be the most efficient.

Predictive analytics for customers' behavior

Cluster models, predictions, collaborative filtering, regression analysis are all applied to spot the correlation patterns in the customers' behavior to predict future tendencies in purchasing.

Predictive analytics to qualify and prioritize leads

Here belong predictive scoring, identification models and automated segmentation. These are related to qualifying and prioritizing leads to make your marketing efforts more effective. Applying these models, you can make sure that the most effective ready to purchase leads will get your call to action correctly.

Predictive analytics to bring the right product to the market

In this case, data visualization helps the marketing team to make the right decision about what product or service should be delivered to the market.

Predictive analytics for targeting

This is related to a whole bunch of predictive analytics models like affinity analysis, response modeling, churn analysis. These models are used to identify the highest value customers and address them with the right offer at the right time.

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Recommendation engines.

Recommendation engines are powerful tools in attempts to provide a personalized experience and high satisfaction rates to the customers. Marketers are those people who should pay particular attention to the application of the recommendation engines.

The key idea of the recommendation engines is to match the preferences of a customer with product features he or she might like. For this purpose, recommendation engines usually use the following models and algorithms: regression, decision tree, K-nearest neighbor, support vector machines, neural networks, etc.

Recommendation engines are a key targeted marketing tool for email and online marketing campaigns.  

Market basket analysis

Market basket analysis refers to the unsupervised learning data mining techniques intended to learn the buying patterns and to disclose the co-occurrence relationships between purchases. Application of these techniques allows predicting future purchase decisions.

Moreover, market basket analysis can significantly improve the efficiency of the marketing message. Besides the type of the marketing message, whether it is direct offer, email, social media, phone call or newsletter you can offer the next best product suitable for a particular customer.

Optimization of marketing campaigns 

The main task of the marketing team is to create an efficient, customer oriented, targeted marketing campaign dedicated to delivering the right message to the right people at the right time.  

Optimization of marketing campaign involves the application of smart algorithms and models allowing to increase the efficiency. Modern technologies bring automation to the data collection and analysis process, reduce time spent on them, provide real-time results and spot the slightest changes in patterns. Smart data algorithms treat each customer individually. Thus, the high personalization level becomes more achievable.

The optimization process includes several steps that are equally important and require attention. Let us outline these steps:

Choose the right tools

Invest money in those tools that will efficiently gather and analyze data. Make sure the tools you choose can work together for the benefit of your campaign. Integrate the tools with existing systems and data.

Measure the metrics

Measuring metrics allows to identify processes and strategies that need improvement. Measure the parameters comparing them to your marketing goals.

Draw conclusions

Make right data-based decisions to make your marketing campaign as successful as possible.

Lead scoring

Customers' path through the sales funnel is staffed with various opportunities, options, and choices. Lead scoring is applied to identify those prospective customers who will go through the funnel and make their choice to the benefit of your product or service. What is the trick?

Lead scoring ranks the prospect according to a scale representing the value of each lead. The value of each lead may be identified differently, but often they are referred to as hot, warm or cold ones. 

Lead scoring involves data collection concerning customers' demographics, responsiveness, purchase history, preferences, web page view, visits, likes, shares and even the type of e-mails they often react to. 

As a result of lead scoring, the salespeople get qualified prospects regarding who is highly intended to buy. Thus, when products are offered to the right people, the sales boost. 

Optimal campaign channels and content

The essence of all the marketing efforts is to reach the right customer. However, the marketing landscape has been changed and moved to the online world. Thus, the main task for the companies is to assure a strong online presence for the brand.

The leading part here is given to the selection of optimal digital marketing channels: email marketing, pay-per-click advertisement, search engine optimization, display advertising, Social Media Marketing, content marketing, affiliate marketing, online public relations. The choice is vast. To make this choice more comfortable, take the following steps:

define goals

allocate budget

determine your audience.  

In its turn, a digital marketing challenge determines the type of content the brand can use. Blog posts, articles, videos, stories etc. All these types prove to be more or less effective depending on the channel used to distribute them.

The use cases mentioned above prove the statement that application of data science brings numerous benefits to marketing campaigns of various brands. Considering the amount of data available today it is essential not just to freeze it but to use it for the benefit of the company.

Transformation of data into meaningful insights is crucial for decision making. Our list of top data science use cases in marketing reveals specific features of data application in this area and real positive effects it may cause.

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Top 25 Data Science Case Studies [2024]

In an era where data is the new gold, harnessing its power through data science has led to groundbreaking advancements across industries. From personalized marketing to predictive maintenance, the applications of data science are not only diverse but transformative. This compilation of the top 25 data science case studies showcases the profound impact of intelligent data utilization in solving real-world problems. These examples span various sectors, including healthcare, finance, transportation, and manufacturing, illustrating how data-driven decisions shape business operations’ future, enhance efficiency, and optimize user experiences. As we delve into these case studies, we witness the incredible potential of data science to innovate and drive success in today’s data-centric world.

Related: Interesting Data Science Facts

Top 25 Data Science Case Studies [2024]

Case study 1 – personalized marketing (amazon).

Challenge:  Amazon aimed to enhance user engagement by tailoring product recommendations to individual preferences, requiring the real-time processing of vast data volumes.

Solution:  Amazon implemented a sophisticated machine learning algorithm known as collaborative filtering, which analyzes users’ purchase history, cart contents, product ratings, and browsing history, along with the behavior of similar users. This approach enables Amazon to offer highly personalized product suggestions.

Overall Impact:

• Increased Customer Satisfaction:  Tailored recommendations improved the shopping experience.
• Higher Sales Conversions:  Relevant product suggestions boosted sales.

Key Takeaways:

• Personalized Marketing Significantly Enhances User Engagement:  Demonstrating how tailored interactions can deepen user involvement and satisfaction.
• Effective Use of Big Data and Machine Learning Can Transform Customer Experiences:  These technologies redefine the consumer landscape by continuously adapting recommendations to changing user preferences and behaviors.

This strategy has proven pivotal in increasing Amazon’s customer loyalty and sales by making the shopping experience more relevant and engaging.

Case Study 2 – Real-Time Pricing Strategy (Uber)

Challenge:  Uber needed to adjust its pricing dynamically to reflect real-time demand and supply variations across different locations and times, aiming to optimize driver incentives and customer satisfaction without manual intervention.

Solution:  Uber introduced a dynamic pricing model called “surge pricing.” This system uses data science to automatically calculate fares in real time based on current demand and supply data. The model incorporates traffic conditions, weather forecasts, and local events to adjust prices appropriately.

• Optimized Ride Availability:  The model reduced customer wait times by incentivizing more drivers to be available during high-demand periods.
• Increased Driver Earnings:  Drivers benefitted from higher earnings during surge periods, aligning their incentives with customer demand.
• Efficient Balance of Supply and Demand:  Dynamic pricing matches ride availability with customer needs.
• Importance of Real-Time Data Processing:  The real-time processing of data is crucial for responsive and adaptive service delivery.

Uber’s implementation of surge pricing illustrates the power of using real-time data analytics to create a flexible and responsive pricing system that benefits both consumers and service providers, enhancing overall service efficiency and satisfaction.

Case Study 3 – Fraud Detection in Banking (JPMorgan Chase)

Challenge:  JPMorgan Chase faced the critical need to enhance its fraud detection capabilities to safeguard the institution and its customers from financial losses. The primary challenge was detecting fraudulent transactions swiftly and accurately in a vast stream of legitimate banking activities.

Solution:  The bank implemented advanced machine learning models that analyze real-time transaction patterns and customer behaviors. These models are continuously trained on vast amounts of historical fraud data, enabling them to identify and flag transactions that significantly deviate from established patterns, which may indicate potential fraud.

• Substantial Reduction in Fraudulent Transactions:  The advanced detection capabilities led to a marked decrease in fraud occurrences.
• Enhanced Security for Customer Accounts:  Customers experienced greater security and trust in their transactions.
• Effectiveness of Machine Learning in Fraud Detection:  Machine learning models are greatly effective at identifying fraud activities within large datasets.
• Importance of Ongoing Training and Updates:  Continuous training and updating of models are crucial to adapt to evolving fraudulent techniques and maintain detection efficacy.

JPMorgan Chase’s use of machine learning for fraud detection demonstrates how financial institutions can leverage advanced analytics to enhance security measures, protect financial assets, and build customer trust in their banking services.

Case Study 4 – Optimizing Healthcare Outcomes (Mayo Clinic)

Challenge:  The Mayo Clinic aimed to enhance patient outcomes by predicting diseases before they reach critical stages. This involved analyzing large volumes of diverse data, including historical patient records and real-time health metrics from various sources like lab results and patient monitors.

Solution:  The Mayo Clinic employed predictive analytics to integrate and analyze this data to build models that predict patient risk for diseases such as diabetes and heart disease, enabling earlier and more targeted interventions.

• Improved Patient Outcomes:  Early identification of at-risk patients allowed for timely medical intervention.
• Reduction in Healthcare Costs:  Preventing disease progression reduces the need for more extensive and costly treatments later.
• Early Identification of Health Risks:  Predictive models are essential for identifying at-risk patients early, improving the chances of successful interventions.
• Integration of Multiple Data Sources:  Combining historical and real-time data provides a comprehensive view that enhances the accuracy of predictions.

Case Study 5 – Streamlining Operations in Manufacturing (General Electric)

Challenge:  General Electric needed to optimize its manufacturing processes to reduce costs and downtime by predicting when machines would likely require maintenance to prevent breakdowns.

Solution:  GE leveraged data from sensors embedded in machinery to monitor their condition continuously. Data science algorithms analyze this sensor data to predict when a machine is likely to disappoint, facilitating preemptive maintenance and scheduling.

• Reduction in Unplanned Machine Downtime:  Predictive maintenance helped avoid unexpected breakdowns.
• Lower Maintenance Costs and Improved Machine Lifespan:  Regular maintenance based on predictive data reduced overall costs and extended the life of machinery.
• Predictive Maintenance Enhances Operational Efficiency:  Using data-driven predictions for maintenance can significantly reduce downtime and operational costs.
• Value of Sensor Data:  Continuous monitoring and data analysis are crucial for forecasting equipment health and preventing failures.

Related: Data Engineering vs. Data Science

Case Study 6 – Enhancing Supply Chain Management (DHL)

Challenge:  DHL sought to optimize its global logistics and supply chain operations to decreases expenses and enhance delivery efficiency. It required handling complex data from various sources for better route planning and inventory management.

Solution:  DHL implemented advanced analytics to process and analyze data from its extensive logistics network. This included real-time tracking of shipments, analysis of weather conditions, traffic patterns, and inventory levels to optimize route planning and warehouse operations.

• Enhanced Efficiency in Logistics Operations:  More precise route planning and inventory management improved delivery times and reduced resource wastage.
• Reduced Operational Costs:  Streamlined operations led to significant cost savings across the supply chain.
• Critical Role of Comprehensive Data Analysis:  Effective supply chain management depends on integrating and analyzing data from multiple sources.
• Benefits of Real-Time Data Integration:  Real-time data enhances logistical decision-making, leading to more efficient and cost-effective operations.

Case Study 7 – Predictive Maintenance in Aerospace (Airbus)

Challenge:  Airbus faced the challenge of predicting potential failures in aircraft components to enhance safety and reduce maintenance costs. The key was to accurately forecast the lifespan of parts under varying conditions and usage patterns, which is critical in the aerospace industry where safety is paramount.

Solution:  Airbus tackled this challenge by developing predictive models that utilize data collected from sensors installed on aircraft. These sensors continuously monitor the condition of various components, providing real-time data that the models analyze. The predictive algorithms assess the likelihood of component failure, enabling maintenance teams to schedule repairs or replacements proactively before actual failures occur.

• Increased Safety:  The ability to predict and prevent potential in-flight failures has significantly improved the safety of Airbus aircraft.
• Reduced Costs:  By optimizing maintenance schedules and minimizing unnecessary checks, Airbus has been able to cut down on maintenance expenses and reduce aircraft downtime.
• Enhanced Safety through Predictive Analytics:  The use of predictive analytics in monitoring aircraft components plays a crucial role in preventing failures, thereby enhancing the overall safety of aviation operations.
• Valuable Insights from Sensor Data:  Real-time data from operational use is critical for developing effective predictive maintenance strategies. This data provides insights for understanding component behavior under various conditions, allowing for more accurate predictions.

This case study demonstrates how Airbus leverages advanced data science techniques in predictive maintenance to ensure higher safety standards and more efficient operations, setting an industry benchmark in the aerospace sector.

Case Study 8 – Enhancing Film Recommendations (Netflix)

Challenge:  Netflix aimed to improve customer retention and engagement by enhancing the accuracy of its recommendation system. This task involved processing and analyzing vast amounts of data to understand diverse user preferences and viewing habits.

Solution:  Netflix employed collaborative filtering techniques, analyzing user behaviors (like watching, liking, or disliking content) and similarities between content items. This data-driven approach allows Netflix to refine and personalize recommendations continuously based on real-time user interactions.

• Increased Viewer Engagement:  Personalized recommendations led to longer viewing sessions.
• Higher Customer Satisfaction and Retention Rates:  Tailored viewing experiences improved overall customer satisfaction, enhancing loyalty.
• Tailoring User Experiences:  Machine learning is pivotal in personalizing media content, significantly impacting viewer engagement and satisfaction.
• Importance of Continuous Updates:  Regularly updating recommendation algorithms is essential to maintain relevance and effectiveness in user engagement.

Case Study 9 – Traffic Flow Optimization (Google)

Challenge:  Google needed to optimize traffic flow within its Google Maps service to reduce congestion and improve routing decisions. This required real-time analysis of extensive traffic data to predict and manage traffic conditions accurately.

Solution:  Google Maps integrates data from multiple sources, including satellite imagery, sensor data, and real-time user location data. These data points are used to model traffic patterns and predict future conditions dynamically, which informs updated routing advice.

• Reduced Traffic Congestion:  More efficient routing reduced overall traffic buildup.
• Enhanced Accuracy of Traffic Predictions and Routing:  Improved predictions led to better user navigation experiences.
• Integration of Multiple Data Sources:  Combining various data streams enhances the accuracy of traffic management systems.
• Advanced Modeling Techniques:  Sophisticated models are crucial for accurately predicting traffic patterns and optimizing routes.

Case Study 10 – Risk Assessment in Insurance (Allstate)

Challenge:  Allstate sought to refine its risk assessment processes to offer more accurately priced insurance products, challenging the limitations of traditional actuarial models through more nuanced data interpretations.

Solution:  Allstate enhanced its risk assessment framework by integrating machine learning, allowing for granular risk factor analysis. This approach utilizes individual customer data such as driving records, home location specifics, and historical claim data to tailor insurance offerings more accurately.

• More Precise Risk Assessment:  Improved risk evaluation led to more tailored insurance offerings.
• Increased Market Competitiveness:  Enhanced pricing accuracy boosted Allstate’s competitive edge in the insurance market.
• Nuanced Understanding of Risk:  Machine learning provides a deeper, more nuanced understanding of risk than traditional models, leading to better risk pricing.
• Personalized Pricing Strategies:  Leveraging detailed customer data in pricing strategies enhances customer satisfaction and business performance.

Related: Can you move from Cybersecurity to Data Science?

Case Study 11 – Energy Consumption Reduction (Google DeepMind)

Challenge:  Google DeepMind aimed to significantly reduce the high energy consumption required for cooling Google’s data centers, which are crucial for maintaining server performance but also represent a major operational cost.

Solution:  DeepMind implemented advanced AI algorithms to optimize the data center cooling systems. These algorithms predict temperature fluctuations and adjust cooling processes accordingly, saving energy and reducing equipment wear and tear.

• Reduction in Energy Consumption:  Achieved a 40% reduction in energy used for cooling.
• Decrease in Operational Costs and Environmental Impact:  Lower energy usage resulted in cost savings and reduced environmental footprint.
• AI-Driven Optimization:  AI can significantly decrease energy usage in large-scale infrastructure.
• Operational Efficiency Gains:  Efficiency improvements in operational processes lead to cost savings and environmental benefits.

Case Study 12 – Improving Public Safety (New York City Police Department)

Challenge:  The NYPD needed to enhance its crime prevention strategies by better predicting where and when crimes were most likely to occur, requiring sophisticated analysis of historical crime data and environmental factors.

Solution:  The NYPD implemented a predictive policing system that utilizes data analytics to identify potential crime hotspots based on trends and patterns in past crime data. Officers are preemptively dispatched to these areas to deter criminal activities.

• Reduction in Crime Rates:  There is a notable decrease in crime in areas targeted by predictive policing.
• More Efficient Use of Police Resources:  Enhanced allocation of resources where needed.
• Effectiveness of Data-Driven Crime Prevention:  Targeting resources based on data analytics can significantly reduce crime.
• Proactive Law Enforcement:  Predictive analytics enable a shift from reactive to proactive law enforcement strategies.

Case Study 13 – Enhancing Agricultural Yields (John Deere)

Challenge:  John Deere aimed to help farmers increase agricultural productivity and sustainability by optimizing various farming operations from planting to harvesting.

Solution:  Utilizing data from sensors on equipment and satellite imagery, John Deere developed algorithms that provide actionable insights for farmers on optimal planting times, water usage, and harvest schedules.

• Increased Crop Yields:  More efficient farming methods led to higher yields.
• Enhanced Sustainability of Farming Practices:  Improved resource management contributed to more sustainable agriculture.
• Precision Agriculture:  Significantly improves productivity and resource efficiency.
• Data-Driven Decision-Making:  Enables better farming decisions through timely and accurate data.

Case Study 14 – Streamlining Drug Discovery (Pfizer)

Challenge:  Pfizer faced the need to accelerate the process of discoverying drug and improve the success rates of clinical trials.

Solution:  Pfizer employed data science to simulate and predict outcomes of drug trials using historical data and predictive models, optimizing trial parameters and improving the selection of drug candidates.

• Accelerated Drug Development:  Reduced time to market for new drugs.
• Increased Efficiency and Efficacy in Clinical Trials:  More targeted trials led to better outcomes.
• Reduction in Drug Development Time and Costs:  Data science streamlines the R&D process.
• Improved Clinical Trial Success Rates:  Predictive modeling enhances the accuracy of trial outcomes.

Case Study 15 – Media Buying Optimization (Procter & Gamble)

Challenge:  Procter & Gamble aimed to maximize the ROI of their extensive advertising budget by optimizing their media buying strategy across various channels.

Solution:  P&G analyzed extensive data on consumer behavior and media consumption to identify the most effective times and channels for advertising, allowing for highly targeted ads that reach the intended audience at optimal times.

• Improved Effectiveness of Advertising Campaigns:  More effective ads increased campaign impact.
• Increased Sales and Better Budget Allocation:  Enhanced ROI from more strategic media spending.
• Enhanced Media Buying Strategies:  Data analytics significantly improves media buying effectiveness.
• Insights into Consumer Behavior:  Understanding consumer behavior is crucial for optimizing advertising ROI.

Related: Is Data Science Certificate beneficial for your career?

Case Study 16 – Reducing Patient Readmission Rates with Predictive Analytics (Mount Sinai Health System)

Challenge:  Mount Sinai Health System sought to reduce patient readmission rates, a significant indicator of healthcare quality and a major cost factor. The challenge involved identifying patients at high risk of being readmitted within 30 days of discharge.

Solution:  The health system implemented a predictive analytics platform that analyzes real-time patient data and historical health records. The system detects patterns and risk factors contributing to high readmission rates by utilizing machine learning algorithms. Factors such as past medical history, discharge conditions, and post-discharge care plans were integrated into the predictive model.

• Reduced Readmission Rates:  Early identification of at-risk patients allowed for targeted post-discharge interventions, significantly reducing readmission rates.
• Enhanced Patient Outcomes: Patients received better follow-up care tailored to their health risks.
• Predictive Analytics in Healthcare:  Effective for managing patient care post-discharge.
• Holistic Patient Data Utilization: Integrating various data points provides a more accurate prediction and better healthcare outcomes.

Case Study 17 – Enhancing E-commerce Customer Experience with AI (Zalando)

Challenge:  Zalando aimed to enhance the online shopping experience by improving the accuracy of size recommendations, a common issue that leads to high return rates in online apparel shopping.

Solution:  Zalando developed an AI-driven size recommendation engine that analyzes past purchase and return data in combination with customer feedback and preferences. This system utilizes machine learning to predict the best-fit size for customers based on their unique body measurements and purchase history.

• Reduced Return Rates:  More accurate size recommendations decreased the returns due to poor fit.
• Improved Customer Satisfaction: Customers experienced a more personalized shopping journey, enhancing overall satisfaction.
• Customization Through AI:  Personalizing customer experience can significantly impact satisfaction and business metrics.
• Data-Driven Decision-Making: Utilizing customer data effectively can improve business outcomes by reducing costs and enhancing the user experience.

Case Study 18 – Optimizing Energy Grid Performance with Machine Learning (Enel Group)

Challenge:  Enel Group, one of the largest power companies, faced challenges in managing and optimizing the performance of its vast energy grids. The primary goal was to increase the efficiency of energy distribution and reduce operational costs while maintaining reliability in the face of fluctuating supply and demand.

Solution:  Enel Group implemented a machine learning-based system that analyzes real-time data from smart meters, weather stations, and IoT devices across the grid. This system is designed to predict peak demand times, potential outages, and equipment failures before they occur. By integrating these predictions with automated grid management tools, Enel can dynamically adjust energy flows, allocate resources more efficiently, and schedule maintenance proactively.

• Enhanced Grid Efficiency:  Improved distribution management, reduced energy wastage, and optimized resource allocation.
• Reduced Operational Costs: Predictive maintenance and better grid management decreased the frequency and cost of repairs and outages.
• Predictive Maintenance in Utility Networks:  Advanced analytics can preemptively identify issues, saving costs and enhancing service reliability.
• Real-Time Data Integration: Leveraging data from various sources in real-time enables more agile and informed decision-making in energy management.

Case Study 19 – Personalizing Movie Streaming Experience (WarnerMedia)

Challenge:  WarnerMedia sought to enhance viewer engagement and subscription retention rates on its streaming platforms by providing more personalized content recommendations.

Solution:  WarnerMedia deployed a sophisticated data science strategy, utilizing deep learning algorithms to analyze viewer behaviors, including viewing history, ratings given to shows and movies, search patterns, and demographic data. This analysis helped create highly personalized viewer profiles, which were then used to tailor content recommendations, homepage layouts, and promotional offers specifically to individual preferences.

• Increased Viewer Engagement:  Personalized recommendations resulted in extended viewing times and increased interactions with the platform.
• Higher Subscription Retention: Tailored user experiences improved overall satisfaction, leading to lower churn rates.
• Deep Learning Enhances Personalization:  Deep learning algorithms allow a more nuanced knowledge of consumer preferences and behavior.
• Data-Driven Customization is Key to User Retention: Providing a customized experience based on data analytics is critical for maintaining and growing a subscriber base in the competitive streaming market.

Case Study 20 – Improving Online Retail Sales through Customer Sentiment Analysis (Zappos)

Challenge:  Zappos, an online shoe and clothing retailer, aimed to enhance customer satisfaction and boost sales by better understanding customer sentiments and preferences across various platforms.

Solution:  Zappos implemented a comprehensive sentiment analysis program that utilized natural language processing (NLP) techniques to gather and analyze customer feedback from social media, product reviews, and customer support interactions. This data was used to identify emerging trends, customer pain points, and overall sentiment towards products and services. The insights derived from this analysis were subsequently used to customize marketing strategies, enhance product offerings, and improve customer service practices.

• Enhanced Product Selection and Marketing:  Insight-driven adjustments to inventory and marketing strategies increased relevancy and customer satisfaction.
• Improved Customer Experience: By addressing customer concerns and preferences identified through sentiment analysis, Zappos enhanced its overall customer service, increasing loyalty and repeat business.
• Power of Sentiment Analysis in Retail:  Understanding and reacting to customer emotions and opinions can significantly impact sales and customer satisfaction.
• Strategic Use of Customer Feedback: Leveraging customer feedback to drive business decisions helps align product offerings and services with customer expectations, fostering a positive brand image.

Related: Data Science Industry in the US

Case Study 21 – Streamlining Airline Operations with Predictive Analytics (Delta Airlines)

Challenge:  Delta Airlines faced operational challenges, including flight delays, maintenance scheduling inefficiencies, and customer service issues, which impacted passenger satisfaction and operational costs.

Solution:  Delta implemented a predictive analytics system that integrates data from flight operations, weather reports, aircraft sensor data, and historical maintenance records. The system predicts potential delays using machine learning models and suggests optimal maintenance scheduling. Additionally, it forecasts passenger load to optimize staffing and resource allocation at airports.

• Reduced Flight Delays:  Predictive insights allowed for better planning and reduced unexpected delays.
• Enhanced Maintenance Efficiency:  Maintenance could be scheduled proactively, decreasing the time planes spend out of service.
• Improved Passenger Experience: With better resource management, passenger handling became more efficient, enhancing overall customer satisfaction.
• Operational Efficiency Through Predictive Analytics:  Leveraging data for predictive purposes significantly improves operational decision-making.
• Data Integration Across Departments: Coordinating data from different sources provides a holistic view crucial for effective airline management.

Case Study 22 – Enhancing Financial Advisory Services with AI (Morgan Stanley)

Challenge:  Morgan Stanley sought to offer clients more personalized and effective financial guidance. The challenge was seamlessly integrating vast financial data with individual client profiles to deliver tailored investment recommendations.

Solution:  Morgan Stanley developed an AI-powered platform that utilizes natural language processing and ML to analyze financial markets, client portfolios, and historical investment performance. The system identifies patterns and predicts market trends while considering each client’s financial goals, risk tolerance, and investment history. This integrated approach enables financial advisors to offer highly customized advice and proactive investment strategies.

• Improved Client Satisfaction:  Clients received more relevant and timely investment recommendations, enhancing their overall satisfaction and trust in the advisory services.
• Increased Efficiency: Advisors were able to manage client portfolios more effectively, using AI-driven insights to make faster and more informed decisions.
• Personalization through AI:  Advanced analytics and AI can significantly enhance the personalization of financial services, leading to better client engagement.
• Data-Driven Decision Making: Leveraging diverse data sets provides a comprehensive understanding crucial for tailored financial advising.

Case Study 23 – Optimizing Inventory Management in Retail (Walmart)

Challenge:  Walmart sought to improve inventory management across its vast network of stores and warehouses to reduce overstock and stockouts, which affect customer satisfaction and operational efficiency.

Solution:  Walmart implemented a robust data analytics system that integrates real-time sales data, supply chain information, and predictive analytics. This system uses machine learning algorithms to forecast demand for thousands of products at a granular level, considering factors such as seasonality, local events, and economic trends. The predictive insights allow Walmart to dynamically adjust inventory levels, optimize restocking schedules, and manage distribution logistics more effectively.

• Reduced Inventory Costs:  More accurate demand forecasts helped minimize overstock and reduce waste.
• Enhanced Customer Satisfaction: Improved stock availability led to better in-store experiences and higher customer satisfaction.
• Precision in Demand Forecasting:  Advanced data analytics and machine learning significantly enhance demand forecasting accuracy in retail.
• Integrated Data Systems:  Combining various data sources provides a comprehensive view of inventory needs, improving overall supply chain efficiency.

Case Study 24: Enhancing Network Security with Predictive Analytics (Cisco)

Challenge:  Cisco encountered difficulties protecting its extensive network infrastructure from increasingly complex cyber threats. The objective was to bolster their security protocols by anticipating potential breaches before they happen.

Solution:  Cisco developed a predictive analytics solution that leverages ML algorithms to analyze patterns in network traffic and identify anomalies that could suggest a security threat. By integrating this system with their existing security protocols, Cisco can dynamically adjust defenses and alert system administrators about potential vulnerabilities in real-time.

• Improved Security Posture:  The predictive system enabled proactive responses to potential threats, significantly reducing the incidence of successful cyber attacks.
• Enhanced Operational Efficiency: Automating threat detection and response processes allowed Cisco to manage network security more efficiently, with fewer resources dedicated to manual monitoring.
• Proactive Security Measures:  Employing predictive cybersecurity analytics helps organizations avoid potential threats.
• Integration of Machine Learning: Machine learning is crucial for effectively detecting patterns and anomalies that human analysts might overlook, leading to stronger security measures.

Case Study 25 – Improving Agricultural Efficiency with IoT and AI (Bayer Crop Science)

Challenge:  Bayer Crop Science aimed to enhance agricultural efficiency and crop yields for farmers worldwide, facing the challenge of varying climatic conditions and soil types that affect crop growth differently.

Solution:  Bayer deployed an integrated platform that merges IoT sensors, satellite imagery, and AI-driven analytics. This platform gathers real-time weather conditions, soil quality, and crop health data. Utilizing machine learning models, the system processes this data to deliver precise agricultural recommendations to farmers, including optimal planting times, watering schedules, and pest management strategies.

• Increased Crop Yields:  Tailored agricultural practices led to higher productivity per hectare.
• Reduced Resource Waste: Efficient water use, fertilizers, and pesticides minimized environmental impact and operational costs.
• Precision Agriculture:  Leveraging IoT and AI enables more precise and data-driven agricultural practices, enhancing yield and efficiency.
• Sustainability in Farming:  Advanced data analytics enhance the sustainability of farming by optimizing resource utilization and minimizing waste.

Related: Is Data Science Overhyped?

The power of data science in transforming industries is undeniable, as demonstrated by these 25 compelling case studies. Through the strategic application of machine learning, predictive analytics, and AI, companies are solving complex challenges and gaining a competitive edge. The insights gleaned from these cases highlight the critical role of data science in enhancing decision-making processes, improving operational efficiency, and elevating customer satisfaction. As we look to the future, the role of data science is set to grow, promising even more innovative solutions and smarter strategies across all sectors. These case studies inspire and serve as a roadmap for harnessing the transformative power of data science in the journey toward digital transformation.

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Application of Data Science in Healthcare and Medicine for improved Patient outcomes[2024]

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Data Engineer vs Data Scientist vs AI Engineer: Key Differences [2024]

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15 KPIs Every Data Team Should Care About [2024]

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Three Short Marketing Analytics Case Studies to Inspire You to Love Data

Written by Anna Sonnenberg

Published Feb. 28 2022

Table of Contents

From engagement statistics to content analytics to conversion metrics, data is a big part of most social media managers’ responsibilities. But that doesn’t necessarily mean you enjoy processing marketing data or drawing conclusions from it.

If data isn’t exactly your favorite part of the job, these marketing analytics case studies may change your mind.

Find out how marketing analytics helped three major brands grow their businesses—and you might develop a whole new appreciation for marketing data in the process.

What Is Marketing Analytics?

Marketing analytics is the process of collecting and evaluating metrics to understand how much value marketing efforts generate. With analytics, you can assess the return on investment (ROI) of anything from social media posts and ad campaigns to landing pages and native platform features.

For many organizations and their marketing team, marketing analytics are essential for improving offerings and driving growth.

Here are common goals you can achieve with marketing analytics.

Improving marketing campaigns

Some social media marketing campaigns are more successful than others. Analytics can help your organization pinpoint exactly what works. By analyzing metrics like engagement, click-through rate (CTR), conversions, and ROI, you can determine what resonates best with its audience. By using data science, you can craft a marketing strategy that gets you better results from your campaigns.

Decreasing expenses

Ineffective marketing campaigns, usability issues, and poorly optimized algorithms can all lead to dissatisfied customers and unnecessarily high retention costs.

By investing in marketing analytics, your organization can take steps to identify points of friction and reduce expenses.

Forecasting results

Reviewing past outcomes is useful, but forecasting the results your campaigns are likely to generate is even more valuable. With marketing analytics, you can model results and get a better sense of how marketing initiatives can impact growth over time.

Marketing Analytics Case Studies: Progressive Insurance

In the early 2000s, Progressive’s website was routinely considered one of the best in the insurance industry. When the insurance provider’s customers began switching to mobile devices a decade later, the organization aimed to develop a mobile app as effective as its desktop site.

But what did that mean exactly? And what was the insurance provider’s mobile app missing?

To determine what would make the mobile app more successful, Progressive pursued an in-depth analysis of the organization’s marketing data.

As Progressive Data & Analytics Business Leader Pawan Divakarla explains , the insurance provider’s analytics team has always sought insight into how customers are using the company’s tools.

In his words, “At Progressive, we sell insurance. But if you think about it, our product is actually data.”

After launching the mobile app, Progressive began looking for ways to optimize the user experience. As this Progressive case study explains, the organization aimed to streamline the login process and improve user satisfaction to meet its ultimate goals of increasing customer loyalty and new customer acquisition.

Because Progressive’s mobile app generated so much information, the organization needed data visualization tools for collection and processing. To analyze customers’ experiences and actions, the company opted to use a combination of Google Analytics 360 and Google Tag Manager 360.

This choice was a relatively simple one for Progressive because the company already used these tools to run A/B tests and optimize its website.

Using Google’s analytical tools to review the company’s mobile app would allow Progressive to understand what features to test and how to optimize the user experience across countless mobile devices and operating systems.

Progressive used the two Google tools for separate yet complementary functions:

• With Google Analytics 360, Progressive could track user sessions and demographics. The company integrated BigQuery for more insight into user behaviors.
• With Google Tag Manager 360, Progressive could easily implement tracking tags to measure various actions, conversions, and navigation patterns.

To get the insights the company needed to improve its mobile app, Progressive took a three-pronged approach:

User device data

First, Progressive aimed to identify which devices and operating systems were most common among the app’s user base. With this information, the company would be able to develop more effective tests for its mobile app.

App crash data

Next, Progressive wanted to analyze app crash data. The company planned to use Google Analytics 360 and BigQuery data to understand the cause for the crash and how users reacted when the app stopped working abruptly.

Login and security data

Finally, Progressive aimed to learn how users responded when failed login attempts locked them out of the app. The company planned to use Google Analytics 360 and BigQuery to see what actions users took. It planned to then test new prompts that would guide users more effectively.

Outcome of this marketing analytics case study

Using marketing analytics tools , Progressive was able to process customer behavior, identify appropriate tests, and implement successful solutions.

Here’s how each of the three approaches generated useful results that helped Progressive reach its ultimate acquisition and loyalty goals.

First, Progressive developed session-based reports that reflected the most common mobile devices and operating systems for the app’s user base. With those insights, the company identified which device and operating system combinations to prioritize for its mobile app tests.

As a result, the company reduced testing time by 20% for its mobile app—allowing the organization to find solutions much more quickly than its typical timeline would have allowed.

Next, Progressive reviewed the actions customers took right before the app crashed. The company pinpointed a server issue as the cause of a major crash that disrupted countless mobile app sessions.

Using this data, Progressive could address the server issue and prevent it from happening again.

Finally, Progressive created a custom funnel in Google Analytics 360 to evaluate users’ typical login path. After learning that many users who became locked out of their accounts never attempted to log in again, the company developed a workflow that provided better guidance.

The new workflow sends users to a Forgot Password page, which has increased logins by 30%.

Marketing Analytics Case Studies: Netflix

When companies take a digital-first approach to customer loyalty, they can collect an incredible amount of user data. With these marketing analytics, companies can improve their products, build better marketing campaigns, and drive more revenue.

As this Netflix case study shows, the online content streaming platform has leveraged its user data in a variety of helpful ways.

By using data to improve its content recommendation engine, develop original content, and increase its customer retention rate, Netflix has positioned itself far ahead of the competition.

With so much data to leverage, Netflix had wide-ranging goals for the company’s marketing analytics. However, all of the organization’s goals contributed to the company’s larger business objectives—which focus on customer retention.

Netflix aimed to go beyond basic user demographics and understand what customers want from a streaming platform—and what was likely to convince them to stay. With this knowledge, Netflix could create better products and services for happier customers.

Access issues, service outages, and platform flaws can all lead to unhappy customers and negative sentiment—which can cause customers to seek out an alternative solution.

By identifying problems early through marketing analytics, Netflix could improve its products and continue to innovate.

To work toward its customer retention objective, Netflix collected data from virtually every interaction with its 150+ million subscribers. The company then used marketing analytics tools to process this native data and evaluate everything from how customers navigate the platform to what they watch.

By creating such detailed customer profiles, Netflix could make much more personalized recommendations for each user. The more data the company collected, the more it could tailor its algorithm to suggest the ideal content to each individual viewer.

To better understand the platform’s users, Netflix collected such data as:

• The devices viewers used to stream content
• Day of week and time of day when users viewed content
• Number of serial episodes viewers watched in a row
• Whether viewers paused and resumed content
• Number and type of searches users performed

Netflix also welcomed user feedback on content . The company incorporated these content ratings into their analysis to better understand viewer preferences.

According to the streaming platform, the Netflix algorithm is responsible for about 80% of viewer activity . The company has successfully collected relevant data and used marketing analytics to generate recommendations that encourage viewers to continue watching and subscribing.

The revenue metrics suggest that Netflix’s focus on marketing analytics has been hugely beneficial to the company. The company estimates that its algorithm generates $1 billion in value every year, largely due to customer retention.

In recent years, Netflix’s customer retention rate has far surpassed competitors like Hulu and Amazon Prime. Netflix has an impressive 90% retention rate , meaning the vast majority of viewers continue to subscribe to the service month after month. (In contrast, Amazon Prime’s retention rate is 75%, and Hulu’s is 64%.)

For Netflix, customer retention means more than happy viewers. It also means more data, a continually improving algorithm, and substantial business growth.

Netflix has emerged as the world’s most highly valued company, with a total valuation of over $160 billion. Netflix can continue to increase this valuation. It leverages its data by producing original media and recommending the ideal content to viewers every time they access the streaming platform.

Marketing Analytics Case Studies: Allrecipes

As the world’s biggest digital food brand, Allrecipes has 18 websites and more than 85 million users. But the brand also has plenty of competition from other food-focused apps and websites.

To stay ahead of other recipe sites and ensure that it continues to provide all the solutions that users want, Allrecipes relies on marketing analytics.

With marketing analytics, the digital brand can better understand the customer journey and analyze trends as they emerge. As this Allrecipes case study explains, the brand can expand its audience and attract even more lucrative demographics using these insights.

To continue to gain ground as the world’s top digital food brand, Allrecipes established several wide-ranging goals.

Some of the brand’s primary objectives included the following.

Improve user experience

With more than a billion and a half visitors across the brand’s sites every year, Allrecipes generates a ton of traffic. But the company needed a way to understand how visitors were using the site, so it could improve the user experience and gauge the health of the sites.

Increase video engagement

To take advantage of a demand for video content, Allrecipes had decided to invest heavily in video. However, the video production team needed strategic guidance. The brand needed to know what types of content would drive the most engagement.

Drive mobile engagement

To continue to meet the needs of its user base, Allrecipes had to look beyond its websites. As more and more people began using mobile devices to access the brand’s content, Allrecipes realized that the company needed to optimize its mobile app.

Inform product strategy

To promote new features and integrations or pursue partner programs, Allrecipes needed to know what its community wanted. Had they adopted the new integrations yet? Did they need new features to use the site or app more effectively?

Expand user base

Cooking and dining trends come and go, and Allrecipes needed a simple yet effective way to identify these developments.

By responding quickly to trends, the brand would be able to capture a larger user base, including elusive millennials.

Grow advertising revenue

Like many digital brands, Allrecipes has a native advertising program that allows the company to monetize its website. The company aimed to increase its advertising revenue, yet the organization didn’t want to compromise the user experience. To find the right partners to grow this program, Allrecipes needed deeper insights into its audience.

Although the brand’s goals were varied, the approach was relatively straightforward. To process marketing analytics from a wide range of channels, the brand opted to use Tableau, a business intelligence platform.

With Tableau, Allrecipes could establish a single platform for visualizing data from Adobe Marketing Cloud, Hitwise, and comScore. By linking Adobe Marketing Cloud to Tableau, the brand could pull in all of its website and marketing analytics. By linking Hitwise and comScore, the brand could source demographic data.

Using Tableau allowed Allrecipes to build custom dashboards and develop tailored reports to answer all of the brand’s questions. This tool also allowed the brand to pursue collaboration options across the organization.

In fact, departments ranging from marketing and design to product and finance contributed to the tool. Teams used Tableau Server to publish dashboards, creating a single space where stakeholders could visualize or analyze data.

With Tableau, Allrecipes was able to visualize the brand’s data successfully, enabling smarter decisions and making progress toward key goals. Here’s what the brand accomplished using marketing analytics:

Using insights from Tableau, Allrecipes was able to see how visitors typically used the site—including how they submit recipes, share content, and post links on social media channels. The organization then used this data to devise a plan for improving the site.

Knowing how visitors were already engaging with the site allowed the brand to make data-driven, goal-focused decisions.

With Tableau’s marketing analytics, Allrecipes found that out of all types of recipes, dessert typically generated more views and attracted more comments and photos. As a result, the brand opted to focus on this highly engaging niche, creating a separate video hub for dessert recipes.

To increase engagement on mobile devices, Allrecipes devised an A/B test that displayed the brand’s mobile site on all devices. Then the organization used the analytics to identify what drove interactions on mobile. The brand then used insights to improve the mobile site, including optimizing content and encouraging photo uploads.

Tableau’s data visualizations helped Allrecipes understand trends in its user community and respond to preferences more efficiently. Using these insights, the brand was able to promote integrations and features while gathering data for future product enhancements.

By using Tableau’s insights to process trends, Allrecipes was able to segment audiences for various recipe types, ultimately identifying millennial users’ interests and preferences. The brand was then able to create more content geared toward this growing user base—likely responding much more quickly than competitors could.

By tapping into real-time marketing analytics, Allrecipes was able to share popular recipe searches and trending content with its advertising partners during a recent holiday season. Advertisers could then create ads tailored to these interests, generating a better ROI and creating a more appealing experience for users.

What We Learned From These Marketing Analytics Case Studies

As these marketing analytics case studies show, data can tell you a lot about what your customers want—and where your organization succeeds or has room for improvement. Using insights from marketing analytics, a digital marketer can make data-driven decisions to cultivate customer loyalty, generate more revenue, and ultimately grow your business.

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Marketing Data: 5 mini case studies where marketers turned information into insight

This article was originally published in the MarketingSherpa email newsletter .

Finding opportunity in your marketing data often comes down to pattern recognition. Which one of these is not like the other? And why?

Here’s a simple example Flint McGlaughlin, CEO and Managing Director, MECLABS Institute shared in a coaching session about the Data Pattern Analysis (MECLABS is the parent organization of MarketingSherpa).

Do you notice the deviation in the pattern? The analytics platform calls it out in red, but here is a more obvious view.

What does that deviation from the pattern mean? And does it present an opportunity…or just a dead end?

Analyses and questions like these help marketers use their data to identify opportunities and drive results. To get you thinking of ways to find opportunities in your data, here are five mini case studies form your peers.

Mini Case Study #1: Looking beyond average customers led to 40% increase in website conversion for tourism company

“We examine our sales and marketing data constantly. But on occasion, there are some surprises so large that they make us rethink sacred cows in our business,” said Casey Halloran, Co-Founder & CEO, Costa Rican Vacations .

“I might be murdering this, but I think there's an old saying that ‘the problem with average data is that it's average.’ We found this out the hard way,” he said.

The company regularly examined customers’ average spend, average length of stay and average number of travelers. But one day the team decided to analyze the data in clusters and standard deviations. They found that the previous reliance on the average data was providing an inaccurate view of actual customer behavior.

“Our overuse of average wasn't telling us how dramatically different the outliers were from this average. In fact, there weren't that many real customers who looked like this ‘average client’ at all!” Halloran said.

This discovery inspired changes to the website’s search and product offering, catering to the outliers on the bell curve, versus previously grouping so much around what Halloran refers to as the “mythical average customer.” These website changes drove a 40% improvement in the site’s conversion rate.

For example, the slider for total budget was increased to a maximum of $20,000 in the site’s finder tool. 

Creative Sample #1: New homepage search

 “Sometimes you gotta slice the data differently and, ideally, by third parties who don't care about your old assumptions,” Halloran advised.

Mini Case Study #2: Segmenting CLTV helps technology research firm make smarter investments

Here’s another example of looking past average numbers and diving deeper into the data to get customer insights.

SoftwarePundit had calculated customer lifetime value (CLTV) to be around $200. This figure was calculated as an average of the entire customer base. Major inputs into the calculation were average order value (AOV), order frequency, gross margin, and churn.

“While digging into our churn data, we realized that we had a material percentage of one-time purchasers, and if a customer purchased a few times in the first few months, they basically never churned. Given that churn is a major input, we decided to segment and recalculate our CLTV,” said Bruce Hogan, CEO, SoftwarePundit.

The team discovered it had a material percentage of its customer base with a CLTV around $20 and a material percentage with a CLTV closer to $1,000.

“This insight had two significant impacts on our marketing,” Hogan said. First, it increased the amount of money they were able to spend acquiring customers, provided the team could determine the customers weren't one-time purchasers.

Second, they ran a series of lifecycle marketing experiments focused on getting one-time purchasers to repeat purchase at the early stages of their lifecycles. Through A/B testing, they found a few tactics that nudged shoppers to repeat purchase, and for a small fraction, this turned into a habit that increased CLTV.

For example, they sent emails with coupons offering a 10 to 20% discount on subsequent purchases. Of the tactics they tested, the coupons resulted in the biggest absolute increase in repeat shoppers. However, most shoppers who used the coupons did not become habitual buyers after the coupons were no longer sent.

Another effective tactic was product recommendations. The company’s data science team identified the products that were most often purchased in customers’ second and third orders. When first-time buyers returned to the site, they would get advertisements for these products, in addition to email promotions and social media targeting. This tactic had a higher ROI than the coupons but did not have as large of an overall impact.

“It's critical to segment CLTV. You're better off having an accurate average CLTV than not having a trustworthy figure. However, there's a good chance that this figure doesn't actually describe the CLTV of any individual segment in an accurate way. By segmenting your CLTV, you can unlock more dollars for acquisition marketing, and uncover experiments that will increase CLTV,” Hogan said.

Mini Case Study #3: Targeted SEO outreach helps reviews website garner 178 quality links in two months

Trond Nyland, Founder & CEO, Mattress Review set out to build strong SEO for his website by getting lots of links from quality websites using traditional SEO techniques like guest posting and blogger outreach.

Nyland took a data-driven approach to target this outreach. “We used Ahrefs to get data on which high-quality websites give out lots of backlinks. We figured these websites would be most likely to link to us and specifically focused on targeting them,” Nyland said.

His team focused 80% of its effort on these high-potential websites. After two months, they'd gotten about 170 links from the high-potential websites and just eight from all the other websites. That represents a more than 400% improvement in efficiency by targeting websites that, statistically, give out lots of links. “Long live data!” Nyland said.

The team was able to garner a Domain Rating of 51 in about four to five months using this targeted approach.

Mini Case Study #4: A/B testing helps increase Facebook ROI for auto detailing website

Question everything.

And then let the data show you the way.

“We have discovered that when running Facebook advertising campaigns it is more effective – in ROI terms – to duplicate the campaign and deploy additional capital rather than increasing ad spend on the existing campaign,” said James Ford, co-founder, AutoBead .

The car detailing website ran A/B tests across nine recent campaigns to validate this insight. The approach resulted in a 21% increase in revenue for the duplicated campaigns versus increasing the spend on existing ads.

Mini Case Study #5: Heatmapping helps nonprofit decrease homepage exit rate 3.5%

“While marketing data traditionally helps organizations increase their sales or visibility, in the nonprofit sector, marketing data is crucial to support direct services,” said Susan Ruel, Director of Marketing, Momentous Institute .

The social emotional health nonprofit offers a variety of services including a school, therapy and professional training. At the beginning of 2019, the Momentous Institute team noticed homepage click-through rates were decreasing as exit rates increased. “While I believe the data derived from the basic Google Analytics helps alert marketers to an issue, additional data is often needed to properly diagnose the issue and develop a logical solution,” Ruel said.

So the team used website heatmapping to try to better understand visitor behavior. The heatmapping data showed that homepage visitors were clicking the logo as if they believed they weren’t on the homepage and that the search icon on the left-hand side of the navigation was one of the most clicked-on buttons.

Creative Sample #2: Confetti heatmap of nonprofit’s website navigation

Based on the heatmapping data, the team determined that visitors were overwhelmed with the 13 click options and were not being served click options they expected to receive from the homepage.

The marketing team redesigned the homepage, creating a cleaner main navigation that narrowed down click options to eight and included links to all of the nonprofit’s services.

Creative Sample #3: Nonprofit website’s nav before redesign

Creative Sample #4: Nonprofit website’s nav after redesign

Once the redesign was launched, the homepage exit rate decreased by 3.5% and the amount of time visitors spent on the homepage decreased by 4.8%. “While traditionally the goal is to keep visitors for an extended time, I consider this decrease a victory as it shows visitors could find their desired information quicker,” Ruel said.

But most importantly, click-through traffic from the homepage to Momentous Institute’s direct services – Momentous School, therapeutic services and trainings – increased. “As a nonprofit marketer, it is incredibly rewarding utilizing marketing data to bring additional exposure to your organization’s direct services,” she said.

Related Resources

Data Pattern Analysis: Learn from a coaching session with Flint McGlaughlin

Data-Driven Marketing: 7 examples of using data as a force for the good

Get Your Free Simplified MECLABS Institute Data Pattern Analysis Tool to Discover Opportunities to Increase Conversion

What is Data? A discussion about getting value from your marketing analytics

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10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses.  We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

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Table of Contents

Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.

So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science  at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

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iii) Packing Optimization 

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering. 

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

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Let us explore data analytics case study examples in the entertainment indusry.

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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience. 

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics. 

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn 

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

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9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used. 

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato. 

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time. 

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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Scatterplot selection for dimensionality reduction in multidimensional data visualization

• Regular Paper
• Published: 23 August 2024

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• Kaya Okada   ORCID: orcid.org/0000-0002-2552-0163 1 &
• Takayuki Itoh 1  

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Dimensionality reduction (DR) techniques for multidimensional data serve as powerful tools for visualization and understanding of the structure of the data. Various DR methods have been developed to extract specific features of the data over the years. However, selection of the optimal DR method and fine-tuning parameters are still challenging, as these choices vary based on the characteristics of the dataset. Consequently, data scientists often rely on their experience or undertake extensive experimentation to identify the most suitable approach. This paper proposes a semi-automatic method for selecting appropriate DR techniques through scatterplot evaluation. Initially, our approach applies a range of DR methods to the given multidimensional data to compute two-dimensional values. Next, we generate scatterplots from the two-dimensional data and calculate scores reflecting the distribution and spatial relationships among the points. Scatterplots that provide insights achieve higher scores, enabling an efficient selection of DR methods based on their visualization. We demonstrate the effectiveness of the presented method through two case studies: The first one is an e-commerce review dataset, and the second focuses on a dataset derived from music feature extraction.

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Okada, K., Itoh, T. Scatterplot selection for dimensionality reduction in multidimensional data visualization. J Vis (2024). https://doi.org/10.1007/s12650-024-01025-6

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