Delays at airports happen. While thinking about my past trip to Norway and my upcoming trip to Manchester I thought about the delays people experience. What causes them? How many are there? To see interactive visuals about this topic, select the Medium Article button below to see the original article I wrote.
Data alone doesn’t necessarily tell a story, or better yet, isn’t appealing to look at unless you’re a numbers person. However, even then, data should draw you in, show a trend, reveal something about what you’re looking at on screen or paper. Enter data storytelling, but like data that supports, it too is not the end all. This blog is about data storytelling and how it can be persuasive. However, before we get into it, we need to ensure we’re using common terminology. The terms data analysis, data storytelling, data visualization, and infographics are sometimes used interchangeably. Let’s define each term for clarity.
Data analysis examines the data and interprets what the numbers are showing to help drive decisions. Essentially, it means looking at the number side of data, running prediction models, and determining what the data shows. During the analysis a pattern or trend is revealed as outcomes or findings. So data analysis shows “here are the numbers and this is what their telling us.”
Data storytelling takes it a step further, building on the data analysis. Based on the outcomes presented, a narrative is crafted to engage the audience through emotional connection. Through the narrative and emotional connection data storytelling employs one of two types of visualization methods to show the trend and influence decision-making; one is data visualization, the other is infographics.
Data visualization is a subset of data storytelling that supports the narrative. Tools like Datawrapper, Tableau, and PowerBi, allow a designer to create visually appealing charts, graphs, and maps based on the data they input. Therefore, without the narrative surrounding the visualization, it’s simply text and numbers on a chart, graph, or map. Sure you can gain insight and see patterns or trends, but there’s no anchor, no emotional tie to the data. During her webinar How to turn data into stories, Cole Nussbaumer Knaflic (Founder of Storytelling with Data), walked through taking disparate graphs and visualizing them into great graphs to support the story you are telling. Knaflic walked through a scenario step-by-step and showed how making deliberate changes to graphs they can be improved.
Infographics are yet another way to visualize and support the data story. The difference between data visualization and infographics is that the latter is usually presented in a poster format with images, text and narrative together. Essentially an infographic can stand alone and convey the story. Take a look at the COVID-19 Infographic below from the CDC showing how the virus is transmitted.
CDC Infographic – How COVID-19 Spreads
Now back to data storytelling. Pratibha Kumari, Chief Digital Officer at Data Thick, said in her LinkedIn article Data Storytelling & Data Visualization there are definitely benefits to data storytelling, which include better communication, improved decision-making, increased engagement, greater impact, and enhanced data storytelling skills. She goes on to say “By using data to tell stories, we can convey important insights and information in a way that resonates with people on an emotional level” (Kumari J., 2023).
The Microsoft Power BI article, What is data storytelling? discusses the benefits of data storytelling. Among them are adding value to your data and providing insights into complex information. Other benefits include interpreting complex information, highlighting essential key points to your audience, and putting a human touch to your data. Additionally, you offer value to your audience and build credibility as an industry and topic thought leader. The article goes on to discuss the importance of ensuring your data is valuable and staying objective about your data. That is, you want to look for both data that supports your theory and data that doesn’t support it. The goal is to add data that helps guide your narrative without bias.
Another important point in the Microsoft article is to ensure your data story points to where it can help support an action or bring about a change. During your story, you’re helping to unveil patterns and trends while providing context insight, streamlining the data processed by your audience, and ultimately gaining or improving your audience’s engagement. According to the article, data storytelling has three vital elements: building a narrative, using visuals to provide that “ah-ha” moment, and showing the supporting data to create an emotional connection and response with your audience.
“Sometime reality is too complex. Stories give it form.”
― Jean Luc Godard
In a short video for the Harvard Business Review, Telling Stories with Data in 3 Steps (Quick Study), Scott Berinato, author of Good Charts, discusses the power of telling a story using your data in this. Berinato breaks it down into three easy steps: Setup, Conflict, and Resolution. Much like traditional storytelling for books, he demonstrates how data has those three elements as well. He shows how the setup is the “before state” of the data. The conflict is “how the data changes,” and you can ask what is the cause? Finally, the resolution is the “after state” or what the change creates as the “new reality.” Berinato goes on to show how you break down a chart to show the different parts of the story. Each story chart should have descriptive titles based on its data and not be generic. In his example about the global housing market, he shows how to break the data down and tell where the story is.
It’s one thing to discuss all the key components of what makes data storytelling persuasive, however its another to show examples. Below are three very different examples of how data can frame a narrative and help tell the story. The story doesn’t always drive a call to action, but can help bring awareness, level set preconceived ideas, or bring a dose of reality to a particular topic.
In his TED Talk, The Best Stats You’ve Ever Seen, Hans Rosling describes data and explains the importance of data through data storytelling. One point he makes is that looking at the “average” data doesn’t help see what the data is honestly telling. His use of data storytelling was powerful and at times humorous, but importantly, it drove home the the importance of data and the ability to visualize and make better decisions. At one point he used South Africa as an example. In the example, he shows how even within Africa, the data can be disparate between neighboring states such as Uganda, Niger, and South Africa. When looking at the quintile sets, the data tells a different story. Therefore, we must look at “raw,” not aggregated data, when telling a data story. This helps to keep your narrative unbiased. Rosling finishes by saying that access to all data worldwide should be consumable for everyone to gather, animate, and determine the story being told by the data.
This is an interesting example of telling a story using data. In the article How Many High School Stars Make it in the NBA?, the authors wanted to find out “are these top 100 recruit lists any indication of making it to the NBA, let alone becoming a star?” (Thomas & Samora, 2019). Through the use of data they were able to determine 92% of those ranked and drafted continued their careers in the NBA. On the flip side, looking at the unranked players, 73% of them continued their careers with only seven making it to Superstar Status, like Stephen Curry.
This example is a little different in that there is very little narrative text until you dive deeper in the section below the fold labeled What This Data Reveals. From the start it is clear the data itself is the story. The data visualization is interactive and allows us to view more or less data, depending on what we the audience want to see. As we look at the data, we see the total number of deaths in the upper left. However, what’s interesting is the stolen years number shown in the graph. This number is a cumulative estimate based on how long each of the victims may have lived had they not been shot (see image). The data lines themselves are interactive and show the ethnicity, age, and gender of each victim. Additionally, the data shows when, where, who, and how the victim was killed, and an approximation of how long they might have lived (see image).
In conclusion, data storytelling is a powerful tool for persuasion because it goes beyond mere data presentation to create a narrative that resonates emotionally with audiences. By crafting a story around data analysis, it helps to engage people, making complex information more relatable and memorable. This approach not only highlights essential trends and patterns, but also provides context that aids in decision-making. Through the use of data visualization and infographics, data storytelling adds a human touch to data, making it more accessible and impactful. By employing key elements of traditional storytelling, such as setup, conflict, and resolution, data storytelling maintains objectivity while creating an emotional connection with the audience. This method can enhance communication, improve engagement, and ultimately influence decision-making by providing insights that are both informative and emotionally engaging.
Berinato, S. (2019, October 30). Telling Stories with Data in 3 Steps (Quick Study). https://www.youtube.com/watch?v=r5_34YnCmMY
Cote, C. (2021, November 23). Data storytelling: How to tell a story with data. Havard Business School Online – Business Insights Blog. https://online.hbs.edu/blog/post/data-storytelling
Dykes, B. (2024, July 2). The future of data storytelling is augmented, not automated. Forbes. https://www.forbes.com/sites/brentdykes/2024/02/27/the-future-of-data-storytelling-is-augmented-not-automated/
Kesari, G. (2024, January 17). The enduring power of data storytelling in the generative AI era: Ganes Kesari. MIT Sloan Management Review. https://sloanreview.mit.edu/article/the-enduring-power-of-data-storytelling-in-the-generative-ai-era/
Kumari J., P. (2023, May 3). Data Storytelling & Data Visualization. LinkedIn. https://www.linkedin.com/pulse/data-storytelling-visualization-pratibha-kumari-jha/
Mattison, R. (2023, March 14). Data storytelling: How to tell a great story with data. ThoughtSpot. https://www.thoughtspot.com/data-trends/best-practices/data-storytelling
Nussbaumer, C. (2023, October 25). How to turn data into stories. Storytelling with Ddata. https://www.youtube.com/watch?v=Hfx1X9WSGYQ
Periscopic. (2019). United States Gun Death Data Visualization by periscopic. U.S. Gun Deaths. https://guns.periscopic.com/?year=2018
Rosling, H. (2006, February). The best stats you’ve ever seen. Hans Rosling: The best stats you’ve ever seen | TED Talk. https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seen?subtitle=en
Team, P. B. (n.d.). What is data storytelling?. What is Data Storytelling and Data Storytelling Examples | Microsoft Power BI. https://powerbi.microsoft.com/en-in/data-storytelling/
Thomas, A., & Samora, R. (2019, February). How many high school stars make it in the NBA?. The Pudding. https://pudding.cool/2019/03/hype/
In the world of data visualization, maps are not just tools; they are storytellers that transform complex datasets into compelling narratives. Among the various types of maps, choropleth maps, symbol maps, and locator maps each have unique characteristics and applications. Knowing when and how to use these maps can significantly enhance the clarity and impact of your data presentations. Here’s a casual dive into how these map types can be your best friends in visual storytelling.
For this blog I used three different datasets to tell three different stories; US airport delays in 2023, EV shares by country in 2023, and EV Drive Distances from Kulpsville. Additionally, I used the Datawrapper tool to visualize each of the datasets. Let’s look a little closer.
First up is the choropleth map. These are the go-to for thematic mapping. They use color gradients to represent data values across predefined regions, such as states or countries. This makes them perfect for displaying data like voting results or unemployment rates. The key is to use these maps for data that is aggregated over geographic areas (Monmonier, 1991). However, beware of their limitations: larger regions can dominate the visual field, skewing perception. To counter this, use rates or per capita figures instead of absolute numbers (Datawrapper, 2021). As I learned with Datawrapper, it’s crucial to know the story you want to tell and ensure your dataset is clean before selecting this map type.
For this story, I wanted to answer the question Which Airport in 2023 had the most weather delays? Before I could start for any of the maps, the process was the same; clean the data. That is, remove duplicates, unnecessary columns and/or rows that didn’t help tell the story. This dataset was challenging because there were over 10,000 rows of data just for the year 2023. The data was broken down by month, airport, airport name, and so on. Additionally, I had over 304 rows which were not recognized by Datawrapper. This meant going back into the dataset and filtering until I identified those 304 rows. It turns out it was airports outside the contiguous 48 states. Once this was fixed I was able to produce the Choropleth map below.
Symbol maps, or proportional symbol maps, are all about pinpoint precision. They use symbols of varying sizes to represent data values at specific locations, making them ideal for showing quantitative data like city populations or earthquake magnitudes. Unlike choropleth maps, symbol maps can display two variables at once—size and color—offering a richer data story (Cairo, 2016). These maps shine when your data is tied to specific points rather than areas, helping to highlight patterns or outliers with ease (Datawrapper, 2021). Remember, refining the data until your story is clear but not misinterpreted is key.
Next up was my dataset to help show which country had the largest percentage of EV shares across the world. This dataset was challenging too. There were two factors adding to the challenge. The first issue I ran into, after cleaning the data was that Datawrapper threw errors. Datawrapper couldn't use the percentage of new electric cars raw data because some were in decimal value and other values were whole numbers. For example, Chile and South Africa were in decimal value, the rest of the countries were whole numbers. This caused countries like Norway, whose value was 93 to be 9300%. Once I converted the whole numbers to decimals the data provided more accurate percentages in Datawrapper. Another cleanup I had to do was to obtain and input latitudes and longitudes for each of the countries for more accuracy. The outcome of this map can be seen below.
Locator maps are your navigational aids. They provide context by showing the location of something within a broader geographical setting. These maps are typically static and serve as a reference point, helping viewers understand where an event occurred or where a specific place is located. They are often used alongside other map types to give context, such as placing a city within a country (Monmonier, 1991). When the goal is orientation rather than data complexity, locator maps are your best bet.
My locator map in Datawrapper used a dataset that combined two pieces of data merged into one. From there I went to https://www.511pa.com/#:Alerts and manually looked up distances from my starting point. Using each of the reported EV range at 70mph I looked up the distances and randomly plugged them into the end location field on my dataset. I thought it would be fun to see how far each of the reported EV's could get from the start location in Kulpsville, PA. In Datawrapper I also included the car details (year, make & model), tire size, and distance in miles inside the tooltip for each locator. This allows the audience to hover over and see the information so there is a reference point.
Choosing the right map type is crucial for effective data storytelling. Choropleth maps are best for regional data, symbol maps excel in representing data at specific locations, and locator maps provide essential geographical context. By understanding the strengths and limitations of each map type, you can craft visual stories that are both informative and engaging. And remember, with tools like Datawrapper, the process becomes all the more intuitive and rewarding.
Additionally, there are a number of tools for data visualization, but especially data mapping. For this blog I used Datawrapper. However, for future projects I plan to explore ARCGIS with my student plan for $100 annually. I will also plan to see about how to use Flourish as it offers more interactivity for my audience.
Monmonier, M. (1991). *How to Lie with Maps*. University of Chicago Press.
Cairo, A. (2016). *The Truthful Art: Data, Charts, and Maps for Communication*. New Riders.
Datawrapper. (2021). *How to show symbols on choropleth maps*. Retrieved from https://academy.datawrapper.de/article/334-how-to-show-symbols-on-choropleth-maps
Datawrapper. (2023). *From news media to Datawrapper: Six things I've learned in my first year*. Retrieved from https://blog.datawrapper.de/media-to-datawrapper-first-year/
Datawrapper Academy. (n.d.). *A collection of Datawrapper pro tips*. Retrieved from https://academy.datawrapper.de/article/256-a-collection-of-datawrapper-pro-tips
This week for our Data Visualization class we were asked to reflect and collect data on ourselves. Why? You ask. Well, for this first we have to understand what data is. Data is well, “anything that can be mapped, counted, or measured” according to Giorgia Lupi and Stefanie Posavec. You see, Lupi and Posavec, are two award-winning designers, who one day decided to use analog means to capture all the data they could…about themselves. They were curious what they could learn about themselves. So, each agreed to observe, collect, and draw their experiences on a postcard. Each week, the completed postcard was mailed. Giorgia would send her postcard to Stephanie in London. Likewise, Stephanie would reciprocate and mail hers to Giorgia in NYC. They called their analog project Dear Data. Lupi shares that “we can use data to become more humane and to connect with ourselves and others at a deeper level.”
With this in mind, I first needed to determine what I wanted to collect data about in my life. This was a little hard because my initial thought holding me back was “who cares about your daily data points? No one really wants to know because you’re not an internet influencer, celebrity, or royalty.” Once I got past this, I thought about what has been happening with me over the past month. There was a lot and one thing I was curious about was how distracting is my phone? What is the most distracting phone app? Do I have an addiction? Using this as my basis for my ultimate reason for collecting and mapping out my data. I began with the following question: “What are the most common apps I use on my phone?”
Over the next week, I paid attention to my app usage and jotted it down in my Remarkable tablet. This typically went like this: open the app, write down the time I started the app, close the app, write down the time I closed the app. Okay, this really only occurred the first day because then I remembered to work smarter not harder. You see I have an iPhone and all these data points are collected in Settings>Screen Time. Therefore, for the rest of the week, I would review my screen time usage the next morning so that I was capturing the full day before.
At the end of the week, I analyzed the data I had collected. During the week I collected the data, I could see a trend start to emerge. When I was bored at work, I would tend to go on Instagram or answer a text message. The next step was to see if my assumptions about the data was true. It was time to visualize the data now!
After analyzing the data, my next step was to figure out how I wanted to represent the data. While apps have their own icon, I wanted to ensure it reflected what I saw in the data and to provide a legend. This process took me longer than expected. After contemplating it I came up with simple icons to represent the apps (based on their digital versions) and simple shapes to represent four distinct times of day. Below is the outcome of my data mentioned earlier and the legend/icons I described above.
Reflecting on this project, I believe I did learn that I’m more distracted with my phone throughout the day. My initial thought is that I need more detailed data to make any behavioral changes. However, the data I did collect have helped me to become more self-aware of my phone-to-app behavior. Because of this, I will be more conscious when I go to use my phone and ask myself: “Do you really need to use that app?” “Will the world stop if you don’t use the app?” Ultimately, I believe this sort of data self-reflection exercise can be therapeutic in this age of everything digital.
The project. Dear Data. (n.d.). https://www.dear-data.com/theproject
Visualization has existed for thousands of years, but how and when we use data to tell our visual story is still in its infancy. While researching about data types and what they mean, I stumbled upon a Harvard Business Review article. In Visualizations That Really Work, author, Scott Berinato provides a reminder before he begins “Know what message you’re trying to communicate before you get down in the weeds.” (Berinato, 2016)
Okay, let’s say you follow Berinato’s advice and you know who your audience is. You think, “Great! I can jump right in and create a visual that will really show the audience how those data points all relate. It’ll be easy.” Well, not so fast. Although the data visualization field is relatively new, there are some basic parameters to creating meaningful data visuals. It begins with a couple of simple questions:
The two questions above form a simple 2×2 grid which shows your data visualization can fall into one of four quadrants; conceptual-declarative, conceptual-exploratory, data-driven-declarative, or data-driven-exploratory (see image below). With this defined, let’s look a little closer at each of the quadrants to understand them better.
Image Credit: Tony DeRose
First up is conceptual-declarative or idea illustration. This type of visualization is used to convey complex processes into a more simpler concept so the audience can comprehend the relationships within the concept.
In the example below Nir Eyal’s Hooked Model conveys the idea of how good companies ultimately create a never-ending loop to keep you coming back for more of their products, services, etc. Triggers are given (either internally or externally) which cause the consumer to take action. This action has an award associated with it which then gets the consumer to invest and want more or to keep returning. While the words above describe what the Hooked Model is, simply looking at the visual below tells the whole story more quickly than words can. Idea illustration per IBM’s What is Data Visualization goes on to say “It is commonly used in learning settings, such as tutorials, certification courses, centers of excellence…” (IBM, 2021).
Image Credit: Tony DeRose
Next up is conceptual-exploratory or idea generation. This is a “think tank” type visualization. This could start out with a simple question someone is trying to answer or something that involves an entire team. The idea on the back of a napkin, collaborative whiteboard iterations by a team are examples of the type of idea generation. The goal is to solve a problem, answer a question, come up with a new innovation. One of the most common of these illustrations is the mind map. Below in my mind map, I show how humans and AI together can be used. On the left side I show the current and future state of AI advancement. On the right side, I show where AI can be applied thus benefiting more efficient use of time and resources.
Image Credit: Tony DeRose
Third is data-driven-declarative or everyday dataviz. This type of visualization is more of your day-to-day charts and graphs. Think sales performance data or customer retention quota data. These types of visualization can typically be found in reports presented to upper management where time is of the essence. Therefore, the data storyteller has the job of providing context and affirming the data in an easy to digest chart or graph. The Coronavirus pandemic is an excellent example of data-driven-declarative visualization. The world was in crisis and experts needed to assure and affirm what was happening with regard to number of cases, mortality rate, affected regions, etc. To this day Our World in Data maintains an ongoing slue of data about COVID-19. Below is an example of a visualization for case fatality rate between September 2020 and June 2024 among the United States, France, Canada, Germany, India, and the United Kingdom.
Image Credit: Our World Data website accessed 21-Jul-2024
The last type is data-driven-exploratory or visual discovery. This is probably the hardest quadrant due to the complexity. This is where the data is big data and can even be dynamic. If you’re answer to the two original questions lead to this quadrant, then you are doing more data analysis, spotting trends. You’re probably in business intelligence or a data scientist. Here you can either use your visuals to confirm or explore a theory. Using the Our World in Data site again, I chose to look at the monthly CO2 emissions commercial airliners emit. This large dataset would look dull and not convey well in table format (1st image) nor what you be able to see the trend that month over month Oceania countries emit the most CO2 (2nd image).
Image Credit: Our World Data website accessed 21-Jul-2024
Berinato, S. (2020, September 14). Visualizations that really work. Harvard Business Review. https://hbr.org/2016/06/visualizations-that-really-work
Eyal, N. (2023, November 2). The hooked model: How to manufacture desire in 4 steps. Nir and Far. https://www.nirandfar.com/how-to-manufacture-desire/
Mathieu, E., Ritchie, H., Rodés-Guirao, L., Appel, C., Giattino, C., Hasell, J., Macdonald, B., Dattani, S., Beltekian, D., Ortiz-Ospina, E., & Roser, M. (2020, March 5). Coronavirus (COVID-19) cases. Our World in Data. https://ourworldindata.org/covid-cases
Mullins, D. (2020, February 5). The 4 types of visualisation and the role they play in market research. B2B International. https://www.b2binternational.com/2017/08/11/4-types-visualisations-role-play-market-research-process/
Ritchie, H., Rosado, P., & Roser, M. (2023, December 28). CO₂ and greenhouse gas emissions. Our World in Data. https://ourworldindata.org/co2-and-greenhouse-gas-emissions
What is data visualization?. IBM. (2021, September 28). https://www.ibm.com/topics/data-visualization
What is Data Visualization and is it new? Data Visualization is a visual representation of large amounts of data. This is usually done in the form of an easy-to-understand graphic. Therefore, a large amount of data can be easily consumed and provide greater insight into complex data points. A good data visualization can help identify trends, or see patterns and correlations otherwise lost in columns of data. Visuals can be quantitative or qualitative in nature. However, as to whether or not data visualization is new, “graphic representation of quantitative information has deep roots.” (Friendly, 2006) Because we live in a world of advanced computers we tend to drift toward the thinking that data visualization is new. In fact, according to Heavy.AI, Charles Minard is credited with creating “a groundbreaking statistical graphic in 1869.” (Heavy.AI)
Technology for data visualization has come in many forms over the years. It’s not limited to only the use of computers. It can also be advancements in science, mathematics, or even typography. Advances in these areas helped to advance the field of data visualization. However, when computers arrived in the 1950s and 1960s, the proliferation of data was available at such a rate. Then, when personal computers arrived in the 1980s, the advancement of computer models made it easier to represent and present the data visually. With the advent of new technology comes great responsibility. Now, let’s look at some pros and cons regarding technology advancements.
Think about your manager asking you to present the new use of AI voice-over in developing your courses. Your manager wants you to show the Leadership Team (LT) how using AI voice-over is more efficient than traditional methods. You have a week to prepare. You’ve already been collecting data about how AI voice-over provides an efficiency increase of 45% comparatively. Because you have the data in an Excel spreadsheet, technological advancements can assist you in getting a quick comparison chart. Great, but remember you’re trying to convince LT to approve the budget for this new technology. A simple comparison chart doesn’t tell them everything. It would be best if you built a story. While tools like Excel only get you so far. You still need to create an appealing visual design. According to Tableau, “Sometimes people can accidentally (or even purposefully) misrepresent data.” (Tableau, 2024) Therefore, it’s essential to not rely solely on technology.
Good Charts should be simple to understand. You should be able to digest and see the information the chart conveys quickly. Is it a comparison of data, or is it showing a trend? Knowing which chart to use when another piece of a chart is good. Using a pie chart is not helpful when showing a trend or pattern. Another piece of the good chart puzzle is having a good legend. Let the audience know what the data is and what the critical pieces of data are.
I’m visual, so accounting or statistical data alone isn’t enough. I need to see correlations, patterns, etc. Additionally, I’m a gamer. How does that fit into data visualization, you ask? Well, gaming (computer, console, etc.) has prominent storylines and minigames built into them. Take, for example, one of my favorite games, Diablo IV. It would be boring if you were to play this game only using dice, a tally sheet, and story cards (think old-school dungeons and dragons). Enter in advancements in computer graphics and you have player maps of the places you’re visiting, you can design your character based on visual queues of skills and attributes. This visual representation of computer databases allows me to build the best Druid or Sorcerer class character to defeat the monsters at the next level!
In my other favorite game, Star Citizen, a Heads-up Display (HUD) and Mobiglass charts provide the player with critical game-making data to act on.
Data visualization can tell a story that text and numbers alone cannot. Today, more than ever, there is access to many data points. Charts and graphs, while good, are only as good as what they were meant to convey. Before you begin visualizing data, a crucial piece of the data visualization journey is to know the story you want to tell. Then, with that story in hand, collect, and analyze the data. Understand what the data is conveying and which visualization best supports it. Then, be selective when choosing one visualization type over another. Finally, ensure you remain objective to allow your audience to digest and see what the data says.
Data Visualization – a complete introduction. HEAVY.AI. (n.d.). https://tinyurl.com/DataViz-HeavyAI
Hazzard, E. (2024, February 25). Data Visualization in games. GameAnalytics. https://gameanalytics.com/blog/data-visualization-games/
Sridharan, M. A., & Friendly, M. (2024, January 20). History of data visualization. Think Insights. https://thinkinsights.net/data/data-visualization-history/
Tov-Ly, G. (2023, October 26). Diablo IV’s Amazing launch: Data analysis by Overwolf. Medium. https://tinyurl.com/DiabloIV-DataAnalysis
Wang, L., Wang, G., & Alexander, C. A. (2015, July 22). Big Data and visualization: Methods, challenges and technology progress. Digital Technologies. https://pubs.sciepub.com/dt/1/1/7/
What are the advantages and disadvantages of data visualization?. Tableau. (n.d.). https://tinyurl.com/Tableau-DataViz-Adv-vs-Disadv