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The Power of Data Visualization

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Example Of Essay On Data Visualization

Type of paper: Essay

Topic: The Reader , Science , Understanding , Information , Virtual Reality , Presentation , Thinking , Statistics

Words: 1100

Published: 03/06/2020

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Data visualization is the arrangement of information, statistics or other quantifiable data in a visual manner that educates and informs the reader. Data visualization aims to bring across the meaning of narratives or presentations that are burdened with abstract or schematic information. It should clearly and effectively communicate the key aspects and provide insight through graphical methods such as: charts; bar or pie, graphs which are good for structure, relationships, plots, maps, images, animation and many others. These data visualizations, often called information graphics should effectively encapsulate the idea being conveyed, and also give perspective to the information or storyline. /> Data visualization usually covers both information such as statistics or quantitative data, and scientific conceptions. Types of data visualization include scientific visualization, information visualization, and information graphics also called ‘infographics’. Scientific visualization involves presenting data that has essentially correspondence of a physical or geometric nature. Which generally refers to data that has close connections to real world objectives with realistic properties. The goal of scientific visualization is to combine visual information with data that has less accessibility directly, in an effort to generate images that give greater meaning and interpretation. Scientific visualization is often used in the medical field to generate MRI scans, or the travel industry to generate air flow over an airplane wing. Information visualization focuses on theoretical data, which provides the means for the transformation and presentation of data in a way that enables and encourages human interaction. Which means information visualization fosters the analysis of data by means of exploration instead of only reasoning abilities. Therefore allowing users, viewers or readers to develop their understanding of the structures and connections illustrated. Information Visualization is being used extensively by people in the fields of business, media, and engineering to aid in the analysis and understanding of information. It is used broadly in many other fields like economics and finance. It utilizes mostly charts and graphics depicting statistical data, and also uses visual images or comparisons to represent different sets of data. Infographic or information graphic is a type or sub-category of visualization that incorporates a variety of statistics and visuals along with a narrative, and sometimes even persuasive elements. Infographics utilizes information, data and knowledge to present complicated information clearly and quickly, such as maps, signs, teaching material, journalism and technical writing. Information graphics brings across the key points of mathematicians, statisticians, and computer scientists in a simple comprehensive way to the reader. Today it is used vastly by the media, in manuals and road sins, in published works, and has become popular on the World Wide Web. Communicating and illustrating information that would otherwise be cumbersome in text form, thereby creating shorthand for everyday concepts, such as the road signs used daily by motorist. Infographics can be seen most commonly in newspapers depicting the weather, in site plans, and some books but mostly in children’s books. A good visualization of data should be effective in allowing the viewer to easily interpret the information. It should also be accurate by having sufficient quantitative data allowing the reader to make correct evaluations. The choice of visualization should allow efficiency in presentation by minimizing any redundant data, unwieldy text, and unnecessary charts. Creating a visualization of data also takes into consideration the aesthetics and adoptability. The design or arrangement of the data should not be offensive to the viewer, and the design should be able to serve and adjust to multiple needs. Data visualization is very important as its use dates as far back as the 2nd century, when Egyptians used tables to organize information on astrology to be used as a navigational tool. A table is primarily a textual illustration of data that uses visual attributes of rows and columns to arrange the data. Tables, diagrams and graphs fall into the data representation class of charts. Through visualization people are better able to see things that were not obvious before, and even with massive amounts of data patterns, trends, and key/focal points can be identified easily and quickly. Visualization continues to be of great importance as it allows the sharing of ideas to be conveyed in a common but effective manner. The factors in a situation is simplified and clarified instantaneously with the aid of data visualization, allowing individuals to share their perspective on various matters outside the scope and specialized areas of many learned individuals. There are many graphical presentations that people can use to bring understanding to their presentations, therefore, choosing the graphics that best depicts a person’s viewpoint has some technique to it. Person’s that decide to use visualization should ensure they have a precise understanding of the data they wish to visualize. Then determine what aspects of the information they are presenting and the complex areas that will need to be visualized. When designing their presentation they need to have knowledge of their audience, knowing what will appeal to that demographic. Finally, they should use simple forms of visuals that will best convey their information to their audience. In this technological era, where people read a lot less than past eras, the use of visual aids to bring across ideas in an easily comprehendible manner is imperative. Visualization of data is becoming increasingly important to the exploration of patterns and trends in businesses, especially with the growth in e-commerce. Visualization of data lets individuals and businesses quantify vast amounts of data, quickly and efficiently to bring insights needed in decision making.

Few, Stephen. (2007). Data Visualization Past, Present, and Future. COGNOS Innovative Center. Retrieved from http://www.perceptualedge.com/articles/Whitepapers/Data_Visualization.pdf Johnston, John. (n.d.). What is an Infographic? Customer Magnetism. Digital Marketing Agency. Retrieved from http://www.customermagnetism.com/infographics/what-is-an- infographic/ Information Visualization and Presentation. (2014). UC Berkeley School of Information. Retrieved from http://www.ischool.berkeley.edu/courses/i247 Stasko, John. (2013). Information Visualization. Information Visualization. Retrieved from http://www.cc.gatech.edu/~stasko/7450/ Ward, Matthew. (n.d.). Overview of Data Visualization. (n.p.). Retrieved from http://web.cs.wpi.edu/~matt/courses/cs563/talks/datavis.html Zoss, Angela. (2013). Introduction to Data Visualization. Dukes University Libraries. Retrieved from http://guides.library.duke.edu/content.php?pid=355157&sid=2904817

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What Is Data Visualization: Brief Theory, Useful Tips and Awesome Examples

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By Al Boicheva

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Updated: June 23, 2022

To create data visualization in order to present your data is no longer just a nice to have skill. Now, the skill to effectively sort and communicate your data through charts is a must-have for any business in any field that deals with data. Data visualization helps businesses quickly make sense of complex data and start making decisions based on that data. This is why today we’ll talk about what is data visualization. We’ll discuss how and why does it work, what type of charts to choose in what cases, how to create effective charts, and, of course, end with beautiful examples.

So let’s jump right in. As usual, don’t hesitate to fast-travel to a particular section of your interest.

Article overview: 1. What Does Data Visualization Mean? 2. How Does it Work? 3. When to Use it? 4. Why Use it? 5. Types of Data Visualization 6. Data Visualization VS Infographics: 5 Main Differences 7. How to Create Effective Data Visualization?: 5 Useful Tips 8. Examples of Data Visualization

1. What is Data Visualization?

Data Visualization is a graphic representation of data that aims to communicate numerous heavy data in an efficient way that is easier to grasp and understand . In a way, data visualization is the mapping between the original data and graphic elements that determine how the attributes of these elements vary. The visualization is usually made by the use of charts, lines, or points, bars, and maps.

• Data Viz is a branch of Descriptive statistics but it requires both design, computer, and statistical skills.
• Aesthetics and functionality go hand in hand to communicate complex statistics in an intuitive way.
• Data Viz tools and technologies are essential for making data-driven decisions.
• It’s a fine balance between form and functionality.
• Every STEM field benefits from understanding data.

2. How Does it Work?

If we can see it, our brains can internalize and reflect on it. This is why it’s much easier and more effective to make sense of a chart and see trends than to read a massive document that would take a lot of time and focus to rationalize. We wouldn’t want to repeat the cliche that humans are visual creatures, but it’s a fact that visualization is much more effective and comprehensive.

In a way, we can say that data Viz is a form of storytelling with the purpose to help us make decisions based on data. Such data might include:

• Tracking sales
• Identifying trends
• Identifying changes
• Monitoring goals
• Monitoring results
• Combining data

3. When to Use it?

Data visualization is useful for companies that deal with lots of data on a daily basis. It’s essential to have your data and trends instantly visible. Better than scrolling through colossal spreadsheets. When the trends stand out instantly this also helps your clients or viewers to understand them instead of getting lost in the clutter of numbers.

With that being said, Data Viz is suitable for:

• Annual reports
• Presentations
• Social media micronarratives
• Informational brochures
• Trend-trafficking
• Candlestick chart for financial analysis
• Determining routes

Common cases when data visualization sees use are in sales, marketing, healthcare, science, finances, politics, and logistics.

4. Why Use it?

Short answer: decision making. Data Visualization comes with the undeniable benefits of quickly recognizing patterns and interpret data. More specifically, it is an invaluable tool to determine the following cases.

• Identifying correlations between the relationship of variables.
• Getting market insights about audience behavior.
• Determining value vs risk metrics.
• Monitoring trends over time.
• Examining rates and potential through frequency.
• Ability to react to changes.

5. Types of Data Visualization

As you probably already guessed, Data Viz is much more than simple pie charts and graphs styled in a visually appealing way. The methods that this branch uses to visualize statistics include a series of effective types.

Map visualization is a great method to analyze and display geographically related information and present it accurately via maps. This intuitive way aims to distribute data by region. Since maps can be 2D or 3D, static or dynamic, there are numerous combinations one can use in order to create a Data Viz map.

COVID-19 Spending Data Visualization POGO by George Railean

The most common ones, however, are:

• Regional Maps: Classic maps that display countries, cities, or districts. They often represent data in different colors for different characteristics in each region.
• Line Maps: They usually contain space and time and are ideal for routing, especially for driving or taxi routes in the area due to their analysis of specific scenes.
• Point Maps: These maps distribute data of geographic information. They are ideal for businesses to pinpoint the exact locations of their buildings in a region.
• Heat Maps: They indicate the weight of a geographical area based on a specific property. For example, a heat map may distribute the saturation of infected people by area.

Charts present data in the form of graphs, diagrams, and tables. They are often confused with graphs since graphs are indeed a subcategory of charts. However, there is a small difference: graphs show the mathematical relationship between groups of data and is only one of the chart methods to represent data.

Infographic Data Visualization by Madeline VanRemmen

With that out of the way, let’s talk about the most basic types of charts in data visualization.

They use a series of bars that illustrate data development.  They are ideal for lighter data and follow trends of no more than three variables or else, the bars become cluttered and hard to comprehend. Ideal for year-on-year comparisons and monthly breakdowns.

These familiar circular graphs divide data into portions. The bigger the slice, the bigger the portion. They are ideal for depicting sections of a whole and their sum must always be 100%. Avoid pie charts when you need to show data development over time or lack a value for any of the portions. Doughnut charts have the same use as pie charts.

They use a line or more than one lines that show development over time. It allows tracking multiple variables at the same time. A great example is tracking product sales by a brand over the years. Area charts have the same use as line charts.

Scatter Plot

These charts allow you to see patterns through data visualization. They have an x-axis and a y-axis for two different values. For example, if your x-axis contains information about car prices while the y-axis is about salaries, the positive or negative relationship will tell you about what a person’s car tells about their salary.

Unlike the charts we just discussed, tables show data in almost a raw format. They are ideal when your data is hard to present visually and aim to show specific numerical data that one is supposed to read rather than visualize.

Data Visualisation | To bee or not to bee by Aishwarya Anand Singh

For example, charts are perfect to display data about a particular illness over a time period in a particular area, but a table comes to better use when you also need to understand specifics such as causes, outcomes, relapses, a period of treatment, and so on.

6. Data Visualization VS Infographics

5 main differences.

They are not that different as both visually represent data. It is often you search for infographics and find images titled Data Visualization and the other way around. In many cases, however, these titles aren’t misleading. Why is that?

• Data visualization is made of just one element. It could be a map, a chart, or a table. Infographics , on the other hand, often include multiple Data Viz elements.
• Unlike data visualizations that can be simple or extremely complex and heavy, infographics are simple and target wider audiences. The latter is usually comprehensible even to people outside of the field of research the infographic represents.
• Interestingly enough, data Viz doesn’t offer narratives and conclusions, it’s a tool and basis for reaching those. While infographics, in most cases offer a story and a narrative. For example, a data visualization map may have the title “Air pollution saturation by region”, while an infographic with the same data would go “Areas A and B are the most polluted in Country C”.
• Data visualizations can be made in Excel or use other tools that automatically generate the design unless they are set for presentation or publishing. The aesthetics of infographics , however, are of great importance and the designs must be appealing to wider audiences.
• In terms of interaction, data visualizations often offer interactive charts, especially in an online form. Infographics, on the other hand, rarely have interaction and are usually static images.

While on topic, you could also be interested to check out these 50 engaging infographic examples that make complex data look great.

7. Tips to Create Effective Data Visualization

The process is naturally similar to creating Infographics and it revolves around understanding your data and audience. To be more precise, these are the main steps and best practices when it comes to preparing an effective visualization of data for your viewers to instantly understand.

1. Do Your Homework

Preparation is half the work already done. Before you even start visualizing data, you have to be sure you understand that data to the last detail.

Knowing your audience is undeniable another important part of the homework, as different audiences process information differently. Who are the people you’re visualizing data for? How do they process visual data? Is it enough to hand them a single pie chart or you’ll need a more in-depth visual report?

The third part of preparing is to determine exactly what you want to communicate to the audience. What kind of information you’re visualizing and does it reflect your goal?

And last, think about how much data you’ll be working with and take it into account.

2. Choose the Right Type of Chart

In a previous section, we listed the basic chart types that find use in data visualization. To determine best which one suits your work, there are a few things to consider.

• How many variables will you have in a chart?
• How many items will you place for each of your variables?
• What will be the relation between the values (time period, comparison, distributions, etc.)

With that being said, a pie chart would be ideal if you need to present what portions of a whole takes each item. For example, you can use it to showcase what percent of the market share takes a particular product. Pie charts, however, are unsuitable for distributions, comparisons, and following trends through time periods. Bar graphs, scatter plots,s and line graphs are much more effective in those cases.

Another example is how to use time in your charts. It’s way more accurate to use a horizontal axis because time should run left to right. It’s way more visually intuitive.

3. Sort your Data

Start with removing every piece of data that does not add value and is basically excess for the chart. Sometimes, you have to work with a huge amount of data which will inevitably make your chart pretty complex and hard to read. Don’t hesitate to split your information into two or more charts. If that won’t work for you, you could use highlights or change the entire type of chart with something that would fit better.

Tip: When you use bar charts and columns for comparison, sort the information in an ascending or a descending way by value instead of alphabetical order.

4. Use Colors to Your Advantage

In every form of visualization, colors are your best friend and the most powerful tool. They create contrasts, accents, and emphasis and lead the eye intuitively. Even here, color theory is important.

When you design your chart, make sure you don’t use more than 5 or 6 colors. Anything more than that will make your graph overwhelming and hard to read for your viewers. However, color intensity is a different thing that you can use to your advantage. For example, when you compare the same concept in different periods of time, you could sort your data from the lightest shade of your chosen color to its darker one. It creates a strong visual progression, proper to your timeline.

Things to consider when you choose colors:

• Different colors for different categories.
• A consistent color palette for all charts in a series that you will later compare.
• It’s appropriate to use color blind-friendly palettes.

5. Get Inspired

Always put your inspiration to work when you want to be at the top of your game. Look through examples, infographics, and other people’s work and see what works best for each type of data you need to implement.

This Twitter account Data Visualization Society is a great way to start. In the meantime, we’ll also handpick some amazing examples that will get you in the mood to start creating the visuals for your data.

8. Examples for Data Visualization

As another art form, Data Viz is a fertile ground for some amazing well-designed graphs that prove that data is beautiful. Now let’s check out some.

Dark Souls III Experience Data

We start with Meng Hsiao Wei’s personal project presenting his experience with playing Dark Souls 3. It’s a perfect example that infographics and data visualization are tools for personal designs as well. The research is pretty massive yet very professionally sorted into different types of charts for the different concepts. All data visualizations are made with the same color palette and look great in infographics.

My dark souls 3 playing data by Meng Hsiao Wei

Greatest Movies of all Time

Katie Silver has compiled a list of the 100 greatest movies of all time based on critics and crowd reviews. The visualization shows key data points for every movie such as year of release, oscar nominations and wins, budget, gross, IMDB score, genre, filming location, setting of the film, and production studio. All movies are ordered by the release date.

100 Greatest Movies Data Visualization by Katie Silver

The Most Violent Cities

Federica Fragapane shows data for the 50 most violent cities in the world in 2017. The items are arranged on a vertical axis based on population and ordered along the horizontal axis according to the homicide rate.

The Most Violent Cities by Federica Fragapane

Family Businesses as Data

These data visualizations and illustrations were made by Valerio Pellegrini for Perspectives Magazine. They show a pie chart with sector breakdown as well as a scatter plot for contribution for employment.

PERSPECTIVES MAGAZINE – Family Businesses by Valerio Pellegrini

Orbit Map of the Solar System

The map shows data on the orbits of more than 18000 asteroids in the solar system. Each asteroid is shown at its position on New Years’ Eve 1999, colored by type of asteroid.

An Orbit Map of the Solar System by Eleanor Lutz

The Semantics Of Headlines

Katja Flükiger has a take on how headlines tell the story. The data visualization aims to communicate how much is the selling influencing the telling. The project was completed at Maryland Institute College of Art to visualize references to immigration and color-coding the value judgments implied by word choice and context.

The Semantics of Headlines by Katja Flükiger

Moon and Earthquakes

This data visualization works on answering whether the moon is responsible for earthquakes. The chart features the time and intensity of earthquakes in response to the phase and orbit location of the moon.

Moon and Earthquakes by Aishwarya Anand Singh

Dawn of the Nanosats

The visualization shows the satellites launched from 2003 to 2015. The graph represents the type of institutions focused on projects as well as the nations that financed them. On the left, it is shown the number of launches per year and satellite applications.

WIRED UK – Dawn of the by Nanosats by Valerio Pellegrini

Final Words

Data visualization is not only a form of science but also a form of art. Its purpose is to help businesses in any field quickly make sense of complex data and start making decisions based on that data. To make your graphs efficient and easy to read, it’s all about knowing your data and audience. This way you’ll be able to choose the right type of chart and use visual techniques to your advantage.

You may also be interested in some of these related articles:

• Infographics for Marketing: How to Grab and Hold the Attention
• 12 Animated Infographics That Will Engage Your Mind from Start to Finish
• 50 Engaging Infographic Examples That Make Complex Ideas Look Great
• Good Color Combinations That Go Beyond Trends: Inspirational Examples and Ideas

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Al Boicheva

Al is an illustrator at GraphicMama with out-of-the-box thinking and a passion for anything creative. In her free time, you will see her drooling over tattoo art, Manga, and horror movies.

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Table of Contents

Ai, ethics & human agency, collaboration, information literacy, writing process, review of best practices in data visualization.

• CC BY-NC-ND 4.0 by Katherine McGee

Learning Objectives

• Design documents, visuals, and data displays that are rhetorically effective, accessible, and usable for specific audiences
• Recognize ethical, legal, and cultural issues in business and the professions

Think of the maps you see produced by the television station or website from which you get your weather information. While the meteorologist explains that northern Florida has highs in the 70s, central Florida has highs in the 80s, and southern Florida has highs in the 90s, that information is accompanied by a map. That map may simply have numbers on it, but it also likely has color added to it, with yellow representing a certain range of temperatures, orange representing a warmer range of temperatures, and red representing even hotter temperatures. These colors provide the audience with an additional way of understanding weather patterns. It’s not that we don’t understand that the “110” hovering over Brownsville, Texas is extremely hot; the dark red color of that area simply reinforces the idea, making it easy for us to quickly absorb information without having to process the actual posted high temperatures.

Sometimes it is beneficial to add visuals into your technical writing, especially if there are a lot of numbers or other types of data involved. Data visualizations such as graphs, charts, tables, and maps organize and communicate information in a way that is accessible to multiple audiences. Depending on the type of visualization, the visualization can help the user look up specific values or identify patterns hidden within the data.

There are many different types of data visualizations that can be and are used, depending on the purpose of the writer and the needs of the audience. Maps can be used to show not only high and low temperatures but also to identify voting patterns or patterns of income across a country. Records of high and low temperatures for the past ten years, or how much people in different professions make annually, can be presented in tables. Tables can also be used to compare and contrast different products. Sales records may be tracked using a line graph in order to see the rise and fall of sales throughout a quarter or a year; these same records could be presented in a table if the purpose is to be able to look up how many of a particular type of item was sold in a given month, quarter, or year.

Sometimes, more complex visualizations are necessary in order to identify the patterns hidden within the information. Consider, for instance, some of the patterns that David McCandless identifies in the visualizations he presents in this TED Talks ( http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization?language=en ) video, or McCandless’s visual depiction of 20th Century Death ( http://www.informationisbeautiful.net/visualizations/20th-century-death/ ), where he breaks down the number of deaths according to categories, such as Humanity, and subcategories, such as War, Drugs, and Air Pollution. By breaking down causes of death in the 20th Century Death chart, McCandless enables readers to visualize the impact that diseases such as Smallpox, Tuberculosis, and Whooping Cough had during the 1900s, and compare them to other causes of death such as Road Traffic and Air crashes.

Purpose of Data Visualizations

According to Stephen Few, the purpose of data visualizations “is to communicate important information effectively” (9). As technical communicators, it is our job to use design in order to help our readers understand what the information says the numbers say (9).

More specifically, the purposes of tables, graphs, and other types of data visualizations are to

• —Clearly indicate how values relate to one another
• —Represent quantities accurately
• —Make it easy to compare quantities
• Make it easy to see ranked order of values
• —Make obvious how people should use the information
• (Few, “Data Visualization for Human Perception”)

An extensive description of every type of data visualization would be beyond the scope of the article; there are entire books written on this topic, and if you want to design in-depth, effective visuals, you should read more of Stephen Few’s or Edward Tufte’s works. The following are some descriptions of common genres and best practices for some of the types of visualizations you may use in your professional and technical communications classes and real world writing experiences.

Tables, “lists of data presented in a system of rows and columns” (Dobrin, et al), are useful if you want the reader to be able to look up specific values. Your professional and technical communications instructor, for instance, likely keeps some form of a gradebook such as depicted in Table 1:

With a table, the data is organized in clearly defined rows and columns, and the instructor can easily look up the grade that each student received on an individual assignment.

Design Tips for Tables

You’ve probably seen a lot of tables in your lifetime, and some have probably been easier to read than others. The following are some guidelines for designing tables effectively:

• Label each row and column clearly so that it is easy to look up values.
• Avoid using too much ink. If you include solid lines between all items, the table can become more difficult to read (Few, Show, 160-61). See, for instance, Table 2, with the same information redesigned to have lines between each item. Notice that the table appears to be more cluttered than it did previously, even though it still has the same amount of information; this amount of clutter will increase if the table has more items. If necessary, light fill colors (Few, Show, 163) such as those used in Table 1 can be used to allow the reader to scan across lines more effectively.
• Use a legible font, and use that same font throughout the table (Few, Show, 177).

Graphs and Charts

Graphs and charts are useful when the patterns within the data tell a story. [1] While the information was relatively easy to understand already, we’ll use the information from Table 1 as an example. By transferring the numbers for the students’ exam scores into Graph 1, it is easy to identify that Student 12345 did better on Exam 1 than he or she did on Exam 2. Students 34567 and 56789, on the other hand, performed much better on Exams 2 and 3 than they did on Exam 1. Students 23456 and 45678, however, performed at approximately the same standard on each exam that they took. These patterns of scores can tell the instructor several pieces of information: Those students who performed consistently likely studied equal amounts for each test, while those who performed better on Exams 2 and 3 than on Exam 1 probably did not study effectively or were having a bad test day for Exam 1.

[1] According to Ask.com, the difference between a graph and a chart is that “Graphs are meant to be focused on the data in question and how it trends. Graphs have exact numerical figures shown on axes, usually organized on the left and bottom of the graph,” and “Charts are designed to show differences in things like surveys and figures in a more aesthetically pleasing way. Charts have numerical figures in line or popping out of the visual representations themselves.”

As data visualization designers, you are certainly not limited to bar graphs. On the contrary, there are numerous types of graphs and charts that you can use. While Graph 1 was created with Microsoft Word software, there are many alternative software available, including several free resources online. See the list of Additional Sources at the end of this article for some examples of these resources.

Design Tips for Graphs and Charts

• Label your axes clearly.
• If an axis contains quantitative information (numbers that have meaning) and shows only positive numbers, then the axis should begin at 0. In some rare cases, it is acceptable to use a different starting point, but if you are starting with a number other than 0, make sure that you mark that clearly. Some designers use varying axes to distort information, which is unethical.
• Make sure that the spacing is even down columns and across rows. Numerical increments should also be equal down columns and across rows. Using unequal increments is another way to distort information.

General Design Tips

When designing tables, graphs, and charts, it is necessary to remember general design tips as well. For starters, let’s consider the CRAP (Contrast, Repetition, Alignment, and Proximity) principles of design, which Robin Williams explains in detail in The Non-Designers’ Design Book and Danielle Nicole DeVoss describes more briefly, with examples, in this video ( https://www.youtube.com/watch?v=qa3Ig3NslEY&feature=youtu.be ).

When we write essays in Microsoft Word, the default colors are black letters on a white background. The stark contrast between white and black make the words easier to read. Consider, however, how difficult it would be to read yellow font on a white background or red font on a maroon background. It’s more difficult to read those words; we have to pause or squint in order to read them—if we can read them at all.

When we design data visualizations, we sometimes have to use color to show different categories or to make the image more visually appealing; we don’t want to be limited to black lettering with a white background. We do, however, need to maintain that level of contrast with the images we design. Look back at Graph 1; notice that the graph uses blue, red, and green for the bars. These colors differ from each other enough that we can see where one bar ends and the next begins without having to pause or squint. If, however, the bars were all various shades of blue, the data would be more difficult to read. Graph 2 demonstrates this lack of contrast. While the graph is still readable because it has relatively few items, Graph 1 is easier to read quickly.

Repeating ideas and colors provides cohesion. Consider the headings and subheadings you see throughout this textbook. Each article heading is written with the same font type, size, and color. This repeated design allows our brains to understand, each time we see that design, that we are looking at a new article within the same textbook.

To apply the principle of repetition to data visualizations, consider Graph 1 once again. Exam 1 is identified with blue ink for each student, Exam 2 is identified with red ink, and Exam 3 is identified with green ink. The repeated color scheme allows us to quickly associate the exam number and compare that exam across the students. The principle of repetition is especially important if you have multiple visualizations throughout your document. If, for instance, you have three tables in a proposal, you want to demonstrate a sense of cohesion among the tables by using the same font, colors, line width, etc.

It is essential to align texts and graphics in a readable manner. Think about the ways in which books and newspapers in the United States are formatted. Generally, text is left aligned or justified; depending on the genre, the beginning of each paragraph may be indented. Because we read from left to right, and our brains are accustomed to seeing text aligned that way, you will rarely will see large amounts of text that are right aligned. For a more in-depth discussion of alignment, look at Clayton Benjamin’s article “Formatting Pages.”

Alignment is perhaps most essential when dealing with tables, especially if you eliminate or reduce the amount of lines you use. In fact, proper use of alignment can save you the ink of having a lot of dividing lines. Align quantitative numbers to the right, thus allowing the ones, tens, etc. digits to align. If numbers are present in non-quantitative forms (e.g. the Student IDs in Tables 1 and 2), they can be left aligned. Most other information should be left aligned (Few, Show, 171).

When two images or pieces of information are placed close to each other, we assume them to be connected in some way. If two images or pieces of information are placed further away from each other, we assume that they are not related or are not closely related. For instance, consider the subheading “Proximity.” There is a space between the last Alignment paragraph and the word Proximity while there is not a space between Proximity and this paragraph. Thus, before we even read the content of the paragraphs, we know that this paragraph belongs with the subheading “Proximity” and not “Alignment,” or another section.

When designing tables, charts, and graphs, it is necessary to place related information close together. For instance, labels for parts of a graph (e.g. the Student IDs numbers in Graphs 1 and 2) are placed as close as possible to that particular student’s scores.

Problematic Chart Practices

One type of chart that is particularly popular is the pie chart. While some use them often because they allow the audience to see a basic part-to-whole relationship, they are also often criticized. Few, for instance, indicates that pie charts are ineffective because “[they] [encode] values as visual attributes . . . that we cannot easily perceive and compare” (“Data Visualization”). In other words, a quick glance at a pie chart informs us of general ratios, but it is often difficult to see the difference between amounts. For instance, if one section represents 20% and another represents 23%, the average human eye cannot discern the difference.

Another issue to be aware of is the use of 3D, which many also consider ineffective because the effect renders the information difficult to read accurately. Few indicates that 3D “cannot be applied effectively to graphs on a flat surface (e.g., a page or computer screen), which relies on illusory cues of light and shadow, occlusion, and size to simulate depth. Even if 3-D position could be used effectively on a flat surface, it is not an effective attribute to use in table and graph design because our perception of depth is weak compared to our perception of height and width” (Show 71).

Look at this graph ( http://www.originlab.com/doc%5Cen/UserGuide/images/3D_Bar_Graph/Image149.png ), for instance. While the numbers for 2009 are readable, the other values are not as easy to discern. Compare, also, the two graphs presented in this article ). As the author indicates, the bars don’t touch the wall, which makes the data difficult to read (“Use 3D”).

How should we refer to the visualizations?

Notice how the tables and graphs were discussed in this article. In order to effectively communicate the information in data visualizations, you need to label the visualizations properly, refer to them in the body of the text, and place them as close as possible to the relevant text. In a sentence or two (or maybe even a paragraph, if necessary) identify the most crucial piece of information you want your audience to get from your visualization. Remember that part of the reason you’re including these visualizations is to reach a wider audience. By pointing out the most relevant information in the text, you can more effectively reach various types of learners.

• Type “ineffective charts,” “ineffective graphs,” “unethical charts,” or some version thereof into Google Images. Find two or three differently-designed graphs and charts and discuss why the data visualizations are not effective.
• Find a textbook, report, or other document that includes some sort of data visualization. What types of information did the author choose to visualize? As a reader, how does the visualization help you to better understand the information presented?
• Consider those same data visualizations, this time looking at how the writer talks about the visualization. How does the writer introduce the visualization within the text? Where is the visualization placed in relation to its textual reference?

Dobrin, S. I., Keller, C. J., & Weisser, C. R. (2008). Technical communication in the twenty-first century . Pearson Prentice Hall.

Few, S. (2012). Show me the numbers: Designing tables and graphs to enlighten (2nd ed.). Analytics Press.

Few, S. (2014). Data visualization for human perception. In M. Soegaard, & R. F. Dam (eds.), The Encyclopedia of Human-Computer Interaction (2nd ed). The Interaction Design Foundation. Available online at https://www.interaction-design.org/encyclopedia/data_visualization_for_human_perception.html

Coutu, A. (n.d.). Use 3D charts and graphs at your own risk. Consultant Journal .

What is the difference between a chart and a graph?” Ask. (n.d.) Retrieved June 23, 2015 from .

Additional Resources

The following resources help you to create charts and graphs:

• Online ChartTool: http://www.onlinecharttool.com/
• Chartgo: The simple chart maker: http://www.chartgo.com/
• Tech & Learning’s list of top 10 chart-making sites: http://www.techlearning.com/default.aspx?tabid=100&entryid=3768

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Data Visualization: What it is and why it matters?

Checked : Luis G. , Eddy L.

Latest Update 18 Jan, 2024

Table of content

What is Data Visualization for?

Why it matters:, visualize for understanding:, display to predict, view to share, display to decide, visualize discovering new points of view, the advantages of data visualization in the company, set up a report to view data effectively, big data is a reality and we need to understand its meaning, the different types of visualization.

For Data Visualization it means a combination of techniques to visualize the data and explore them interactively. Data Visualization is an increasingly central activity in the field of Analytics. We often talk about Data Design, that is, a structured approach to creating visual and interactive reports that allow those who observe them to easily interpret them and quickly grasp the most significant information.

Of course, the generation of Business Intelligence and Data Analytics systems offer an interface already prepared for Data Visualization, but human processing is still a fundamental step. Understanding how to master the possibilities of visual processing offered by these software, in fact, allows us to make decision-making processes faster within the company.

Data Visualization is the answer to the Big Data paradox: thanks to increasingly complex information systems we have an enormous amount of data at our disposal. But often, precisely because of this excess quantity, we cannot identify the significant ones and make them “talking "", that is to transform them into insights useful to our business.

Analyzing the data collected by your ERP or your CRM observing the visual representation allows you to:

• understand them faster and more thoroughly
• make better predictions
• share your analyzes more easily
• guide the decision-making process
• discover new points of view

Images have a capacity for synthesis greater than those of words: they allow information to be conveyed more quickly even to non-experts, but at the same time they are open to different interpretations. A graph representing sales trends can communicate immediately if the trend is positive or negative, pushing the viewer to search for the reason for this trend, interpreting it also concerning its background of skills. In this sense, Data Visualization allows an ""intelligent reading"" of business processes.

By immediately offering an intelligible representation of business processes, Data Visualization, in particular when presented in an interactive form, allows us to investigate the underlying causes of a given trend by relating different data sets. This allows you to identify opportunities and problems and, consequently, to formulate forecasts for the future with greater precision and knowledge of the facts.

Creating visual and interactive reports allows entrepreneurs and managers of different departments to have access to all business information in one place. And interact with data already composed in an understandable form, without having to resort to IT or rely on interpretations already performed by consultants and analytics experts. Thanks to its simplicity, comprehensibility, and accessibility, Data Visualization allows you to share the information gathered with the entire company team, allowing everyone to provide their interpretative input regarding their knowledge.

Dealing with information systems for business management, we often find ourselves talking about the need to adopt a data-driven decision-making process, i.e. guided by concrete data collected by software and interpreted through human ingenuity. By contributing to a better interpretation of information, Data Visualization also influences the way decisions are made within a company.

Data Visualization activates perceptive mechanisms that stimulate lateral thinking, often providing new analysis inputs that reveal new points of view concerning the progress and development of one's business. In this sense, the graphic representation of the data makes a fundamental contribution to the so-called ""data-driven innovation"", that is, it gives companies the ability to use Big Data for innovative processes, services, products and open up new possibilities for development.

The company data collected with Business Intelligence tools or through CRM software are characterized by a high volume, high variety, and ever-increasing variability and complexity. As we have seen so far, however, thanks to visualization, the brain can process, absorb and interpret large amounts of information more successfully.

Therefore, processing the data in graphic form allows you to achieve a series of advantages:

• Save time when analyzing and interpreting data
• Encourage collaboration between departments
• Allow each manager or team member to understand statistical reports
• Optimize the decision-making process from a data-driven perspective

Although the Data Visualization tools included in the Business Intelligence software offer advanced functionalities able to provide excellent processing without excessive human intervention, setting up and following a method of analysis appropriate to your business is fundamental to obtain the information you have need.

A good visual report is built on three fundamental points:

• The specific objectives of the analysis in progress
• The characteristics of the audience to which it will be presented
• The communication objectives according to which the report is drawn up

The distinction between point one and point three is subtle but fundamental. The objective of the analysis can be, for example, to discover the underlying causes of a production slowdown. The communication objective, on the other hand, depends on how you want the public to whom you present the report to make your decisions regarding this problem. Data Visualization always reflects the intentions of those who create graphic representations: it is good not to forget it to avoid incurring evaluation errors.

We are advancing in the era of big data and visualization is an increasingly necessary tool to give meaning to the many billions of data lines that are generated every day. Data visualization helps to explain the contents, organizing the data more understandable and highlighting trends and outliers. An effective visualization allows exposing the contents eliminating from the data the superfluous and bringing in the foreground the useful information.

However, it is not easy and immediate how to simply embellish a graphic to make it more pleasant or show the information section of an infographic. Effective visualization of data requires a subtle balance between form and functionality. A very simple chart may be too boring to attract attention or to expose the salient points effectively; on the other hand, the most surprising of the views may fail to convey the right message or be too distracting. The data and the visual part must interact optimally: combining an excellent analysis with a perfect narration is an art.

When you think of data visualization you usually tends to imagine simple bar or pie charts. They can indeed be included in the data display and constitute a widespread reference for many graphs, but the right visualization must be associated with the right set of information. The simple graphs represent only the tip of the iceberg. There are many visualization methods available to present data effectively and interestingly.

Some generic types of tools to view data:

• infographics

Some more specific examples of methods for displaying data:

• Box diagram and mustache
• Bubble diagram
• Horizontal bar chart
• Map of the distribution of points
• Gantt chart
• Heat-map chart
• Highlight table
• Network diagram
• Radial shaft
• Scatter chart (2D or 3D)
• Stream graph
• Text tables
• Tree diagram
• Graphic wedge stack
• Cloud of words
• And any combination of them in a dashboard.

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Every field of science, engineering, technology and mathematics benefits from a better understanding of the data and the same is true for the public sector, finance, marketing, history, consumer goods, services, school, sport and so on.

Even if we always talk poetically about data visualization (after all, you are browsing the Tableau website) there are practical and real applications of undeniable importance. And being so prolific, visualization is also one of the most useful professional skills to be acquired. The better you expose the topics visually, through a dashboard or a series of slides, the better you will be able to exploit the information.

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Blog Graphic Design What is Data Visualization? (Definition, Examples, Best Practices)

What is Data Visualization? (Definition, Examples, Best Practices)

Written by: Midori Nediger Jun 05, 2020

Words don’t always paint the clearest picture. Raw data doesn’t always tell the most compelling story. 

The human mind is very receptive to visual information. That’s why data visualization is a powerful tool for communication.    

But if “data visualization” sounds tricky and technical don’t worry—it doesn’t have to be. 

This guide will explain the fundamentals of data visualization in a way that anyone can understand. Included are a ton of examples of different types of data visualizations and when to use them for your reports, presentations, marketing, and more.

Table of Contents

• What is data visualization?

What is data visualization used for?

Types of data visualizations.

• How to present data visually  (for businesses, marketers, nonprofits, and education)
• Data visualization examples

Data visualization is used everywhere. 

Businesses use data visualization for reporting, forecasting, and marketing. 

CREATE THIS REPORT TEMPLATE

Nonprofits use data visualizations to put stories and faces to numbers. 

Source:  Bill and Melinda Gates Foundation

Scholars and scientists use data visualization to illustrate concepts and reinforce their arguments.

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Reporters use data visualization to show trends and contextualize stories. 

While data visualizations can make your work more professional, they can also be a lot of fun.

What is data visualization? A simple definition of data visualization:

Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart , infographic , diagram or map. 

The field of data visualization combines both art and data science. While a data visualization can be creative and pleasing to look at, it should also be functional in its visual communication of the data. 

Data, especially a lot of data, can be difficult to wrap your head around. Data visualization can help both you and your audience interpret and understand data. 

Data visualizations often use elements of visual storytelling to communicate a message supported by the data. 

There are many situations where you would want to present data visually. 

Data visualization can be used for:

• Making data engaging and easily digestible
• Identifying trends and outliers within a set of data
• Telling a story found within the data
• Reinforcing an argument or opinion
• Highlighting the important parts of a set of data

Let’s look at some examples for each use case.

1. Make data digestible and easy to understand

Often, a large set of numbers can make us go cross-eyed. It can be difficult to find the significance behind rows of data. 

Data visualization allows us to frame the data differently by using illustrations, charts, descriptive text, and engaging design. Visualization also allows us to group and organize data based on categories and themes, which can make it easier to break down into understandable chunks. 

Related : How to Use Data Visualization in Your Infographics

For example, this infographic breaks down the concept of neuroplasticity in an approachable way:

Source: NICABM

The same goes for complex, specialized concepts. It can often be difficult to break down the information in a way that non-specialists will understand. But an infographic that organizes the information, with visuals, can demystify concepts for novice readers.

CREATE THIS INFOGRAPHIC TEMPLATE

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2. Identify trends and outliers

If you were to sift through raw data manually, it could take ages to notice patterns, trends or outlying data. But by using data visualization tools like charts, you can sort through a lot of data quickly. 

Even better, charts enable you to pick up on trends a lot quicker than you would sifting through numbers.

For example, here’s a simple chart generated by Google Search Console that shows the change in Google searches for “toilet paper”. As you can see, in March 2020 there was a huge increase in searches for toilet paper:

Source: How to Use SEO Data to Fuel Your Content Marketing Strategy in 2020

This chart shows an outlier in the general trend for toilet paper-related Google searches. The reason for the outlier? The outbreak of COVID-19 in North America. With a simple data visualization, we’ve been able to highlight an outlier and hint at a story behind the data. 

Uploading your data into charts, to create these kinds of visuals is easy. While working on your design in the editor, select a chart from the left panel. Open the chart and find the green IMPORT button under the DATA tab. Then upload the CSV file and your chart automatically visualizes the information. 

3. Tell a story within the data

Numbers on their own don’t tend to evoke an emotional response. But data visualization can tell a story that gives significance to the data. 

Designers use techniques like color theory , illustrations, design style and visual cues to appeal to the emotions of readers, put faces to numbers, and introduce a narrative to the data. 

Related : How to Tell a Story With Data (A Guide for Beginners)

For example, here’s an infographic created by World Vision. In the infographics, numbers are visualized using illustrations of cups. While comparing numbers might impress readers, reinforcing those numbers with illustrations helps to make an even greater impact. 

Source: World Vision

Meanwhile, this infographic uses data to draw attention to an often overlooked issue:

Read More:  The Coronavirus Pandemic and the Refugee Crisis

4. Reinforce an argument or opinion

When it comes to convincing people your opinion is right, they often have to see it to believe it. An effective infographic or chart can make your argument more robust and reinforce your creativity. 

For example, you can use a comparison infographic to compare sides of an argument, different theories, product/service options, pros and cons, and more. Especially if you’re blending data types.

5. Highlight an important point in a set of data

Sometimes we use data visualizations to make it easier for readers to explore the data and come to their own conclusions. But often, we use data visualizations to tell a story, make a particular argument, or encourage readers to come to a specific conclusion. 

Designers use visual cues to direct the eye to different places on a page. Visual cues are shapes, symbols, and colors that point to a specific part of the data visualization, or that make a specific part stand out.

For example, in this data visualization, contrasting colors are used to emphasize the difference in the amount of waste sent to landfills versus recycled waste:

Here’s another example. This time, a red circle and an arrow are used to highlight points on the chart where the numbers show a drop: 

Highlighting specific data points helps your data visualization tell a compelling story.

6. Make books, blog posts, reports and videos more engaging

At Venngage, we use data visualization to make our blog posts more engaging for readers. When we write a blog post or share a post on social media, we like to summarize key points from our content using infographics. 

The added benefit of creating engaging visuals like infographics is that it has enabled our site to be featured in publications like The Wall Street Journal , Mashable , Business Insider , The Huffington Post and more. 

That’s because data visualizations are different from a lot of other types of content people consume on a daily basis. They make your brain work. They combine concrete facts and numbers with impactful visual elements. They make complex concepts easier to grasp. 

Here’s an example of an infographic we made that got a lot of media buzz:

Read the Blog Post: Every Betrayal Ever in Game of Thrones

We created this infographic because a bunch of people on our team are big Game of Thrones fans and we wanted to create a visual that would help other fans follow the show. Because we approached a topic that a lot of people cared about in an original way, the infographic got picked up by a bunch of media sites. 

Whether you’re a website looking to promote your content, a journalist looking for an original angle, or a creative building your portfolio, data visualizations can be an effective way to get people’s attention.

Data visualizations can come in many different forms. People are always coming up with new and creative ways to present data visually. 

Generally speaking, data visualizations usually fall under these main categories:

An infographic is a collection of imagery, charts, and minimal text that gives an easy-to-understand overview of a topic. 

While infographics can take many forms, they can typically be categorized by these infographic types:

• Statistical infographics
• Informational infographics
• Timeline infographics
• Process infographics
• Geographic infographics
• Comparison infographics
• Hierarchical infographics
• List infographics
• Resume infographics

Read More: What is an Infographic? Examples, Templates & Design Tips

Charts 

In the simplest terms, a chart is a graphical representation of data. Charts use visual symbols like line, bars, dots, slices, and icons to represent data points. 

Some of the most common types of charts are:

• Bar graphs /charts
• Line charts
• Bubble charts
• Stacked bar charts
• Word clouds
• Pictographs
• Area charts
• Scatter plot charts
• Multi-series charts

The question that inevitably follows is: what type of chart should I use to visualize my data? Does it matter?

Short answer: yes, it matters. Choosing a type of chart that doesn’t work with your data can end up misrepresenting and skewing your data. 

For example: if you’ve been in the data viz biz for a while, then you may have heard some of the controversy surrounding pie charts. A rookie mistake that people often make is using a pie chart when a bar chart would work better. 

Pie charts display portions of a whole. A pie chart works when you want to compare proportions that are substantially different. Like this:

CREATE THIS CHART TEMPLATE

But when your proportions are similar, a pie chart can make it difficult to tell which slice is bigger than the other. That’s why, in most other cases, a bar chart is a safer bet.

Here is a cheat sheet to help you pick the right type of chart for your data:

Want to make better charts? Make engaging charts with Venngage’s Chart Maker .

Related : How to Choose the Best Types of Charts For Your Data

Similar to a chart, a diagram is a visual representation of information. Diagrams can be both two-dimensional and three-dimensional. 

Some of the most common types of diagrams are:

• Venn diagrams
• Tree diagrams
• SWOT analysis
• Fishbone diagrams
• Use case diagrams

Diagrams are used for mapping out processes, helping with decision making, identifying root causes, connecting ideas, and planning out projects.

CREATE THIS DIAGRAM TEMPLATE

Want to make a diagram ? Create a Venn diagram and other visuals using our free Venn Diagram Maker .

A map is a visual representation of an area of land. Maps show physical features of land like regions, landscapes, cities, roads, and bodies of water. 

Source: National Geographic

A common type of map you have probably come across in your travels is a choropleth map . Choropleth maps use different shades and colors to indicate average quantities. 

For example, a population density map uses varying shades to show the difference in population numbers from region to region:

Create your own map for free with Venngage’s Map Maker .

How to present data visually (data visualization best practices)

While good data visualization will communicate data or information clearly and effectively, bad data visualization will do the opposite. Here are some practical tips for how businesses and organizations can use data visualization to communicate information more effectively. 

Not a designer? No problem. Venngage’s Graph Maker  will help you create better graphs in minutes.

1. Avoid distorting the data

This may be the most important point in this whole blog post. While data visualizations are an opportunity to show off your creative design chops, function should never be sacrificed for fashion. 

The chart styles, colors, shapes, and sizing you use all play a role in how the data is interpreted. If you want to present your data accurately and ethically, then you need to take care to ensure that your data visualization does not present the data falsely. 

There are a number of different ways data can be distorted in a chart. Some common ways data can be distorted are:

• Making the baselines something other than 0 to make numbers seem bigger or smaller than they are – this is called “truncating” a graph
• Compressing or expanding the scale of the Y-axis to make a line or bar seem bigger or smaller than it should be
• Cherry picking data so that only the data points you want to include are on a graph (i.e. only telling part of the story)
• Using the wrong type of chart, graph or diagram for your data
• Going against standard, expected data visualization conventions

Because people use data visualizations to reinforce their opinions, you should always read data visualizations with a critical eye. Often enough, writers may be using data visualization to skew the data in a way that supports their opinions, but that may not be entirely truthful.

Read More: 5 Ways Writers Use Graphs To Mislead You

Want to create an engaging line graph? Use Venngage’s Line Graph Maker to create your own in minutes.

2. Avoid cluttering up your design with “chartjunk”

When it comes to best practices for data visualization, we should turn to one of the grandfather’s of data visualization: Edward Tufte. He coined the term “ chartjunk ”, which refers to the use of unnecessary or confusing design elements that skews or obscures the data in a chart. 

Here’s an example of a data visualization that suffers from chartjunk:

Source: ExcelUser

In this example, the image of the coin is distracting for readers trying to interpret the data. Note how the fonts are tiny – almost unreadable. Mistakes like this are common when a designers tries to put style before function. 

Read More : The Worst Infographics of 2020 (With Lessons for 2021)

3. Tell a story with your data

Data visualizations like infographics give you the space to combine data and narrative structure in one page. Visuals like icons and bold fonts let you highlight important statistics and facts.

For example, you could customize this data visualization infographic template to show the benefit of using your product or service (and post it on social media):

USE THIS TEMPLATE

  This data visualization relies heavily on text and icons to tell the story of its data:

This type of infographic is perfect for those who aren’t as comfortable with charts and graphs. It’s also a great way to showcase original research, get social shares and build brand awareness.

4. Combine different types of data visualizations

While you may choose to keep your data visualization simple, combining multiple types of charts and diagrams can help tell a more rounded story.

Don’t be afraid to combine charts, pictograms and diagrams into one infographic. The result will be a data visualization infographic that is engaging and rich in visual data.

Design Tip: This data visualization infographic would be perfect for nonprofits to customize and include in an email newsletter to increase awareness (and donations).

Or take this data visualization that also combines multiple types of charts, pictograms, and images to engage readers. It could work well in a presentation or report on customer research, customer service scores, quarterly performance and much more:

Design Tip: This infographic could work well in a presentation or report on customer research, customer service scores, quarterly performance and much more.

Make your own bar graph in minutes with our free Bar Graph Maker .

5. Use icons to emphasize important points

Icons are perfect for attracting the eye when scanning a page. (Remember: use visual cues!)

If there are specific data points that you want readers to pay attention to, placing an icon beside it will make it more noticeable:

Design Tip: This infographic template would work well on social media to encourage shares and brand awareness.

You can also pair icons with headers to indicate the beginning of a new section.

Meanwhile, this infographic uses icons like bullet points to emphasize and illustrate important points. 

Design Tip: This infographic would make a great sales piece to promote your course or other service.  

6. Use bold fonts to make text information engaging

A challenge people often face when setting out to visualize information is knowing how much text to include. After all, the point of data visualization is that it presents information visually, rather than a page of text. 

Even if you have a lot of text information, you can still create present data visually. Use bold, interesting fonts to make your data exciting. Just make sure that, above all else, your text is still easy to read.

This data visualization uses different fonts for the headers and body text that are bold but clear. This helps integrate the text into the design and emphasizes particular points:

Design Tip: Nonprofits could use this data visualization infographic in a newsletter or on social media to build awareness, but any business could use it to explain the need for their product or service. 

As a general rule of thumb, stick to no more than three different font types in one infographic.

This infographic uses one font for headers, another font for body text, and a third font for accent text. 

Read More: How to Choose Fonts For Your Designs (With Examples)

Design Tip: Venngage has a library of fonts to choose from. If you can’t find the icon you’re looking for , you can always request they be added. Our online editor has a chat box with 24/7 customer support.

7. Use colors strategically in your design

In design, colors are as functional as they are fashionable. You can use colors to emphasize points, categorize information, show movement or progression, and more. 

For example, this chart uses color to categorize data:

Design Tip : This pie chart can actually be customized in many ways. Human resources could provide a monthly update of people hired by department, nonprofits could show a breakdown of how they spent donations and real estate agents could show the average price of homes sold by neighbourhood.

You can also use light colored text and icons on dark backgrounds to make them stand out. Consider the mood that you want to convey with your infographic and pick colors that will reflect that mood. You can also use contrasting colors from your brand color palette.

This infographic template uses a bold combination of pinks and purples to give the data impact:

Read More: How to Pick Colors to Captivate Readers and Communicate Effectively

8. Show how parts make up a whole

It can be difficult to break a big topic down into smaller parts. Data visualization can make it a lot easier for people to conceptualize how parts make up a whole.

Using one focus visual, diagram or chart can convey parts of a whole more effectively than a text list can. Look at how this infographic neatly visualizes how marketers use blogging as part of their strategy:

Design Tip: Human resources could use this graphic to show the results of a company survey. Or consultants could promote their services by showing their success rates.

Or look at how this infographic template uses one focus visual to illustrate the nutritional makeup of a banana:

CREATE THIS FLYER TEMPLATE

9. Focus on one amazing statistic

If you are preparing a presentation, it’s best not to try and cram too many visuals into one slide. Instead, focus on one awe-inspiring statistic and make that the focus of your slide.

Use one focus visual to give the statistic even more impact. Smaller visuals like this are ideal for sharing on social media, like in this example:

Design Tip: You can easily swap out the icon above (of Ontario, Canada) using Venngage’s drag-and-drop online editor and its in-editor library of icons. Click on the template above to get started.

This template also focuses on one key statistic and offers some supporting information in the bar on the side:

10. Optimize your data visualization for mobile

Complex, information-packed infographics are great for spicing up reports, blog posts, handouts, and more. But they’re not always the best for mobile viewing. 

To optimize your data visualization for mobile viewing, use one focus chart or icon and big, legible font. You can create a series of mobile-optimized infographics to share multiple data points in a super original and attention-grabbing way.

For example, this infographic uses concise text and one chart to cut to the core message behind the data:

CREATE THIS SOCIAL MEDIA TEMPLATE

Some amazing data visualization examples

Here are some of the best data visualization examples I’ve come across in my years writing about data viz. 

Evolution of Marketing Infographic

Graphic Design Trends Infographic

Stop Shark Finning Nonprofit Infographic

Source: Ripetungi

Coronavirus Impact on Environment Data Visualization

What Disney Characters Tell Us About Color Theory

World’s Deadliest Animal Infographic

Source: Bill and Melinda Gates Foundation

The Secret Recipe For a Viral Creepypasta

Read More: Creepypasta Study: The Secret Recipe For a Viral Horror Story

The Hero’s Journey Infographic

Read More: What Your 6 Favorite Movies Have in Common

Emotional Self Care Guide Infographic

Source: Carley Schweet

Want to look at more amazing data visualization? Read More: 50+ Infographic Ideas, Examples & Templates for 2020 (For Marketers, Nonprofits, Schools, Healthcare Workers, and more)

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How to Do Data Visualization in Your Essays

Last updated: July 2020

How to do data visualization in essays:

• Try flash spatial.
• Remember of maps.
• Create videos.
• Record vox pops.
• Draw an expressive design chart.
• Consider interactive charts, too.
• Make GIFs to illustrate street views/panoramas.
• Create infographics.
• Try data visualization software.

What picture do you imagine when hearing of data visualization?

Here is ours:

Source: Bored Panda

Yes, it’s a pie chart, a pictorial representation of numerical data. Perfect for visualizing your research papers, right?

Not really.

Today, when people don’t read but scan, the world goes crazy over videos and podcasts, and interactive content runs the show, data visualization goes far beyond pie charts, graphs, and diagrams. It takes so many forms that they even have a special course for it in colleges now! Students don’t just write essays ; they write visual essays and think of creative ways to represent their research to professors.

Proper visualization is the best way to make your research papers stand out. When illustrating a content asset by visuals, you increase its view rate by 94% and produce 180% more engagement, which multiplies its chances to get shared and remembered.

(Okay, when a student, you hardly think of likes and shares. But what about impressing a teacher and, finally, getting that damned and so desired A+, huh?)

Studies don’t lie:

So, Why and When Use Data Visualization

Visuals are a new way to communicate information and make it easier for users to perceive. When you create a graphic essay or write a long read for your blog, visual elements are perfect to represent data because otherwise it would take many pages to convey.

More than that, people respond to visual content better. Reasons are many:

• The brain processes visual information 60,000 faster than text. (While it takes about 1/4 second to process a symbol meaning, it takes about 6 seconds to read 20 words.)
• Ideas presented graphically are easier to comprehend and remember than those presented as words.
• 65 percent of the population are visual learners.

That’s why all bloggers and marketing specialists focus on data visualization in research articles for more reads, shares, and sales. Video is on its rise, interactive content ( surveys , quizzes , calculators, infographics , e-books, emails) call the shots, and all they boost engagement, increase brand loyalty, and help marketers capture more data for their research.

“Hey, I’m a student, okay? What’s in there for my essay writing? Do all these visual tricks meet academic requirements?”

Yes, they do.

Most handbooks on evaluating research papers count tables and diagrams as the “organization” criteria and give 20% of a total grade for them. “Content originality” brings another 30%. Therefore, if you decide to do data visualization in essays – you can get half of your paper grade in one go!

The only detail to know is the proper places for visuals in your paper. Two options to consider:

1) Within your essay, just after the reference to a visual.

And remember of citing and referencing your visuals! This and this document will help you learn how to place visual data in the text of your essay , how to relate visuals to your writing, and how to create a caption.

2) At the end of your essay, after the list of references.

Known as the appendix, it’s a separate page at the end of your paper, including the additional information for readers. You can put visuals with corresponding captions there.

If you have multiple items to include, create a separate appendix for each visual. Don’t forget to format it according to essay standards (APA, MLA, etc.)

The first mentioned is the most common way to use visuals in essays. But whatever option you choose, be consistent: there’s no need to place images or graphs both in the text and appendixes. Follow the instructions your teachers give, or just choose one option and keep to it.

And now, for the most interesting part:

Visual Essays

They are quite a different story.

To define visual essay, let’s make it clear: it’s a set of pictures/other visual elements you use instead of words to complete a writing assignment. As a rule, it’s a mix of visuals and short written passages explaining what happens in your visual content.

Images you use to write visual essays should be original, i.e. , made by you. (Keep on reading to find out what tools to use for data visualization.)

Here goes a terrific example, “Who cares about the forest?” by Franke James):

Full version here

He combined photos, illustrations, and writing in 20+ visuals to craft an essay on the red hot issue of deforestation and its influence on our planet. Storytelling is not about words only, huh?

The best visual essays include:

• A short introduction, with hooks and thesis statement .
• A sequence of visual elements, related to each other and set consistently.
• A short analysis of each visual element, explaining what’s in there.
• Conclusion . (Yes, short again.)

An average visual essay consists of 10-12 visuals and 500-700 words of the accompanying text, formatted in Word Doc or PDF. When teachers give you a graphic essay assignment, they’ll evaluate your critical thinking, presentation skills, cultural competence, and ability to convey arguments and facts in graphic form.

Writing visual essays, you stimulate creativity, attention, memory, and analytical skills.

Want to get a better idea on how to create a graphic essay? Let us know in comments, and we’ll craft an ultimate guide on visual essay writing here on Bid4Papers blog. 🙂

What Essay Elements to Visualize

Together with “how to write essays with visuals,” content creators often ask what exact elements to visualize and how to know if a particular method is relevant to their topic or niche.

Here’s how you solve this:

First, consider the audience.

If you are a student writing essays for teachers, everything will depend on many factors: a subject, a format (printed or online), and other specific requirements your teacher gives.

• For Geography -related majors, research papers require at least one map. Make it interactive et voilà , it brings engagement.
• For Business and Management, Marketing and Data Science specialties, statistics presentation is a crucial skill that will get you extra points. Get wild with the charts, and you can do so much better than that old boring pie.
• History papers “tell the story” with data. GIFs are perfect for illustrating timelines in just one pic.
• English and Literature will rock if you support the text with visual features. For example, if writing an essay about Dickens – create a collage with that-era bookshelf and add pop-up descriptions of each book cover.

If a digital copywriter or marketer, think of a specific visual presentation format that would engage your audience most. Consider a platform where you’ll land it: is it a blog post, your landing page, a YouTube channel, cold email, or slideshow presentation at a conference?

Two questions to answer:

• How can this visual best appeal to my audience in this specific setting?
• What is the simplest method for displaying this data? Which one will represent my information better? What data do I need to highlight most?

And second, rely on these criteria when deciding what argument or fact to visualize:

• Is it exciting? – Boring topics will rarely benefit from visual adaptation.
• Is it complex? – Copybook maxims don’t need visualization.
• Does it spark a discussion? – If it’s unequivocal evidence, then it’s not worth the effort.

But when it’s not a fact of common knowledge, and you understand it’s worth visualizing, the question arises as to what option and tool to choose for it.

How to Do Data Visualization in Essays and Other Content

When you write visual essays or create content assets for marketing goals, forget about ready-made templates. They are boring. And big chances are, your peers or competitors have used them already.

Be creative , design visuals on your own! Data visualization tools are many, and the most common ones are:

• Adobe Flash
• Windows Movie Maker
• Microsoft Word

But if your ambitions go beyond diagrams, charts, and infographics, here go alternative options on how to visualize data.

1. Flash spatial

Perfect for Geography, History, Politics, or Social Sciences papers. If you have any map-based data, overlap two pictures and let the reader compare them by moving the slider back and forth. A small step from spatial comparison, yet far more engaging and impressive.

Carto is a terrific tool for making amazing interactive maps. Leaflet is another one. Location-related facts will help to illustrate any scientific research. Gather the metrics and build up a visible backdrop for your thesis.

Nothing says “I gave a major damn about this research” like a video file in your professor’s inbox. Presenting your research in a 3-minute speech is the best way to sift through the backbone facts and wrap them in the perfect form. Don’t overlook the screen recording option!

And another one:

4. Vox Pops

The favorite tool of many marketers, it works best with a study involving people and opinions.

Record your respondents in their natural habitat or via Skype and edit into a convincing illustrative interview to support your thesis. Choose between “audience opinion” vs. “expert’s opinion,” depending on your subject.

Check the examples of non-video vox pops here .

5. Expressive Design Charts

Stuck with charts to state your theses in Business and Management and related disciplines? Thy this trick: keep the chart but pimp it up in a creative way, so your paper will immediately stand out and get remembered.

6. Interactive Charts

Nothing gets your reader more engaged than the possibility of tailoring your research to them personally. With a 5-dollar-help from a freelance programmer, you can turn your data into a fun personalized illustration. Add some back-end work: gather the data from your professors or peers for further use.

• Years You Have Left to Live, Probably
• How You Will Die

7. Street Views / Panoramas

Ten years from its launch, Google Street View has become a powerful tool for illustrating Geography and Communities research. You can use it to predict the city areas development over time, for example. Pull several layers in a timeline GIF to illustrate your findings.

QUOTE: “It’s always nice when your research checks out with the view outside your window.” – Scott Duke Kominers, the Harvard Business School MBA Class of 1960 Associate Professor

8. Infographics

Infographics are a great help in learning because this content format requires you to summarize concepts and identify certain things as significant. It helps your professor to better evaluate your knowledge, too. A whole host of handy (and free) tools are out there to help you with the designs: Visual.ly, Tableau, Venngage , and Infogram are just several to mention.

NB: If you also need to promote the research among college communities or online, a comprehensive infographic increases your chances of getting noticed by far.

Examples of infographic templates: here and here .

9. SaaS Data Visualization Software

Many businesses leverage data visualization tools through a SaaS subscription. These tools help organizations translate big data and their key metrics into digestible charts and graphics, helping them better understand how they’re tracking towards their goals.

But students, universities, professors, and other researchers can use these tools as well to create dashboards and turn big data/research sets into easy-to-interpret visualizations. Those looking to implement a tool such as these should look at online review sites to compare the over 200 data visualization software products on the market.

Examples of SaaS Data Visualization Software:

What Else Do You Need to Know?

If you choose to work on your data visualization with a freelancer, make sure not to leave it to the final day before submission. It’s time-consuming, so save yourself several wiggle days to input extra data as needed.

When creating a complex visualization, skim to just one per academic paper to avoid clutter.

One final piece of advice:

Don ’ t assume that data visualization is research – an essay maker should use data visualization as a cherry on the top of their impressive essay . Your paper still needs a strong hook , good thesis , and all the required chapters included to make it a delicious cake .

• Data-Driven Guides: Supporting Expressive Design for Information Graphics
• Researchers Use Google Street View to See the Future of Cities

Related posts

• How to Write a Mission Statement?
• Unlocking Your Potential: Improving Memory Techniques
• What are Scholarly Sources?

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6 Data Visualization Examples To Inspire Your Own

• 12 Jan 2017

Data informs virtually every business decision an organization makes. Because of this, it’s become increasingly important for professionals of all backgrounds to be adept at working with data.

While data can provide immense value, it’s important that professionals are able to effectively communicate the significance of the data to stakeholders. This is where data visualization comes into play. By transforming raw data into engaging visuals using various data visualization tools , it’s much easier to communicate insights gleaned from it.

Here are six real-world examples of data visualization that you can use to inspire your own.

What Is Data Visualization?

Data visualization is the process of turning raw data into graphical representations.

Visualizations make it easy to communicate trends in data and draw conclusions. When presented with a graph or chart, stakeholders can easily visualize the story the data is telling, rather than try to glean insights from raw data.

There are countless data visualization techniques , including:

• Scatter plots

The technique you use will vary based on the type of data you’re handling and what you’re trying to communicate.

6 Real-World Data Visualization Examples

1. the most common jobs by state.

Source: NPR

National Public Radio (NPR) produced a color-coded, interactive display of the most common jobs in each state in each year from 1978 to 2014. By dragging the scroll bar at the bottom of the map, you’re able to visualize occupational changes over time.

If you’re trying to represent geographical data, a map is the best way to go.

2. COVID-19 Hospitalization Rates

Source: CDC

Throughout the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) has been transforming raw data into easily digestible visuals. This line graph represents COVID-19 hospitalization rates from March through November 2020.

The CDC tactfully incorporated color to place further emphasis on the stark increase in hospitalization rates, using a darker shade for lower values and a lighter shade for higher values.

3. Forecasted Revenue of Amazon.com

Source: Statista

Data visualizations aren’t limited to historical data. This bar chart created by Statista visualizes the forecasted gross revenue of Amazon.com from 2018 to 2025.

This visualization uses a creative title to summarize the main message that the data is conveying, as well as a darker orange color to spike out the most important data point.

4. Web-Related Statistics

Source: Internet Live Stats

Internet Live Stats has tracked web-related statistics and pioneered methods for visualizing data to show how different digital properties have ebbed and flowed over time.

Simple infographics like this one are particularly effective when your goal is to communicate key statistics rather than visualizing trends or forecasts.

5. Most Popular Food Delivery Items

Source: Eater

Eater, Vox’s food and dining brand, has created this fun take on a “pie” chart, which shows the most common foods ordered for delivery in each of the United States.

To visualize this data, Eater used a specific type of pie chart known as a spie chart. Spie charts are essentially pie charts in which you can vary the height of each segment to further visualize differences in data.

6. Netflix Viewing Patterns

Source: Vox

Vox created this interesting visualization depicting the viewing patterns of Netflix users over time by device type. This Sankey diagram visualizes the tendency of users to switch to streaming via larger device types.

Visualizing Data to Make Business Decisions

The insights and conclusions drawn from data visualizations can guide the decision-making and strategic planning processes for your organization.

To ensure your visualizations are relevant, accurate, and ethical, familiarize yourself with basic data science concepts . With a foundational knowledge in data science, you can maintain confidence in your data and better understand its significance. An online analytics course can help you get started.

Are you interested in improving your data science and analytical skills? Download our Beginner’s Guide to Data & Analytics to learn how you can leverage the power of data for professional and organizational success.

This post was updated on February 26, 2021. It was originally published on January 12, 2017.

Introductory essay

Written by the educators who created Visualizing Data, a brief look at the key facts, tough questions and big ideas in their field. Begin this TED Study with a fascinating read that gives context and clarity to the material.

The reality of today

All of us now are being blasted by information design. It's being poured into our eyes through the Web, and we're all visualizers now; we're all demanding a visual aspect to our information...And if you're navigating a dense information jungle, coming across a beautiful graphic or a lovely data visualization, it's a relief, it's like coming across a clearing in the jungle. David McCandless

In today's complex 'information jungle,' David McCandless observes that "Data is the new soil." McCandless, a data journalist and information designer, celebrates data as a ubiquitous resource providing a fertile and creative medium from which new ideas and understanding can grow. McCandless's inspiration, statistician Hans Rosling, builds on this idea in his own TEDTalk with his compelling image of flowers growing out of data/soil. These 'flowers' represent the many insights that can be gleaned from effective visualization of data.

We're just learning how to till this soil and make sense of the mountains of data constantly being generated. As Gary King, Director of Harvard's Institute for Quantitative Social Science says in his New York Times article "The Age of Big Data":

It's a revolution. We're really just getting under way. But the march of quantification, made possible by enormous new sources of data, will sweep through academia, business and government. There is no area that is going to be untouched.

How do we deal with all this data without getting information overload? How do we use data to gain real insight into the world? Finding ways to pull interesting information out of data can be very rewarding, both personally and professionally. The managing editor of Financial Times observed on CNN's Your Money : "The people who are able to in a sophisticated and practical way analyze that data are going to have terrific jobs." Those who learn how to present data in effective ways will be valuable in every field.

Many people, when they think of data, think of tables filled with numbers. But this long-held notion is eroding. Today, we're generating streams of data that are often too complex to be presented in a simple "table." In his TEDTalk, Blaise Aguera y Arcas explores images as data, while Deb Roy uses audio, video, and the text messages in social media as data.

Some may also think that only a few specialized professionals can draw insights from data. When we look at data in the right way, however, the results can be fun, insightful, even whimsical — and accessible to everyone! Who knew, for example, that there are more relationship break-ups on Monday than on any other day of the week, or that the most break-ups (at least those discussed on Facebook) occur in mid-December? David McCandless discovered this by analyzing thousands of Facebook status updates.

Data, data, everywhere

There is more data available to us now than we can possibly process. Every minute , Internet users add the following to the big data pool (i):

• 204,166,667 email messages sent
• More than 2,000,000 Google searches
• 684,478 pieces of content added on Facebook
• $272,070 spent by consumers via online shopping
• More than 100,000 tweets on Twitter
• 47,000 app downloads from Apple
• 34,722 "likes" on Facebook for different brands and organizations
• 27,778 new posts on Tumblr blogs
• 3,600 new photos on Instagram
• 3,125 new photos on Flickr
• 2,083 check-ins on Foursquare
• 571 new websites created
• 347 new blog posts published on Wordpress
• 217 new mobile web users
• 48 hours of new video on YouTube

These numbers are almost certainly higher now, as you read this. And this just describes a small piece of the data being generated and stored by humanity. We're all leaving data trails — not just on the Internet, but in everything we do. This includes reams of financial data (from credit cards, businesses, and Wall Street), demographic data on the world's populations, meteorological data on weather and the environment, retail sales data that records everything we buy, nutritional data on food and restaurants, sports data of all types, and so on.

Governments are using data to search for terrorist plots, retailers are using it to maximize marketing strategies, and health organizations are using it to track outbreaks of the flu. But did you ever think of collecting data on every minute of your child's life? That's precisely what Deb Roy did. He recorded 90,000 hours of video and 140,000 hours of audio during his son's first years. That's a lot of data! He and his colleagues are using the data to understand how children learn language, and they're now extending this work to analyze publicly available conversations on social media, allowing them to take "the real-time pulse of a nation."

Data can provide us with new and deeper insight into our world. It can help break stereotypes and build understanding. But the sheer quantity of data, even in just any one small area of interest, is overwhelming. How can we make sense of some of this data in an insightful way?

The power of visualizing data

Visualization can help transform these mountains of data into meaningful information. In his TEDTalk, David McCandless comments that the sense of sight has by far the fastest and biggest bandwidth of any of the five senses. Indeed, about 80% of the information we take in is by eye. Data that seems impenetrable can come alive if presented well in a picture, graph, or even a movie. Hans Rosling tells us that "Students get very excited — and policy-makers and the corporate sector — when they can see the data."

It makes sense that, if we can effectively display data visually, we can make it accessible and understandable to more people. Should we worry, however, that by condensing data into a graph, we are simplifying too much and losing some of the important features of the data? Let's look at a fascinating study conducted by researchers Emre Soyer and Robin Hogarth . The study was conducted on economists, who are certainly no strangers to statistical analysis. Three groups of economists were asked the same question concerning a dataset:

• One group was given the data and a standard statistical analysis of the data; 72% of these economists got the answer wrong.
• Another group was given the data, the statistical analysis, and a graph; still 61% of these economists got the answer wrong.
• A third group was given only the graph, and only 3% got the answer wrong.

Visualizing data can sometimes be less misleading than using the raw numbers and statistics!

What about all the rest of us, who may not be professional economists or statisticians? Nathalie Miebach finds that making art out of data allows people an alternative entry into science. She transforms mountains of weather data into tactile physical structures and musical scores, adding both touch and hearing to the sense of sight to build even greater understanding of data.

Another artist, Chris Jordan, is concerned about our ability to comprehend big numbers. As citizens of an ever-more connected global world, we have an increased need to get useable information from big data — big in terms of the volume of numbers as well as their size. Jordan's art is designed to help us process such numbers, especially numbers that relate to issues of addiction and waste. For example, Jordan notes that the United States has the largest percentage of its population in prison of any country on earth: 2.3 million people in prison in the United States in 2005 and the number continues to rise. Jordan uses art, in this case a super-sized image of 2.3 million prison jumpsuits, to help us see that number and to help us begin to process the societal implications of that single data value. Because our brains can't truly process such a large number, his artwork makes it real.

The role of technology in visualizing data

The TEDTalks in this collection depend to varying degrees on sophisticated technology to gather, store, process, and display data. Handling massive amounts of data (e.g., David McCandless tracking 10,000 changes in Facebook status, Blaise Aguera y Arcas synching thousands of online images of the Notre Dame Cathedral, or Deb Roy searching for individual words in 90,000 hours of video tape) requires cutting-edge computing tools that have been developed specifically to address the challenges of big data. The ability to manipulate color, size, location, motion, and sound to discover and display important features of data in a way that makes it readily accessible to ordinary humans is a challenging task that depends heavily on increasingly sophisticated technology.

The importance of good visualization

There are good ways and bad ways of presenting data. Many examples of outstanding presentations of data are shown in the TEDTalks. However, sometimes visualizations of data can be ineffective or downright misleading. For example, an inappropriate scale might make a relatively small difference look much more substantial than it should be, or an overly complicated display might obfuscate the main relationships in the data. Statistician Kaiser Fung's blog Junk Charts offers many examples of poor representations of data (and some good ones) with descriptions to help the reader understand what makes a graph effective or ineffective. For more examples of both good and bad representations of data, see data visualization architect Andy Kirk's blog at visualisingdata.com . Both consistently have very current examples from up-to-date sources and events.

Creativity, even artistic ability, helps us see data in new ways. Magic happens when interesting data meets effective design: when statistician meets designer (sometimes within the same person). We are fortunate to live in a time when interactive and animated graphs are becoming commonplace, and these tools can be incredibly powerful. Other times, simpler graphs might be more effective. The key is to present data in a way that is visually appealing while allowing the data to speak for itself.

Changing perceptions through data

While graphs and charts can lead to misunderstandings, there is ultimately "truth in numbers." As Steven Levitt and Stephen Dubner say in Freakonomics , "[T]eachers and criminals and real-estate agents may lie, and politicians, and even C.I.A. analysts. But numbers don't." Indeed, consideration of data can often be the easiest way to glean objective insights. Again from Freakonomics : "There is nothing like the sheer power of numbers to scrub away layers of confusion and contradiction."

Data can help us understand the world as it is, not as we believe it to be. As Hans Rosling demonstrates, it's often not ignorance but our preconceived ideas that get in the way of understanding the world as it is. Publicly-available statistics can reshape our world view: Rosling encourages us to "let the dataset change your mindset."

Chris Jordan's powerful images of waste and addiction make us face, rather than deny, the facts. It's easy to hear and then ignore that we use and discard 1 million plastic cups every 6 hours on airline flights alone. When we're confronted with his powerful image, we engage with that fact on an entirely different level (and may never see airline plastic cups in the same way again).

The ability to see data expands our perceptions of the world in ways that we're just beginning to understand. Computer simulations allow us to see how diseases spread, how forest fires might be contained, how terror networks communicate. We gain understanding of these things in ways that were unimaginable only a few decades ago. When Blaise Aguera y Arcas demonstrates Photosynth, we feel as if we're looking at the future. By linking together user-contributed digital images culled from all over the Internet, he creates navigable "immensely rich virtual models of every interesting part of the earth" created from the collective memory of all of us. Deb Roy does somewhat the same thing with language, pulling in publicly available social media feeds to analyze national and global conversation trends.

Roy sums it up with these powerful words: "What's emerging is an ability to see new social structures and dynamics that have previously not been seen. ...The implications here are profound, whether it's for science, for commerce, for government, or perhaps most of all, for us as individuals."

Let's begin with the TEDTalk from David McCandless, a self-described "data detective" who describes how to highlight hidden patterns in data through its artful representation.

David McCandless

The beauty of data visualization.

i. Data obtained June 2012 from “How Much Data Is Created Every Minute?” on http://mashable.com/2012/06/22/data-created-every-minute/ .

Relevant talks

Hans Rosling

The magic washing machine.

Nathalie Miebach

Art made of storms.

Chris Jordan

Turning powerful stats into art.

Blaise Agüera y Arcas

How photosynth can connect the world's images.

The birth of a word

Data Visualization Review and Analysis Essay

Introduction, data visualization key insights and accessibility to audience.

The selected visualization, “World Cup Penalty Shootouts by Athan Mavrantonis,” is intended to provide an overview of penalty shootout performance in World Cup tournaments. I selected this visualization because it presents an engaging and informative analysis of World Cup penalty shootouts. The visual strategy used to present the information and analysis effectively provides a comprehensive overview of the data, making it easy to understand without becoming overwhelming or difficult to digest. The use of color coding makes the data easier to interpret, while the interactivity allows the user to explore different aspects of the data and draw conclusions.

Athan Mavrantonis uses an informative and engaging approach to address the audience. His visualization is designed to give football fans an interactive way to explore penalty shootout results from the World Cup (Mavrantonis, 2022). The data is presented in a visual timeline with individual tabs for each match year, allowing viewers to compare shootouts over the years. Additionally, the visuals are designed to highlight patterns and trends in penalties – for example, by color-coding each table according to success rate. This helps the audience to quickly identify how successful a particular year’s shootout was compared to other years.

Athan Mavrantonis’ Data Visualization aims to provide an engaging, comprehensive look at penalty shootout performance in World Cup matches. The visualization clarifies its purpose through a concise title and description and uses bold colors and intuitive visuals to convey information quickly (Mavrantonis, 2022). It effectively combines historical and modern data, allowing users to compare different World Cup eras. The data is presented in a way that allows users to easily explore and compare the success rates of each team and nation participating in penalty shootouts.

The presentation employs color, ordering, layout, and hierarchy to organize the data into a visually striking display that prioritizes information for viewers. Color is used to categorize teams by the percentage of penalties scored – red (saved), yellow (missed), and blue (scored) (Mavrantonis, 2022). The teams are then ordered by continent, with the most successful team within each region at the top. The layout organizes teams into a coherent grid-like structure that allows viewers to recognize patterns between countries.

In response to the two peers, data visualizations make information more accessible to their audience by using color, layout, and hierarchy to prioritize the information. Color helps viewers quickly identify patterns in a visualization while ordering helps viewers track the progress over time. The layout is important for helping viewers focus on specific visualization elements and organizing related elements together. Lastly, hierarchy establishes relationships between different levels of information. For example, in one of the visualizations discussed, it is clear that the overall trend is a peak in business formation during July 2020, with more detailed insights provided by looking at each state’s data (Ayodimeji, 2022). Therefore, to make information more accessible to the audience, authors should use color, layout, and hierarchy to prioritize information.

To make key insights in visualizations more digestible to an audience, I recommend color coding and labeling to draw attention to important elements of the visualization. For instance, in the visualization analysis of the Superstore Dashboard, the author used a combination of colors and labels to emphasize areas of the highest importance (Padham, 2022). Additionally, it is important to ensure that any graphical elements are easily understandable. This can be done by providing a legend or symbol explanation, which will help viewers better interpret the data.

Additionally, I recommend using clear and concise titles for each section to help viewers navigate the visualizations easily. To avoid this, I recommend against cluttering visualization with too much information, as this can create confusion and distract from the overall insights. It is also important to ensure consistency in labeling and visual elements so viewers can interpret the data accurately.

Ayodimeji, Z. (2022). Business Formation in the US Pandemic Year #MakeoverMonday . Public.tableau.com. Web.

Padham, P. (2022). Superstore Dashboard . Public.tableau.com. Web.

Mavrantonis, A. (2022). World Cup Penalty Shootouts . Public.tableau.com. Web.

• Aspects of Visualizations and Graphics
• Microsoft Power BI Software's Features
• "The Crow" (1994): Plot Segmentation
• Information Technology in Microsoft's Business as a Project Topic
• Reliability of Information Found on the Internet
• The British Library: Big Data Management
• Kinds of Big Data: Foundations of Business Intelligence
• Decisions Improved by Using Big Data
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2024, January 20). Data Visualization Review and Analysis. https://ivypanda.com/essays/data-visualization-review-and-analysis/

"Data Visualization Review and Analysis." IvyPanda , 20 Jan. 2024, ivypanda.com/essays/data-visualization-review-and-analysis/.

IvyPanda . (2024) 'Data Visualization Review and Analysis'. 20 January.

IvyPanda . 2024. "Data Visualization Review and Analysis." January 20, 2024. https://ivypanda.com/essays/data-visualization-review-and-analysis/.

1. IvyPanda . "Data Visualization Review and Analysis." January 20, 2024. https://ivypanda.com/essays/data-visualization-review-and-analysis/.

Bibliography

IvyPanda . "Data Visualization Review and Analysis." January 20, 2024. https://ivypanda.com/essays/data-visualization-review-and-analysis/.

• Documentation

The Evolution of Data Visualization

Posted by Allen Hillery on October 19, 2020 Chartio

Data visualization is the graphical representation of data.

Although we may think of data visualization as a modern innovation, data has been visually recorded since writing was invented. It started as tick marks on tablets and cave walls, as in the image above. Each group of tick marks with a slash represents a set of five, making the total easier to sum up.

But as the amount of data worth recording grew, symbols were needed to represent larger groupings — such as the Roman numerals “C” and “M” to represent 100 and 1,000, respectively. Eventually, more complex data than just raw counts needed to be documented, such as where to find food (maps), when to plant crops (almanacs), and how much tax needed to be collected (aggregations).

People have been visualizing data for a long time, but their ability to visualize large complex data sets quickly and easily is relatively new. The most game-changing tool for data visualization was Microsoft Excel , which was introduced less than 40 years ago. Excel empowers people to work with data in unaggregated forms in rows and columns and then, with the click of a button, produce a visual representation of this data.

But the amount of data needed to be analyzed has continued to grow, and new tools have been created to handle it.

Here is a timeline of the major data visualization milestones:

There have been many new data visualization tools created since Microsoft Excel. These tools have the ability to handle larger data sets and offer a wider array of chart types. These data visualization tools have evolved from static counts to interactive displays composed of multiple data sources. Data visualization has traditionally been a highly specialized field where practitioners have been cartographers, statisticians, and scientists. These professionals knew their data, and data visualization was their medium to share findings. Now, business professionals who are not as trained and familiar with their company’s data want to do the same thing.

Data visualization is now more focused on use cases

While data visualization tools have grown in power, they have also become more user-friendly. Unfortunately there still remain gaps in people’s ability to use them due to a lack of data literacy. Data literacy is the ability to read, understand, create, and communicate data as information. Many data visualization tools do not provide the context needed to understand the underlying data. Knowing how to best calculate metrics or represent the data requires education.

In an attempt to solve the lack of data literacy among business users, data visualization tools are now evolving again to become more use case-focused.

The four main use cases for new data visualization tools are:

BI Reporting. Data visualization tools have given analysts more capabilities to discover and communicate key trends and insights to broader audiences. These findings are primarily delivered in the form of dashboards, where executive audiences are given tools to make decisions for their business. Dashboards have been slowly replacing spreadsheets and pivot reports and provide a varying degree of interactivity to allow users to explore trends in their data. BI reporting has allowed broader audiences to become more familiar with various charts and graphs, making them more “conversational” with data but not necessarily “fluent.”

Custom Data Visualization. While traditional tools are designed to accommodate the most common charts, sometimes advanced users want to add more animation, use rare chart types, or otherwise customize the appearance of their data. Developers have leveraged R and Python to push the boundaries of data visualization tools to create complex data models. Some use cases are using these platforms for doing more complicated data blending outside of the data visualization tool, or building advanced statistical models. In a business setting, decision makers would have to be guided through these data models or be given very concise recommendations on how to proceed with their business.

Information Visualization. Traditionally, data visualization has been used within companies and universities to support specific lines of inquiry. Now tools have made it easier to share insights publicly. A trend that has been taking place in journalism is the use of data visualization to provide news and insights via data tables, graphs, maps, and infographics. This can allow the public to spend more time investigating a topic in a way that might have been hard to consume over a broadcast. Some tools make it simple to post dashboards publicly, while others make it easy to embed charts within news stories. We can see visualizations like this in the New York Times , which strings graphics together to create stories that help convey complex topics to readers.

Exploratory Data Analysis (EDA). EDA is an approach to analyzing data sets to summarize their main characteristics. Gone are the days of prepared data sets; they just take too much time, especially when data is constantly changing. Tools like Chartio provide schema-level access and click-and-drag interfaces to write SQL for you. This allows many more people in the organization to explore data and ask questions. They can begin to spot trends or outliers in the data. Becoming familiar with the data begins to build the user’s data literacy .

Looking to the future, as visualization tools become easier to use, more and more people will become data storytellers. They’ll explore data sets, discover insights, and share takeaways with others as part of their everyday workflows. As they do, they’ll want to create great-looking charts that grab their audiences’ attention and communicate insights persuasively. They’ll want more customization options, so they can tailor their charts to, say, match the look and feel of their company’s brand. And they’ll want a great user experience, with charts that load, render, and scale quickly and reliably.

At Chartio, we’re about to reshape the future of data visualization with our totally new, open-source chart library. Based on the Vega charting language, our new library will let you create beautiful, performant visualizations so you can more effectively tell stories with data. We’d love to show you a sneak peek of our new chart library, and share more details about all you can accomplish with Chartio.

Join Chartio CEO/Founder Dave Fowler and Visualization Engineer Casey Haber for an exclusive webinar on Thursday, November 19th to learn more. Register now.

(This guest post is written by Allen Hillery . Allen is an adjunct professor at Columbia University and a writer at Data Visualization Society.)

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16. What we talk about when we talk about beautiful data visualizations

From the book data visualization in society.

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Data visualization basics.

Learn the purpose and benefits of using data visualization, and find out what you will need before starting a project.

Infographics

Understand the difference between data visualization and infographics.

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Discover how basic mapping differs from complex spatial analysis.

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Understand why you might want to use a programming language to create data visualizations.

Find videos, web tutorials and workshops at UNC-Chapel Hill for learning different tools and practices related to data visualization.

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How To Write the MIT Sloan Pre-Interview Essays

Oct 25, 2022

After spending months fine-tuning your MBA application essays, you’ve finally received that long-awaited MIT interview invitation — only to discover that now you need to write more essays!

With more and more competitive candidates applying every year, elite MBA programs like MIT Sloan are always looking for new ways to distinguish the “admits” from the “dings.” 

Though they may seem small and relatively unimportant at first, MIT Sloan’s interview essays are an important element of your overall application and should be carefully crafted. That’s why we’re sharing our top tips on how to approach and write your own interview essays . By following these tips, you can ensure you stand out and land a spot at MIT Sloan. 

The MIT Sloan Pre-Interview Essay Questions 

MIT Sloan has also long required candidates who are called to interview to submit an additional essay. This year, MIT Sloan’s interview question is as follows:

Required Question #1 (Diversity)

The mission of the MIT Sloan School of Management is to develop principled, innovative leaders who improve the world and to generate ideas that advance management practice. We believe that a commitment to diversity, inclusion, equity, and well-being is a key component of both principled leadership and sound management practice. 

. In 250 words or less, please describe a time when you contributed toward making a work environment or organization more welcoming, inclusive, and diverse.

How to approach your answer

At MIT, diversity is a core value . This means that at Sloan, you’ll constantly interact with people who think and act differently than you do. 

Succeeding in such an environment requires a great deal of adaptability and flexibility, as well as a willingness to learn from those who are different. This type of environment, however, is not for everyone, which is why MIT seeks to gauge how you respond to diversity in this essay. 

We suggest you start by considering the brand you are presenting to MIT and examine which examples about diversity you can share that will add value to the stories you told in your cover letter and video. Then, make sure you narrow this list down to your single best story . In a 250-word essay , you won’t have time to fully explore multiple examples, so limit yourself to one killer story. 

Required Question #2 (Data)

We are interested in learning more about how you make data-driven decisions and communicate results. Please select one of the following prompts to respond to. 

• Please select an existing data visualization and in 250 words or less explain why it matters to you. The data visualization should be uploaded as a PDF. Examples may come from current events, a business analysis, or personal research (e.g. climate change, COVID maps, etc).
• In 250 words to less, please describe a recent data-driven decision you had to make, and include one slide presenting your analysis. The slide may include a data visualization example and should present data used in a professional context. Your slide must be uploaded as a PDF.

Considering how short the MIT application is, you should also use this as another opportunity to add something new to your application. 

With this question, MIT seeks to understand how you are able to use data to make important decisions. MIT states that this example should come from data used in a professional context, so keep this in mind. When designing your slide, remember that you’re being judged on how you present the information visually, not on the data itself.

In your essay, make sure to explain how you analyzed the data and used it to make a decision . If you are not able to show how you applied the insights gained from the data you presented in a clear, concrete way, you may want to consider a different approach. 

If you don’t have a job that requires you to use data on a regular basis, question 1 would be a better fit for you. If you do use data frequently at work, question 1 may still be a good option, but showing a real and measurable way in which you used data to make a decision is an excellent way to show the adcom you possess a great fit with their program. 

TOP TIP: Make sure not to share confidential information. If you need to “sanitize” the data, this is acceptable. 

MIT Sloan Pre-Interview Essay Examples

The mission of the MIT Sloan School of Management is to develop principled, innovative leaders who improve the world and to generate ideas that advance management practice. We believe that a commitment to diversity, inclusion, equity, and well-being is a key component of both principled leadership and sound management practice.

In 250 words or less, please describe a time when you contributed toward making a work environment or organization more welcoming, inclusive, and diverse.

As a volunteer at ORG, I helped educate 400 students from all over my city, some in very marginalized situations. Although we were providing a low-cost pre-college course, I began to notice how everyday norms we took for granted were inadvertently exacerbating difficulties faced by the underprivileged. 

For instance, I noticed that I never saw certain students at lunchtime. Lunch was in an open space where most students ate together and forged friendships. However, students with nothing to eat routinely stayed in other parts of the building and were not just isolated but, more importantly, hungry. To address this situation, I prospected potential donors, developed a proposal, and presented it in an internal all-hands meeting, gaining approval. After coordinating with the university community, I helped build a fundraising department. Our first donor, NGO, provided a sandwich and juice to every student, so those in need would not feel ashamed to be singled out. The fundraising department has since continued to build partnerships to benefit students.

This experience taught me that there are norms in place that prevent us from seeing persistent inequalities. It would have been easy to dismiss the absence of certain students as a result of shyness or simply not notice at all. Commitment to creating inclusive environments means noticing systemic problems in everyday contexts and working collaboratively toward solutions. 

Looking for MIT Sloan pre-interview essay examples? Our MBA Resource Center includes multiple examples of both the diversity and data prompts, as well as countless other resources, including interview mock sequences, sample interview responses, and so much more. Sign up here !

We are interested in learning more about how you make data-driven decisions and communicate results. Please select one of the following prompts to respond to:

• Please select an existing data visualization and in 250 words or less explain why it matters to you. The data visualization should be uploaded as a PDF. Examples may come from current events, a business analysis, or personal research (e.g. climate change, COVID maps, etc). 
• In 250 words or less, please describe a recent data-driven decision you had to make, and include one slide presenting your analysis. The slide may include a data visualization example and should present data used in a professional context. Your slide must be uploaded as a PDF. 

A few years ago, I helped lead the sale of a payment company to a strategic investor. Negotiations included a price adjustment based on the company’s future performance. When the adjustment time came, the company was performing well below the business plan, despite not having reported abrupt operational changes. After thoroughly analyzing its financial data, I realized that its credit fund, responsible for providing credit to thousands of merchants, was underperforming. While it could be easily explained by external market issues, I decided to investigate other possible explanations, such as higher default levels. I asked the fund administrator to provide me with the credit database and developed an algorithm to parse data from hundreds of thousands of transactions, which allowed me to identify a spike in default levels. After filtering the data for individual client performance, an unusually high number of reliable clients seemed to have stopped paying.

To further investigate, I talked with several departments, discovering the fund administrator had changed right at the spike period and uncovering the underlying issue: an integration problem with the new administrator corrupted the database. I asked the fund administrator to refile the financials using a corrected database. Since this would incur a fine, however, they denied my request. So, I negotiated with senior leaders and convinced them to use a “simulation” with the corrected database to support negotiations with the investor. By doing so, we almost doubled the amount my client received.

Make Sure You Succeed in any MBA Interview

Though you might not be practicing for your interviews, your competitors are. When you’re competing at such an elite level (and top MBA programs are about as elite as it gets), practice is the only way to get the edge you need. 

Working with highly-trained professionals, like our team of interview experts, can also be an excellent step to include in your interview process. 

• Maybe you don’t know where to start preparing.  
• Maybe you ramble on and lose your focus while answering. 
• Maybe you’re concerned about making errors, discussing your strengths, or addressing weaknesses.
• Maybe you’ve downloaded interview guides or have spent hours reading interview report forums—but still have questions and doubts. 

Our interview prep and practice service focuses on helping you determine what to highlight in your interview depending on the program while using appropriate, impact-driven language without being artificial, or worse, robotic. We also help you choose (and perfect) culturally appropriate examples based on the schools you’re interviewing for. 

As our client João, who was admitted to MIT Sloan, said:

“I would like to thank Ellin and her team for the extremely careful service they provided. After talking to and working with a few consulting firms, her work was the most structured I found.

Ellin helped me with my interview preparation and provided specific feedback about my pre-interview essays, especially for the MIT Sloan application. During all sessions, Ellin and her team demonstrated a deep knowledge of every school and offered excellent suggestions on how to improve both the content and the structure of my answers. Their service was definitely critical for my acceptance.”

Don’t undersell your success. Set up your 1:1 interview prep and show that you belong at a top MBA program! 

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Big Data: What It Is and Why It’s Important

These huge datasets reveal patterns and trends for better decision making.

What Is Big Data?

Big data refers to large, diverse data sets made up of structured, unstructured and semi-structured data. This data is generated continuously and always growing in size, which makes it too high in volume, complexity and speed to be processed by traditional data management systems.

Big Data Definition

Big data refers to massive, complex data sets that are rapidly generated and transmitted from a wide variety of sources. Big data sets can be structured, semi-structured and unstructured, and they are frequently analyzed to discover applicable patterns and insights about user and machine activity.

Big data is used across almost every industry to draw insights, perform analytics, train  artificial intelligence  and  machine learning  models, as well as help make data-driven business decisions.

Why Is Big Data Important?

Data is generated anytime we open an app, use a search engine or simply travel place to place with our mobile devices. The result? Massive collections of valuable information that companies and organizations manage, store, visualize and analyze.

Traditional data tools aren’t equipped to handle this kind of complexity and volume, which has led to a slew of specialized  big data software platforms  designed to manage the load.

Though the large-scale nature of big data can be overwhelming, this amount of data provides a heap of information for organizations to use to their advantage. Big data sets can be mined to deduce patterns about their original sources, creating insights for improving business efficiency or predicting future business outcomes.

As a result, big data analytics is used in nearly every industry to identify patterns and trends, answer questions, gain insights into customers and tackle complex problems.  Companies and organizations use the information  for a multitude of reasons like automating processes, optimizing costs, understanding customer behavior, making forecasts and targeting key audiences for advertising.

The 3 V’s of Big Data

Big data is  commonly characterized  by three V’s:

Volume refers to the huge amount of data that’s generated and stored. While traditional data is measured in familiar sizes like megabytes, gigabytes and terabytes, big data is stored in petabytes and zettabytes.

Variety refers to the different types of data being collected from various sources, including text, video, images and audio. Most data is unstructured, meaning it’s unorganized and difficult for conventional data tools to analyze. Everything from emails and videos to scientific and meteorological data can constitute a big data stream, each with their own unique attributes.

Big data is generated, processed and analyzed at high speeds. Companies and organizations must have the capabilities to harness this data and generate insights from it in real-time, otherwise it’s not very useful. Real-time processing allows decision makers to act quickly.

How Big Data Works

Big data is produced from multiple data sources like mobile apps, social media, emails, transactions or  Internet of Things (IoT)  sensors, resulting in a continuous stream of varied digital material. The diversity and constant growth of big data makes it inherently difficult to extract tangible value from it in its raw state. This results in the need to use specialized big data tools and systems, which help collect, store and ultimately translate this data into usable information. These systems make big data work by applying three main actions — integration, management and analysis.

1. Integration 

Big data first needs to be gathered from its various sources. This can be done in the form of  web scraping  or by accessing databases, data warehouses, APIs and other data logs. Once collected, this data can be ingested into a big data pipeline architecture, where it is prepared for processing.

Big data is often raw upon collection, meaning it is in its original, unprocessed state. Processing big data involves cleaning, transforming and aggregating this raw data to prepare it for storage and analysis.

2. Management

Once processed, big data is stored and managed within  the cloud  or on-premises storage servers (or both). In general, big data typically requires  NoSQL databases  that can store the data in a scalable way, and that doesn’t require strict adherence to a particular model. This provides the flexibility needed to cohesively analyze disparate sources of data and gain a holistic view of what is happening, how to act and when to act on data.

3. Analysis 

Analysis is one the final steps of the big data lifecycle, where the data is explored and analyzed to find applicable insights, trends and patterns. This is frequently carried out using big data analytics tools and software. Once useful information is found, it can be applied to make business decisions and communicated to stakeholders in the form of data visualizations.

Uses of Big Data

Here are a few  examples of industries  where the big data revolution is already underway:

Finance and insurance industries  utilize big data  and predictive analytics for fraud detection, risk assessments, credit rankings, brokerage services and blockchain technology, among other uses.  Financial institutions  also use big data to enhance their cybersecurity efforts and personalize financial decisions for customers.

Hospitals, researchers and pharmaceutical companies  adopt big data solutions  to improve and advance healthcare. With access to vast amounts of patient and population data, healthcare is enhancing treatments, performing more effective research on diseases like cancer and Alzheimer’s, developing new drugs, and gaining critical insights on patterns within population health.

Using  big data in education  allows educational institutions and professionals to better understand student patterns and create relevant educational programs. This can help in personalizing lesson plans, predicting learning outcomes and tracking school resources to reduce operational costs.

Retail utilizes big data  by collecting large amounts of customer data through purchase and transaction histories. Information from this data is used to predict future consumer behavior and personalize the shopping experience.

Big data in government  can work to gather insights on citizens from public financial, health and demographic data and adjust government actions accordingly. Certain legislation, financial procedures or crisis response plans can be enacted based on these big data insights. 

Big data in marketing  helps provide an overview of user and consumer behavior for businesses. Data gathered from these parties can reveal insights on market trends or buyer behavior, which can be used to direct marketing campaigns and optimize marketing strategies.

If you’ve ever used Netflix, Hulu or any other streaming services that  provide recommendations , you’ve witnessed big data at work. Media companies analyze our reading, viewing and listening habits to build individualized experiences. Netflix even uses data on  graphics, titles and colors  to make decisions about customer preferences.

Big Data Challenges

1. volume and complexity of data.

Big data is massive, complicated and ever growing. This makes it difficult in nature to capture, organize and understand, especially as time goes on. In order to manage big data, new technologies have to be developed indefinitely and organizational  big data strategies  have to continually adapt. 

2. Integration and Processing Requirements

Aside from storage challenges, big data also has to be properly processed, cleaned and formatted to make it useful for analysis. This can take a considerable amount of time and effort due to big data’s size, multiple data sources and combinations of structured, unstructured and semi-structured data. Processing efforts and identifying what information is useful can also be compounded in the case of excess noisy data or data corruption.

3. Cybersecurity and Privacy Risks

Big data systems can sometimes handle sensitive or personal user information, making them vulnerable to cybersecurity attacks or privacy breaches. As more personal data resides in big data storage, and at such massive scales, this raises the difficulty and costs of safeguarding this data from criminals. Additionally, how businesses collect personal data through big data systems  may not comply  with regional data collection laws or regulations, leading to a breach of privacy for affected users.

Big Data Technologies

Big data technologies describe the tools used to handle and manage data at enormous scales. These technologies include those used for big data analytics, collection, mining, storage and visualization.

Data Analysis Tools

Data analysis tools  involve software that can be used for big data analytics, where relevant insights, correlations and patterns are identified within given data.

Big Data Tools

Big data tools refer to any  data platform ,  database ,  business intelligence tool  or application where large data sets are stored, processed or analyzed. 

Data Visualization Tools

Data visualization tools  help to display the findings extracted from big data analytics in the form of charts, graphs or dashboards.

History of Big Data

“Big data” as a term became popularized  in the mid-1990s  by computer scientist John Mashey, as Mashey used the term to refer to handling and analyzing massive data sets.

In 2001, Gartner analyst Doug Laney characterized big data as having three main traits of volume, velocity and variety, which came to be known as the three V’s of big data.

Starting in the 2000s, companies began conducting big data research and developing solutions to handle the influx of information coming from the internet and web applications. Google created the Google File System  in 2003  and MapReduce  in 2004 , both systems meant to help process large data sets. Using Google’s research on these technologies, software designer Doug Cutting and computer scientist Mike Cafarella developed Apache Hadoop in 2005, a software framework used to store and process big data sets for applications. In 2006, Amazon released Amazon Web Services (AWS), an on-demand cloud computing service that became a popular option to store data without using physical hardware.

In the 2010s, big data gained more prevalence as mobile device and tablet adoption increased. According to IBM  as of 2020 , humans produce 2.5 quintillion bytes of data on a daily basis, with the world expected to produce 175 zettabytes of data by 2025. As connected devices and internet usage continue to grow, so will big data and its possibilities for enhanced analytics and real-time insights.

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How to Write Good TOEFL Integrated Essay- Pro Tips 2024

TOEFL Integrated Essay: In the TOEFL writing section, candidates encounter two distinct tasks: Task 1, known as the TOEFL Integrated Writing Task, and Task 2, referred to as the TOEFL Independent Writing Task. Together, these tasks constitute a 50-minute examination assessing candidates’ reading, writing, and listening abilities in English.

This article primarily focuses on Task 1, providing detailed insights into its structure and a sample question for better understanding.

What is TOEFL Writing Task 1? – TOEFL Integrated Writing Task 

In the Integrated Writing section of the TOEFL, candidates are required to utilize their reading, listening, and writing skills to complete the task. They are given three minutes to prepare for an essay by reading a brief passage and listening to an audio clip on a single topic.

Following the preparation time, candidates have 20 minutes to compose an essay in response to the two sources. These sources can present two scenarios:

• Contradictory: The audio clip contradicts the information presented in the passage.
• Supportive: The audio clip supports the content of the passage.

Regardless of the scenario, candidates must summarize the topic within a range of 150 to 225 words. Moreover, they are expected to establish a clear connection between the information provided in the passage and the content of the audio clip.

Relationship Between Passage and Audio Lecture

• Argumentative Style (Common): The passage presents a viewpoint, while the lecture presents a contrasting perspective.
• Problem-Solution Style (Less Common): The passage introduces an issue, and the lecture offers potential solutions.
• Solution-Problem Style (Less Common): The passage proposes solutions to a problem, and the lecture critiques or raises concerns about these solutions.

The Question

After the lecture finishes, the question will be shown.  It will look something like this:

• Summarize the points made in the lecture, being sure to explain how they oppose specific points made in the reading passage.
• Summarize the points made in the lecture, being sure to explain how they answer the specific problems presented in the reading passage.
• Summarize the points made in the lecture, being sure to explain how they cast doubt on specific solutions presented in the reading passage.

After you see the question, you will get 20 minutes for planning, writing and revising the essay.

Note-Taking Tips

• Prepare your paper before the question begins by labeling sections as “reading” and “listening” and drawing arrows to indicate connections between them. Even though you’ll have access to the article as you write, it’s advisable to take notes during the reading period to ensure attentive comprehension.

TOEFL Integrated Essay

• Employ shorthand such as “grav” for gravity, “cond” for conditions, and “effec” for effects to save time while taking notes. Use “x” as a placeholder for negatives like “not,” “no,” or “can’t.”
• Immediately after the lecture concludes, expand upon your notes while the details are still fresh in your mind. Use a pencil for practice sessions, as this is the writing implement allowed on test day.

TOEFL Writing Integrated Task Exercises

Question 1: summarize the key points outlined in the passage and provide insights based on the lecture..

Sample Reading Passage: Sea Otters, once abundant along the west coast of North America from California to Alaska, have experienced a rapid decline in population in recent years. This decline has had a direct impact on the coastal ecosystem due to the important role Sea Otters play in maintaining balance. Investigators attribute this decline to two main factors: attacks by predators and environmental pollution. Environmental pollution, particularly from oil rigs along the Alaskan coast, has been identified as a significant contributor to the decline. Water samples taken by investigators revealed high chemical content that can affect the immune systems of marine life, leading to mortality. Similar declines in other sea mammals, such as seals, further support the hypothesis of water pollution as a primary cause. The uneven decline of Sea Otters along the coast is also attributed to pollution concentration influenced by ocean currents and natural factors.

Insights: The lecture provides additional insight into the decline of Sea Otter populations along the Alaskan coast. It emphasizes the role of environmental pollution, particularly from oil rigs, as a major contributing factor. The lecture highlights how pollution affects marine life’s immune systems, leading to increased mortality rates. Furthermore, it suggests that pollution concentration, influenced by ocean currents and natural factors, unevenly affects Sea Otter populations along the coast.

Question 2: Summarize the lecturer’s viewpoint and its relation to the provided reading passage.

Sample Reading Passage: The International Astronomical Union Conference announced the reclassification of Pluto as a dwarf planet on August 24, 2006. Initially discovered in 1930, Pluto was thought to be similar in size to Earth but was later found to be smaller than Earth’s moon and other moons. The discovery of numerous planetary objects similar to Pluto, such as Eris, led to its reclassification. While the news may disappoint Pluto enthusiasts, it reflects the progress of science. Despite the emotional attachment to Pluto as a planet, its reclassification has been widely accepted, especially among current students who recognize only eight planets in the solar system.

Insights: The lecture reinforces the reclassification of Pluto as a dwarf planet and its acceptance as a scientific advancement. It acknowledges the emotional attachment to Pluto as a planet but emphasizes the importance of scientific progress in understanding celestial bodies. Additionally, it highlights how current students recognize only eight planets in the solar system, reflecting the widespread acceptance of Pluto’s reclassification.

TOEFL Integrated Writing Template to Follow for Better Score

To effectively tackle the integrated writing task in the TOEFL exam, candidates should adhere to the recommended template outlined below:

• Introduction Paragraph: Commence the essay by stating that both the lecture and the reading passage address the topic of (topic). Highlight that while the reading passage supports the notion that (thought), the lecturer expresses a different viewpoint by stating (sayings). Emphasize that there are points of agreement/disagreement between the lecturer and the author, which will be elaborated upon in the subsequent essay.
• Body Paragraphs: In the first body paragraph, discuss the assertion made in the reading passage (statement 1) regarding (discussion). Provide context to the point and mention how it is either challenged or affirmed by the lecturer in the audio clip, who states (statement).
• Move on to the second body paragraph , focusing on the second statement mentioned in the reading passage. Explain the point made by the author and how it relates to the topic. Then, present the lecturer’s response, which either aligns with or contradicts the statement. Provide the lecturer’s perspective by including (audio content) and elaborating on their stance with (discussion).
• Transition to the third body paragraph , addressing the third statement proposed by the author. Clarify the suggestion made and its significance in the context of the topic. Subsequently, incorporate the lecturer’s stance, indicating whether they agree or disagree with the statement. Support the lecturer’s viewpoint with (statement) and provide further explanation with (explanation).
• Conclusion (Optional): Optionally, conclude with a brief summary of the main points discussed in the essay, reinforcing the key arguments presented and the relationship between the reading passage and the lecturer’s perspective.

Tips for TOEFL Writing Tasks

Here are essential strategies for TOEFL writing success:

• Allocate your time wisely, ensuring each paragraph fits within the 20-minute timeframe. Reserve the final 5 minutes for proofreading and correcting any grammatical mistakes.
• Develop a habit of concise writing from the outset of your preparation.
• While listening to the audio, actively jot down key points to ensure your essay encompasses all crucial details.
• Incorporate transitional phrases to enhance the coherence and cohesion of your writing.
• Utilize abbreviated notes during the listening phase to save time and aid in recall during the writing process.

TOEFL Integrated Essay is a chance for test-takers to show how well they understand and explain information from different sources. It’s not just about understanding—it’s about organizing thoughts clearly, giving reasons for ideas, and writing well.

To do well, it is important to practice and learn how to approach this task. With practice and focus, test-takers can do well on the TOEFL writing section.

Related Links

• TOEFL Full Form
• TOEFL Exam 2024
• R ole of TOEFL Score for US Universities 2024
• TOEFL Exam Dates 2024

TOEFL Integrated Essay- FAQs

What is the toefl integrated essay.

The TOEFL Integrated Essay is the first writing task on the TOEFL test. It requires test-takers to read a passage and listen to a lecture on the same topic. Then, they must write an essay that summarizes the key points and explains how they relate to each other.

How much time do I have for the TOEFL Integrated Essay?

Test-takers have a total of 20 minutes to complete the TOEFL Integrated Essay. This includes three minutes for reading the passage, listening to the lecture, and taking notes, and 17 minutes for writing the essay.

What is the structure of the TOEFL Integrated Essay?

The essay consists of an introduction, three body paragraphs, and a conclusion. In the introduction, test-takers introduce the topic and briefly mention the main points. Each body paragraph discusses one main point from the passage and the corresponding point from the lecture. Finally, the conclusion summarizes the main points and restates the overall relationship between the passage and the lecture.

How should I prepare for the TOEFL Integrated Essay?

To prepare for the TOEFL Integrated Essay, practice reading academic passages and listening to lectures on various topics. Work on taking effective notes during the reading and listening phases, and practice writing essays that summarize the main points and explain their relationship.

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Dow Theory Flashes Bear Signal

David Keller

Dow Theory is based on the foundational work of Charles Dow, considered the "Father of Technical Analysis." Many of the tools we employ to better analyze market structure and investor sentiment, from trend analysis to index construction, are derived from Dow's original essays from the early 20th century.

Dow compared two indexes to gauge the strength of the economy: the Dow Industrials which represented the producers of goods, and the Dow Railroads, which tracked the distributors of goods. If both of these indexes confirmed new highs, then economic conditions were strong and the markets were in a bullish phase.

In the modern age of financial analysis, when the Dow Industrials and Dow Transports (which evolved from the original railroad index) do not confirm a new high, we have a "bearish confirmation," which indicates trend exhaustion. One of my guests this week on The Final Bar , Jeff Huge of JWH Investment Partners, highlighted this Dow Theory signal, leading us to dig deeper into the methodology. Today, we'll walk through what this indicator implies for stocks in Q2, and also explore two more modern versions of Dow Theory.

Here, we are showing the traditional version of Dow Theory, with the Dow Industrials in the top panel and the Dow Transports in the bottom panel. Note how the Dow Industrials made a new all-time high recently, pushing above 40,000 for the first time in history. Also note how the Dow Transports have not confirmed this recent high, actually trending lower over the last two months. Because only one of the indexes has made a new high, and that breakout was not confirmed by the other index, Dow would suggest that there is internal weakness in the market and a likely major market top.

Proponents of traditional Dow Theory would cite the long-term success of these signals, which have often provided an excellent early warning sign of danger ahead. Detractors would explain that the Dow Industrials index in 2024 includes lots of "non-industrial" names like banks and service companies. For example, technology stocks like Microsoft and Salesforce are part of the Dow Industrials! And companies like Amazon.com are not included in the Dow Transports, even though they have an exceptionally robust built-in distribution network.

So what else can we do to apply Dow's theory in 2024? What about if we use the S&P 500 to represent the "old economy" names and the Nasdaq Composite to track the "new economy" companies?

When we go with this "New Dow Theory", we realize that both the S&P 500 and Nasdaq Composite have already achieved new all-time highs in May 2024. So there is no bearish non-confirmation as we see with traditional Dow Theory. The problem with this approach? The mega-cap growth stocks dominate both of these market cap-weighted indexes! So we're essentially double counting the largest names.

This has led to me to what I call the "Newer Dow Theory", using equal-weighted indexes for the S&P 500 and the Nasdaq 100. This should effectively neutralize the mega-cap overweight, particularly in the S&P 500 index.

This is where things get a little more concerning for market bulls. While the S&P 500 has already made a new all-time high in Q2, the equal-weighted S&P is still below its March high. And the equal-weighted Nasdaq 100, while holding up quite well in recent weeks, has also failed to push to a new all-time high.

For now, this Newer Dow Theory comparison suggests market weakness, using Charles Dow's simple analysis of market trends. As he determined over 120 years ago, an uptrend is a pattern of higher highs and higher lows. So, if the highs aren't getting higher, the uptrend should be brought into question.

Thus, while traditional Dow Theory has already flashed a bearish signal, I'm watching this Newer Dow Theory as May transitions into June. If we get a bearish non-confirmation on this final chart, or if neither equal-weighted index manages to score another new high, market bears may be proven right as we enter the summer months!

P.S. Ready to upgrade your investment process? Check out my free behavioral investing course !

David Keller, CMT

Chief Market Strategist

StockCharts.com

Disclaimer: This blog is for educational purposes only and should not be construed as financial advice. The ideas and strategies should never be used without first assessing your own personal and financial situation, or without consulting a financial professional.

The author does not have a position in mentioned securities at the time of publication. Any opinions expressed herein are solely those of the author and do not in any way represent the views or opinions of any other person or entity.

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