Category Archives: Data Visualization

DataViz: Crayola Crayons – How Color Has Changed

Readers:

When I was a young boy, I loved to color with my big box of Crayola Crayons. I would pull out blank sheets of paper and create multi-colored masterpieces (at least my mother said so).

eight_crayons-200x138Crayola’s crayon chronology tracks their standard box, from its humble eight color beginnings in 1903 to the present day’s 120-count lineup. According to Crayola, of the seventy-two colors from the official 1975 set – sixty-one survive. [1]

A creative dataviz type who goes by the name Velociraptor (referred from here as “Velo”) created the chart below to show the historical crayonology (I just made that word up!) of Crayola Crayons colors.

 

crayola_crayon_color_chart-520x520Velo gently scraped Wikipedia’s list of Crayola colors, corrected a few hues, and added the standard 16-count School Crayon box available in 1935.

Except for the dayglow-ski-jacket-inspired burst of neon magentas at the end of the ’80s, the official color set has remained remarkably faithful to its roots!

Ever industrious, Velo also calculated the average growth rate: 2.56% annually. For maximum understandability, he reformulated it as “Crayola’s Law,” which states:

The number of colors doubles every 28 years!

If the Law holds true, Crayola’s gonna need a bigger box, because by the year 2050, there’ll be 330 different crayons! [1]

A Second Version

Velo was not satisfied with his first version, so he produced the second version below. [2]

Crayola Color Chart

A Third Version (and interactive too!)

Click through to the interactive version for a larger view with mouseover color names!

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References:

[1] Stephen Von Worley, Color Me A Dinosaur, The History of Crayola Crayons, Charted, Data Pointed, January 15, 2010, http://www.datapointed.net/2010/01/crayola-crayon-color-chart/.

[2] Stephen Von Worley, Somewhere Over The Crayon-Bow, A Cheerier Crayola Color Chronology, Data Pointed, October 14, 2010, http://www.datapointed.net/2010/10/crayola-color-chart-rainbow-style/.

Infographic: Happy Diwali!

Readers:

INDIAN GIRL LIGHTS A DEEPAWALI LAMP IN AHMEDABADWhile called the “Festival of Lights,” Diwali is most importantly a day to become aware of one’s “inner light.” In Hindu philosophy there is an idea of “Atman,” something beyond the body and mind which is pure, infinite and eternal. Today is a celebration of “good” versus “evil”; A day when the light of higher knowledge dispels ignorance. With this awakening comes compassion and joy.

The background story and practices vary region to region. Many people celebrate by lighting fireworks and sharing sweets and candies. Diwali is a holiday celebrated across a vast array of countries and religions. It is celebrated in India, Nepal, Sri Lanka, Myanmar, Mauritius, Guyana, Trinidad & Tobago, Suriname, Malaysia, Singapore and Fiji, by Hindus, Jains, Sikhs and Buddhists.

This informative infographic is from 2012, but I like the information about Diwali it provides and thought of sharing.

Namaste!

Michael

diwali_infographic_final1

Source: Metal Gaia, Happy Diwali!, November 13, 2012, http://metal-gaia.com/2012/11/13/happy-diwali/.

Stephen Few: Now You See It

Portland

Readers:

Stephen_Few2I was in Portland, Oregon last week attending three data visualization workshops by industry expert, Stephen Few. I was very excited to be sitting at the foot of the master for three days and soak in all of this great dataviz information.

Last Thursday, was the third workshop, Now You See It which is based on Steve’s best-selling book (see photo below).

To not give away too much of what Steve is teaching in the workshops, I have decided to discuss one of our workshop topics, human perceptual and cognitive strengths.

You can find future workshops by Steve on his website, Perceptual Edge.

Best Regards,

Michael

Now You See It

 

Designed for Humans

Good visualizations and good visualization tools are carefully designed to take advantage of human perceptual and cognitive strengths and to augment human abilities that are weak. If the goal is to count the number of circles, this visualization isn’t well designed. It is difficult to remember what you have and have not counted.

Quickly, tell me how many blue circles you see below.

Design for Humans 1

The visualization below, shows the same number of circles, however, is well designed for the counting task. Because the circles are grouped into small sets of five each, it is easy to remember which groups have and have not been counted, easy to quickly count the number of circles in each group, and easy to discover with little effort that each of the five groups contains the same number of circles (i.e., five), resulting in a total count of 25 circles.

Design for Humans 2

The arrangement below is even better yet.

Design for Humans 3

Information visualization makes possible an ideal balance between unconscious perceptual and conscious cognitive processes. With the proper tools, we can shift much of the analytical process from conscious processes in the brain to pre-attentive processes of visual perception, letting our eyes do what they do extremely well.

Stephen Few: Information Dashboard Design

Readers:

Stephen_Few2I am in Portland, Oregon this week attending three data visualization workshops by industry expert, Stephen Few. I am very excited to be sitting at the foot of the master for three days and soak in all of this great dataviz information.

Today, was the second workshop, Information Dashboard Design which is based on Steve’s best-selling book (see photo below).

To not give away too much of what Steve is teaching in the workshops, I have decided to discuss one of the dashboard exercises we did in class. The goal here was to find what we feel is wrong with the dashboard.

I will show you the dashboard first. Then, you can see our critique below.

You can find future workshops by Steve on his website, Perceptual Edge.

Best Regards,

Michael

Information Dashboard Design

 

Dashboard To Critique

CORDA Airlines Dashboard

Critique Key Points

  • Top left chart – Only left hand corner chart has anything to do with flight loading
  • Top left chart – are flight numbers useful?
  • Two Expand/Print buttons – Need more clarity (right-click on chart would be a better choice)
  • Top right chart – Poor use of pie charts – size of pies are telling largest sales channel – use small multiple bar charts, total sales as a fourth bar chart
  • Redundant use of “February” – In the title and in charts
  • Bottom left chart – why does it have a pie chart in it?
  • Bottom right chart – map may be better as a bar chart (geographical display could be useful if we had more information). Current way bubbles are being expressed is not useful (use % cancellations instead). Symbols may have a different meaning every day
  • Bottom right chart – CORDAir Logo – is this necessary?
  • Location of drop-down. Not clear if it applies to top left chart or all charts
  • Backgrounds – heavy colors, gradients
  • Instructions should be in a separate help document. Only need to learn this once.
  • Top left chart: Faint Image in background. Suppose to look like a flight seating map. Do you really want to see this every day? It is a visual distraction.
  • IMPORTANT: Is there visual context offered with any of the graphs? No. This is critical.

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Dashboard Example Source: Website of Corda Technologies Incorporated, which has since been acquired by Domo.

Stephen Few: Show Me The Numbers

Readers:

Stephen_Few2I am in Portland, Oregon this week attending three data visualization workshops by industry expert, Stephen Few. I am very excited to be sitting at the foot of the master for three days and soak in all of this great dataviz information.

Yesterday, was the first workshop, Show Me the Numbers which is based on Steve’s best-selling book (see photo below).

To not give away too much of what Steve is teaching in the workshops, I have decided to give one “before and after” example each day with Steve’s explanation of why he made the changes he did.

You can find future workshops by Steve on his website, Perceptual Edge.

Best Regards,

Michael

Show Me the Numbers

 

“Before” Example

In the example below, the message contained in the titles is not clearly displayed in the graphs. The message deals with the ratio of indirect to total sales – how it is declining domestically, while holding steady internationally. You’d have to work hard to get this message the display as it is currently designed.

Before - Show Me the Numbers

 

“After” Example

The revised example below, however, is designed very specifically to display the intended message. Because this graph, is skillfully designed to communicate, its message is crystal clear. A key feature that makes this so is the choice of percentage for the quantitative scale, rather than dollars.

After - Show Me the Numbers

Additional Thoughts From Steve

The type of graph that is selected and the way it’s designed also have great impact on the message that is communicated. By simply switching from a line graph to a bar graph, the decrease in job satisfaction among those without college degrees in their later years is no longer as obvious.

More Thoughts - Show Me the Numbers

DataViz: Squaring the Pie Chart (Waffle Chart)

Readers:

Robert-Kosara-Tableau-Software-200x200In the past, I would have highly condemned pie charts without giving you much explanation why. However, Dr. Robert Kosara (photo, left), posted a great thought study of pie charts on his wonderful blog, EagerEyes.org, that I want to share with you.

Dr. Kosara is a Visual Analytics Researcher at Tableau Software, with a special interest in the communication of, and storytelling with, data. He has a Ph.D. in Computer Science from Vienna University of Technology.

Also, as part of his blog post, Robert offers an alternative way to create pie charts: using waffle charts or square pie charts.

Dr. Kosara is also one of the great minds behind Tableau’s new storytelling feature. I hope you enjoy his creative thoughts as much as I do.

Best Regards,

Michael

The Pie Chart

Dr. Kosara contends that pie charts are perhaps the most ubiquitous chart type; they can be found in newspapers, business reports, and many other places. But few people actually understand the function of the pie chart and how to use it properly. In addition to issues stemming from using too many categories, the biggest problem is getting the basic premise: that the pie slices sum up to a meaningful whole.

Touchstone Energy Corporation Pie Chart
Robert points out that the circle (the “pie”) represents some kind of whole, which is made up of the slices. What this means is that the pie chart first and foremost represents the size relationship between the parts and the entire thing. If a company has five divisions, and the pie chart shows profits per division, the sum of all the slices/divisions is the total profits of the company.

Five Slices

 

If the parts do not sum up to a meaningful whole, they cannot be represented in a pie chart, period. It makes no sense to show five different occupations in a pie chart, because there are obviously many missing. The total of such a subsample is not meaningful, and neither is the comparison of each individual value to the artificial whole.

Slices have to be mutually exclusive; by definition, they cannot overlap. The data therefore must not only sum up to a meaningful whole, but the values need to be categorized in such a way that they are not counted several times. A good indicator of something being wrong is when the percentages do not sum up to 100%, like in the infamous Fox News pie chart.

The Infamous Fox News Pie Chart

Fox News Pie Chart

In the pie chart above, people were asked which potential candidates they viewed favorably, but they could name more than one. The categories are thus not mutually exclusive, and the chart makes no sense. At the very least, they would need to show the amount of overlap between any two (and also all three) candidates. Though given the size of the numbers and the margin of error in this data, the chart is entirely meaningless.

When to Use Pie Charts

Dr. Kosara points out that there are some simple criteria that you can use to determine whether a pie chart is the right choice for your data.

  • Do the parts make up a meaningful whole? If not, use a different chart. Only use a pie chart if you can define the entire set in a way that makes sense to the viewer.
  • Are the parts mutually exclusive? If there is overlap between the parts, use a different chart.
  • Do you want to compare the parts to each other or the parts to the whole? If the main purpose is to compare between the parts, use a different chart. The main purpose of the pie chart is to show part-whole relationships.
  • How many parts do you have? If there are more than five to seven, use a different chart. Pie charts with lots of slices (or slices of very different size) are hard to read.

In all other cases, do not use a pie chart. The pie chart is the wrong chart type to use as a default; the bar chart is a much better choice for that. Using a pie chart requires a lot more thought, care, and awareness of its limitations than most other charts.

Alternative: Squaring the Pie

A little-known alternative to the round pie chart is the square pie or waffle chart. It consists of a square that is divided into 10×10 cells, making it possible to read values precisely down to a single percent. Depending on how the areas are laid out (as square as possible seems to be the best idea), it is very easy to compare parts to the whole. The example below is from a redesign Dr. Kosara did a while ago about women and girls in IT and computing-related fields.

Kosara Square Pie

Links to Examples of Waffle Charts

I did a little Googling and found a few great examples of Waffle Charts. I have provided links to examples in Tableau, jQuery, R and Excel. I hope in the new month or so to create an example for you using MicroStrategy.

Squaring The Pie

Sources:

WIRED: A Redesigned Parking Sign So Simple That You’ll Never Get Towed

web-snow-day-1

Your car gets towed, and who do you blame? Yourself? God no, you blame that impossibly confusing parking sign. It’s a fair accusation, really. Of all the questionable communication tools our cities use, parking signs are easily among the worst offenders. There are arrows pointing every which way, ambiguous meter instructions and permit requirements. A sign will tell you that you can park until 8 am, then right below it another reading you’ll be towed. It’s easy to imagine that beyond basic tests for legibility, most of these signs have never been vetted by actual drivers.

Like most urban drivers, Nikki Sylianteng was sick of getting tickets. During her time in Los Angeles, the now Brooklyn-based designer paid the city far more than she would’ve liked to. So she began thinking about how she might be able to solve this problem through design. She realized that with just a little more focus on usability, parking signs could actually be useful. “I’m not setting out to change the entire system,” she says. “It’s just something that I thought would help frustrated drivers.” [1]

Sylianteng notes: [2]

I’ve gotten one-too-many parking tickets because I’ve misinterpreted street parking signs. The current design also poses a driving hazard as it requires drivers to slow down while trying to follow the logic of what the sign is really saying. It shouldn’t have to be this complicated.

The only questions on everyone’s minds are:
1. “Can I park here now?”
2. “Until what time?”

My strategy was to visualize the blocks of time when parking is allowed and not allowed. I kept everything else the same – the colors and the form factor – as my intention with this redesign is to show how big a difference a thoughtful, though conservative and low budget, approach can make in terms of time and stress saved for the driver. I tried to stay mindful of the constraints that a large organization like the Department of Transportation must face for a seemingly small change such as this.

01 two-step

The sign has undergone multiple iterations, but the most recent features a parking schedule that shows a whole 24 hours for every day of the week. The times you can park are marked by blocks of green, the times you can’t are blocked in a candy-striped red and white. It’s totally stripped down, almost to the point of being confusing itself. But Sylianteng says there’s really no need for the extraneous detailed information we’ve become accustomed to. “Parking signs are trying to communicate very accurately what the rules actually are,” she says. “I’ve never looked at a sign and felt like there was any value in knowing why I couldn’t park. These designs don’t say why, but the ‘what’ is very clear.”

Sylianteng’s design still has a way to go. First, there’s the issue of color blindness, a factor she’s keenly aware of. The red and green are part of the legacy design from current signs, but she says it’s likely she’d ultimately change the colors to something more universal like blue. Then there’s the fact that urban parking is a far more complex affair than most of us care to know. There’s an entire manual on parking regulations; and Sylianteng’s design does gloss over rules concerning different types of vehicles and space parameters indicating where people can park. She’s working on ways to incorporate all of that without reverting back to the information overload she was trying to avoid in the first place. [1]

redesigned-parking-inline2

Sylianteng also posted on her blog an illustration of the problem in terms of biocost, as part of her Cybernetics class with Paul Pangaro. [2]

Biocost_ParkingSign

Sylianteng has been going around Manhattan and Brooklyn hanging up rogue revamped parking signs. “A friend of mine called it functional graffiti,” she says. She’ll stick a laminated version right below the city-approved version and ask drivers to leave comments. In that way, Sylianteng’s design is still a ways away from being a reality, but so far, she’s gotten pretty good feedback. “One person wrote: ‘The is awesome. The mayor should hire you.’” [1]

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Sources:

[1] Liz Stinson, A Redesigned Parking Sign So Simple That You’ll Never Get Towed, Wired, July 15, 2014, http://www.wired.com/2014/07/a-redesigned-parking-sign-so-simple-youll-never-get-towed-again.

[2] Nikki Sylianteng, blog, http://nikkisylianteng.com/project/parking-sign-redesign/.

12 JavaScript Libraries for Data Visualization

Readers:

This is from a blog post by Thomas Greco.

Thomas GrecoThomas is a web developer / graphic designer living in New York City. When Thomas isn’t striving towards front­end perfection, he enjoys hanging with friends, going to concerts, and exploring through the wilderness!

Thomas has provided twelve JavaScript frameworks that are extremely useful for data visualization. Thomas feels that a more heavy focus is being placed on JavaScript as a data visualization tool.

I tried the demos for these JavaScript frameworks and they are very impressive. I hope you enjoyed this information as much as I did.

Best regards,

Michael

Dygraphs.js

The Dygraphs.js library allows developers to create interactive charts using the X and Y axis to display powerful diagrams. The more data being parsed, the higher the functionality of the graph. That being said, Dygraphs was built for these visualizations to contain a multitude of views. For example, Dygraphs.js makes it capable to analyze separate portions of a data-set, such as specific months, in addition to the timeframe in its entirety. Also, the Dygraphs.js library is compatible across all major web browsers, and can responds to touch sensitivity, making it a thoroughougly solid choice as a data visualization framework.

D3.js

Eventually becoming the successor to Protovis.js, D3 is capable of creating stunning graphics via dynamically updating the DOM. An acronym for Data-Driven Document, D3.js makes use of chained methods when scripting visualizations, subsequently creating dynamic code that is also reusable. Due to its reliance on the DOM, D3 has been created in accordance with W3C web standards so that the library may render correctly across web browsers. Lastly, D3′s path generator function, defined as d3.svg.line(), gives developers the capability to produce a handful of SVGs by defining different paths, and their properties.

InfoVis

Commonly referred to as InfoVis, the JavaScript InfoVis Toolkit (JIT) also earned its stripes as a JavaScript library for data visualization. Equipped with WebGL support, InfoVis has been trusted by names like Mozilla and AlJazeera, showing its solidarity as a visualization tool. Along with the D3 framework, InfoVis also makes use of chained methods to manipulate the DOM, making it a reliable library for developers of any skill set.

The Google Visualization API

Hailing from the Google Developers Console (GDC), Google’s Visualization API can be called with barely any code. In addition to easy DOM modification, this Google API makes it easy for its user to easily define custom modifier functions that can then be placed into custom groups. Furthermore, this interface’s usability, matched with its support from the GDC’s open source network, place it among the top of the list of data visualization tools.

Springy.js

Springy.js is a JavaScript library that relies on an algorithm to create force-directed graphs, resulting in nodes reacting in a spring-like manner on the web page. Although Springy.js comes configured with a predefined algorithm, options such as spring stiffness and damping can easily be passed as parameters. Springy.js was developed by Dennis Hotson as a library for developers to build off of – a fact that he makes clear.

Polymaps.js

Polymaps.js makes use of SVGs to generate interactive web maps with cross browser compatibility in mind. At the heart of Polymaps lies vector tiles, which help ensure both optimal load speeds and optimal zoom functionality. Although it may come configured with components, Polymaps.js is easily customized, and is able to read data in the form of vector geometry, GeoJSON Files, and more. Check out the graph below of the U.S. created by the U.S. Census borough.

Dimple

This past January, the Dimple API was developed so that analysts at Align-Alytics could develop strong data visualizations without having to possess much development knowledge. That being said, Dimple makes it easy for anyone, analyst or not, to develop stunning, three dimensional graphics without any real JavaScript training. Moreover, dimplejs.org displays several demonstrations, which can be easily manipulated by one’s personal data to render a graph with the same configuration, but different values. So, if you, or anyone you know is trying to segway into the depths of JavaScript, then these examples are perfect for beginners to vist and poke around.

Sigma.js

For people looking to build highly advanced line graphs, Sigma.js provides an unbelievable amount of interactive settings inside its library, and also within its plug-ins. Hailing a motto that states “Dedicated to Graph Drawing”, those developing using Sigma.js cannot help but feel like they have chosen a reliable library to work with. Moreover, Sigma’s developers encourage people to re-configure this library and create plug-ins, which has resulted in a large open-source network. Having said all that, I was extremely pleased with various aspects of Sigma, and it is among my favorite libraries for creating graphical representations in JavaScript.

Raphael.js

The Raphael.js library was created with an emphasis on browser compatibility. The framework follows the SVG W3C Recommendation, which is a set of standards that ensure images are completely scalable and without pixelation. In addition to the use of SVGs, Raphael.js even reverts to the Vector Model Language (VML) if rendered in Internet Explorer browsers prior to IE9. Although VML is very rarely used today, the support for it does a great job of showing the attention to detail that the Raphael.js team placed on this project when developing the library.

gRaphaël

Although Raphael.js is a library used to for the creation of SVGs, it was not built with a total focus on the representation of large datasets. In turn, the gRaphaël JavaScript library was created. Weighing in at a mere 10KB, gRaphaël.js has proven to be a worthy extension to Raphael.js. Although it may have not been developed behind things like a force-driven algorithm, nor does it come pre-configured with any physics properties, gRaphaël is still a well respected library for reasons ranging from its cross-compatible SVG structure, to its ease of use. As long as it coincides with the task at hand, I believe that gRaphaël.js should always be looked at as a viable resource to complete a project.

Leaflet

Whether developing for a smartphone, tablet, or desktop, the Leaflet JavaScript library has ranked atop the list of interactive mapping libraries for several reasons. Lead by the founder of MapBox, Vladimir Agafonkin, the Leaflets team of developers worked to create a library “designed with simplicity, performance, and usability in mind.” Along with Polymaps, Leaflet shares the ability to render SVG pattens via vector tiles, however only Leaflet has been developed to support Retina display. Furthermore, Leaflet can interpret various forms of data such as GeoJSON, making it perfect for a number of tasks.

Ember Charts

For those who already use the juggernaut that is Ember.js, the developers at Addepar Open Source have created a few add-on libraries to extend the Ember experience: Ember Table, Ember Widgets, and Ember Charts. A child of Ember.js and D3.js, Ember Charts utilizes the properties of flat-design. Although limited, the library does have a handful of options that deal with properties such as color and size, making it fairly simple to create impressive visualizations. Nonetheless, Ember’s presence in the front end could really help Ember Chart’s popularity in the future.

Stephen Few: Why Do We Visualize Quantitative Data?

Readers:

Stephen_FewIt has been a while since I have discussed some of the latest creative thoughts on data visualization from Stephen Few. I have read all of Steve’s books, attended several classes from him, and religiously follow his blog and newsletter on his website, Perceptual Edge.

For those of you who don’t know, Stephen Few is the Founder & Principal of Perceptual Edge. Perceptual Edge, founded in 2003, is a consultancy that was established to help organizations learn to design simple information displays for effective analysis and communication.

Steve has stated that his company will probably always be a company of one or two people, which is the perfect size for him. With 25 years of experience as an innovator, consultant, and educator in the fields of business intelligence and information design, he is now considered the leading expert in data visualization for data sense-making and communication.

Steve writes a quarterly Visual Business Intelligence Newsletter, speaks and teaches internationally, and provides design consulting. In 2004, he wrote the first comprehensive and practical guide to business graphics entitled Show Me the Numbers, now in its second edition. In 2006, he wrote the first and only guide to the visual design of dashboards, entitled Information Dashboard Design, also now in its second edition. In 2009, he wrote the first introduction for non-statisticians to visual data analysis, entitled Now You See It.

Here is his latest thoughts from his newsletter.

Best regards,

Michael

 

Why Do We Visualize Quantitative Data?

Per Stephen Few, we visualize quantitative data to perform three fundamental tasks in an effort to achieve three essential goals:

Web

These three tasks are so fundamental to data visualization, Steve used them to define the term, as follows:

Data visualization is the use of visual representations to explore, make sense of, and communicate data.

Steve poses the question of why is it that we must sometimes use graphical displays to perform these tasks rather than other forms of representation? Why not always express values as numbers in tables? Why express them visually rather than audibly?

Essentially, there is only one good reason to express quantitative data visually: some features of quantitative data can be best perceived and understood, and some quantitative tasks can be best performed, when values are displayed graphically. This is so because of the ways our brains work. Vision is by far our dominant sense. We have evolved to perform many data sensing and processing tasks visually. This has been so since the days of our earliest ancestors who survived and learned to thrive on the African savannah. What visual perception evolved to do especially well, it can do faster and better than the conscious thinking parts of our brains. Data exploration, sensemaking, and communication should always involve an intimate collaboration between seeing and thinking (i.e., visual thinking).

Despite this essential reason for visualizing data, people often do it for reasons that are misguided. Steve dispels a few common myths about data visualization.

Myth #1: We visualize data because some people are visual learners.

While it is true that some people have greater visual thinking abilities than others and that some people have a greater interest in images than others, all people with normal perceptual abilities are predominantly visual. Everyone benefits from data visualization, whether they consider themselves visual learners or not, including those who prefer numbers.

Myth #2: We visualize data for people who have difficulty understanding numbers.

While it is true that some people are more comfortable with quantitative concepts and mathematics than others, even the brightest mathematicians benefit from seeing quantitative information displayed visually. Data visualization is not a dumbed-down expression of quantitative concepts.

Myth #3: We visualize data to grab people’s attention with eye-catching but inevitably less informative displays.

Visualizations don’t need to be dumbed down to be engaging. It isn’t necessary to sacrifice content in lieu of appearance. Data can always be displayed in ways that are optimally informative, pleasing to the eye, and engaging. To engage with a data display without being well-informed of something useful is a waste.

Myth #4: The best data visualizers are those who have been trained in graphic arts.

While training in graphic arts can be useful, it is much more important to understand the data and be trained in visual thinking and communication. Graphic arts training that focuses on marketing (i.e., persuading people to buy or do something through manipulation) and artistry rather than communication can actually get in the way of effective data visualization.

Myth #5: Graphics provide the best means of telling stories contained in data.

While it is true that graphics are often useful and sometimes even essential for data-based storytelling, it isn’t storytelling itself that demands graphics. Much of storytelling is best expressed in words and numbers rather than images. Graphics are useful for storytelling because some features of data are best understood by our brains when they’re presented visually.

We visualize data because the human brain can perceive particular quantitative features and perform particular quantitative tasks most effectively when the data is expressed graphically. Visual data processing provides optimal support for the following:

1. Seeing the big picture

Graphs reveal the big picture: an overview of a data set. An overview summarizes the data’s essential characteristics, from which we can discern what’s routine vs. exceptional.

The series of three bar graphs below provides an overview of the opinions that 15 countries had about America in 2004, not long after the events of 9/11 and the military campaigns that followed.

graph-of-country-opinions

Steve first discovered this information in the following form on the website of PBS:

table-of-country-opinions

Based on this table of numbers, he had to read each value one at a time and, because working memory is limited to three or four simultaneous chunks of information at a time, he couldn’t use this display to construct and hold an overview of these countries’ opinions in his head. To solve this problem, he redisplayed this information as the three bar graphs shown above, which provided the overview that he wanted. Steve was able to use it to quickly get a sense of these countries’ opinions overall and in comparison to one another.

Bonus: Here is a link to where Steve discusses the example above on his website.

2. Easily and rapidly comparing values

Try to quickly compare the magnitudes of values using a table of numbers, such as the one shown above. You can’t, because numbers must be read one at a time and only two numbers can be compared at a time. Graphs, however, such as the bar graphs above, make it possible to see all of the values at once and to easily and rapidly compare them.

3. Seeing patterns among values

Many quantitative messages are revealed in patterns formed by sets of values. These patterns describe the nature of change through time, how values are distributed, and correlations, to name a few.

Try to construct the pattern of monthly change in either domestic or international sales for the entire year using the table below.

table-of-sales-data

Difficult, isn’t it? The line graph below, however, presents the patterns of change in a way that can be perceived immediately, without conscious effort.

graph-of-sales-data

You can thank processes that take place in your visual cortex for this. The visual cortex perceives patterns and then the conscious thinking parts of our brains make sense of them.

4. Comparing patterns

Visual representations of patterns are easy to compare. Not only can the independent patterns of domestic and international sales be easily perceived by viewing the graph above, but they can also be compared to one another to determine how they are similar and different.

In Summary

These four quantitative features and activities require visual displays. This is why we visualize quantitative data.

Forbes: Data Visualization Is The Future – Here’s Why

Readers:

Dorie ClarkI read this blog post from Dorie Clark back in March. I keep notes on interesting blogs and articles I come across and wanted to share this one with you regards the importance of data visualization.

Dorie Clark is a marketing strategist and professional speaker who teaches at Duke University’s Fuqua School of Business. Learn more about her new book Reinventing You: Define Your Brand, Imagine Your Future (Harvard Business Review Press) and follow her on Twitter.

I hope you find this helpful in your data visualization endeavors.

Best regards,

Michael

Data Visualization Is The Future – Here’s Why

We’ve all heard that Big Data is the future. But according to Phil Simon’s new book The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions, that may not be quite right. Big Data is a powerful discovery tool for companies seeking to glean new insights. But without the right framework for understanding it, much of that knowledge may go unrecognized. Oftentimes, it’s data visualization that allows Big Data to unleash its true impact.

The Visual Organization is fundamentally about how progressive organizations today are using a wide array of data visualization (dataviz) tools to ask better questions of their data – and make better business decisions,” says Simon, citing the example of companies such as Amazon, Apple , Facebook, Google, Twitter, and Netflix, among others.

Phil Simon
Data visualization allows Big Data to unleash its true impact, as author Phil Simon explains.

Two recent factors have conspired to make this the moment for data visualization. First, says Simon, is the rise of Big Data and the growing public awareness of its power. “Today more than ever, professionals are being asked to argue their cases and make their decisions based on data,” he says. “A new, data-oriented mind-set is permeating the business world.”

But that push outside IT circles means that many non-technical professionals must now produce and comprehend insights from Big Data. Visualization can help, and a raft of new tools makes that possible. “IBM, Cognos, SAS, and other enterprise BI (business intelligence) stalwarts are still around, but they are no longer the only game in town,” he says. “Today, an organization need not spend hundreds of thousands or millions of dollars to get going with dataviz. These new tools have become progressively more powerful and democratic over the last decade. Long gone are the days in which IT needed to generate reports for non-technical employees. They have made it easier than ever to for employees to quickly discover new things in increasingly large datasets. Examples include Visual.ly, Tableau, Vizify, D3.js, R, and myriad others.”

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Source: Dorie Clark, Data Visualization Is The Future – Here’s Why, Forbes, March 10, 2014, http://www.forbes.com/sites/dorieclark/2014/03/10/data-visualization-is-the-future-heres-why/.
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