If you know anything about the history of data visualization, Edward Tufte is a name you'll readily recognize. If he's your source of inspiration, then you ought to be in good hands.
A statistician and Yale University professor emeritus of political science, statistics, and computer science, Tufte is known as a supreme master of the art (and science) of delivering information visually. He's inspired many of today's data visualizers, among them Jonathan Schwabish, a US economist who says he considers Tufte the "godfather of modern data visualizations." For him, a one-day Tufte workshop turned into an eye-opening experience on how "we can use graphics and data visualization in a more strategic way and a way in which we can show our data in clearer and more innovative ways."
Since then, Schwabish has been putting what he's learned into his work, creating data visualizations in his role as a policy analyst. "I put those things into practice, and keep thinking about ways and better ways in which we can present our data," says Schwabish, who now runs his own workshops about visualizing and presenting data for people in public policy.
Jonathan Schwabish, US policy analyst
Schwabish joined us yesterday to present his data visualization dos and don'ts during our first live Facebook video chat, which you can view on demand here on AllAnalytics.com. I've included four takeaways below. Watch the video for the full rundown!
Know the differences between exploratory and explanatory data visualizations. Interactive data visualizations, those available through a web interface, for example, can be exploratory or explanatory in nature. Exploratory interactive visualizations encourage users to go into the data and play around with it, maybe even coming up with their own conclusions from it, Schwabish said. In explanatory visualizations, the data visualization "tells a story and the interactivity is leading you down that path."
Static visualizations are explanatory in purpose, too, since the user doesn't have the capability of working with the data or playing with the graphics. Static data visualizations include your basic bar charts and pie charts, as well as those towering infographics -- compilations of text, graphics, and images that have become so popular today. (View our own latest such data visualization, 3 Levels of Analytical Sophistication, and see one of Schwabish's below.)
Devote time to plotting out the infographic. Don't give short shrift to the amount of time needed to think about how to present and tell the story of your data, said Schwabish, adding that he spends about 70 to 80 percent of his time laying out his story for the larger infographics he creates. "I'm in an analog world here, actually sketching with pen and paper and colored pencils." Then you can move into the graphics software.
Understand what your audience wants. While you might gravitate toward wanting to create the fun and fancy type of data visualization, don't do it if it serves no purpose for your audience. For example, Schwabish mostly prepares static data visualizations because he's trying to provide members of Congress and their staffs the bottom-line, statistic, or headline piece of information. "At this point, I'm not sure my audience is really interested in an interactive infographic where they have to weave and explore and click -- that's not my audience." The standard static data visualization serves as much purpose as the interactive one for the right audience.
Deliver insight. Regardless of type, data visualizations ought to give users fresh insight. "If you can give your users insight that they may not have gotten from some other means, be it the written report, some other website, or some other source, that, I think, is a successful visualization."
I would agree. How about you?
All Analytics will be continuing our ongoing series of video chats on data visualization next week, when business intelligence consultants Tricia Aanderud and Ben Zenick join us for a conversation on how to create great data visualizations. You'll find us on Facebook next Wednesday, June 5, at 2:00 p.m. ET. I hope you can tune in!
I won't argue against imagination for interpretation but I think if you get too imaginative you lose the audience. There is a real skill/talent needed to make numbers look pretty and keep them from getting confusing.
@Beth, to make matters more complicated continuing with your writing analogy, I think there are times -- particularly longer research papers type projects -- when even someone who doesn't typically use outlines might need to use an outline to get their heads around a particularly complex topic. Same might go for visualizations.
I'll add imagination for interpretation, which supports the "artistic eye". See how a graph can be shown with respect to code can be tricky, but imagination has to be at work to get it down to an appreciable level.
@SaneIT, I would suspect it's a lot like the writing process. Some people swear by the outline; can't write cohesively without it. Others don't like the outlining process or the structure associated with it. I think forcing one way or the other on a person results in poorer quality than would result by letting the person do what works best for him or her.
I think if you've got the talent to sit down and organize everything by hand and draw it out chances are that you've got a better eye for aesthetics than I've got and that does go a long way in making visual representations that people will actually look at. I do think that it's a skill but it involves more than just interpreting data, if you have that artistic eye I'm sure it helps a lot.
It'd be interesting to compare data visualizations -- the "tell a story" sorts with graphics, text, and images, at least -- done each way. I would think depending on the complexity of the topic, how much information you're trying to convey, and the audience, storyboarding could really help.
""I'm in an analog world here, actually sketching with pen and paper and colored pencils." Then you can move into the graphics software."
This really surprised me. I figured most of the people building these were using visualization packages that generate the charts for them. I never imagined someone sitting there drawing out the charts by hand and putting colors and labels on that way. I know I just fire up the software pull in the data I want and do all the pretty changes inside the software.
I for one love the update Linked In creatd - it permits additional material to describe the profile. Given the importance of data visualization, it can give an opportunity to see how far a report with a visualization can go.
2015 Visual Analytics Interactive RoadshowSAS(r) experts are coming to a city near you in a series of live, interactive workshops focused on SAS Visual Analytics, including how to prepare your data for VA, the integration of VA with Office Analytics and a Visual Statistics demo.
January 22: King of Prussia, PA
February 24: Austin, TX
March 26: Redwood City, CA
April 22: NYC, NY (1st of 2 stops)
May 13: Seattle, WA
June 18: Minneapolis, MN
July 21: Rockville, MD
August 18: Chicago, IL
September 24: Irvine, CA
October 9: Cary, NC (during SAS Championship)
October 21: NYC, NY (2nd of 2 stops)
November 17: Orlando, FL
December 8: Atlanta, GA
LEADERS FROM THE BUSINESS AND IT COMMUNITIES DUEL OVER CRITICAL TECHNOLOGY ISSUES
The Current Discussion
Visual Analytics: Who Carries the Onus? The Issue: Data visualization is an up-and-coming technology for businesses that want to deliver analytical results in a visual way, enabling analysts the ability to spot patterns more easily and business users to absorb the insight at a glance and better understand what questions to ask of the data. But does it make more sense to train everybody to handle the visualization mandate or bring on visualization expertise? Our experts are divided on the question. The Speakers: Hyoun Park, Principal Analyst, Nucleus Research; Jonathan Schwabish, US Economist & Data Visualizer
The hospitality industry gathers massive amounts of customer data, and mining that data effectively can yield tremendous results in terms of improved CRM, better-targeted marketing spend, and more efficient back-end processes. Roger Ares, vice president of analytics at Hyatt Corp., discusses the ways he and his staff use big data.
Charged with keeping track of travel assets, including employees, iJET International relies on data management best-practices and advanced analytics to keep its clients in the know on current and potential world events affecting travel, Rich Murnane, Director of Enterprise Data Operations & Data Architect, told All Analytics in an interview from the 2014 SAS Global Forum Executive Conference.
Jason Dorsey, chief strategy officer for the Center for Generational Kinetics and keynote speaker at last month's SAS Global Forum 2014, describes how Gen Y professionals are enhancing the makeup of multigenerational analytics organizations.
From analytics talent development to the power of visual analytics, All Analytics found a variety of common themes circulating throughout the exhibition floor and session discussions at the 2014 SAS Global Forum and SAS Global Forum Executive Conference events held last month in Washington, DC.
Talking with All Analytics live from the 2014 SAS Global Forum Executive Conference, Eric Helmer, senior manager of campaign design and execution for T-Mobile, discussed the importance of customer data -- starting internally -- in devising the mobile operator's marketing plans.
The big-data analytics market can be a confusing place. Among the vendors vying for your dollars are traditional database management providers, Hadoop startup services, and IT giants. In this video, All Analytics editors Beth Schultz and Michael Steinhart sit down in a Google+ Hangout on Air with Doug Henschen, executive editor of InformationWeek. Henschen discusses use cases for big-data analytics, purchase considerations, and his recent roundup of the top 16 big-data analytics platforms.
At the National Retail Federation BIG Show last month, All Analytics executive editor Michael Steinhart noted a host of solutions for tracking and analyzing customer activity in retail stores. From Bluetooth beacons to RFID tags to NFC connections to video analytics, retailers must find the right combination of tools to help optimize the shopper experience, streamline operations, and boost revenues.
The days when historical shipment trends and gut feelings were enough to forecast retail demand accurately are long over. SAS chief industry consultant Charles Chase outlines the benefits of pulling real-time sales information from point-of-sale and product scanner systems, then flowing that data into dynamic forecasting tools from SAS.
With today's advanced visual analytics tools, you can stream data into memory for real-time processing, provide users the ability to explore and manipulate the data, and bring your data to life for the business.
Dynamic data visualizations let analysts and business users interact with the data, changing variables or drilling down into data points, and see results in a flash. Advance your use of data visualization with tools that support features like auto-charting, explanatory pop-ups, and mobile sharing.
No doubt your enterprise is amassing loads of data for fact-based decision-making. Hand in hand with all that data comes big computational requirements. Can traditional IT infrastructure handle the increasing number and complexity of your analytical work? Probably not, which is why you need a backend rethink. Big data calls for a high-performance analytics infrastructure, as Fern Halper, a partner at the IT consulting and research firm, Hurwitz & Associates, discusses here.
Redbox's bright-red DVD kiosks are all but ubiquitous these days, located in more than 28,000 spots across the country. Jayson Tipp, Redbox VP of Analytics and CRM, provides an insider's look at how the company has accomplished its phenomenal nine-year growth.
InterContinental Hotels Group (IHG), a seven-brand global hotelier, has woven analytics into the fabric of its operations. David Schmitt, director of performance strategy and planning, shares IHG's analytics story and his lessons learned.