Are visual analysis tools poised to become pervasive?

I spent most of last week at InfoVis 2008 in Columbus, Ohio. You might remember that I delivered the capstone presentation last year at InfoVis 2007, which also served as the keynote presentation for VAST 2007 (Visual Analytics Science and Technology). Last week the 2008 edition of this presentation was delivered by Christian Chabot, cofounder and CEO of Tableau Software. Chabot and I share the belief that visual analysis software is needed by a broad audience of people, not just those who have the term “analyst” in their titles. We also share the belief that with well-designed visual analysis tools like Tableau, visual analytics is poised to explode.

Participants in the conference consisted primarily of academics—professors and graduate students who spend their days inventing and refining visualization tools and techniques for making better sense of data. Chabot clearly wanted to challenge this audience to direct more of their efforts toward the practical needs of a broad audience of potential users.

Chabot identified four conditions that have set the stage for the current readiness of visual analytics to take off:

  • Data explosion
  • Technological advances
  • General awareness
  • Industry consolidation

The overwhelming amount of information that people now face has created a desperate need for tools that will help them make sense of it. Modern computer hardware and the Web have provided the infrastructure that is needed for people to interact with and share information effectively. Awareness of the visualization’s potential has reached a critical mass. Traditional business intelligence vendors, along with their tired, low-yield approaches to data analysis, have been bought up by large software corporations where they will languish, which has opened the door for better approaches to capture the attention of market. By rejecting the sins of traditional business intelligence vendors, refusing to compete for the hearts and wallets of customers through a litany of useless and ineffective pseudo-analytical features, software companies such as Tableau that are thoughtful, agile, design-oriented, and well-informed, have differentiated themselves from the pack and are now reaping the rewards of their commitment to give people analysis tools that really work. One result that we’re beginning to see is the gradual spread of data analysis tools to organizations of all sizes (from Google to the local bakery), and their proliferation throughout all parts of those organizations.

When the founders of Tableau Software were initially crafting their vision, they identified five core principles of visual analytics’ adoption:

  • People adopt visual analytics primarily to help them see and understand complex data.
  • People adopt visual analytics primarily to help them see and understand massive data.
  • People adopt visual analytics primarily to help them see and understand new visual paradigms.
  • People adopt visual analytics primarily to help them see and understand hidden insights.
  • People adopt visual analytics primarily to help analysts save time.

Chabot is a Stanford MBA who worked for years after graduation as a high-end analyst—one of those guys that spend their days tackling complex analytical problems using complex analytical techniques. The other founders of Tableau, Chris Stolte, who earned his doctorate in computer science at Stanford by developing the prototype for Tableau’s eventual product, and Pat Hanrahan, the Stanford professor who supervised Stolte’s work, were immersed in the world of academic information visualization research. Their assumptions about what it would take to get people to adopt visual analytics made perfect sense, given their perspective at the time. As time passed, however, they kept their eyes open and learned that each of their assumptions turned out to be flawed.

Flawed Principle #1: People adopt visual analytics primarily to help them see and understand complex data.

Although sometimes complex, the data sets that people analyze are usually fairly simple. Chabot advised those of us in the information visualization community to start simple. Rather than focusing most of our attention on solving the complex, highly-specialized needs of a few, we can solve much more widespread problems that are just as important by making it easier for people to do the simple stuff that they must do over and over again each day, which are now unnecessarily onerous and time-consuming.

Flawed Principle #2: People adopt visual analytics primarily to help them see and understand massive data.

Although sometimes massive, the data sets that most people analyze are not particularly large. Chabot recommended that we start small, making it easy for people to work not just with huge corporate databases, but also with small files stored in Access and Excel.

Flawed Principle #3: People adopt visual analytics primarily to help them see and understand new visual paradigms.

Although there are times when new visual paradigms must be invented to solve peoples’ needs, most problems can be solved with proven visualizations, such as bar charts, line graphs, and scatterplots. Chabot suggested that we start proven by making it easier for people to use what we already know to work well in a seamless fashion.

Flawed Principle #4: People adopt visual analytics primarily to help them see and understand hidden insights.

While it is true that one of the great benefits of visual analytics is the discovery of previously hidden insights—those “Aha!” moments that we all crave—the primary reason, by far, that people want good visual analytics tools is more mundane, though no less useful: to save time. Chabot pointed out that we can design great tools that get out of the way, allowing people to become engaged in the act of thinking about data, rather than distracted by the mechanics of using the software.

Flawed Principle #5: People adopt visual analytics primarily to help analysts save time.

While analysts desperately need better tools to help them do their jobs, even greater benefit can be gained by providing tools that anyone can use, enabling everyone who must make sense of information to do their jobs and, as a consequence, freeing up analysts to spend their time solving the more complicated problems. With religious zeal, Chabot warned that we can no longer serve the needs of small groups with specialized needs, but should invite everyone to the table.

At the end of his presentation, Chabot reviewed his message and challenged us with these final facts:

  • Millions of people need visual analytics technologies to help them understand information.
  • The current state-of-the-art in business analytics (what most people rely on to do their jobs) is tragic.
  • The primary barriers to visual analytics’ adoption are (1) awareness, (2) misperception, (3) ease of trial, (4) ease of deployment, (5) ease of use, and (6) ease of price.

What business intelligence vendors have still failed to do, a new breed of software company with roots in information visualization research, is poised to finally deliver. The world needs what we have to offer. To get it into the hands of those who need it, we must bridge the chasm that divides academic research and commercial software. Tableau and a few other ventures have done that. They’re inviting others to join them—not tomorrow, but now—because the time is ripe.

Take care,

8 Comments on “Are visual analysis tools poised to become pervasive?”

By Michael W Cristiani. October 28th, 2008 at 6:16 pm


Amen! Preach it, Brother!

Christan Chabot’s talk was, like Tableau Software itself, revolutionary in some ways. Not just hype or the stylings of a CEO doing his job of promoting his company and its products. His message is right on the mark, and it has developed over time as Tableau has engaged its customers and learned from them in many critical ways. What its customers instinctively understand about is that these folks are missionary and zealous, and the really want to help change the world. They remind me of ESRI in the GIS space, whose mantra is “Geography Matters…”

Just this week, more evidence of the pending pervasive path of visual analytics: the City of Charlotte hosted a visualization challenge competition among city departments using Tableau Software. Since when…?

Thanks for doing such a fine job summarizing Christan’s message and bringing it forward.

Peace and All Good!
Michael W Cristiani
Market Intelligence Group

By dave. October 28th, 2008 at 6:19 pm

“visual analysis software is needed by a broad audience of people, not just those who have the term “analyst” in their titles”

True, and I agree with this post, but I sure hope that if this becomes a reality, businesses don’t begin to think that such software should replace the professional analyst.

While visual analytics can help understand information quickly, any business that really wants to do powerful things with their data needs professionals. I’ve seen lots of data that doesn’t seem very complex (simple tabular format) where I am amazed at all the important information that was able to be extracted that couldn’t have been done just with a fancy visualization tool.

By Enrico Bertini. October 29th, 2008 at 3:20 am

Great post! … and great presentation, I guess. I attended your keynote last year but could not come this time.

I am intrigued by all of the flaws you have reported but I am very fond of Flawed Principle #4: “People adopt visual analytics primarily to help them see and understand hidden insights.”. I totally agree with it and I am always surprised to see how much weight is given to the idea of insights rather than efficiency.

I am totally convinced that infovis is not primarily a means to do things that cannot be done without it, but rather and mostly a means to do these things faster, better, and with less of a hassle.

By Stephen Few. October 29th, 2008 at 11:57 am


As you point out, there will always be data analysis problems that can only be solved by those who have been trained in sophisticated statistical analysis techniques. It will always be important for people who handle simpler analytical tasks to recognize when they are up against a challenge that requires more sophisticated skills, so they can seek the help of statisticians when that happens.

The other point worth making is that good visual analysis tools such as Tableau are not only useful to casual analysts, but to sophisticated analysts as well. All good statisticians know that much of their work is done most easily, thoroughly, and efficiently by using good visualization tools.

By Wayne Morris. October 29th, 2008 at 12:48 pm

This is great. At myDIALS, we’ve seen how the stated principals are flawed as we work with our customers. It’s all about making timely, relevant information available through a highly intuitive, interactive, visual analysis interface directly in the hands of decision makers. While professionals still have a place to play, quicker, more informed decisions lead to better operational results.

I agree with the barriers to adoption, however the SaaS model combined with Web 2.0 technologies goes a long way to solving the ease of use, ease of deployment, ease of trial and ease of cost issues.

By jerome cukier. October 30th, 2008 at 5:36 am

Hi Stephen,
what I really enjoyed in Chabot’s talk was that contrary to other VisWeek speakers who have a dubious understanding of non-infovis experts (which they refer to as “uninformed users” or “the masses”) his main point is that everyone is an analyst.
no matter what’s your job title, your industry or your salary, everyone manipulates data on a daily basis, and have to make sense out of data.
And for that, you don’t need exotic visualizations or sophisticated tools.

By Juhan Sonin. December 2nd, 2008 at 12:57 pm

Where is the OPEN SOURCE dataviz service that produces beautiful viz, beautiful evidence for everyday citizens (versus the big $$$$ Tableau and SpotFire)?


By Stephen Few. December 2nd, 2008 at 1:16 pm


Very nice to hear from you. How are things at MITRE?

As far as I know, a good open source data visualization solution does not yet exist. Many Eyes ( provides a useful and free means for people to share data visually, but it wasn’t designed as a full-fledged data analysis platform.

Compared to traditional business intelligence (BI) products, products such as Tableau seem to be quite affordable. I was recently approached by a fellow who wished to thank me because he learned about Tableau from something I wrote, and as a consequence, found the solution he needed for 1% ($10,000 vs. $1,000,000) of what he was braced to spend for a more traditional BI solution.