Month: February 2010

Tableau Public launches visual analysis for the masses

I’m sorry to tell you serious types out there, but visual analysis is often a game — in fact, one of the best games in town with Tableau Software’s visual analysis tool. Now Tableau Public is going to bring it to the masses.

In the same way that YouTube spawned a surge of new filmmakers, Tableau Public — free, running the same engine as its desktop sibling, and embedable — will bring on a new generation of data players and spectators.

I was a spectator at a data visualization conference one afternoon two years ago. Tableau Software director of visual analysis Jock Mackinlay had finished his presentation and another person had started his. Yet someone at the control board forgot to flip a switch, and Jock’s live screen remained on one of the room’s big screens. Jock assumed his screen had been hidden, and he kept playing with the data. I don’t have to tell you who seemed to have the audience’s attention until someone pointed out the problem.

The mere visual distraction was minor. Even without narration, I got caught up in the apparent drama as he tried one look at the data after another.

Not long after that, I wondered aloud to someone at Tableau about data hobbyists. I imagined people who foraged for data to analyze then publicize it to start conversations, collaboration, or duels. Data would be their raw material of choice just as scrap metal is to some sculptors or overheard conversations is to some fiction writers.

There was no such community visible then. But I realized this week that I know one now: Dan Murray, a skilled, dedicated Tableau user. He jokes that he’s a “freak” because he analyzes data from the federal budget and posts his often provocative analyses. He’s already been answered by at least one who disagrees with him.

In beta and since its February 11 launch, Tableau Public has hosted a flurry of visualizations, including these: a map of top venture capital firms investments by U.S. region; a chart showing how long it takes to build a technology empire; a history of earthquakes in Haiti; a neighborhood breakdown of housing supply in Seattle; trends in U.S. high school graduation; and studies of deprivation and marginalization in education. In most cases, spectators can become players by selecting subsets of the data to find answers to their own questions.

With popularity comes some misuse. Many of the charts will break rules, such as what happens in another kind of game, YouTube. A New York film editor I know complains that many YouTube-acculturated film editors have neglected basic editing principles. She writes that they rely so much on special effects that they “can’t put two shots together and have them work as an unembellished edit.” On Tableau Public, there will be pie charts, chart junk, and even baselines that do not start at zero. We’ll survive it.

But what’s all this got to do with the very serious practice of business intelligence?

Like monks must have done when printing presses began producing books for the masses, many priests of business intelligence will stand aside, arms folded in the aspe chapel. But I predict that before long even they will appreciate a wider, deeper pool of analytical talent ripening for training and employment.

I suspect that the new bunch will have been sharpened by the give and take of public exposition. They’ll also learn from playing in a huge community the way artists and craftspeople of all kinds improve their skills when they bump into peers every day.

This is a new clue for the future of BI. It can’t help but improve data analysis in business. So let the games begin.

Tools and those who enable their misuse

To get a data architect I know worked up, just ask him about how customers end up buying the wrong tools.

How about sales people who push federation tools on those who actually need data warehouses?

“It all sounds extremely sexy,” says my source, who works for a major business intelligence vendor and whom I can’t identify. “You have a lot of people who exaggerate their ability to combine data to provide business solutions. … They don’t prototype, they don’t profile, they don’t actually think about the problem or do testing or even send some high school data analyst out with Excel to put something together that [the customer] might want. They don’t do that.”

Many sales people tout EII because that’s what they have to sell, he says. “The EII tools give you your data, warts and all,” he says. It’ll work fine as a data warehouse substitute “if the data’s pretty clean to start with, if it has a somewhat similar structure, if you can define the data you need, if the data’s relatively common across all the sources, and if there’s not much duplication.”

Even if the salesperson has a more appropriate tool than what the customer asks for, the customer may never hear about it. “‘Fine!,'” thinks the salesperson. “‘If you want to buy a hammer, that’s fine. If you want to buy a wrench, that’s fine. It’s not like I care. It’s just sales to me.'”

Just once, says my source, he’d like to hear one of these questions: “How long does it take for a novice to become OK at this task?” Or, “How long would it take for an expert to become proficient at these two things?” Or, “If I have a failure, what is your tool’s usual process for recovery, and what gives your tool more integrity than others?”

Mark Madsen, meanwhile, has been been thinking about similar problems but from a different perspective. He’s research director at the Third Nature consultancy and a keynote speaker at this month’s TDWI conference in Las Vegas.

One source of problems he sees is vendor marketing. “It’s all about ‘our tool does this’ or ‘has these features,'” he writes in email. “A lot of people don’t think about them that way. They think about them as ‘what this tool is for.'” People end up using an ETL tool for real-time synchronization, for example, or a federation tool in place of a data warehouse.

Even product documentation can lead users down dark paths. “All those docs that say what the features are help when you know what feature you want,” he writes. “When you’re trying to accomplish a task, you’re thinking in a different way.” A common result: convoluted solutions.

“I once did something in an ETL tool,” he writes, “and the product developer said, ‘That’s not how you do that.’ They had built around an improper conception of how users apply it.”

Design schools tell you that every user has a theory of how anything works, he writes, which determines their approach to it. Wrong theories explain why people push on doors that need to be pulled, for example. He says that this insight has made him change his approach to teaching his courses or showing clients.

“I’ve realized that I need to start with the ‘what this thing is for’ and move into what you do with it, and how it works.”

Mark may go into this more in his keynote at this month’s TDWI World Conference in Las Vegas. His long-running “Clues to the Future of Business Intelligence” — perhaps the “Cats” of tech presentations — has been one of the most interesting I’ve seen in any tech industry. I expect “Stop Paving the Cowpath” to be worthwhile.