Tableau Touts Its Data Visualization Chops
Tableau probably isn’t the first name that comes to mind when you think of data visualization software. Tableau on Monday announced version 1.5 of its data viz suite, touting usability and connectivity improvements—such as out-of-the-box support for Microsoft’s SQL Server 2005 database.
Tableau Software probably isn’t the first name that comes to mind when you think of data visualization software.
Tableau on Monday announced version 1.5 of its data visualization suite, touting usability and connectivity improvements, such as out-of-the-box support for Microsoft’s forthcoming SQL Server 2005 database release.
Data visualization proponents often position the technology as a complement to conventional operational reporting and analytic tools. Tableau CEO and co-founder Christian Chabot, on the other hand, says data visualization is a more basic animal altogether.
“It’s so often been seen as sort of like a Holy Grail thing, where you’ve got hundreds of guys trying to crack it, and nobody really has,” Chabot says, arguing that many proponents come at the technology from an unrealistic perspective. “Advances in graphics technology might actually be the key to unlocking one of our nation’s great unsolved problems, which is that databases are too hard to use. We believe [visualization is] ideal for interacting with structured databases. You can interact with a database with very little understanding about how it works and actually approach it using a completely graphical environment.”
For this reason, Chabot maintains, data visualization—à la Tableau Software, anyway—will be the business intelligence (BI) industry’s next killer app.
“We really believe this is going to be one of those products that comes along literally every five or ten years that is a multi-million desktop opportunity. It’s something that speaks to a visceral need of what knowledge workers want to do at their desktops,” he argues.
Just what kind of “visceral” requirements does Tableau purport to address? Chabot—much like his counterpart at data visualization competitor Advizor Solutions Inc.—says it has a lot to do with information overload. “The visceral need we speak of is to present quantitative information—data—in a really compelling visual way, and to look at and explore data visually, rather than with just lists of numbers.”
At the same time, Chabot asserts, you don’t have to be overwhelmed by your information assets to benefit from data visualization technology. “It is a myth that you need a lot of data to do visual analysis,” he says, noting that half of Tableau’s sales are to garden-variety Excel users.
More to the point, Chabot insists, visualization typically offers distinct value in three scenarios: rapid-fire outlier analysis, trending, and the fast discovery of multidimensional business relationships.
The common upshot, he says, is the rapid identification of critical business insights—or “gem-finding,” in Tableau’s parlance. “The only way for mere mortal knowledge workers at their desks to query databases and uncover important relationships is to have a user interface that lets them visually drag-and-drop the [data] they want to analyze,” he says. “So you can do rapid-fire outlier analysis, where—just by dragging-and-dropping [columns or rows] from an Excel spreadsheet—you can identify every single outlier district where you spend a fortune on marketing and actually lose money.”
Chabot uses the example of a gourmet reseller being stumped by the underperformance of one of its products in the Eastern region, which is otherwise its most profitable district. By importing an Excel spreadsheet—or by using a wizard interface to connect to a relational or multidimensional data store—users can drag and drop product information into the Tableau UI.
In this example, while product inventory information might be within the accepted parameters for the East region, perhaps the product marketing budget for Café Mocha is an as-yet-unknown problem area. In the space of three drag-and-drop movements (viz., product sales information; inventory level by product; marketing costs by product), a user can immediately see as much: when plotted on a graph, the product marketing budget for Café Mocha is the outlier.
The data visualization space isn’t exactly bereft of competition. And—in many respects—Tableau’s pitch is similar to those of its competitors. But Tableau differs from its data viz rivals, Chabot insists, because it’s also targeting rank-and-file knowledge workers—in addition to business analysts, power users, and others of the end user aristocracy. (Some of Tableau’s competitors, such as Advizor, make the same argument.)
“In our new release, we have what’s called the ‘Show Me Bar.’ The only thing a user has to know to use Tableau is what they care about—what fields interest them [in a spreadsheet or data source],” he explains. “Tableau automatically queries the database, brings back the records, does the calculations, and renders not just any result, but the best fit result for the data.”
At the same time, Chabot says, Tableau can be a boon to power users. He concedes there’s no shortage of Excel experts who can reduplicate Tableau’s trending and outlier analysis on their own. But such efforts can take a significant amount of time, whereas Tableau’s Visual Query Language is designed from the ground up to do it for them. “Their frustration is that these cross tabs and lookups and formulas and pivot tables are really useful for basic facts, but as soon as you start turning up the cognitive complexity of what you want to know even slightly, this model of slicing and dicing corporate databases falls like a house of cards,” he concludes.
Stephen Swoyer is a Nashville, TN-based freelance journalist who writes about technology.