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TDWI Blog: Data 360

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Big Data Analytics: The View from Tableau Software

Blog by Philip Russom
Research Director for Data Management, TDWI

I just got off the phone with Ellie Fields, the director of product marketing at Tableau Software. Ellie has a lot to say to about intersections among big data, analytics, and data visualization. So allow me to recount the high spots of the conversation.

Philip Russom: Tableau is often pigeon-holed as a data visualization vendor. But the Tableau users I’ve met are using the tool for analytics. How does Tableau position itself?

Ellie Fields: Our customers use Tableau in different ways. For example, many use us as their primary, enterprise BI platform. Others use us for specific BI applications within a department. Still other customers use Tableau for fast analytics, as a complement to a legacy BI platform. Given the breadth of use, we see ourselves as a multi-purpose BI platform.

Philip Russom: I’ve seen demonstrations of the Tableau tool, so I know that ease-of-use is high. But is it high enough to enable self-service BI?

Ellie Fields: The Tableau tool was designed with self-service in mind for a broad range of BI users. For example, with a few mouse clicks, a user can access a database, identify data structures of interest, and bring data into server memory for reporting or analysis. The user needs to know the basics of enterprise data, but doesn’t need to wait for assistance from IT. With a few more clicks, you can publish your work for colleagues to use. Going back to your question about positioning, we describe this quick and easy method as “rapid fire business intelligence.”

Philip Russom: What’s the relationship between data visualization and big data?

Ellie Fields: As you know, Tableau is strongly visual. In fact, the visual images representing data are an extension of the user interface, in that you grab your mouse and – with simple drag-and-drop methods – you interact directly with the visualization and other visual controls to form queries, reports, and analyses. Analysis is iterative, and iterations need to flow fast. The drag-and-drop environment enables an analyst to work quickly, without losing the train of thought, and even to collaborate with others on live data. So, we’re fast with results – even against big data.

When working with big data, all of our visualizations scale up and down, in that they can represent ten data points from a spreadsheet or ten million rows of big data. And when working with big data, visualization is even more important. It’s how humans explore and consume information to arrive at a conclusion. Analytics without good visualization is hamstrung from the beginning.

Philip Russom: What types of analytic applications have you seen in your customer base recently?

Ellie Fields: Many of our customers practice what we call “exploratory analytics.” This is especially important with big data, where the point is to explore and discover things you didn’t already know. For example, we have a lot of Web companies as customers, and they depend on advertizing for revenue. As they explore big data, they’re answering analytic questions like: “How do small ads compare to big ones? Or which colors in an ad sell the most?” Yahoo! is a customer, and they analyze online ads by many dimensions, including size, color, location, frequency, Web site locations, revenue, and so on.

High tech manufacturing stands out as a growing area, especially analytics for monitoring product and supply quality. Healthcare, finance, and education companies have also adopted Tableau. One healthcare client analyzes its supply chain to be sure all locations are equipped adequately. Another hospital uses analytics to optimize nurse staffing. And a university client analyzes trends in SAT scores to enlighten decisions about recruitment, scholarships, and educational curricula.

So, what do you think, folks? Let me know. Thanks!

Note: The next TDWI Solution Summit, September 25-27 in San Diego, will feature case studies focused on the theme of “Deep Analytics for Big Data.”

Posted by Philip Russom, Ph.D. on May 19, 2011


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