Big data hype obscures reality of the marketplace, confuses users (interview)

This sponsored post is produced by BrightTALK.

Big data is more than a buzzword now. It’s a drumbeat that underpins strategic thinking across all industries, businesses, and regions. Despite the value of big data projects, there’s still confusion around what is hype, what is myth, and what is real.

At least that’s what Greg Laughlin, CEO of Statwing, has been seeing in the marketplace. In his experience, most people don’t know how to cut through the big data hype and find out what’s real about business intelligence, visualization, and analytics.

Laughlin is speaking on this topic at BrightTALK’s Cloud and Mobile Analytics Summit on May 14, but we caught up with him in the meantime to learn more about his take on these major trends in the BI and big data industry. Here’s what he had to say:

Q: What is the hype vs. the reality of the BI market?

A: To understand where the market is heading, it’s important to understand its roots. BI tools originated as a way to describe a company’s key data using basic visualization, dashboarding, and querying.

Flash-forward to the present day and the data explosion. There’s more data, so there’s much more latent value. Users don’t just want to query or see basic charts of a number moving over time. They want to really dig deep into the data, asking lots of questions and exploring lots of hypotheses. And they want to integrate predictive analytics — understanding which customers are likely to churn, for example. As Gartner puts it, they want to move from “descriptive” uses of data to “diagnostic” and “predictive” uses.

Wisely, the BI industry is meeting the “predictive” need with integrations and acquisitions. But they’ve met the “diagnostic” need by building more advanced visualization and then claiming that visualization is equivalent to analysis. But analysis is about how many hypotheses you can explore in how short a time period, and these tools just weren’t built for that. Just ask an analyst if they’d rather play with data in a BI tool or in an analysis tool like Excel or R or Statwing. Or try running a correlation in most BI tools — you can’t.

And I’m just describing one piece of the data stack. We’re witnessing an explosion of great data tools that can help in myriad, frequently complementary ways, and that explosion is making it very confusing to understand which tool does what. Which BI tools tap into an existing data warehouse or database, and which ones move my data into their own engine? How much data do I need before some Hadoop distribution starts being the right answer? It’s daunting.

Q: What’s the solution? How can you find the right tools to use?

A: This is actually a really hard problem. There’s not a lot of non-expert information out there from vendor-neutral sources. Analysts and decision makers need to do their due diligence before making a decision. This could take the form of reading Gartner and IDC reports, asking colleagues, finding bloggers who write in plain language, and testing out tools. In the end, users bear the responsibility of seeking out the best tool for their use case. Don’t settle. We live in the future; if it feels clunky, there’s probably something better out there.

There is a message for vendors here, too: Be frank about what your tool can and can’t do and how your tool differs from the others. As the industry continues to mature, it will become clearer who does what and how well. Until then, the user needs to be vigilant.

To hear more from Greg Laughlin, join his live webinar, BI’s Big Lie: The Difference Between Visualization and Analysis, on May 14 at 10 a.m. PDT, and tune in to the entire Cloud and Mobile Analytics Summit to learn more about the latest in the BI industry.

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