3 Big Data Technology Blunders You Must Avoid

November 21, 2013
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"Big Data can transform business thinking, if the business transforms how it thinks about Big Data." Source: Attivio.comBig Data can transform business thinking, if the business transforms how it thinks about Big Data.

"Big Data can transform business thinking, if the business transforms how it thinks about Big Data." Source: Attivio.comBig Data can transform business thinking, if the business transforms how it thinks about Big Data.

That might sound like a Zen koan, but it’s the key to gaining breakthrough insights: You must look beyond the limitation of what you think can be accomplished – and instead think about and ask for what you wish you could derive from the data you have available.

And yet, many organizations surprisingly fail to apply such fresh thinking to their Big Data initiatives – and end up suffering serious project failures.

There are three primary areas of misguided thinking – “Big Data blunders”, if you will – which you must dispel in order to make new business insights a reality. Left unchallenged, these blunders will lead directly to ill-advised initiatives that will fail to deliver meaningful business value:

Blunder #1: Reacting from a “FOMO” perspective. Fueled by a Fear Of Missing Out, many organizations dived headfirst into Big Data infrastructure projects so they wouldn’t “fall behind.” One survey reported in MIT Sloan Management Review noted the soaring popularity of Big Data led some executive committees at large companies to issue mandates to managers along the lines of, “we don’t know what this big data thing is, but we better be attacking it immediately.”

Such knee-jerk reactions have resulted in boil-the-ocean projects like blindly building out Hadoop clusters vaguely targeted to take 12 to 24 months – with NO thought invested in actual use cases explaining how that will help boost revenue, cost savings or competitiveness! FOMO-driven decision making is clearly a one-way ticket to Big Data failure.

Blunder #2: Focusing primarily on volume. My colleague Randy McLaughlin recently observed that the term “Big Data” has so many competing definitions, that they limit the usefulness of the term. Early definitions, for example, equated “Big” with “volume.” This definition, incomplete at best, still persists; many still mistakenly think of Big Data as synonymous with Hadoop.

That’s a problem, because focusing heavily on volume will lead to “big mistakes.” That’s the warning from a recent Harvard Business Review blog article: Does Bigger Data Lead to Better Decisions? The authors cite long-standing research that shows decision makers will often selectively use and interpret information for self-enhancement or to confirm existing beliefs. Existing sacred cows of conventional corporate wisdom are unlikely to be challenged by merely pumping up data volume.

Perhaps that’s the primary reason why, to quote BusinessWeek, “of the countless companies trying to leverage vast amounts of data, only a few have been truly successful” (emphasis added). The solution to this issue is not to “re-engineer decision making processes” as that article suggested, but rather, re-engineer the organization’s strategy – away from volume as the primary technology focus and towards managing variety!

Blunder #3: Failing to focus on primarily the variety of information. The HBR article authors also noted that “big volume” is actually old; financial services firms have had big volume for decades. What’s really new today is the variety of information sources, which enables new business insights.

The article points out that diverse business teams are more creative than homogeneous groups; diverse data merged together confers similar benefits. “So perhaps we shouldn’t be talking about Big [volume] making decisions better, but about Diverse Data connecting the dots using new technologies, processes, and skills.”

Imagine, for example, integrating, correlating and analyzing transactional databases together with customer likes and dislikes expressed on social media, websites, email, IM chats and call center notes. The result: a true 360 degree view of the customer solution that delivers a new level of actionable customer insights to reduce customer churn while maximizing customer service, loyalty, and successful up-selling and cross-selling. That’s the business-transforming power of Big Data variety.

It’s important to note evidence is mounting that organizations are starting to “get” that the real game-changing payoffs will be realized through successfully managing information variety. For example, the Big Data survey I mentioned earlier also found the large corporations surveyed were all about “managing the variety of data and… all about integrating information from diverse sources… Across the board, that was really the primary focus of how firms wanted to use big data, and that included incorporating unstructured data.”

So, if your organization is not yet exploring managing variety as your primary Big Data business value driver and primary technology focus, make it a priority to do so now – before your competition does.