The Big Data Challenge: How to Develop a Winning Strategy

Developing a winning Big Data strategy is challenging because it is as much about getting ahead of the trend and acquiring talent as it is about investing in new technology. To be successful you will need Data Scientists on your team, professionals who are adept with the analytical and visualization tools required to process and recognize patterns in data and who are equally comfortable with business concepts and operations. EMC's Howard Elias recommends three steps to help ensure your organization has the people it needs.

By Howard Elias, EMC Information Infrastructure and Cloud Services
Thu, June 14, 2012

CIO — Big Data is exciting stuff, but don't take my word for it. Ask the Academy of Arts and Sciences, who nominated Moneyball -- a movie about Big Data -- for six Oscars. If Hollywood is on board, you know Big Data has gone mainstream. What's more, Tinsel Town managed to do what most industry commentators haven't: Spin a tale that is both interesting and illustrative of Big Data's transformative potential.

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And yet, while a baseball team's creative use of statistical data may have helped it to go from perennial doormat to scrappy contender, it doesn't begin to describe what Big Data can do for an organization.

Howard Elias, President and COO, EMC Information Infrastructure and Cloud Services
Howard Elias, President and COO, EMC Information Infrastructure and Cloud Services

What is Big Data, anyway? Gartner defines Big Data as having three primary characteristics: volume (amount), velocity (speed of creation and utilization), and variety (types and sources of unstructured data, such as social interaction, video, audio -- anything that isn't neatly categorized within a database). I describe Big Data as datasets so large and diverse, they break traditional IT infrastructures.

What Big Data is, however, isn't as important as what you can do if you harness its potential and uncover new business opportunities through Big Data analytics.

Even in these early days of Big Data, its applications across diverse industries are compelling. Retailers embrace Big Data to combine RFID sensor data, social media data, and GPS coordinates to evaluate location, product selection, and individual profiles in order to deliver geo-specific product promotions to a mobile device. Big Data has been used to analyze entire forests in order to identify individual trees for harvest in order to maximize health, yield, and profit.

We are using Big Data to better understand massive amounts of data associated with processes and costs related to supplier parts, manufacturing, logistics, quality control, customer service, and more, and have used it to establish predictive performance models to address quality issues before they can have a negative effect on customer satisfaction. Yet even these examples only scratch the surface of how Big Data can effect business transformation.

Big Data's potential goes beyond traditional "rear view" business intelligence, revealing patterns in near real time to facilitate making a quantum leap from incremental improvement to predictive business processes and even entirely new business models -- what I call the Art of the Possible.

But the Art of the Possible requires practitioners who understand the unique mix of "art and science" that characterizes the most transformative Big Data breakthroughs. It requires people who are as comfortable with business concepts and operations as they are adept with the analytical and visualization tools required to process and recognize patterns in the data. We call these professionals Data Scientists. They're able to make quantifiable connections between previously unknown causes and effects; they're adroit at seeing associations others have missed; and they're able to understand how these new insights can be used to fundamentally change operational practices and business and organizational models.

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