Structuring a big data strategy
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"Big data" has quickly climbed up the hype cycle and continues to do so. But along the way, the conversation has evolved. First, the industry had to name and then define the quickly ballooning quantities of data generated in multiple structures, such as text. Though many experts will say data has always been big, the industry still gravitated toward the concise three V's checklist: Volume, velocity and variety.
But Colin White, president and founder of BI Research, has no desire to discuss either the original big data definition or the additional V's the industry seems intent on adding to the big data lineup. He is interested, however, in advancing the conversation from management to analytics.
"The data management piece is important, but what's of equal if not more importance is the analytics," said White. "It's what you do with the data that matters."
White copresented one of the five sessions at the 11th annual Pacific Northwest BI Summit: his was on big data and big data analytics. He and fellow presenter Harriet Fryman, director of business analytics software at IBM, introduced examples of use cases that don't hinge on massive data volumes or come from giant Internet companies, as so many of the use cases often do.
Against the backdrop of the summit's traditional setting in Grants Pass, Ore., White spoke with SearchBusinessAnalytics.com's news editor Nicole Laskowski discussing the shift of big data management to big data analytics.
In this video, viewers will also learn about:
- How the big data conversation has changed in the last year
- Trends on real-life big data use cases
- How big data is fitting into the new business model of producing information and selling services to other companies
- Why big data analytics is more important than big data management
- How to get started with big data analytics