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From the Editors: Getting started on a 'big data' analytics program

Does your organization have a firm grasp on what "big data" is? That's a good place to begin on a big data analytics project, but don’t forget about basic IT blocking and tackling.

Seemingly everyone is talking about “big data” and the potential business benefits of capturing, storing and analyzing it. But there are various definitions of big data floating around out there, some taking large amounts of both structured and unstructured data into account, others involving the latter only. The differing points of view create the possibility that you might be talking up the potential value of big data analytics inside your organization without corporate executives and business managers having a firm grasp on what you’re talking about.

To avoid confusion that could stall a big data analytics program before it even gets going, it’s a good idea to start by talking the talk on big data analytics, according to Enterprise Strategy Group analyst Julie Lockner. That means agreeing internally on what big data is in the first place, says Lockner, the author of a new report that refers to it as “data sets that exceed the boundaries and sizes of normal processing capabilities, forcing you to take a nontraditional approach.” Another important starting point, she advises, is to check how vendors of big data analytics platforms define the term and make sure their interpretations mesh with yours -- and your business needs.

Big data was also a big discussion topic for user and vendor executives affiliated with the International Institute for Analytics, a market research firm that recently issued a series of predicted developments for analytics programs in 2012. The list includes a broader focus on analyzing unstructured and semi-structured data as well as heightened investments in social media analytics, balanced by the need to re-evaluate data privacy policies to make sure they reflect the realities of all the information you’re collecting and how you’re using it.

Once a big data analytics program is off the ground, though, its success or failure is likely to come down to the same kind of blocking and tackling issues that are central to conventional business intelligence (BI) and analytics projects. Analysts and IT pros say that familiar project management best practices, such as a clear understanding of business requirements and a well-defined plan, still apply. Similarly, potential pitfalls on big data analytics include a lack of internal skills and training shortfalls.

If you have a definition of big data -- maybe even an irreverent, satirical one -- feel free to email it to me. I’d also like to hear about your plans for or experience with big data analytics as well as other BI and analytics technologies.

Twitter: @BizAnalytics_TT

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