How to avoid big data analytics failures

Big data and analytics initiatives can be game-changing, giving you insights to help blow past the competition, generate new revenue sources, and better serve customers. Big data and analytics initiatives can also be colossal failures, resulting in lots of wasted money and time–not to mention the loss of talented technology professionals who become fed up at frustrating management blunders.

[ Also on InfoWorld: Harness the power of Hadoop[1]–find out how in InfoWorld’s Deep Dive report. | 18 essential Hadoop tools for crunching big data[2]. ]

How can you avoid big data failures? Some of the best practices are the obvious ones from a basic business management standpoint: be sure to have executive buy-in from the most senior levels of the company, ensure adequate funding for all the technology investments that will be needed, and bring in the needed expertise and/or having good training in place.

If you don’t address these basics first, nothing else really matters.

But assuming that you have done the basics, what separates success from failure in big data analytics is how you deal with the technical issues and challenges of big data analytics.

Here’s what you can do to stay on the success side of the equation.

References

  1. ^ Harness the power of Hadoop (www.infoworld.com)
  2. ^ 18 essential Hadoop tools for crunching big data (www.infoworld.com)

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