How to avoid big data analytics failures

Follow these six best practices to blow past the competition, generate new revenue sources, and better serve customers

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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.

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.

1. Carefully choose your big data analytics tools

Many technology failures stem from the fact that companies buy and implement products that prove to be an awful fit for what they are trying to accomplish. Any vendor can slap the words “big data” or “advanced analytics” onto their product descriptions to try to take advantage of the high level of hype around these terms.

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