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Matt Asay
Matt Asay
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8 Reasons Big Data Projects Fail

Most companies remain on the big data sidelines too long, then fail. An iterative, start-small approach can help you avoid common pitfalls.

Big data is all the rage, and many organizations are hell bent on putting their data to use. Despite the big data hype, however, 92% of organizations are still stuck in neutral, either planning to get started "some day" or avoiding big data projects altogether. For those that do kick off big data projects, most fail, and frequently for the same reasons.

It doesn't have to be this way.

The key to big data success is to take an iterative approach that relies on existing employees to start small and learn by failing early and often.

Herd mentality
Big data is a big deal. According to Gartner, 64% of organizations surveyed in 2013 had already purchased or were planning to invest in big data systems, compared with 58% of those surveyed in 2012. More and more companies are diving into their data, trying to put it to use to minimize customer churn, analyze financial risk, and improve the customer experience.

[Read the rest in InformationWeek]

Matt Asay is Vice President of Community at MongoDB. He was previously VP of Business Development at Nodeable. You can reach him at mjasay@mac.com and follow him on Twitter @mjasay. View Full Bio

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