Four Steps to a Successful Big Data Project

Too often, investments in big data yield little value. Companies tend to get stuck in constant proof-of-concept efforts and investments that never capture the full potential of their big data vision.

Big data projects often fall into two proof-of-concept traps. The IT science project: This is often led by architects who decide to create a data pool, or even a data lake, from data that no one has had any need to use previously. These architects hope that business users and value will magically appear from newly accessible data, and that they can get funding for an even grander big data project. Not surprisingly, what often results is a big data investment that yields low user adoption, a negligible return on investment, and little hope of continued funding.

Author: Christopher Sowa

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