By Krish Krishnan
Big data is the biggest buzzword in the industry today. Every
organization—big or small—is looking into understanding and
deploying a big data program. Big data doesn’t just refer to having
larger volumes of data. We must consider the source(s) of the data.
One purpose of a big data implementation is to incorporate
additional data sets into the current data infrastructure to help
the enterprise question anything from the data. Although the
possibility of accomplishing this goal seems realistic with the
evolution of technology and commoditization of an enterprise’s
infrastructure, there are several critical pitfalls to avoid. In this
Ten Mistakes to Avoid, we will look at the most common mistakes
that occur when implementing a big data program to help
you enhance your analytical insights and the decision support
processes in your enterprise.
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