How to avoid messing up big data analytics

How to avoid messing up big data analytics

How to avoid messing up big data analytics

“Big Data” is still in the news these days, but the story has evolved from describing what it is to how an organization can actually use all that data for actionable intelligence, or “Big Data Analytics”. In fact, Forbes just reported on a Honeywell study of 200 manufacturing executives and found the majority of them (68 percent) are currently investing in Big Data Analytics as they felt it is essential for improving manufacturing intelligence and operational performance.

The reality is that while many understand the value of Big Data Analytics, far too many are messing it up, but why?

As businesses continue to amass terabytes upon terabytes of data, they are in reality creating Big Data systems and assembling volumes of information that are considered “big” by any reasonable measure based largely on a specific line of business or business objective. The trouble is these systems are not single repositories and that makes all the difference. If you look inside these systems, you’ll find that they’re made up of multiple Big Data systems: one for marketing, one for finance, one for HR, etc.

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The result is that you have the same problem you had before: the data is separated into silos, and it cannot easily be integrated. Before, the Big Data “explosion” these silos existed in separate, physical data centers, on servers from different vendors, which might not work well with each other. This is especially true if they are systems from the ’80s that were still in use for one reason or another. The difference is now these independent silos are all residing in the same Big Data system, yet they remain just that – silos.



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