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10 Pitfalls That Can Undermine Big Data Analytics’ Potential

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Nov 06, 2015

The honeymoon phase of big data analytics is beginning to end. After the initial whirlwind romance with flashy demos and sleek visualization tools, businesses are now coming to terms with the reality of implementation. In many cases, it isn’t going well. Why? No specialty analytics tool can fix underlying problems with data quality and mismanagement. Due to scattered enterprise data environments, analytics teams are having trouble accessing and analyzing data in a timely manner, particularly for the “human” content of unstructured data. Enterprises need to unlock the value of unstructured “people data,” in a proactive manner, to enhance business decision-making and performance. eWEEK interviewed officials at ZL Technologies, a provider of information governance solutions, to get an update on the analytics landscape. “The mainstream analytics paradigm is broken,” said Kon Leong, CEO and co-founder of ZL Technologies. “Tedious sampling and use of point tools undermine big data’s potential.” This eWEEK slide show examines some of the biggest pitfalls of today’s analytics projects.

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