Eight Classic Big Data Mistakes

By Dennis McCafferty  |  Posted 07-01-2014 Email

Demand for big data analytics will only continue to soar as the overall market is expected to grow at a compound annual growth rate of 27% through 2017, amounting to a $32.4 billion market, according to industry research. Yet, with all of the investments into analytics tools and talent, CIOs and other IT leaders can lose sight of what they're ultimately seeking to do. Collecting lots of big data, after all, doesn't amount to much if teams don't know how to effectively mine and translate it. Or if the quality of the data itself is highly suspect. Or if team members fail to understand the potential for missing context or ignore the existence of ill-advised personal biases in forming conclusions. These are among the following eight classic big data analytics mistakes we've compiled in order to highlight the common existing traps and lend guidance about how to avoid them. They were adapted from a number of online resources, including those posted by IBM and Oracle. Clearly, there is no surefire script which will guarantee success, given that many organizations still face a considerable learning curve here. But developing awareness of these trouble signs will certainly help minimize obstacles. For more about the mistakes from IBM, click here. For more about the mistakes from Oracle, click here.

Dennis McCafferty is a freelance writer for Baseline Magazine.


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