Big Data is quickly becoming more than just a buzzword. A plethora of organizations have made significant investments in the technology that surrounds Big Data and are currently starting to leverage the content within to find real value.
Even so, there is still a great deal of confusion about Big Data, similar to what many information technology (IT) managers have experienced in the past with disruptive technologies. Big Data is disruptive in the way that it changes how business intelligence (BI) is used in a business—and that is a scary proposition for many senior executives.
That situation puts chief technology officers, chief information officers, and IT managers in the unenviable position of trying to prove that a disruptive technology will actually improve business operations. Further complicating this situation is the high cost associated with in-house Big Data processing, as well as the security concerns that surround the processing of Big Data analytics off-site.
Perhaps some of the strife comes from the term Big Data itself. Nontechnical people may think of Big Data literally, as something associated with big problems and big costs. Presenting Big Data as “Big Analytics” instead may be the way to win over apprehensive decision makers while building a business case for the staff, technology, and results that Big Data relies upon.
The trick is to move beyond the accepted definition of Big Data—which implies that it is nothing more ...