Big data doesn’t inherently lead to better results.
Although big data already is — and will continue to be — a relentless driver of revolutionary business change (just ask Jeff Bezos, Larry Page or Reid Hoffman), too many organizations don’t quite grasp that being “big data-driven” requires more qualified human judgment than cloud-enabled machine learning. Web 2.0 juggernauts like Google, Amazon and LinkedIn have the inborn advantage of being built around both big data architectures and cultures. Their future success is contingent upon becoming disproportionately more valuable as more people use them. Big data is both enabler and byproduct of “network effects.” The algorithms that make these companies run need big data to survive and thrive. Ambitious Algorithms love Big Data and vice versa.
Similarly, breakthrough big data systems such as IBM’s Watson — the Ken Jennings-killing Jeopardy champion — are designed with a mission of clarity and specificity that makes their many, many terabytes intrinsically indispensable.
By contrast, the overwhelming majority of enterprise IT systems can’t quite make up their digital minds……
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