Five steps to master Big Data and predictive analytics in 2014

As recently as the past two years, one of the seminal issues regarding Big Data was storage, especially with respect to the exponential growth and size of unstructured data that did not fit into databases (e.g., video feeds, PowerPoint presentations), Forbes reports.

Indeed petabytes and exabytes of data exist in science, technology, commerce, national defense, telecommunications, and other fields. Today, however, the competitive landscape is very different. Proper storage is merely a pre-condition to finding the real jewels in Big Data—turning data from massive streams into knowledge, and thereby actionable intelligence in real time as events unfold.

The following five steps are imperative to master Big Data and drive business growth.

1.  Infer, Infer, Infer. Understand that not all Big Data is useful data. According to the renowned AT&T Bell Telephone Laboratories statistician John Tukey, “data may not contain the answer. The coordination of some data and an aching desire for an answer will not ensure that a reasonable answer can be extracted from a given body of data.”

In an era of data-centric science, we now have advanced analytics that permit inferences from granular data. Inferences transform data into knowledge, which results in greater process transparency and improvements.

… 2. Empower a C-Level Data and Predictive Analytics Champion. With big data analytics changing rapidly and straining information structures, corporations and governments need what McKinsey calls “executive horsepower” or “top-management muscle” behind its data initiatives. Id. Accordingly, a C-level officer (e.g., Chief Data Officer, CTO, or Chief Analytics Officer) who comes from both a supply chain and analytics background must have the mandate to lead model analytic centers.

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