In the past several years, one of the seminal issues regarding Big Data has been storage, especially with respect to the exponential growth and size of unstructured data that did not fit into databases. Exabytes (1021 bytes) of data exist in science, technology, commerce, national defense, telecommunications, and other fields. Today, however, the competitive landscape is very different. Data storage is merely a precondition to finding the real jewels in Big Data—turning data from massive streams and algorithms into knowledge, and thereby actionable business intelligence in real time as events unfold.
The following four steps are imperative to master predictive analytics and thereby drive business growth.
1. Infer Actionable Intelligence From Your Data Initiatives.
Understand that not all Big Data is useful data. According to the renowned AT&T Bell Telephone Laboratories statistician John Tukey (1915-2000), “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 business process transparency and improvements. When evaluating the need to institute analytics as part of your data strategy, remember that actionable knowledge is not inherent in data per se; it must be extracted based upon established rules and algorithms. While extraction and inference require the type of open innovation platform described below, this should not be overly complicated. As the National Research Council of the National Academies states, even “naive users” should be able to “carry out massive data analysis without a full understanding of systems and statistical uses.” Data scientists play an indispensable role in today’s corporation, but business line executives should not have to rely on them to run analytics and make the inferences that are the basis for decisions.  As McKinsey & Company puts it, “sophisticated analytics solutions . . . must be embedded in frontline tools so simple and engaging that managers and frontline employees will be eager to use them daily.” 
2. Empower Your C-Level Predictive Analytics and Business Intelligence 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.  Accordingly, a C-level officer (e.g., Chief Data Officer, CTO, or Chief Analytics Officer) with a strong business background (one hopes) must have the mandate to lead model analytics centers. In order to succeed, corporate analysts with deep data experience fortified by the use of expert-global-third-party-based innovation platforms outside their four walls must have a clear strategy with defined initiatives to achieve business results. A forward-thinking analytics strategy thus needs to take place at the business unit level. Why? First, priorities will differ by business unit; the treatment of data in one business unit may have little utility in another. Second, management priorities have to reinforce functional level goals with targets and metrics. A C-level executive who can work with business line managers and still champion analytics in the C-suite is a must.
3. Use Case: Assess The Strength of your Your Supply Chain.
Address Weaknesses with Analytics. Consider supply chain management (“SCM”) and logistics as a Use Case. Do not examine your supply chain without first considering logistics at a macro level. According to the World Economic Forum’s Outlook on the Logistics & Supply Chain Industry (2013), “ratios of trade to GDP for the world as a whole have increased from 39% in 1990 to 59% in 2012.”  This change is in large part the result of a “targeted and concerted effort by industry and governments to increase economic growth and jobs.”
How does your SCM fit into this larger context? Consider this: a corporation’s failure to maximize the knowledge in its data and thereby contribute to unnecessary logistics costs imposes upon itself and others (as well as international trade) what amounts to an inefficiency-based tax. How are corporations faring on other fronts? They spend an astonishing average of eight percent of net sales on transportation, warehousing, customer service, administration, and inventory carrying costs. Yet many do not have a comprehensive view of their data, let alone their upstream or downstream logistics functions. 
Being in the blind has transformative impacts—and not the kind you want. What effect does your logistics framework have on your company? On customer loyalty? How can you most effectively leverage your data both past and present, and what technology do you need to do so? And while an analysis of your supply chain will ultimately include your relationships with parties such as customers, manufacturers, providers and retailers, it should begin with an inward-facing assessment of key assets.
4. Use Case: Rely On A Core Open Innovation Platform That Derives Intelligence And Real-Time Knowledge
Building a robust SCM platform from scratch within one’s four walls or in concert with traditional vendors by combining point solutions is nearly impossible. From the perspective of cost alone, it is much more effective to partner with a third-party cloud-based solution provider. Your criteria when you choose a platform should be stringent. For example, Appirio’s community of 660,000 global experts in data science, application development, and design provide an incomparable competitive advantage for those who tap into its world-class skills. Your platform must also provide robust IP and security measures, as set forth in our recent White Paper on the subject, How To Drive Open Innovation Without Risking Your Intellectual Property.
Cloud computing is not going anywhere. Combined with open innovation platforms, it will form the Human Cloud and usher in new Services models. A critical element of these models is the use of Data Science and mastery of predictive analytics and business intelligence, by which enterprises can extract critical knowledge from what otherwise might remain mere stored and even wasted data.
 John Gertner, The Idea Lab and the Great Age of American Innovation (The Penguin Press 2012)
 SeeFrontiers in Massive Data Analysis (National Academy of Sciences 2013)
 Mobilizing Your C-Suite For Big Data Analytics (McKinsey & Company 2013)
 Outlook on the Logistics & Supply Chain Industry (World Economic Forum 2013)
6] See Supply Chain Logistics As A Driver of Business Strategy and Profitability (C.H. Robinson 2013).