As a business leader, you are use Data to design more precise marketing campaigns, to detect unusual incidences of fraud, to identify indicators of risk, or to find the next major oil field, each of these problems presents unique analysis challenges.

Today we are witnessing dynamic shifts in business strategy — enabled by Digital Technology. Digital transformation creates extraordinary opportunities for consumers, companies, industries and countries. We examining how industries embrace the Internet of Things, cloud and cognitive computing. It's clear, we are embarking on something truly life changing as we enter the Cognitive Business Era.

Like many emerging technologies, cognitive computing is not easy. First, cognitive computing represents a new way of creating applications to support business and research goals. Second, it is a combination of many different technologies that have matured enough to become commercially viable. Some technologies or methods such as machine learning algorithms and natural language processing (NLP) have been seen in artificial intelligence applications for many decades. Other technologies such as advanced analytics have evolved and grown more sophisticated over time. Dramatic changes in deployment models such as cloud computing and distributed computing technology have provided the power and economies of scale to bring computing power to levels that were impossible only a decade ago.

With huge advancements in technology in the last 30 years, the ability to gain insights and actions from data hasn't changed much. In general, applications are still designed to perform predetermined functions or automate business processes, so their designers must plan for every usage scenario and code the logic accordingly. They don't adapt to changes in the data or learn from their experiences. Computers are faster and cheaper, but not much smarter. Of course, people are not much smarter than they were 30 years ago either. That is about to change, for humans and machines. A new generation of an information system is emerging that departs from the old model of computing as process automation to provide a collaborative platform for discovery.

In order to cope with these uncertainties, a basic procedure in the engineering/economic practice is to replace first the unknown parameters by some chosen nominal values, e.g., estimates, guesses, of the parameters. Then, the resulting and mostly increasing deviation of the performance (output, behavior) of the structure/system from the prescribed performance (output, behavior), i.e., the "tracking error", is compensated by (online) input corrections. However, the online correction of a system/structure is often time-consuming and causes mostly increasing expenses (correction or recourse costs). Very large recourse costs may arise in case of damages or failures of the plant. This can be omitted to a large extent by taking into account already at the planning stage the possible consequences of the tracking errors and the known prior and sample information about the random data of the problem. Hence, instead of relying on ordinary deterministic parameter optimization methods—based on some nominal parameter values—and applying then just some correction actions, stochastic optimization methods should be applied: Incorporating stochastic parameter variations into the optimization process, expensive and increasing online correction expenses can be omitted or at least reduced to a large extent.