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Decision making and analytics have coexisted since before the advent of IT. IT simply made it easier to leverage analytics and apply it to decision making.

Advances in modern decision analytics are highly correlated with the evolution of IT. The advent of the mainframe (the first platform) enabled access to decision support system (DSS) tools during the 1970s and 1980s. The introduction of the PC (the second platform), client/server computing, and cheaper storage gave rise to data warehousing and business intelligence (BI) in the 1990s and 2000s. The evolution of smartphones (the third platform) coincided with cloud, social, and mobile computing and today’s emphasis on big data and decision analytics in the 2010s, and will extend into the 2020s.

The big change that is coming in analytics is the transition from looking backward to looking forward. Descriptive analytics give managers a historical view of how the business is performing. Business intelligence and data warehousing are prime examples of how descriptive analytics have been put to use. Although the term historical means past, it can also mean recent past, meaning up to the current point in time. The utility in looking backward is that data is no longer a moving target for analysis. This removes ambiguity from the analysis and enables a factual point of view to be established and captured in a system of record. Looking backward is a core competency that all organizations should exercise and is a prerequisite for looking forward.

Looking forward is where new opportunities exist in decision analytics. Predictive analytics includes a wide variety of analytic techniques that leverage historical data and relationships to help us identify and evaluate the opportunities and risks that will shape the future. Once these opportunities and risks have been identified and evaluated, this knowledge can be leveraged to make informed decisions. While enterprises may struggle to get their heads around some of the concepts associated with predictive analytics, there are obvious entry points, such as the use of “scoring models,” that have broad familiarity. Vendors that have a long tenure in decision analytics, including FICO, IBI, IBM, Oracle, Pegasystems, SAP, SAS, and TIBCO, are actively pursuing ways to make decision analytics easier to understand, adopt, and implement.

Decision Analytics Defined

Decision analytics is the process of rendering decisions supported by analytic capabilities that improve the decision making process and reduce decision time, complexity, and uncertainty. Decision analytics therefore includes an analytic component that performs analysis and a decisioning component that uses the outcome of the analysis to either make or refine a decision. Automation is an important goal of decision analytics—but not all decision analytics activities, especially strategic ones, lend themselves to automation.