Cognitive Analytics for Making Better Evidence-Based Decisions

Abstract

The actions associated with business decisions are guided by a range of variables, that include: opportunities, funding types, customer categories, competencies, proposal status, resource feasibility, technical feasibility, capability increase, risk level and commitment. The analysis of these decision variables or more likely the data associated with them is based on using descriptive, predictive, or prescriptive analytics as a means of “searching for answers” to the business problems and issues confronting the enterprise. Cognitive analytics embodies a fourth area of decision support that facilitates the analysis of structured and unstructured data sources and the use of natural language processing, learning and reasoning capabilities to enhance hypothesis generation. In short, cognitive analytics enables the enterprise to “ask the right questions” surrounding the evidence. This research highlights the impact of cognitive analytics in making evidence-based decision actions – specifically by modeling “what if” scenarios concerning the impact of resource and schedule on project risk associated with the development of a new product using IBM SPSS Modeler and IBM Watson Analytics.