James Taylor is the CEO of Decision Management Solutions and is a leading expert in how to use business rules and analytic technology to build decision management systems. He is passionate about using decision management systems to help companies improve decision-making and develop an agile, analytic and adaptive business. He provides strategic consulting to companies of all sizes, working with clients in all sectors to adopt decision-making technology. James is an expert member of the International Institute for Analytics and is the author of multiple books and articles on decision management, decision modeling, predictive analytics and business rules, and writes a regular blog at JT on EDM. James also delivers webinars, workshops and training. He is a regular keynote speaker at conferences around the world.

Abstract: Applying machine learning, predictive analytics and artificial intelligence to decision-making is a hot topic. Organizations have more data than ever and these analytical techniques offer the promise of insights that will dramatically improve decision-making. The challenge for decision professionals is that the decisions you can improve with analytics are operational ones. The day to day, transactional decisions of an organization are where data is generated and where analytics can be applied.

A clear understanding of these decisions is a necessary condition for success. A new standatd – the Decision Model and Notation standard – is establishing itself as the premier way to describe operational decision-making and support digitization and the effective application of analytic insight. Decision Modeling documents known aspects of the current situation, reveals and resolves ambiguity, and supports definition of multiple alternative solutions. It’s highly iterative and often results in an a-ha moment. Decision models capture how human experts make decisions in a way that makes it clear what should be automated and how analytic insight can be applied.

This tutorial introduces decision modeling and the DMN notation, shows how to use decision models to apply analytic insight and discusses how models of repeatable decisions can support the continuous improvement of decision-making.