As business decisions are increasingly made by computers these days, business analysts could benefit from a better understanding what it takes to build software that makes decisions. Using lessons learned from numerous customer projects, the presenter will use as much plain English as possible to shed light on: getting data ready for automation, modeling decisions to support automation, the iterative software development life cycle (SDLC), common risks and pitfalls in decision automation, and more. This session is geared towards people without a formal development background. Attendees should leave this session with an understanding of how to communicate better with the developers they’ll work alongside as the next business decision gets automated.

Learning Objectives:

• Understand data modeling concepts such as structured and normalized data, data relationships, and validity
• Review how automated decisions are iteratively written, tested, and implemented following the Software Development Life Cycle (SDLC)
• Learn how to identify common risks for decision automation project failure and how to raise them sooner