Abstract: Our hope is that we can turn marketers and other business users into pretty good data scientists by giving them self-service and automated tools. Sometimes this has resulted in disaster. Sometimes this has resulted in success. In this talk, I will share current research into the required new features that make automated predictive analytics tools safe and effective for both the ordinary citizen as well as the accomplished data scientist.
The attendee will learn:
- Business cases where automation worked
- Business cases where automation failed
- The criteria for when automation can be used safely and effectively
- Where to find and how to create ‘citizen data scientists’ within your organization
- How to evaluate predictive analytics tools
- How today’s PA offerings need to change to realize the promise of automation
Bio: Steve Smith is the practice leader for data science at the Eckerson Group. His unique perspective comes from his real-world experience in building the predictive analytics products Darwin, Discovery Server and Optas. These products were among the first to deliver machine learning on an MPP computer architecture, implement algorithms directly in SQL, and embed model results in an OLAP tool. He has written the best-selling business technology books: “Data Warehousing, Data Mining and OLAP” and “Building Data Mining Application for CRM” with McGraw-Hill. He received his undergraduate degree in engineering from MIT and his graduate degree from Harvard in machine learning. His current research is on the limits of automation in data science.