Poor data quality is enemy number one to the widespread, profitable use of machine learning.” A scary claim. Especially because so many of us know the issue exists, but we don’t know how to address it.
In this webinar, we will first explore why the “garbage-in, garbage-out” problem in analytics and decision making has remained intractable for generations. Then we’ll explore concrete steps you can take to start getting your data quality issues under control.
You’ll also have an opportunity to ask your own questions and get expert answers gleaned from practical experience, applying hard-won lessons to the incredibly steep quality demands of machine learning. By the end of the webinar, you’ll come away with a better insight into the challenges and approaches to creating a comprehensive and well-executed data quality program.
With more than 20 years’ experience in the software industry, Michael Corcoran works with the executive management team to develop and communicate corporate and product strategy. During his career, Michael has played key roles in the development and acquisition of major technologies, including business intelligence (BI), integration middleware, Internet technology, data warehouse, application development, and expert systems. Michael is a frequent speaker at industry and financial investor conferences and has lectured at the Yale School of Management, Columbia University, University of Michigan, New York University, and St. John’s University.
Dr. Thomas C. Redman is the president of Data Quality Solutions where he helps start-ups and multinationals; senior executives, chief data officers, and leaders buried deep in their organizations, chart their courses to data-driven futures, with special emphasis on quality and analytics. Tom’s most important article is “Data’s Credibility Problem” (Harvard Business Review, December 2013). He has a Ph.D. in Statistics and holds two patents.