Do More with Your Data: Deep Analytics Using Big DataRegister Online
Analytics are evolving to a new world from a focus on transactions to a focus on interactions. The analysis of detailed transactional data provides insight into the value created from customer relationships whereas the analysis of interactions provides insight into the experience provided to a customer. Analytic models for predicting defection, buying patterns, etc. can be significantly enhanced when interaction data is used to augment traditional transaction data. And yet managing the immense volume of interaction data comes with a unique set of challenges for cost effective data warehouse deployment. Moreover, the sources for interaction data are different than transactional data and the methods by which interaction data is stored for optimal usage is often quite different than what has been done with traditional data warehouse deployments. This session will explore the best practices for getting full value out of new analytic capabilities targeted at big data.
Stephen Brobst is the Chief Technology Officer for Teradata Corporation. Stephen performed his graduate work in Computer Science at the Massachusetts Institute of Technology where his Masters and PhD research focused on high-performance parallel processing. He also completed an MBA with joint course and thesis work at the Harvard Business School and the MIT Sloan School of Management. Stephen is a TDWI Fellow and has been on the faculty of The Data Warehousing Institute since 1996. During Barack Obama's first term he was also appointed to the Presidential Council of Advisors on Science and Technology (PCAST) in the working group on Networking and Information Technology Research and Development (NITRD). He was recently ranked by ExecRank as the #4 CTO in the United States (behind the CTOs from Amazon.com, Tesla Motors, and Intel) out of a pool of 10,000+ CTOs.