Title:
IBM Machine Learning for z/OS; Where customers can bring Predictive Analytics to their Data
Presentation Abstract:
In order to stay competitive in the current market, organizations are planning or have begun a Digital Transformation journey. This effort has exposed organizations to the truth that they are data intensive, process laden and time insensitive. Too much data, too many iterative steps to process that data and their time dimension does not meet the needs of the business. In their quest to understand the data for better insight, customers are concluding that they are “data rich but insight poor”. With core business and functional applications hosted on System z, customer’s most critical data is resident on the mainframe. There is an ongoing need to make informed decisions in systems of record (SoR) and increase the level of personal interaction with systems of engagement (SoE). With IBM Machine Learning for z/OS, customers can bring Predictive Analytics to their data and optimize the intersection between their Digital Business and Digital Intelligence. Speaker Bio:
Jerome Gilbert – North America zAnalytics Technical Leader. Jerome is the leader of the North America zAnalytics technical sales team where he has an Enterprise wide role driving analytics solutions on System z with a focus on creating and delivering data strategies and analytic agendas for IBM’s customers. Jerome has been with IBM 5 years and was a professional hire after 25 years in the Financial Services industry where his role was managing corporate data. Jerome has been a long-time member of IBM’s customer councils and continues to be an advocate for the field in the areas of the DB2 engine, DB2 Tools and Real Time Analytics. Jerome and his team are currently delivering the positioning and value statement for bringing Predictive Analytics to the data through the implementation of IBM Machine Learning for z/OS. Chart:
Audio:
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