Many companies delay addressing core improvements such as increasing revenues and reducing costs and risk exposure by tying these changes to a to-be-hired data scientist. Companies need data scientists for many advanced machine learning approaches. But success requires domain experience and machine learning. Starting with the domain experience and augmenting that with more data and analytics can provide excellent results that can then be continually improved. And starting with the data actually lays a better foundation for immediate and continued success.
Jack Norris shares three customer case studies to answer questions and illustrate approaches.
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Jack Norris is the senior vice president of data and applications at MapR Technologies, where he works with leading customers and partners worldwide to drive the understanding and adoption of new applications enabled by data and analytics. With over 25 years of enterprise software experience, he has demonstrated success from identifying new markets to defining new products to launching companies. Jack’s background includes senior executive positions with establishing analytic, virtualization, and storage companies. Jack was an early employee of MapR Technologies and held senior executive roles with EMC, Brio Technology, and Bain and Company.
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