Tom Deutsch

Client Technical Leader at Ibm

Based in Lake Forest, United States

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Seniority

Manager

Department

Information Technology

Location

Lake Forest

Industry

IT Services and IT Consulting

Company size

345K

Contact information

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Email

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t•••••••@ibm.com

Phone

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Background

About Tom Deutsch

Current role is all things IBM technology for 60+ Healthcare relationships in the West. Prior role in Product Management for IBM's Governance, Risk and Compliance (GRC) technologies. Prior to this I was CTO for IBM Watson Financial Services and IBM's Industry Solutions and helped drive technology planning on Cognitive solutions that spanned IBM Watson, Software, Cloud, Research and GBS. Created & launched data science as a service offerings and authored papers on emerging analytics methodologies that changed the art of the possible. In Q1 2015 the programs that I invented were expanded to a global IBM initiative. Prior to this I was Program Director on IBM’s Big Data Team. I played a formative role moving NoSQL technology from Research to IBM Software Group. Started IBM's Hadoop-based products, and worked with 300+ customers on Hadoop, Streaming, Cassandra big data technologies. Immediately prior I worked in the Information Management CTO’s office focused on emerging technology and IBM’s innovative Enterprise Mashups and early Cloud offerings. I joined IBM through the FileNet acquisition, where I helped spearhead FileNet product initiatives. More than 25 years’ experience, several startups, expert on the technical, strategic and business information management issues facing the Enterprise today. BA from Fordham University in New York and an MBA degree from University of Maryland University College. Co-authored Understanding Big Data & Harness The Power Of Big Data. Served as an Employer Advisory Board member at Harrisburg University of Science and Technology & on Advisory Board for Data Science at California State University, Fullerton. Skills include Big Data, Cloud, NoSQL technologies and applied analytics including machine learning / advances statistical modeling, cognitive computing initiatives, business strategy and architecture.

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