Tom Deutsch

Tom Deutsch (Twitter: @thomasdeutsch) is chief technology officer (CTO) for the IBM Industry Solutions Group, and focuses on data science as a service. Tom played a formative role in the transition of Apache Hadoop–based technology from IBM Research to the IBM Software Group, and he continues to be involved with IBM Research's big data activities and the transition from research to commercial products. In addition, he created the IBM® InfoSphere® BigInsights™ Hadoop–based software, and he has spent several years helping customers with Hadoop, InfoSphere BigInsights, and InfoSphere Streams technologies by identifying architecture fit, developing business strategies, and managing early stage projects across more than 200 engagements. Tom came to IBM through the FileNet acquisition, where he had responsibility for FileNet’s flagship content management product and spearheaded FileNet product initiatives with other IBM software segments, including the Lotus and InfoSphere segments. Tom has also worked in the Information Management in the CTO’s office and with a team focused on emerging technology. He helped customers adopt innovative IBM enterprise mash-ups and cloud-based offerings. With more than 20 years of experience in the industry, and as a veteran of two startups, Tom is an expert on the technical, strategic, and business information management issues facing the enterprise today. Most of his work has been on emerging technologies and business challenges, and he brings a strong focus on the cross-functional work required to have early stage projects succeed. Tom has coauthored a book on big data and multiple thought-leadership papers. He earned a bachelor’s degree from Fordham University in New York and an MBA degree from the University of Maryland University College.

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Key Considerations for Third-Party Information Sources

IBM experience with data sources fosters a timely primer as the number of external data sources grows

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Data Lakes, Analyst Observations, and Reality

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Analytics Hype: The Next Wave in Big Data Backlash

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Recapping 2014: Significant Trends for Big Data

The months ahead represent the year that was when it comes to prognosticating big data, analytics, and more

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Mobile, Hyperpersonalized Experiences and Big Data

Intersecting mobile hyperpersonalization and big data can forever change the dynamics of interaction

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A Different Methodology for Big Data

Get acquainted with minimum viable insight for moving analytics-driven big data projects forward

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Putting Big Data Myths to Rest

Avoid giving credence to these misconceptions when making decisions about big data

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Real Time Versus Customer Time

For big data, how fast is fast enough?

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Ten Years Later, Does IT Matter?

Taking a look back on Nicholas Carr’s seminal article from 2003

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Why is Schema on Read So Useful?

A primer on why flexibility—not scale—often drives big data adoption

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Don’t Overhype Data Science Expectations

Why it’s important to keep outsized data science claims in perspective

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Getting Past the Big Data Hype—and Backlash

Why arguments at both ends of the spectrum are missing the point

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