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Tom Deutsch

Tom Deutsch (Twitter: @thomasdeutsch) serves as a Program Director in IBM’s Big Data Team. He played a formative role in the transition of Hadoop-based technology from IBM Research to IBM Software Group, and he continues to be involved with IBM Research Big Data activities and transition from Research to commercial products. Tom created the IBM BigInsights Hadoop based product, and then has spent several years helping customers with Apache Hadoop, BigInsights and Streams technologies identifying architecture fit, developing business strategies and managing early stage projects across more than 200 customer engagements. Tom has co-authored a Big Data book and multiple thought papers. Prior to that, Tom worked in the Information Management in the CTO’s office. Tom worked with a team focused on emerging technology and helped customers adopt IBM’s innovative Enterprise Mashups and Cloud offerings. 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. With more than 20 years in the industry, and as a veteran of two startups, Deutsch is an expert on the technical, strategic and business information management issues facing the Enterprise today. Most of Tom’s 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 project succeed. Deutsch earned a bachelor’s degree from Fordham University in New York and an MBA degree from the University of Maryland University College.


Key Considerations for Third-Party Information Sources

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


Data Lakes, Analyst Observations, and Reality

Fit-for-purpose architectures can bring business outcomes down to earth


Analytics Hype: The Next Wave in Big Data Backlash

When considering big data technologies, don’t confuse data collection with smart, operational use of the collected data


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


Mobile, Hyperpersonalized Experiences and Big Data

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


A Different Methodology for Big Data

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

Putting big data myths to rest

Putting Big Data Myths to Rest

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

Real time versus customer time

Real Time Versus Customer Time

For big data, how fast is fast enough?

Ten Years Later, Does IT Matter

Ten Years Later, Does IT Matter?

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

Why is Schema on Read So Useful

Why is Schema on Read So Useful?

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


Don’t Overhype Data Science Expectations

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

Getting Past the Big Data Hype and Backlash

Getting Past the Big Data Hype—and Backlash

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