Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA

Managing data science at scale

Matthew Granade (Domino Data Lab)
11:50am12:30pm Wednesday, March 7, 2018
Average rating: **...
(2.00, 1 rating)

Who is this presentation for?

  • Data science managers, IT managers, CIOs, and heads of data science and infrastructure

What you'll learn

  • Learn how to identify and leverage flexible tools to implement successful infrastructure across data teams while maintaining control and ensuring security and compliance


Predictive analytics and artificial intelligence have become critical competitive capabilities across industries. Data-driven decision making is reshaping every aspect of the modern enterprise, from hiring to forecasting to manufacturing to sales and marketing. Yet for IT, supporting data science programs has become far more complex than supporting other departments.

Researchers demand flexibility when it comes to tools and compute resources, particularly with high-performance computing infrastructure. But security and business imperatives require regulations around the use of data and the deployment of models. If these controls are too limiting, the result is a “wild west” of shadow IT and siloed projects, and business risk increases even as data science capabilities are handicapped. CIOs are stuck between a rock and hard place. They need to centralize data science infrastructure in a way that maintains governance without constraining data scientists’ freedom and flexibility.

Matthew Granade explains how IT managers can end data science shadow IT projects, stop the complaints from data science teams about infrastructure, and enable teams to leverage modern tools while maintaining control and ensuring security and compliance. Along the way, he details how leading banks, insurance and pharmaceutical companies, and others manage data science at scale.

Photo of Matthew Granade

Matthew Granade

Domino Data Lab

Matthew Granade is a cofounder of Domino Data Lab, which makes a workbench for data scientists to run, scale, share, and deploy analytical models, where he works with companies such as Quantopian, Premise, and Orbital Insights. He also invests in, advises, and serves on the boards of startups in data, data analysis, finance, and
 financial tech. Previously, Matthew was co-head of research at Bridgewater Associates, where he built and managed teams that ensured Bridgewater’s understanding of the global economy, created new systems for generating alpha, produced daily trading signals, and published Bridgewater’s market commentary, and an engagement manager at McKinsey & Company. He holds an undergraduate degree from Harvard University, where he was president of the Harvard Crimson, the university’s daily newspaper, and an MBA with highest honors from Harvard Business School.