Managing data science at scale

Description

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.