Lockheed Martin accelerates data science with Domino Data Lab Posted on : Feb 16 - 2021

The defense contractor has empowered its data scientists with self-service capabilities and centralized modeling, says Matt Seaman, chief data and analytics officer of enterprise operations.

Enterprise data scientists are frustrated by the Sisyphean struggle to get the technology assets they require to build data models. But that’s hardly the only hurdle: Because these projects slow-cook in siloes, data science teams often duplicate efforts. It’s a maddening combination of requisitioning hell and redundancies.

No stranger to such challenges, defense contractor Lockheed Martin installed a software platform to make the development of machine learning (ML) and artificial intelligence (AI) models more efficient. The platform centralizes assets required to build data models, reducing the costs of the company’s ML and AI projects by $20 million a year, says Matt Seaman, Lockheed Martin’s chief data and analytics officer of enterprise operations.

The self-service capabilities are critical for the company’s approach to democratizing access to data, Seaman says. “We’re reducing the barriers to start and run new projects that will help us make better and faster decisions with data.”

Adoption of self-service technology is soaring, representing the next phase of a consumerization phenomenon that put mobile computers and applications into the hands of millions of workers more than a decade ago. But perhaps nowhere is the interest greater than in data science, in which the potential of advanced analytics that helps discover business insights has been constrained by the same clunky processes that have long held back companies from reaching their potential.

Clearing the provisioning hurdle

Lockheed Martin is neutralizing the problem with the help of Domino Data Lab, whose collaborative data science platform helps the company’s 300-plus data scientists both build data models more efficiently and lay a foundation for future data scientists coming into the company, says Seaman.

Before landing on Domino Data, Lockheed Martin’s data scientists spent an inordinate amount of time identifying computing resources they needed and requesting them from IT. These staffers waited for IT to build, install and configure the integrated development environment (IDE) and other programming tools on a server, which they logged into every time they needed to access their projects and resources. But many data scientists are working on multiple projects, often requiring multiple systems, servers and IDEs, creating a constant cycle of blocking and tackling infrastructure. View More