Eli Lilly data strategy paves way for AI in drug discovery

With model-driven drug discovery on the horizon, Eli Lilly is building an organizational and technological structure for enterprise-wide AI. That task got underway about four years ago when Gopal joined the company as its first chief data and analytics officer. Among his earliest initiatives was studying Eli Lilly’s data environment, which he found in need of modernization.

“Historically, like every other pharma company, we grew up in silos,” Gopal said. “The data in the research and discovery part was managed differently compared to clinical development and manufacturing and commercial.”

Eli Lilly embarked on a modernization program to create an enterprise-wide data environment, providing data quality, consistent security polices and the ability to rapidly find and access data. The program follows a cloud-first philosophy, using components such as a cloud data warehouse, to eliminate data silos and offer improved data visibility.

Eli Lilly needed such a foundation to harness AI’s potential, considering, as a rule of thumb, data scientists spend 80% of their time identifying, aggregating and cleansing data, Gopal noted. The resulting data latency, as Gopal calls it, hinders AI efforts. Data latency exists in a continuum that includes analytics latency, which manual approaches exacerbate, and decision latency, which stems from the other forms.

“What we’re trying to do is to reduce the data latency, and quite often that’s the big one,” Gopal said. Reduced data drag, coupled with an effort to speed up data analytics, translates into faster decision-making, he noted.