NASA famously maintains a “ lessons learned ” database containing valuable information from its past programs and projects. But the vast system, which has been online since 1994, is not always easy to navigate. Now the agency is modernizing it with help from a tool more familiar to social media than space missions: graph databases.

The genesis of the change began about a year and a half ago when an engineer, attempting to search “lessons learned” for relevant documents, found the number of possible results overwhelming. “He was getting things that really were not relevant to what he was looking for,” David Meza, NASA’s chief knowledge architect, recalls.

Looking to make the database more useful, and help users investigate relationships beyond what basic keyword searches could uncover, Meza experimented with storing the information in a graph database—that is, a database optimized to store information in terms of data records and the connections between them. In recent years, such network graphs have become a familiar feature of online social networks.

The individual lesson write-ups themselves were nodes in the network, as were topics to which the lessons were associated by a machine learning algorithm. And to store and organize that data, Meza turned to Neo4j, a database system that’s specifically designed to store graph data more efficiently than traditional, SQL-powered relational databases.

“We frequently have customers telling us that we’re a thousand times faster or a million times faster than a relational database,” says Emil Eifrem, CEO of Neo Technology, the San Mateo, California, company behind Neo4j.

The tool was also notably used by the International Consortium of Investigative Journalists to map connections between people and companies identified in the massive leaked collection of offshore finance data dubbed the Panama Papers. And, says Eifrem, it’s frequently used by e-commerce companies looking to generate automated product recommendations based on relationships between users and products, and by financial institutions looking to identify suspicious sets of transactions—even in cases where the individual transactions aren’t independently off-looking.

“A fraud ring is all about relationships,” says Eifrem.