Why NASA Converted Its Lessons Learned Database into a Knowledge Graph

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Learning from mistakes—your own and those made by others—is a mark of effective organizations. Fostering a learning environment means seeing every possible outcome as a learning opportunity. And every project manager knows the importance of documenting and continuously reviewing these lessons learned.

Many companies maintain databases for their lessons learned which contain heaps of valuable information, potentially critical to the success of new projects. More often than not, though, they are incredibly difficult to search and navigate, rendering them unusable.

Even mature and knowledge-driven organizations like NASA find themselves struggling to find the answers they need in their own knowledge bases. Nothing illustrates that better than the infamous case of the “lost tapes”, as they came to be referred to by the press.

In the early 2000s, a team of retired NASA employees set out on a search after the tapes containing the original footage of the Apollo 11 moonwalk by Neil Armstrong and Buzz Aldrin. After an exhaustive, three-year search, NASA was forced to admit to an embarrassing mishap: the tapes were most likely improperly labeled and ended up being erased and reused at some point in the 1980s.

While this case is certainly an outlier, it does reflect the challenges that come with managing knowledge. NASA famously maintains an automated database called the Lessons Learned Information System (LLIS). It contains impressive volumes of data collected from past tests and missions, both successful and failed, and is used in the planning of future projects and expeditions into space. As the database absorbed more and more information, it became apparent to NASA that in order to maintain its usability the system needed to be modernized.

Collecting and storing the lessons learned is only half the battle. Making that knowledge easily discoverable is the real challenge. David Meza, NASA’s Chief Knowledge Architect understood this all too well as he struggled to find answers in LLIS. The system required you to punch in a keyword which would then produce an endless, randomly arranged list of links to documents, every one of which needed to be checked one by one—a process so tedious that NASA engineers hardly ever consulted the system.

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Yves Mulkers

Yves Mulkers is the founder of 7wData and a widely followed voice in the data and AI community. He curates the 7wData and AI Beat newsletters, reaching hundreds of thousands of data and AI professionals, and writes on data strategy, analytics, AI, and the evolving data ecosystem.