Fixing the talent pipeline so that finding rocket scientists doesn’t have to be rocket science
NASA has been set the ambitious targets of taking humans back to the Moon by 2024, then to later make the order-of-magnitude leap on to Mars. Even for the globally renowned space agency, it is a struggle. According to a recent report by the Government Accountability Office (GAO) it still isn’t quite sure how much this will cost, nor how long it will take to get the equipment ready. Hardware challenges abound, including testing an electric ion propulsion system for the orbiting capsule, dubbed Gateway. On top of all that, there is the question of whether NASA has the right people in place to take on the Moon mission and the trip to the Red Planet beyond. Trying to come up with some answers is David Meza, senior data scientist within the agency’s people analytics group, who has begun employing graph database technologies to try and help. Like many HR and personnel IT estates, NASA maintains a mishmash of applications and analytical systems, each reliant on relational databases. “We have SAP, ServiceNow and we’ve got some of the different RDBMSs that hold various different types of information about the employees, about our work roles about our jobs, and so on,” Meza said. But connecting the information between systems and understanding the relationships between it was the tough part. Data about individuals might be on one system, while training was managed on another and databases about projects were somewhere else. “It made it difficult to find the right relationship very easily,” he said. Step forward graph databases. Meza has been working with graph databases for more than 10 years during his time as chief knowledge officer at NASA, applying them to a “lessons learned” database problem. The same features would help with the skills challenge NASA faces: “This is a graph problem,” he said.
