10x Increase in engineering productivity
Engineers no longer spend valuable time manually pulling and wrestling with data and are able to answer questions and make adjustments more quickly
Reduced risk
Stardog enables cross-functional activities across multiple datasets allowing for the identification and subsequent mitigation of previously unrecognized risks
Saved time
The Knowledge Graph reduces back and forth and allows for faster review processes, keeping rockets on schedule for launch
Building a rocket is a complex endeavor of precision, involving huge numbers of people, vast amounts of disparate data, many interconnected pieces, and no margin for error. Using Stardog, NASA can quickly make fully informed mission-critical decisions, improving engineering design while reducing cost and risk.
The Challenge
A successful rocket launch is the product of many interdependent parts and systems that must all work together perfectly. Even small changes to one system can significantly impact another seemingly unrelated system.
For example, engineers must ensure that humans will be able to tolerate the rocket’s vibrations and have created a complex mathematical model that verifies the precise calibration of acceptable vibration. This requires a range of inputs about the amount of vibration that will be generated, as well as information about the impact that a slight change in vibration has on the human body, all of which needs to be aligned with project data on factors like schedule and cost.
It typically took NASA engineers weeks to pull the relevant data manually from each dataset, analyze and qualify it, and create a report. If a review board asked a new question or if the scope changed in any way, the engineers had to go back to the drawing board and repeat the whole process. Because every question created an entirely new project that needed its own complex assessment, the review cycle was long and costly. NASA engineers spent lots of valuable time wrestling with the data. Change became more expensive and riskier due to the connectedness and complexity of the data.