2.7.4: Case Study Analysis

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Page ID: 58290

Felix Amoruwa
De Anza College

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NASA Mars Climate Orbiter: A Data Quality Disaster

In 1999, NASA lost the $125 million Mars Climate Orbiter spacecraft due to what was ultimately a data quality failure. The spacecraft was designed to study Mars's atmosphere and climate. However, during its approach to Mars, it flew too close to the planet and was destroyed.

The root cause was a unit inconsistency: one software component (built by Lockheed Martin) produced thrust data in Imperial units (pound-force seconds), while the navigation system at NASA's Jet Propulsion Laboratory expected metric units (newton-seconds). This mismatch — a data consistency and accuracy issue — caused systematic errors in calculating the spacecraft's trajectory. Over months of flight, these small errors accumulated until the spacecraft entered Mars's atmosphere at the wrong altitude.

Investigation revealed several contributing factors:

No automated validation existed to check that thrust data was in the expected units
The discrepancy produced anomalous trajectory data that some engineers noticed but didn't escalate
Review processes existed but failed to catch the unit mismatch during integration testing
The two teams (Lockheed Martin and JPL) used different internal standards without a shared data governance framework

NASA's subsequent review led to sweeping changes in data quality processes, including mandatory unit verification, automated validation checks, and improved cross-team data governance procedures.

Discussion Questions:

Classify the Mars Orbiter failure using the data quality dimensions from this chapter. Which dimensions were violated (accuracy, consistency, validity, etc.)? Could metadata have prevented this failure? If so, how?
Engineers noticed anomalous trajectory data during the mission but didn't escalate the concern. What does this tell us about the organizational and cultural aspects of data quality? How does data governance address not just technical issues but also human processes?
Compare this 1999 incident to modern data integration challenges. Today's organizations routinely combine data from multiple sources with different formats, units, and standards. What modern tools and practices (ETL validation, schema enforcement, automated testing) could prevent similar failures in data analytics projects?
The Mars Orbiter loss was $125 million. Research or estimate: what is the annual cost of poor data quality to businesses? (Industry estimates suggest trillions globally.) Why do organizations continue to underinvest in data quality despite these costs? What would you recommend to a CEO to justify data quality investment?

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