FG-0508 Designed Data Guarantee
Description
Ensure that every data artifact shipped by the collaboration has been intentionally designed, reviewed, and validated: never randomly generated or arbitrarily chosen. This technique eliminates 'placeholder syndrome' where AI-generated sample data ships as production content. Every data point, every example, every configuration value should exist because someone decided it should, not because an AI needed to fill a field.
Implementation
Establish a review step for all data artifacts: configuration files, sample data, test fixtures, and content. Define what 'designed data' means for your domain (manually authored, reviewed for accuracy, validated against requirements). Flag and replace any generated data that shipped without review. Include data design in the asset review pipeline.
Success Indicators
- No placeholder or randomly-generated data in production
- Data artifacts are reviewed with the same rigor as code
- The team can explain why every data value was chosen
- Data quality issues decrease over time
Failure Modes
- Treating data review as lower priority than code review
- Not recognizing AI-generated sample data as placeholder
- Data review bottleneck: reviewing data takes longer than generating it
- Designed data that's still wrong: reviewed but not validated