Phase 2: Experiment
Prioritize data quality
Garbage in, garbage out. Fix the inputs first.
Name a data quality owner, separate from the AI Coordinator, so neither workstream gets half attention. Map what data you have, where it lives, and how key terms are defined. The same term, like "client served," often means different things in different departments.
Focus on the quality of new data going in rather than cleaning years of old records. Prevention is worth more than cleanup.
Put it to work
- Use standard entry templates.
- Double-check key fields at the source.
- Clean historical data only where it adds clear value.