Data Collection Infrastructure

Prediction requires data. Behavioral performance prediction requires two data streams: profile data (DISC scores, assessment dates, context notes) and performance data (metrics, ratings, ramp times, tenure, departure reasons). The prediction model lives in the correlation between these two streams. Without both streams, you have either profiles without outcomes or outcomes without profiles — and neither is prediction.

1. Profile Data Collection Standardize the assessment instrument, administration process, and data recording format. Every profile should include: full four-dimensional scores, assessment date, context (role, team, organizational conditions), self-validation notes, and any observed discrepancies. Consistency in collection enables comparison across time and people.
2. Performance Data Collection Define the performance metrics that matter for each role: output quantity, output quality, ramp time, collaboration effectiveness, and supervisor assessment. Record these metrics at consistent intervals — quarterly at minimum. The performance data must be granular enough to correlate with specific behavioral dimensions.
3. Linkage and Storage Link profile records to performance records by individual. Store longitudinally — each person's data accumulates over time, enabling developmental tracking and prediction refinement. The data store is the foundation of every prediction model you will build. Invest in its design upfront.