Data Refresh Cadence
Data refresh cadence is how often records and fields are re-checked and updated to reflect new information.
Allocator relevance: Determines how quickly decision-maker changes, mandate shifts, and contact updates are captured—reducing staleness risk.
Expanded Definition
A refresh cadence defines the frequency of re-validation or re-ingestion of data sources. High cadence can improve freshness, but only if paired with accuracy controls; frequent updates without verification can introduce noise. Different fields require different cadences: decision-maker roles may change faster than governance docs; contacts can decay rapidly; AUM may update slower.
For Altss use cases, cadence should be interpreted alongside change detection and last verified timestamps.
How It Works in Practice
Systems schedule periodic re-checks based on field type, source reliability, and observed change rates. Refresh output should log what changed versus what stayed stable, and assign updated confidence and verification statuses.
Decision Authority and Governance
Governance defines field-level refresh rules, materiality thresholds, and when updates require validation. Without governance, refresh cadence becomes a vanity metric rather than a trust metric.
Common Misconceptions
- Faster cadence always means better data.
- Refresh cadence replaces verification.
- One cadence can apply to every field.
Key Takeaways
- Cadence should be field-specific and risk-weighted.
- Pair cadence with change detection to reduce noise.
- “Last verified” is often more actionable than “refreshed.”