The degree to which data is up-to-date and available when needed for decision-making or processing.
Timeliness (also known as currentness) is a critical data quality dimension that measures whether data reflects the current state of the real-world entities it represents and is available at the right time for its intended use.
Key aspects:
- Temporal accuracy: Data values reflect the current state, not outdated information
- Update frequency: Data is refreshed at appropriate intervals
- Availability timing: Data is accessible when users need it for decisions
- Age of data: Time elapsed since data was last updated or validated
Examples:
- Financial trading: Stock prices must be real-time or near-real-time
- Healthcare: Patient vital signs and medication records must be current
- Inventory management: Stock levels must reflect actual warehouse quantities
- Weather forecasting: Meteorological data must be recent for accurate predictions
- Regulatory reporting: Compliance data must be current to meet filing deadlines
Timeliness directly impacts decision quality—outdated data leads to poor decisions, while timely data enables effective action.
See also: data-quality, accuracy
Note: For system performance timing characteristics (response times, throughput rates), see performance and response-time.