Data Timeliness – On-Time Delivery Monitoring¶
AI-Driven Data Timeliness Module for Data Quality and Observability – digna
Purpose¶
The Data Timeliness module ensures that data arrives on time - every time.
It continuously monitors delivery schedules and automatically detects when datasets, tables, or files are delayed, missing, or incomplete.
By combining AI learning with user-defined schedules, digna enables organizations to prevent downstream errors and uphold strict SLA (Service Level Agreement) targets for both Data Quality and observability of data pipelines.
Technical Overview¶
Dual Monitoring Modes¶
-
AI-Learned Arrival Patterns
digna automatically learns the natural rhythm of your data deliveries — daily, hourly, or event-driven — by analyzing historical timestamps and completion times.
It adapts to changes in business calendars, weekends, or month-end peaks. -
User-Defined Schedules
Users can define expected delivery times explicitly (e.g., every weekday before 7:30 AM).
digna compares the actual arrival time with the planned schedule and raises alerts when data is late or missing.
Detection Mechanism¶
- Evaluates metadata timestamps, record counts, and table freshness
- Detects stalled ETL jobs, failed extractions, and partial file arrivals
- Integrates with Data Anomalies and Data Validation for combined insights
Detection Scenarios¶
| Scenario | Description |
|---|---|
| Late data arrival | Daily market data feed delayed by two hours, causing reports to miss SLAs |
| Missing load | A scheduled table or partition not updated for the current date |
| Chained dependency delay | Upstream job delay impacts downstream pipeline refresh |
| Weekend pattern shift | AI model adapts automatically when no data is expected on Sundays |
Architecture and Execution¶
- In-database execution: digna runs timeliness checks directly inside your database or data warehouse.
- Lightweight metadata access: reads job timestamps, record counts, and partition info — no data extraction required.
- Configurable frequency: schedule monitoring per dataset, schema, or pipeline.
- Cross-module alerts: results can trigger visual warnings in Inspection Hub or notifications via email, Slack, or API.
Example Use Cases¶
- Financial Market Feeds: detect delays in price or trading data updates.
- Data Warehouse Loads: monitor when nightly ETL jobs finish later than expected.
- Data Sharing Between Teams: ensure departmental data deliveries occur before daily cutoffs.
- Regulatory Reporting: confirm that submissions include the latest available data snapshot.
Benefits¶
| Area | Benefit |
|---|---|
| Business Continuity | Prevents operational disruptions due to delayed or missing data |
| Data Quality | Improves reliability and consistency of data pipelines |
| Compliance | Ensures SLA adherence and audit transparency |
| Automation | AI eliminates manual schedule tracking |
| Integration | Works seamlessly with Data Analytics to visualize timeliness trends over time |
How digna Learns Expected Delivery Times¶
- Historical Analysis: digna observes previous load times and durations.
- AI Modeling: Machine learning creates a dynamic baseline for expected arrival.
- Monitoring: Each new delivery is compared to the baseline.
- Alerting: Deviations trigger alerts with contextual metrics and confidence scores.
This continuous learning approach adapts to evolving processes while keeping false positives low.
Frequently Asked Questions¶
Can I define my own delivery times?
Yes. digna supports both fixed user schedules and AI-learned patterns.
Can it integrate with my ETL or orchestration tool?
Yes. digna integrates with tools such as Airflow, dbt, Informatica, or custom schedulers.
Where does computation happen?
All analysis runs within your database or cloud warehouse — no external service is used.
What happens when data is late?
digna raises alerts in the dashboard, Inspection Hub, and via API/webhooks to notify operations teams instantly.
digna Data Timeliness helps ensure trust in data, combining AI-driven detection, on-premises execution, and data observability — all within your controlled environment.