DFS Lite Data Quality
Data quality shows whether connector data is usable for operational workflows. Check quality before using DFS data in Inspector, AI Agent, predictive maintenance, dashboards, or simulation workflows.
Open the quality view
Go to:
Data Integration > Data Quality
Use this page to review connector-level quality, trends, quota usage, and alerts.
Quality dimensions
| Dimension | Meaning | Typical issue |
|---|---|---|
| Completeness | Required values are present. | Missing fields, empty rows, sparse tags. |
| Timeliness | Data arrives within the expected time window. | Stale connector, delayed polling, source outage. |
| Accuracy | Values match expected type, range, and format. | Unit mismatch, impossible values, invalid status codes. |
| Overall quality | Combined signal for connector health. | Multiple small issues across fields or time. |
Connector quality checklist
For each important connector, check:
- latest quality score;
- quality trend;
- connector status;
- last sync time;
- points written today;
- recent errors;
- completeness, timeliness, and accuracy details;
- quota usage.
Sync history checks
Use Sync History or Connector Detail to inspect sync runs.
| Field | How to use it |
|---|---|
| Time | Confirm the run happened when expected. |
| Connector | Confirm the run belongs to the source under review. |
| Status | OK means the run completed; partial or failed runs need review. |
| Read | Number of records or points read from source. |
| Written | Number of records or points accepted by DFS. |
| Failed | Number of records or points rejected or failed during processing. |
If read count is high and written count is low, inspect mappings, rejected rows, validation rules, and target identity.
Quality drops
When quality drops, follow this order:
- Check connector status.
- Check last sync time.
- Check recent errors.
- Compare read, written, and failed counts.
- Review recent mapping changes.
- Ask the source owner whether the upstream schema, tag names, units, or source schedule changed.
- Review quota usage if ingestion stopped unexpectedly.
Quota usage
Quota helps teams understand connector and point usage. If quota is exhausted or close to the limit, decide whether to:
- reduce unnecessary mappings;
- reduce polling frequency;
- split sources by priority;
- move historical or analytical needs to DFS Pro datasets;
- request a quota adjustment for the tenant.
Before using data downstream
Before a connector feeds AI Agent, Inspector, predictive maintenance, or BI, confirm:
- source ownership is clear;
- identity mapping is reviewed;
- quality score and trend are acceptable for the use case;
- stale values are visible to users;
- failed rows can be investigated;
- known limitations are recorded.
Next steps
| Situation | Next page |
|---|---|
| First connector quality is acceptable | DFS Pro Datasets |
| Mapping appears wrong | Mapping Source Fields |
| Preparing maintenance signal history | Prepare Signal History for Predictive Maintenance |