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 review flow

Prerequisites

Before reviewing data quality, confirm:

• at least one sync run exists;
• connector owner and source owner are known;
• mappings or dataset targets have been reviewed;
• expected update interval, unit, and value range are documented;
• downstream use case and minimum freshness requirement are clear.

Input signals

Use these signals together rather than relying on a single score:

Input	Use
Connector status	Shows whether the source is currently reachable.
Sync history	Shows read, written, and failed counts.
Quality trend	Shows whether quality is stable or degrading.
Recent errors	Explains current failures or warnings.
Mapping review state	Indicates whether accepted data is bound to the right target.
Quota usage	Shows whether ingestion limits are affecting sync.

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:

1. Check connector status.
2. Check last sync time.
3. Check recent errors.
4. Compare read, written, and failed counts.
5. Review recent mapping changes.
6. Ask the source owner whether the upstream schema, tag names, units, or source schedule changed.
7. 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.

Expected output

After quality review, the team should have a clear decision:

Decision	Meaning
Ready	Data can feed the planned workflow with known limitations.
Conditional	Data can be used only for a limited workflow, dashboard, or pilot scope.
Blocked	Source, mapping, sync, or quota issues must be fixed before downstream use.

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