Data Quality Monitoring
Using the Data Quality Check workflow to ensure Splunk data ingestion quality
Data Quality Monitoring
The Data Quality Check workflow runs a detailed audit of your Splunk data. You get a structured report on ingestion latency, timestamp parsing, line breaking, duplicates, and field coverage.
Navigation
- Parent: Use Cases
- Related: Workflow Templates | Running Workflows
The Scenario
You need to ensure data ingestion quality across your Splunk deployment. Stale data, parsing errors, and inconsistent fields cause downstream issues in dashboards and alerts. You want a systematic check instead of manual spot-checks.
What the Workflow Checks
The Data Quality Check workflow audits five areas:
| Area | What It Checks |
|---|---|
| Ingestion latency | Indexing lag vs real time; delayed or stuck data |
| Timestamp parsing | Parsing errors, timezone issues, malformed timestamps |
| Line breaking | Malformed events, truncation, multi-line handling |
| Duplicates | Duplicate event detection and frequency |
| Field coverage | Missing or inconsistent fields across sourcetypes |
Duration: 5–7 minutes | Complexity: Intermediate
Steps
- Open Workflows — Go to the Workflows section and select the Data Quality Check template.
- Configure scope — Choose indexes and sourcetypes to audit. You can run a broad check or focus on critical data.
- Run the workflow — Start the run. The workflow connects to your Splunk via MCP and executes the checks.
- Review the report — You receive a structured report with findings per category, severity, and suggested fixes.
Interpreting Results
- Pass — No issues detected in that category.
- Warning — Minor issues that may affect some use cases. Review and prioritize.
- Fail — Significant issues requiring action. The report includes suggested SPL and config changes.
Use the suggested fixes to update props.conf, transforms.conf, or inputs.conf. Re-run the workflow after changes to verify improvements.
