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Data Quality Monitoring

Data quality (DQ) monitoring means continuously tracking the quality of business-critical data using business rules.

This process involves automated tools that check for issues and offer insights into areas needing improvement. By integrating DQ monitoring into data workflows, you can take a proactive approach to managing your data and ensure its reliability for reporting, analytics, and decision-making.

To get started with DQ monitoring, choose the monitoring workflow for your use case and follow the steps included in it.

Data quality monitoring workflows

There are two main workflows for DQ monitoring:

Analyze data quality

Evaluate the quality of your data by applying DQ rules to catalog items (tables) and running data quality evaluation. You can schedule DQ evaluation to automate monitoring.

Learn how to evaluate data quality.

Learn how to understand DQ evaluation results.

Investigate DQ issues

Identify and investigate issues in your data by analyzing results of data quality evaluation and profiling.

Learn how to investigate DQ issues.

Report DQ issues

Group tables you want to monitor into DQ Reports. You can, for example, create reports for specific departments, data stewards, or reports from specific data sources.

Learn how to create DQ Reports.

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