Managing data quality
Measure, monitor, and maintain the quality of your data to ensure the data meets your expectations and standards for specific use cases.
Data of good quality is in a state that usually can be defined as fit for use, defect free, or meeting expectations and requirements. Data quality is measured against the quality dimensions Accuracy, Completeness, Consistency, Timeliness, Uniqueness, and Validity.
Data quality analysis provides answers to these questions:
- How good is the overall quality of a data asset?
- Which of the data assets has the better quality?
- How did the quality of a data asset change over time?
- Required services
- Watson Knowledge Catalog
- DataStage
- Data format
- Tables from relational and nonrelational data sources
- Tabular: Avro, CSV, Parquet, ORC :
- Data size
- Any :
- Required permissions
- To view data quality assets, you must have at least the Viewer role in the project.
- To create, edit, or delete data quality assets, you must have the Admin or the Editor role in the project.
Data quality analysis and monitoring
Use data quality analysis and monitoring to evaluate data against specific criteria. Use these evaluation criteria repeatedly over time to see important changes in the quality of the data being validated.
After a data quality check is designed, you have these options:
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Create a data quality definition that defines the logic of the data check irrespective of the data source. The definition contains logical variables or references that you link or bind to actual data (for example, data source, table and column or joined tables) when you create a data quality rule that can be executed.
After you create a data quality rule with the required bindings based on a select data quality definition, that rule can be executed. The rule produces relevant statistics and can generate an output table, depending on the rule configuration.
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Create an SQL-based data quality rule.
The functionality of a data quality rule can range from a simple single column test to evaluating multiple columns within and across data sources.
Assessing data quality
To determine whether your data is of good quality, check in how far the data meets your expectations and identify anomalies in the data. Evaluating your data for quality also helps you to understand the structure and content of your data.
Learn more
- Data quality assets
- Managing data quality definitions
- Managing data quality rules
- Assessing data quality
- Data quality SLA rule compliance and remediation
Parent topic: Preparing data