Data Quality Overview Section
The SaaS version of Collate offers an overview of the data quality test results grouped by dimensions. This gives users a quick insight about data quality performance centered around meaningful categories. The 6 categories are defined as:- Completeness: contains test cases allowing user to validate if any values are missing from a column/table (e.g. Column Values To Be Not Null)
- Accuracy: contains test cases allowing user to validate if any values represent their expected values in the real world (e.g. Column Value Max To Be Between)
- Consistency: contains test cases allowing user to validate the information stored between data processing is consistent with the expectations (e.g. Table Data Diff)
- Validity: contains test cases allowing user to control the data represent the specifications/expectations of the domain (e.g. Column Values To Not Match Regex)
- Uniqueness: contains test cases allowing user to control for potential duplicates in the data (e.g. Column Values To Be Unique)
- Integrity: contains test cases allowing user to validate the integrity of entity attributes (e.g. Table Column Count To Be Between)
