SYNQ analytics gives you a birds-eye view of the health of your data stack so you can systemically improve what matters most
Dimension | Description |
---|---|
Accuracy | Ensures data correctly represents real-world facts (e.g., accepted_values test for valid statuses, custom SQL checks for calculated metrics). |
Completeness | Confirms all necessary data is present (e.g., not_null test for critical columns, row count checks). |
Uniqueness | Ensures no duplicate entries exist (e.g., unique test on primary key columns). |
Timeliness | Checks data freshness and update frequency (e.g., dbt source freshness test, custom timestamp lag checks). |
Validity | Confirms data adheres to formats and rules (e.g., accepted_values test for categorical data, regex-based custom tests for formatting). |
Uncategorised | Everything else |
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functionality to segment insights by the owner, data product, and platform. This helps make the insights actionable and focused.