View asset quality scores - Precisely Data Integrity Suite

View quality scores for your datasources, datasets, and fields. Understand how scores are calculated and use them to identify data quality issues. Quality scores measure how well your data meets defined quality standards. Scores aggregate factors such as freshness, completeness, validity, and accuracy. Higher scores indicate assets you can trust for critical operations. Scores flow hierarchically from individual rule evaluations up through fields, datasets, and datasources. This structure helps you identify issues and track improvements.

1. Go to Catalog and select the Datasources, Datasets, or Fields page.
2. Quality scores display in both card and list views.
   What's changed: In the new user experience, Table View replaces List View; for more information on the new experience, see About the new user experience

   • Card view: scores appear next to the asset name
   • List view: a Quality Score column shows scores for each row
3. Select an asset and go to the Scoring page to view score dimensions, contributing rules, and score rollups.

   The Scoring page displays the overall score, score dimensions, contributing rules, individual rule scores, and the last evaluation timestamp for contributing rules.

   When score rollup data is available, select a dimension to view the rules that contribute to the aggregated score.

4. Each level aggregates the scores below it:

   Score Level	Formula	Example	Key point
   Rule Score	(Records passing / Total records) × 100	950 passing out of 1000 = 95%	Ranges from 0% to 100%
   Field Score	Average of all rule scores applied to the field, along with associated dimensions.	Rules: 98%, 96%, 94% = 96%	All rules weighted equally
   Dataset Score	Average of all field scores in the dataset, along with associated dimensions	Fields: 99%, 96%, 92%, 88% = 93.75%	Only fields with scores included
   Datasource Score	Average of all dataset scores within that datasource, along with associated dimensions	Datasets: 93.75%, 91.5%, 95.2% = 93.48%	Represents overall data health

   Tip:

   • When using functions such as Not Equal, Less Than, or Less Than or Equal, the calculation includes null records; to exclude null values, add a secondary condition using the 'is not null' operator.
   • Fields with lower scores proportionally impact the overall dataset quality assessment
5. Calculate scores when a field has multiple rules across different dimensions.

   Each rule generates its own score, grouped by dimension. Dimension scores are the average of all rules within that dimension, and the field score is the average of all dimension scores. For example, a customer email field has rules across two dimensions:

   • Completeness: Email not null = 98%
   • Validity: Valid email format = 96%, Email domain exists = 94%
   • Validity score = (96 + 94) / 2 = 95%
   • Field score = (98 + 95) / 2 = 96.5%
6. Calculate dataset scores when only some fields have rules assigned.

   Only fields with assigned rules contribute to the dataset score; the dataset score excludes fields without rules. For example, a customer dataset has 10 fields, but only 6 have quality rules with scores of 99%, 96%, 92%, 88%, 94%, and 91%:

   Dataset Score = (99 + 96 + 92 + 88 + 94 + 91) / 6 = 93.33%

   Tip: Assign rules to all critical fields to improve quality assessment.
7. Know the quality score dimensions that determine aggregate scores.

   Dimension	Definition
   Completeness	Assesses whether required data is present and not missing, such as rules checking for null or blank values. If 98 out of 100 records are not null, the completeness score is 98%
   Validity	Checks whether data conforms to predefined formats, standards, or constraints; if the semantic type confidence is 98%, the validity score is 98%
   Field Score	The aggregate score of all quality dimensions for a field.

8. Track when quality scores update based on rule execution. Quality scores update when rules run. The timing depends on how rules are configured:

   Update Type	Behavior
   Manual runs	Scores update immediately when you run a rule from the Rules page or rule details page
   Scheduled runs	Scores update according to the rule's configured schedule; by default, rules follow the discovery schedule
   Cascading updates	When a rule score updates, all dependent scores, including field, dimension, dataset, and datasource, automatically recalculate.

   The timestamp on the Scoring page shows when the most recent rule evaluation occurred.

9. Interpret quality score ranges to assess data quality. Quality scores measure data quality on a scale of 0% to 100%:

   Score Range	Assessment
   90–100%	Excellent quality; data is suitable for critical business operations and analytics
   80–89%	Good quality; data is generally suitable for most operations but may require attention in specific areas
   70–79%	Fair quality; data has notable quality issues that you should address before use in critical operations
   Below 70%	Poor quality; data requires significant remediation before use in production systems

   Tip: Establish your own quality thresholds based on business requirements and risk tolerance.