When you open an asset, the Overview tab displays the composite score for the asset on the Data Quality panel. The score represents the data quality result for all constituent data elements across all data quality dimensions, and appears as a donut chart.
The following image shows the components of a composite score:
1Composite data quality score for the asset. The composite score is the average value of all data elements that are represented by the asset.
2Increase or decrease in score since the last rule run.
3Date of the last rule run. The date indicates the recency and freshness of the score.
•Rule Template. Data elements that are linked to Glossary assets that you specify in a rule template.
•Policy. Data sets and data elements that are linked to Policy assets.
•Process. Data elements that are linked to Process assets.
How composite scores are calculated
The composite score for an asset is derived by calculating the simple average of the rule occurrence scores of immediate child assets or directly related assets.
When you open a technical asset, the composite score is the simple average value of rule occurrence scores for all constituent data elements that are immediate child assets. If you open a data element, the composite score is the rule occurrence score for the data element. When you open a business asset, the composite score is the simple average value of the rule occurrence scores for all directly related assets.
Example
Consider a table called 'Oct2021Revenue' with rule occurrence scores for four data elements:
Week1Revenue
94%
Week2Revenue
86%
Week3Revenue
78%
Week4Revenue
82%
The composite score for the 'Oct2021Revenue' technical asset is the average value of the four rows. Therefore, In this example, the composite score for the 'Oct2021Revenue' technical asset is (94+86+78+82)/4 = 85%. The following diagram depicts this composite score:
Similarly, we can have the following composite scores:
•Composite score for the 'Oct2021Revenue' technical asset = 85%
•Composite score for the 'Nov2021Revenue' technical asset = 77%
•Composite score for the 'Dec2021Revenue' technical asset = 81%
Now, if we have a data container called 'Q4RevenueGeo1' that contains the above three technical assets, the composite score for the 'Q4Revenue' technical asset is (85+77+81)/3 = 81%. The following diagram depicts this composite score:
Similarly, we can have the following composite scores:
•Composite score for the 'Q4RevenueGeo1' technical asset = 81%
•Composite score for the 'Q4RevenueGeo2' technical asset = 64%
•Composite score for the 'Q4RevenueGeo3' technical asset = 93%
•Composite score for the 'Q4RevenueGeo4' technical asset = 90%
Now, if we have a data container called 'Q4RevenueGlobal' that contains the above four technical assets, the composite score for the 'Q4Revenue' technical asset is (81+64+93+90)/4 = 82%. The following diagram depicts this composite score:
In each case, the composite score is derived by calculating the average rule scores of the immediate child assets. The composite score does not include rule scores of subsequent child assets beyond the first level.