Scoring

Scoring can be completely controlled by the end-user with out of the box defaults.

Collibra Data Quality provides a data quality assessment that scans nine dimensions of a data set to assure the integrity of that data. The nine dimensions include behavior, rules, outliers, pattern, source, record, schema, duplicates, and shapes.

OwlCheck produces a data quality score from 0-100. 100 represents no integrity issues found in the data set. The score numerically represents the integrity of that data. For example, the score of 100 tells the data analyst that there are zero data quality issues in that data set.

DQ scans your data with the same frequency. You load your data - Owl scans nine dimensions of DQ and summarizes the results into a score from 0-100.

Aggregate Score

Note Each dimension can be custom weighted and rules can contain custom scoring severity. In this example, the deducted score (59) from the starting score (100) equals an overall score of 41.