Are contacts and email addresses verified and validated?
The objective of assessing data quality is not necessarily to identify errors. Quality teams can spend months designing rule books and error reports, however the real ROI from assessment initiatives is justified by developing a data quality scorecard. By creating scores for each data quality dimension, measurable metrics can be identified to add value to data quality management.
Is data contributing to the success of your campaigns?
Any data intensive business will need to answer such basic questions to make better decisions and gain a bigger picture. With data quality, some of the ‘should have’ metrics are definition, relevancy, currency, measurability, traceability and controllability.
The objective of assessing data quality is not necessarily to identify errors. Quality teams can spend months designing rule books and error reports, however the real ROI from assessment initiatives is justified by developing a data quality scorecard. By creating scores for each data quality dimension, measurable metrics can be identified to add value to data quality management.
As they say, “the devil is in the details”, a clear approach in defining data quality metrics is:
1. Identify and select one aspect of business impact related to bad data quality.
2. Assess data dependencies related to that aspect of business impact.
3. List related data expectations of business clients related to each data dependency.
4. Determine data quality dimension for each data expectation.
5. Specify business rules to establish conformance of the data quality with expectations.
6. Ascertain a process of measuring conformance and establish an acceptability threshold for each business rule.
What a data quality scorecard does, is it provides an analytical tool to determine ROI on initiatives as well as the cost of bad data.
Measure it, Manage it, Maintain it and Make it Better. Develop an insightful Data Quality (DQ) Scorecard.
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