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A Final Word Although there may not be any truly foolproof way to prevent all data integrity issues, making data quality part of the DNA of your environment will go a long way in gaining the trust of your user community.About the Author Wes Flores of Mc Knight Consulting Group has over 20 years of experience in the data management field.Validity is described as the degree to which a research study measures what it intends to measure.There are two main types of validity, internal and external.Have you ever had a set of reports that were distributed for years only to have your business users discover that the reports have been wrong all along and consequently lose trust in your data warehouse environment?Gaining trust is the foundation of user adoption and business value of your data management program.
Data-Issue tracking: By centrally tracking all of your issues in one place, you can find recurring issues, reveal riskier subject areas, and help ensure proper preventive measures have been applied.
Whether you have an in-house-developed statistics collection process or you rely upon metadata captured with your transformation program, you need to ensure you can set alarms based upon trending.
For example, if your loads are typically a specific size day in and day out and suddenly the volume shrinks in half, this should set off an alert and a full investigation should occur for such events.
This situation may not trigger your typical checks, so it's a great way to find those difficult-to-catch situations on an automated basis.
Workflow management: Thinking properly about data quality while you design your data integration flows and overall workflows can allow for catching issues quickly and efficiently.