A critical component of any Data Governance program is the tracking of data quality metrics over the life cycle of the data. When a new record enters a Master Data Management system, it does not stay static; it undergoes updates until the last transaction (and beyond).
After the last transaction, at some point, it should be purged to maintain the freshness of the data. At all these stages, the information’s quality, security and compliance can be prone to compromise. A good data governance program should address measurement at these various stages of the data life cycle. Efforts must be made to build suitable metrics, as the organization progresses through the maturity levels of its data governance program.
Here’s an example. As part of the Data Governance program, a company identified one key metric as the “number of validated Ship To addresses”. Why? Because for a significant number of deliveries, FedEx would return the package and charge the company for giving an undeliverable address. And FedEx, as part of its business process, would not let the company know what was wrong with the address or where the correction was needed.
If a company does a large volume of shipments, even a small percentage of returns amounts to a substantial cost. When a data governance program was instituted, the company ensured that for all new customers’ Ship To addresses, the Customer Hub validated the new addresses via FedEx’s web services. FedEx has an elaborate address validation and other shipment-related web services available on its web site.
The company also ensured that any other projects that touched the customer master were aware of this integration. This was published as an official data governance policy. If any other program or user attempted to update the validated address, an approval workflow was initiated. Periodic system refreshes were also developed that would end-date the validated address and create a new validated Ship To address, using U.S. Postal Service’s National Change of Address service.
For historical customer addresses, the company started doing validations of the “defective” FedEx addresses first and after that set was processed, the remaining addresses were cleansed and validated.
The most important thing to remember is that unless visibility is provided thru a data governance metric, it’s easy for management to lose sight of your accomplishments. Therefore, it’s critical to build the data governance metrics first, even before embarking on an MDM project.