by Angela Guess
A new article discusses several data quality practices that can also improve master data management efforts. The article states, “For years, I’ve watched data integration solutions incorporate functions that originated with data quality tools, especially data profiling and data monitoring. In a similar trend, I’m now seeing MDM solutions incorporating DQ functions for data standardization, deduplication, augmentation, identification, and verification. After all, master and reference data benefits from these functions, just as any data domain would.”
It continues, “DQ success usually depends on the processes of data stewardship. A data steward plays a key role in linking data quality work and standards to specific business goals and business applications. The average data steward can identify and prioritize DQ work that will yield a noticeable return for the business. I’m now seeing a similar stewardship approach to prioritizing MDM work.”
The article adds, “DQ and MDM share very similar goals, in that each strives to improve data, whether the data domain is master data, customer data, product data, financial data, etc. Achieving improvement almost always requires changes to data, applications, and how end-users use applications. Therefore, a change management process is key to effecting improvements. DQ has long standing change management processes via stewardship, plus new options for change management via data governance. MDM’s likelihood of effecting positive change is increased when it taps the data-oriented change management processes that evolved from DQ and stewardship.”
