Mastering Data Management

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Resolving the confusion between data quality and MDM can help put you on the right path

Let’s summarize now and resolve the confusion around MDM vs data quality and answer the questions posed with the first blog in this series.

Many companies initiate master data programs as part of more generic enterprise data management, enterprise information management and enterprise data quality programs.

Master data typically represent only 3-7 percent of the overall enterprise conceptual data model. Since master data are highly distributed across multiple applications and systems their overall importance is disproportionally high.

An enterprise master data program may be able to address 60-70 percent of the most critical, costly and difficult to fix data quality issues. This explains why master data quality (MDQ) is the right focus for many organizations that are looking to jumpstart enterprise data quality or data management initiatives.

Some experts and industry analysts recommend to execute data quality first as a precursor to MDM. Typically this recommendation is based on a premise that a data hub is just a repository of master data cleansed outside the data hub.

With this premise in mind a recommendation to cleanse data first seems reasonable. In reality, today’s modern data hubs are not just data repositories. They operate more like a master data service capable to validate, standardize and cleanse master data to create and maintain the best view of enterprise master entities and relationships.

(This article, Old Thinking Does a Disservice to New Data Hubs, discusses data hub capabilities from the master data service perspective.)

The notion of a data hub as master data service brings another important point: the notion of a data hub as master data service leads to the idea that data hubs coexist naturally with services oriented solutions and frameworks so that SOA and MDM enable each other.

We have also described two key master data quality MDQ processes: Benchmark Development process and Benchmark Proliferation process. These two processes embedded in operational MDM are the foundation of continuous data quality improvement practice. Proper introduction and maintenance of this practice will guarantee quality of master data.

An executive level data governance policy should determine how your organization will treat master data. It is critical for this policy to strategically recognize data as a critical enterprise asset with explicitly defined consequences of this recognition.

An enterprise asset has an economic value, requires quality processes, metrics and accountabilities clearly defined. This strategic document will be referenced to justify MDM, MDQ, Data Governance and other enterprise data-centric initiatives.

Finally we determined that data governance metrics for data quality must be aggregated to characterize quality of large data sets and data areas holistically. These data governance metrics are foundational to enable data governance reporting for enterprise executives, committees, boards of directors, etc and successful execution of data governance programs.