What are your recommendations for developing and implementing metrics for MDM? How can an organization best measure...
an MDM program's effectiveness?
Many organizations want to show that their master data management (MDM) programs have achieved great things, so that the investments made in MDM were "worth it." However, defining those "great things" and "it" are never easy. To demonstrate that any MDM program has achieved some level of success, an organization must accomplish the following steps prior to implementation:
- Develop the MDM program as part of an enterprise information (or data) management initiative -- because good MDM supports the goals of enterprise information management (EIM). If the organization has defined its EIM vision, then defining the goal of the MDM program will be that much easier because the two should be aligned. Part of the EIM strategy should include a strategy for MDM, from the business perspective as well as the technical perspective.
- Develop the MDM program with the support of a formal enterprise data governance program. An enterprise data governance program based on best practices, organized by subject area and focused on business metadata and data, allows the organization to examine critical data assets through standards and policies that can be applied to its master data. Doing this from the beginning allows for the rapid development of managed master data. An MDM strategy is effective when the organization recognizes that shared data must be governed across the organization consistently.
- Develop the MDM program in conjunction with a data quality program to ensure that the master data is at the level of quality required by the organization, as determined by the data governance group. Data quality characteristics for selected critical master data should be established alongside data stewardship efforts.
The metrics used to determine the success of an MDM program can vary by organization. They should be linked to the MDM (and EIM) business case, to show the value that MDM brings to the business. Technology-related metrics can be used in addition to business metrics, but they should be secondary to the business value metrics.
An example of business-based MDM metrics could be an increase in sales over the prior year due to improved management of customer data (e.g., through removal of duplicate customer records, improved linking of customers to purchased products, or improved access to correct product or inventory master data). The secondary technology-related metrics for this example could include savings in disk space and cycle times as a result of the removal of erroneous customer records, as well as time savings for queries against poorly defined product or inventory master files.
Related Q&A from Anne Marie Smith, Ph.D.
When implementing an MDM initiative, businesses should take steps to incorporate effective data quality management practices from the outset.continue reading
Data governance programs often rely on the part-time data steward -- an employee from a business unit who also serves as a data expert.continue reading
Governing big data is much like governing other data, though businesses must adjust governance processes to accommodate larger, more varied data sets.continue reading
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