This content is part of the Essential Guide: Guide to managing a data quality assurance program

Integrate data quality into your master data management strategy

When implementing an MDM initiative, businesses should take steps to incorporate effective data quality management practices from the outset.

What is the relationship between master data management and data quality? And how can data management teams uphold data quality as part of their MDM programs?

When planning a master data management strategy, many businesses don't necessarily think about instituting a data quality program as well -- not until they profile the existing master data and discover all the bad data in their current files.

In addition, many companies don't realize that organizing redundant source data in a master data management initiative produces data quality issues in both the source systems and the MDM target. They view MDM and data quality separately and don't see the connections between the two. Often, they don't see how MDM, data quality management, metadata management and data governance can all be connected through enterprise information management processes.

Yet the businesses that align all those components -- so that they work together to produce master and reference data based on solid governance practices -- are often the businesses that achieve a high level of enterprise data and information management maturity. The data is defined and documented properly, meets the determined level of quality and resides in the appropriate MDM space.

For continuing high-quality master data, data management teams should follow these steps:

1. Ensure that your MDM program includes data quality management practices and the right data quality tools. Most MDM products include some level of data quality capabilities. Determine if those capabilities are sufficient, or if you need to augment them with a separate but compatible product.

2. Emphasize the need to perform data quality management against your data (especially the master data) on a regular basis.

3. Develop and sustain a data quality practice that includes master data, staffed by experienced data quality specialists.

4. Perform the standard data profiling of master data using the proper tools and report the results, establishing a baseline for data quality.

5. Implement rules to load only clean master data into the MDM hub.

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