As time goes on, master data management (MDM) becomes more commonplace in organizations of all sizes. Because of this fact, MDM project management is in high demand. Professionals that are drawn to MDM tend to come from two very distinct backgrounds, both of which can cause certain areas of their work to be less-than-effective.
On one side of the coin, there are those that arrive to the industry with a ton of experience in information systems. They have a complete understanding of things like data integration, modeling, profiling, data quality, and all related concepts. While they have many strengths that will help them succeed in this industry, these people may have a tendency to lack operational focus. These people may have experience with data warehouses, which by nature, allow for delays and errors in timing. MDM often needs to deliver data immediately to an assortment of analytical and operational environments. In contrast, data warehouses can be updated once a day (usually overnight) without users having a lot of latitude in data consistency.
On the other side of the coin, you have people arriving in the master data management industry with extensive experience in the operational systems that either generate or use master data. This group excels in understanding the immediacy of operational issues, but they often fall short in their understanding of data profiling, integration, and quality.
How to Achieve MDM Excellence
The first objective on the path to MDM project management excellence might be a self-assessment. It is important to make sure that any gaps in experience that you (or your data managers) may have are filled with the appropriate education. You may also assemble a team, called a data governance group, to draw on the strengths of multiple people within the organization. The data governance group will be responsible for performing many important functions for the MDM.
The next objective should be labeled ‘the initial requirements gathering.’ The purpose of this meeting is to understand the targets and sources of data; it is also meant to determine where you might need master data management capabilities (like hierarchy management and workflow). In order to achieve this goal, you must have access to the data and gain control of your data models.
There are no shortcuts in these vital steps to achieving data management excellence. When the process is completed, you must have the following: historical data requirements, a target architecture, definitions of the involved subject areas, data governance roles, complete metadata on source and target systems, and conceptual hierarchies. All of these things should be put into the calendar and adjusted, if necessary, for external factors.
Whichever path you decide to choose, make sure to follow all of the steps carefully. The only way you can achieve excellence is by properly maintaining accurate master data. Keep in mind that the person you choose as the project leader is crucial to the program’s success. The best master data managers will be productive, agile, and able to work both business and technology issues. Most importantly, they will strike a good balance between their knowledge of information management and their operational focus.