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Learn the pros and cons of analytical and operational MDM.

In our recent blog we discussed how MDM helps business intelligence and data warehousing by building the content of data warehousing dimensions.

Today, let’s examine two different versions of MDM, with their relative strengths and weaknesses.

Analytical MDM

In analytical MDM the data warehouse “passively” takes the content created and maintained by MDM. This is why analytical MDM is oftentimes referred as a “passive MDM implementation”.

Data quality in the data warehousing dimensions built by a data hub is mostly a data integration issue. The quality of dimensions passively follows the quality of data in the hub.

When a multi-year MDM program is planned, analytical MDM is a frequent candidate for the first implementation phase for several reasons:

• The implementation is non-intrusive from the source systems’ perspective. The user interfaces of operational systems do not change.
• Relatively low systems on-boarding challenges, risks and implementation time. There is no need to support by-directional synchronization.
• Analytical MDM supports a quick win strategy, which is critical for the first phase of MDM to justify investment and build credibility quickly. Low hanging fruit can be a high value business target!

This explains why many MDM implementations begin with analytical MDM.

A limitation of analytical MDM is in that it does not address enterprise data quality issues since the data sources do not receive the benchmark data back from the hub.

Operational MDM

The owners of operational systems wish to retain control over each record in their systems. Consequently these systems cannot accept data feeds from the data hub in bulk. These systems benefit from the data hub by leveraging the ability to search the data hub before a new record is created or edited in the source system (“lookup before create” capability).

This MDM style is known as an active MDM implementation. This implementation type reduces administrative overhead of redundant data entries, eliminates duplicates and inconsistencies, facilitates information sharing across enterprise applications and systems and ultimately improves data quality.

Operational MDM is a powerful technique that enables the enterprise to bring its master data in order, keep the data in a high quality state continuously, and avoid periodic cleansing projects that consume significant resources. Data cleansing efforts are often inefficient since they do not address the root cause of the data quality problems.

Here are some considerations that should be taken into account when operational MDM is planned or implemented:

• Implementation challenges are larger than those for a passive implementation
• The solution must deal with difficulties of bi-directional synchronization
• Such an implementation usually faces higher risks and longer implementation time compared to those for analytical MDM
• System on-boarding processes and procedures are more complex too

Operational MDM requires more significant data governance involvement. For operational MDM, data quality is not just a data integration issue but rather much more a data governance problem: how to synchronize the source systems with the hub.

In order to start and maintain a continuous synchronization with the data hub data quality improvement processes must be defined, built, progress measured, and accountabilities for the progress established.

Sometimes it is not feasible to develop “lookup before create” interfaces. This scenario can occur when application source code is not available, proprietary legacy applications do not allow changes in the interfaces or when there is a plan to decommission a system soon and therefore  system’s enhancements are not considered worthwhile.

A similar scenario can also when the operational systems are outsourced and managed by an external organization that supports some of your business functions.

In my next post, we’ll look at some data governance metrics for evaluating – and improving – data quality.