Data aggregation implementation

This is the second article in the series about MDM based on the article “Master Data Management from a technical perspective” from Microsoft (april 2010). As I mentioned in the first post, i have noticed that there are 4 implementation possibilities of Master Data management:

• Master data registry implementation
• Data aggregation implementation.
• System-of-record-only implementation.
• Complete enterprise master data management implementation.

This blog is about the 'Data aggregation implementation’ also called ‘Watered down’ method. In the article a quote is mentioned : ‘Data aggregation implementations involve the creation of a new system that is neither the system of entry nor the system of record but a downstream system used to aggregate the attributes of multiple systems and pass them to lower level subscribing systems.’ This means that one system is taken as the most business critical and a leading system. This data is loaded in the MDM hub and other systems will take this as a base to compare their data against this.

At first, data aggregation seemed a bit strange to me. Aggregated data is data (measures) aggregated to a higher level (summarized, maximized or whatever). So what is meant by a data aggregation implementation? Data aggregation means from this viewpoint : a single view of the master data. This is in contrast with the multiview approach which is well suited for companies, achieving the 'right view' presented to the right people and processes, at the right time. In a data aggregation implementation there are multiple values unified to one view: the aggregated data.

Advantages:
• Quick wins for a organization.
• Risk to the mission-critical application can be mitigated.
• Less critical applications can begin to source their master data from the master data management application, solving any integration issues that arise without major ramification to the organization.
• Integration processes can be tested and modified in an iterative fashion.

Disadvantages
• Lack of control. It is very difficult for an initial master data management project to get all of the necessary stakeholders to relinquish control of their data to a new system immediately.

So this is it again....
 
Greetz,
Hennie