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Understanding Master Data Management and Customer Data Integration

Originally published August 24, 2005

Data integration is a hot topic and I seem to be spending more and more of my time working in this area. Although many articles and papers are being published about data integration, the number of options and acronyms seem to be increasing, and the area of data integration is as confusing as ever.

Recently I have been conducting an in-depth study on data integration for The Data Warehousing Institute (TDWI). This work is a follow-up to three previous TDWI research reports on ETL, real-time data integration and integrating packaged application data into a data warehouse. The current report (which will be published in October in conjunction with a Webcast) focuses on building enterprise data integration architecture, and looks specifically at bringing together three key data integration technologies: extract transformation and load (ETL); enterprise information integration (EII); and enterprise application integration (EAI).

The results of the TDWI study show that companies are starting to look at data integration from an enterprise perspective, rather than just from a data warehousing viewpoint. It also shows that organizations are beginning to understand that ETL, EAI and EII need to be brought together to form a cohesive data integration environment.

The area of data integration that was still somewhat unknown and confusing to people in the TDWI study was the topic of master data management (MDM) and customer data integration (CDI)—hence this article. This confusion was truly evident in the study which showed that MDM applications were deployed in 30 percent of organizations and CDI applications in 50 percent of organizations. I personally think that CDI is a form of master data management. I suspect that the discrepancy is due to the fact that CDI is often viewed as a data warehousing project, whereas MDM is seen more as being involved with business transaction processing. Given that the respondents to the study are focused on data warehousing, it seems likely that they are more familiar with CDI than MDM. 

What is Master Data Management?

The objective of MDM is to provide and maintain a consistent view of an organization’s core business entities, which may involve data that is scattered across a range of application systems. The type of data involved in this process varies by industry and organization, but examples include customers, suppliers, products, employees and finances.  Presently, many MDM applications concentrate on the handling of customer data because this aids the sales and marketing process, and can help improve sales and thus revenues. A hot new buzzword for customer MDM solutions is customer data integration, or CDI.

MDM and CDI are often presented as technologies, but in reality they are business applications. The objective of both MDM and CDI is to provide a consistent view of dispersed reference data. This view is created using data integration techniques and technologies, and may be used by business transaction applications and/or analytic applications. What MDM and CDI bring to data integration are the business semantics about the reference data as it relates to the business entity and industry involved. The value of the MDM or CDI solution is not only based on the technology platform provided, but also on the power of the business semantic layer. MDM and CDI data can also act as a data source for data warehousing applications. 

Deploying MDM and CDI Applications

The actual techniques and technologies used to deploy MDM and CDI applications are dependent on the business requirements. An example here would be whether the integrated view of the reference data must be kept current with source systems, or whether some level of data latency is acceptable. Another example is whether the MDM and CDI data can be modified or is read-only. Techniques involved in data integration include:

  • Data consolidation captures data from multiple source systems and integrates into a single persistent data store. 
  • Data federation provides a single virtual view of one or more source data files.
  • Data propagation copies data from one location to another.

These techniques may be implemented using ETL, EII or EAI, or other technologies like enterprise data replication (EDR), enterprise content management (ECM), and so forth. It is quite common for a MDM or CDI application to use a hybrid approach that involves several data integration techniques and technologies.

A simple approach to CDI, for example, would be to build a single consolidated customer data store that contains customer data captured from multiple source systems. The latency of the information in the consolidated store will depend on whether batch or on-line data consolidation is used, and how often the updates are applied to the store.

Another approach to CDI is data federation. Here a virtual business view of the customer data in source systems is defined. These views are used by business applications to access current customer information in the source systems. The federated approach may also employ a metadata reference file to connect related customer information together based on a common key.

A hybrid data consolidation and data federation approach may also be appropriate. Common customer data (name, address, etc) could be consolidated in a single store, but customer data that is unique to a specific source application (customer orders, for example) could be federated. This hybrid approach can be extended further using data propagation. If a customer updates his or her name and address during a web store transaction, this change could be sent to the consolidated data store and then propagated to other source systems such as a retail store customer database.

MDM and CDI Products

Several different types of vendors are offering solutions for MDM and CDI, including packaged application vendors like SAP and Siebel, infrastructure vendors like IBM and independent solution vendors like Kalido.

It’s interesting to note that Gartner Research has a magic quadrant for CDI, but not MDM. The CDI quadrant does, however, mention the MDM market, but its positioning of MDM is somewhat confusing. The quadrant also talks about CDI hubs and CDI vertical solutions. This quadrant has a 0.99 probability of confusing people! Key vendors listed on the quadrant include Siebel, Oracle, SAP, SeeBeyond, DWL (which was recently acquired by IBM), Initiate, Dendrite, VisionWare and Siperian.

While there is no question that the use of MDM and CDI is growing—the TDWI results show that 50 percent of organizations will have MDM applications within two years—vendors and analysts need to do a better job of positioning these solutions and explaining their business benefits. I think we are likely to see more articles on the topic of MDM and CDI in future editions of this newsletter.  

  • Colin WhiteColin White

    Colin White is the founder of BI Research and president of DataBase Associates Inc. As an analyst, educator and writer, he is well known for his in-depth knowledge of data management, information integration, and business intelligence technologies and how they can be used for building the smart and agile business. With many years of IT experience, he has consulted for dozens of companies throughout the world and is a frequent speaker at leading IT events. Colin has written numerous articles and papers on deploying new and evolving information technologies for business benefit and is a regular contributor to several leading print- and web-based industry journals. For ten years he was the conference chair of the Shared Insights Portals, Content Management, and Collaboration conference. He was also the conference director of the DB/EXPO trade show and conference.

    Editor's Note: More articles and resources are available in Colin's BeyeNETWORK Expert Channel. Be sure to visit today!

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