Master data model requirements


By evaluating data dependence relationships as part of the process of integrating a data warehouse with a master data repository, one begins to see patterns of at least three key types of accesses. A search or locate access queries the master repository to determine if an entity already exists within the master data set, a read accesses the data, and an update will modify the data in the master repository.

In most cases, a data warehouse would be used for reporting and analysis purposes, and typically would not impose any updates to master data entities. However, it is not unheard of to have business processes in which individuals interacting with the data warehouse might be compelled to update a master copy, so it is important to review the end-user usage scenarios to properly assess the dependence relationships. Based on an understanding of the set of data attributes that are ultimately shared by more than one business process, those dependence details can be used to determine if and how the master data model would need to be adjusted to support integration with the data warehouse, including

  • Modifications to the set of master data attributes managed as shared items
  • Modifications to the set of identifying attributes to ensure unique identifiability as well as ability to ensure precise and accurate entity resolution
  • Modification of existing master data model elements (e.g., types and sizes)
  • Creation of new master data entities
  • Addition of new master data model elements

Aside from directing modifications to the models, these dependence requirements will also suggest the need for methods for assuring consistency between source and target among the MDM repository model, suppliers to the MDM repository and the consumers of data from the MDM repository.


About Author

David Loshin

President, Knowledge Integrity, Inc.

David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. David is a prolific author regarding data management best practices, via the expert channel at and numerous books, white papers, and web seminars on a variety of data management best practices. His book, Business Intelligence: The Savvy Manager’s Guide (June 2003) has been hailed as a resource allowing readers to “gain an understanding of business intelligence, business management disciplines, data warehousing and how all of the pieces work together.” His book, Master Data Management, has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at . David is also the author of The Practitioner’s Guide to Data Quality Improvement. He can be reached at

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