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Aligning Data Governance and Enterprise Data Modeling

Having an organizational understanding of the essential data used across the enterprise, through the development of a Subject Area Model and a foundational Conceptual Data Model, can improve the effectiveness of a Data Governance program

Increasingly, enterprises are recognizing the value of an enterprise approach to the data found in their organizations.  The potential benefits of enterprise data governance include rationalization of data for a common view of the business, alignment of processes that use that data, and creation of a powerful foundation that can coordinate business informational needs throughout the organization.

However, creating unified views of data and the processes that act on that data can be daunting to an organization that has not developed an approach to rationalize data across conflicting, disparate data sources; and this lack of enterprise understanding of data and its value dooms many data governance implementations.  It is important to ensure that the enterprise’s data is defined, understood, appropriately aligned within and across business units, its use conforms to published standards and guidelines, its exceptions properly stewarded and data policies properly implemented.  Developing an enterprise data model as one of the first steps in instituting a data governance program can give the organization this detailed yet enterprise view of their valuable data assets, making an enterprise data model one of the most critical requirements of the data governance solution. Yet, companies often fail to develop an enterprise data model and therefore imperil their data governance efforts at the start.

Enterprise Data Model

Enterprise Data Modeling is the practice of creating a graphical model of the data used by an enterprise or company.  It represents a single integrated definition of data, independent of any system or application, and does not depend on how the data is physically obtained, stored, processed or accessed.  Since the model includes some business rules governing the use of data and enables the identification of shareable and/or redundant data across functional and organizational boundaries it can provide a holistic view of data for the entire organization, a “single version of the truth”.  Having and using an enterprise data model for application development and data management minimizes data redundancy, disparity, and errors in data usage.  Developing an Enterprise Data Model can be one of the first steps undertaken in the creation of a data governance program and should be part of any organization’s enterprise information management initiative.

An Enterprise Data Model (EDM) is built in three stages, and each stage is important to the construction of the model as a whole and to the understanding of the data under the organization’s control, making the EDM an essential part of the foundation for data governance.  The three steps in EDM development are:

  • Enterprise Subject Area Model – defines the major subject areas of the organization (usually between 10 – 15, rarely more than 20) and the relationships between them
  • Enterprise Conceptual Model – each subject area is decomposed into major business concepts (usually between 8 – 15 for a subject area) and shows how these concepts are related
  • Enterprise Entity Model – each business concept is analyzed to discover the major areas of interest within that business area, representing the things important to the business for that concept.  These interests are similar to the “major” entities found within a logical data model, and this stage of the EDM creates relationships between the major entities to show some of the high level business rules within that business area.  This stage should not  be confused with a logical data model, since a logical data model is more detailed and would include “minor” entities and more finite business rules.

An Enterprise Data Model is considered part of the foundation of an organization’s data architecture, and data governance is another part of that foundation.  These two parts contribute semantic understanding to the process of discovering what data the organization considers important, why it is important and how it will be guarded and managed.  The first two points (what and why) are discovered in the enterprise data model development process, the third point (guarding and managing) is the purpose of data governance.  It is impossible to guard and manage data appropriately when one does not know what the data to be managed is and what it means, and why it is important to the organization.

Creating and developing an Enterprise Data Model is one of the basic activities of a solid data governance effort.  Many organizations do not build an enterprise data model since they think it takes too much time, provides little or no benefit, or requires skills beyond those present in the organization.  Since the best enterprise data models are built iteratively, time can be managed and the identification of each subject area, business area, and entity can be accomplished within well-managed data governance council meetings.  Attention to the third level, Enterprise Entities, can be the responsibility of the appropriate data stewardship teams, who will have the entity-level knowledge necessary for development of the entities under their stewardship. 

As to benefits, understanding the data and metadata is essential to any application development effort, to any data warehousing / business intelligence / analytics effort, and to the creation of any data movement effort.  Misunderstood data or incomplete data requirements can affect the successful outcome of any project, making the creation and maintenance of the organization’s Enterprise Data Model a truly beneficial exercise.

Conclusion

In conclusion, creating a culture of data understanding, data usability, and data quality are some of the goals of a data governance program.  Developing and maintaining an Enterprise Data Model can contribute to the realization of all of these goals, and can lead to the success of a data governance program.

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Anne Marie Smith, Ph.D., CDMP

Anne Marie Smith, Ph.D., CDMP is an internationally recognized expert in the fields of enterprise data management, data governance, enterprise data architecture and data warehousing. Dr. Smith is VP of Education and Chief Methodologist of Enterprise Warehousing Solutions, Inc. (EWS), a Chicago-based enterprise data management consultancy dedicated to providing clients with best-in-class solutions. Author of numerous articles and a Certified Data Management Professional (CDMP), Dr. Smith is also a well-known speaker in her areas of expertise at conferences and symposia.

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