Data Governance and Enterprise Data Modeling – Don’t Do One Without the Other!
By Anne Smith
Increasingly, enterprises are recognizing the value of a governance approach to the data found in their organizations. The potential benefits of 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 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 governance solution. Yet, companies often fail to develop an enterprise data model and therefore imperil their governance efforts at the start.
According to Wikipedia,Enterprise Data Modelingis “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 approach to information management.
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) and the relationships between them
- Enterprise Conceptual Model – each subject area is decomposed into major business concepts (usually between 8 – 12) 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. 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
An Enterprise Data Model is considered part of the foundation of an organization’s data architecture, and 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 EDM development process, the third point (guarding and managing) is the purpose of 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 don’t build an EDM 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 EDM’s are built iteratively, time can be managed and the identification of each subject area, business area and entity can be accomplished within well-managed governance council meetings. Attention to the third level, Enterprise Entities, can be the responsibility of the appropriate stewardship teams, who will have the entity-level knowledge necessary for development of the entities under their stewardship.
As to benefits, data understanding is essential to any application development effort, to any data warehousing effort and to the creation of any services-oriented-architecture (SOA) effort. Misunderstood data or incomplete data requirements can affect the successful outcome of any Information Systems project, making the creation and maintenance of the organization’s Enterprise Data Model a truly beneficial exercise.
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 approach.
About the Author
Anne Marie Smith is a leading consultant in Information Management and is a frequent contributor to various IS publications. Anne Marie has over 20 years experience in information management for several corporate entities and has successfully led the development of data resource management departments within corporations and consulting organizations. Anne Marie is active in the local chapter of DAMA and serves on the board of directors of DAMA International, and is an advisor to the DM Forum. She has been an instructor of Management Information Systems (MIS) with Philadelphia, PA area colleges and universities. Anne Marie has taught topics such as: data stewardship and governance, data warehousing, business requirements gathering and analysis, metadata management and metadata strategy, information systems and data warehouse project management. Anne Marie’s areas of consulting expertise include metadata management including data stewardship and governance, information systems planning, systems analysis and design, project management, data warehouse systems assessment and development, information systems process improvement and information resource management/data resource management. Anne Marie holds the degrees Bachelor of Arts and a Master's of Business Administration in Management Information Systems from La Salle University; she has earned a PhD in MIS at Northcentral University. She is a certified logical data and process modeler and holds project management certification. Anne Marie can be reached at firstname.lastname@example.org