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Modern Data Modeling: The Foundation of Enterprise Data Management and Data Governance

The role of data modeling (DM) has expanded to support enterprise data management, including data governance and intelligence efforts. After all, you can’t manage or govern what you can’t see, much less use it to make smart decisions.

Metadata management is the key to managing and governing your data and drawing intelligence from it. Beyond harvesting and cataloging metadata, it also must be visualized to break down the complexity of how data is organized and what data relationships there are so that meaning is explicit to all stakeholders in the data value chain.

Data Governance and Automation

Data models provide this visualization capability, create additional metadata and standardize the data design across the enterprise.

While modeling has always been the best way to understand complex data sources and automate design standards, modern data modeling goes well beyond these domains to ensure and accelerate the overall success of data governance in any organization.

You can’t overestimate the importance of success as data governance keeps the business in line with privacy mandates such as the General Data Protection Regulation (GDPR). It drives innovation too. Companies who want to advance AI initiatives, for instance, won’t get very far without quality data and well-defined data models.

Why Is Data Modeling the Building Block of Enterprise Data Management?

DM mitigates complexity and increases collaboration and literacy across a broad range of data stakeholders.

  • DM uncovers the connections between disparate data elements.

The DM process enables the creation and integration of business and semantic metadata to augment and accelerate data governance and intelligence efforts.

  • DM captures and shares how the business describes and uses data.

DM delivers design task automation and enforcement to ensure data integrity.

  • DM builds higher quality data sources with the appropriate structural veracity.

DM delivers design task standardization to improve business alignment and simplify integration.

  • DM builds a more agile and governable data architecture.

The DM process manages the design and maintenance lifecycle for data sources.

  • DM governs the design and deployment of data across the enterprise.

DM documents, standardizes and aligns any type of data no matter where it lives. 

Realizing the Data Governance Value from Data Modeling

Modeling becomes the point of true collaboration within an organization because it delivers a visual source of truth for everyone to follow – data management and business professionals – to conform to governance requirements.

Information is readily available within intuitive business glossaries, accessible to user roles according to parameters set by the business. The metadata repository behind these glossaries, populated by information stored in data models, serves up the key terms that are understandable and meaningful to every party in the enterprise.

The stage, then, is equally set for improved data intelligence, because stakeholders now can use, understand and trust relevant data to enhance decision-making across the enterprise.

The enterprise is coming to the point where both business and IT co-own data modeling processes and data models. Business analysts and other power users start to understand data complexities because they can grasp terms and contribute to making the data in their organization accurate and complete, and modeling grows in importance in the eyes of business users.

Bringing data to the business and making it easy to access and understand increases the value of  data assets, providing a return on investment and a return on opportunity. But neither would be possible without data modeling providing the backbone for metadata management and proper data governance.

For more information, check out our whitepaper, Drive Business Value and Underpin Data Governance with an Enterprise Data Model.

You also can take erwin DM, the world’s No. 1 data modeling software, for a free spin.

erwin Data Modeler Free Trial - Data Modeling

2 replies on “Modern Data Modeling: The Foundation of Enterprise Data Management and Data Governance”

I missed this presentation, but has a different method of classification than what I propose in my book, “Achieving Buzzword Compliance: Data Architecture Language and Vocabulary”. In my case, “conceptual data model” is any data model that is based on the business, without regard to technology. For me, there are three flavors:
– Overview – Executives view with a limited number of most important entity types
– Semantic – View of the people in the business, rife with specific jargon an departmental terms. Assignment: get all of these views coordinated.
– Essential – A distilation of the semantic view, with more abstract entity types encompassing concepts that cross departmental boundaries.

“Logical data model” is a term I learned from earlier versions of ErWIN, describes data structures with respect to a particular data management Technology. In ERWIN’s case, it is biased towards the relational model (with primary keys and foreign keys), but also includes the object-oriented one (UML), XML Schema, NoSQL, etc.

The physical model is the actual vendor-specific database design, with its tablespaces, partitions, indexes, etc.

I would be happy to give two presentations to this audience: one for conceptual/business-oriented modeling; and one for technology-based modeling. Do contact me at or (713) 464-8316

IMO A logical data model should not be oriented towards any target data model.

It can be ER even if your target physical model is anything other than relational. e.g. Oracle, SQL/Server or Progresql


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