The Unwavering Role of the Conceptual Data Model in Data Governance

At a time when data governance has become almost synonymous with data privacy and data protectionmany aspects of data management are viewed very differently than they traditionally were.

Data modeling, for example, is often considered a dimension of data engineering or data science. From this perspective, data models are manipulated to integrate data between sources so that organizations can load applications or analytics tools with a wealth of data across their various ecosystems.

However, conceptual data models, also called domain models or ontologies, have always remained firmly entrenched in the field of data governance. These models give meaning to data to achieve business goals. Of all the forms of data modeling, conceptual models are probably the most important and the basis for many others (such as logical data models, entity-relationship models, etc.).

According to Aaron Colcord, Privacera Senior Director, Governance and Security, Center of Excellence, these conceptual models are about “how you, as a business, think about yourself”. Subsequently, these fundamental data models include many aspects of organizations, from how they are mapped across business units, to specific terminology, definitions, and taxonomies that influence the meaning of data for different roles.

These concepts are essential for successful data governance so that organizations can benefit from the long-term reuse of data while mitigating risk.

Map the business

Although there is a wide range of ontologies or conceptual data models (ranging from the most basic to the most complex), they at least solidify the way an organization or business unit is structured. This information is integral to assigning data ownership and forming the basics of what the data means according to organizational definitions. When implementing the business concepts attributed to this type of data model, data modelers must integrate the multiple departments, roles, and responsibilities of their business. “The thing is, you’ll always find that every organization, the leaders, know how their business works and that’s how their data is organized,” Colcord remarked.

Capturing this information in a domain model clarifies these facts and becomes the means by which organizations define data for many downstream applications, including metadata management, data cataloging, and data quality. Additionally, to secure data to maintain data privacy and comply with regulations, companies can rely on conceptual data models so they can “now know where the data is, go call it, and find out What the data is,” Colcord commented. This information becomes the basis for masking PII, for example, to comply with PII regulations.


The next progression in utility ontologies ensuring data governance concerns the question of the schema – that’s why data modeling has been somewhat appended to the field of data engineering. However, it is important to realize that even in schema terms, ontologies reflect domain information about business concepts and their meanings. The most comprehensive and utilitarian conceptual data models involve “an ontology or schema of all important objects in a particular domain”, observed Francis CEO Jans Aasman. The amount of detail these ontologies include is vast. These involve not only different concepts such as product types and product hierarchies, but also similar information for users, their roles, and even the relationships between these business objects and users.

The specificity of such ontologies makes them intrinsically unique. “For a bank, of course, it’s completely different than for a hospital or an airline inspector like the FAA,” Aasman noted. The data governance value of these very detailed ontologies is manifold. They standardize the various constructs required to define data so that governance rules can be consistent and uniformly followed. They also provide concrete definitions of the data in relation to these business objects, which helps reinforce the meaning of the data across use cases, business units, and sources. “Before you can share it, you need to know what the data means,” Aasman said. With ontologies providing this meaning, organizations can aggregate data across departments for 360-degree views of customers, for example, to leverage for business purposes while complying with governance mandates.


The uniformity of meaning to which Aasman alludes is characteristic of the most advanced ontologies, which usually include taxonomies. The relationship between the hierarchies of definitions provided by taxonomies and the underlying conceptual data model is not always clear. It is possible to use taxonomies without ontologies (and vice versa), although the more sophisticated ontologies invariably have a component to define words that describe business concepts. This glossary component that lends itself easily to conceptual data models is “where you have the vocabulary, the terminology,” CTO Marco Varone revealed.

The importance of this element of conceptual data models is invaluable for governance purposes. By stipulating exactly what data-related terms mean to the business, any ambiguity is removed for the implementation of data quality and certain facets of metadata management. The meaning of data in relation to business purposes is further clarified by the support for synonyms that this linguistic aspect of conceptual data models provides. Varone called this utility “rather than a thesaurus…the language-specific part”. Clearly defining the words and definitions that support the business concepts reflected in the data is essential for well-governed data sharing across domains and applications. It also helps some forms of artificial intelligence, including inference techniques, symbolic reasoning and “structuring knowledge the right way,” Varone said.

Data Governance 101

Despite current perceptions that suggest otherwise, data modeling remains an important part of data governance. Conceptual data models explain how organizations are structured, the critical business concepts the data is used for, and what the data specifically means in relation to those concepts.

This insight impacts nearly every dimension of data governance, from access control methods to lifecycle management and data cataloging. Creating these domain models, perfecting them, and building data governance with them is fundamental to “trying to know exactly what your data is,” Colcord summarized, which is key to forming the proper rules on which domain governance is based. data and implement them. .

About the Author

Jelani Harper is an editorial consultant serving the information technology market. He specializes in data-driven applications focused on semantic technologies, data governance and analytics.

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