Christopher Bradley, information strategist at DMA Advisors, says the “main purpose of a data model is actually not to design a database – it’s to describe a business.” However, many professionals lack a solid grasp of the data modeling concepts needed to effectively represent their business operations.

This knowledge gap comes at a steep cost. A recent DATAVERSITY study found that while half of all participants use data models, 60% still struggle with “data silos.” Worse, 20 to 40% of organizational IT costs go into evolving and reworking existing data infrastructures to do business.

What is a root cause? Stakeholders and engineers don’t share a common understanding of how the business translates into data structures. Without this alignment, companies develop fragmented systems that fail to meet their requirements.

Closing this divide starts with demystifying the core building blocks of data models: entities, attributes, and relationships. By mastering these data modeling concepts, both business and technical teams can collaborate to accurately describe their operations and leverage technology.

What Are the Benefits of Data Modeling?

Data modeling proves its value by its processes of discovering, analyzing, and scoping company-wide data requirements. It results in clear definitions and knowledge about specifications.

This goal requires everyone to be on the same page and speak with a common vocabulary. Data governance, which facilitates collaboration and guides and enforces standardized data activities and practices, ensures data model alignment. As a result, technology grasps and builds what the business needs. 

However, the amount of time needed to clarify fundamentals impacts the data model quality and the speed at which companies can have accurate and usable data assets. If stakeholders or executives are confused when talking about different entities or real-world things, and feel uncomfortable asking, some major business information can get lost or siloed.

Consequently, they may find that the data types used and saved within enterprise systems describe the data system but not the business needs. So, describing the business requires understanding basic data modeling concepts.

What Are the Basic Data Modeling Concepts?

Basic concepts embrace different parts to clarify and approve any business description by the data model. Entities, attributes, and relationships make up this diagram.

Entities form business building blocks. They are the persons, places, and things on which systems and people operate.

To express entities technically, modelers use a combination of values, tables, systems, hubs, or nodes. For example: an entity may define a Club using the element below: 

Having a Club ID # represents its uniqueness and dependencies during business interactions.

Attributes are the characteristics that describe entities. The specific values entered for an entity create unique instances describing business things in specific contexts.

Examples of attributes include status or gender. In the example above, each club has a unique combination of promotions, period of obligations, canceled, number of members, and units sold. See the example below:

Additionally, attributes may follow specific rules and dependencies. For example, keys connect attributes logically to another entity. A member of a particular club may have the same period of obligation as described in that club entity.

Sometimes entities share attributes and relationships through inheritance. Those categorized as parent entities or super types define the characteristics of other entities. These children or subtypes take on these parent dimensions while embodying other properties.

A relationship depicts the natural association between two or more entities. Through portraying relationships, modelers show how shared information flows through the business.

The diagram below identifies the club and club member entities. Each club member has a name and a net worth as attributes.

The cluster connector entity allows a person to be a member of more than one club and a club to have more than one member. See the diagram below:

Data modelers use relationships among entities and their attributes in different ways, depending on the purpose and the audience. These objectives match different modeling types.

What Are Basic Data Modeling Types? 

Data models diagram entity, attribute, and relationship components for business purposes. They fall into three types.

• Conceptual: Conceptual data models capture the entities, attributes, and relationships a data system contains. This model checks business understanding.
• Logical: Logical data models describe how entities, attributes and relationships work together. It captures business and technical requirements and rules.
• Physical: Physical data models cover how a single project or application works when the technical components are in place.

See “Data Modeling 101” and “What is a Data Modeling? Types, Benefits, Uses” for more information.

What Are Basic Data Modeling Techniques?

Data models not only adapt to their purposes, but they serve business requirements and the technical representations of them. Pascal Desmarets, founder and CEO of Hackolade, notes that modeling techniques need to fit these architectures, through a pragmatic mindset. Find examples of different techniques below.

Relational Modeling: Visit any business and find a relational database, a concrete structure that expresses data elements in tables according to a schema. This popular infrastructure captures the significance of relationships between entities and their attributes, defining how the business operates with its data.

Whether constructing conceptual, logical, or physical models, many organizations use an entity-relationship (ER) technique, a framework of rectangles and lines used for understanding, analyzing, and creating databases. Find a sample ER model below that depicts an airline business through table entities with attributes. The lines represent the relationships.

Learn more about modeling relational data from this webinar produced by Peter Aiken, an acknowledged data management authority.

Dimensional Modeling: Where organizations have lots of data and hundreds to thousands of tables, they turn to the data warehouse. Over 60% of companies have a data warehouse.

The dimensional modeling technique organizes relevant data while separating inconsequential data, making it easier to retrieve data. This modeling practice focuses on the fact entity, a table that contains the KPI or metric.

Attribute tables surround the fact entity, providing additional information about the KPIS. These attributes relate to the foreign-primary keys – from facts to the dimension tables. 

In this example, the central “fact” is the dealership’s revenue. The surrounding tables provide additional context about that revenue, such as which dealer it came from, the branch location, the date of sale, and the specific product sold. Each “dimension” table is related to the central revenue fact table through the ID columns.

See this article on dimensional modeling fundamentals for more.

Network Modeling: Many data systems manage big data, extremely large data sets of varying types: structured, unstructured, and semi-structured. Since big data is a newer, more flexible technology that captures complex relationships and schema on demand, some enterprises decide to skip modeling altogether. 

They do so at their peril, especially where large language models (LLMs) like ChatGPT or Claude thrive from big data. A Forbes article predicts that 90% of pilots will not move into production.

Big data projects, like those that use generative AI, require the network modeling technique, depicting conceptual, logical, and physical models as a system and not schemas. This approach treats entities as nodes or circles while relationships or edges form lines between the entities.

Some network diagrams add attributes, known as properties, to nodes and edges. These properties provide additional context and can carry quantitative values. 

In the education business depicted below, students and teachers represent an entity label. Each node provides user ID, name, and age attributes. Students and teachers relate to each other by who they follow on social media.

Read this article from Datamation to learn more about network data modeling in depth.

Conclusion

Data modeling and understanding its fundamentals bridge the gap between business strategy and technical implementation. Providing a shared language and framework for describing and aligning business needs enables organizations to design, implement, and optimize their data infrastructures, ensuring data quality and operational efficiency.

To realize these benefits, all business and IT stakeholders must understand and apply basic data modeling concepts from the start. This outcome requires knowledge and comfort with entities, attributes, and relationships.

Investing in data modeling education and collaboration is not just a nice-to-have – it is a must-have for any organization that wants to harness the full power of its data assets. By making data modeling types and techniques a priority and a shared responsibility, organizations can unlock new insights, drive better decisions, and achieve lasting competitive advantages.

The alternative – a fragmented, siloed data landscape that fails to meet business needs – is simply too costly. By embracing data modeling as a core competency and a shared language, organizations can ensure that every system and person can effectively describe the business.