What is a Data Model?

| February 12, 2022
What is a Data Model?

A data model is a visual representation of an enterprise’s data elements that defines the logical structure of data. It standardizes how data elements relate to one another and to the properties of real-world entities. It provides conceptual tools that help to define and structure data in the context of relevant business processes and the models supported in the development of effective information systems.

The data models help business and technical resources to collaboratively decide on data storage, data access, data sharing, data updating, and how these processes are leveraged across an organization. A data model includes the data description, data semantics, and consistency constraints of the data.

Data models define how data is connected to each other and how they are processed and stored inside the system. They play a key role in bringing together all segments of an enterprise – IT, management, business analysts, etc. to cooperatively design information systems that require properly defined and formatted data. In support of a consistent, clean exchange of data, these models support a variety of use cases, including database modeling, information system design, and process development.

It’s also important to understand these three different types of data models in order to understand the structure of the databases. Each data model serves a different purpose as one works through the data modeling process.

Conceptual Data Model

Also known as a domain model, this is the most abstract. The conceptual data model explores and details your high-level, static business structures and concepts as they are used frequently during the discovery stage of a new project. You’ll find elements like basic business rules that need to be applied, the categories or entity classes of data that you plan to include, and any other regulations that may limit your layout options. Often, these models are created as precursors or alternatives to the next stage: logical data models.

Logical Data Model

This considers more relational factors than the conceptual data model. This type of data model describes data elements in detail and is used to develop visual understandings of data entities, attributes that define those entities, keys, and the relationships between them. This model is particularly useful in data warehousing plans.

Physical Data Model

This is the most detailed model, and usually the final step before database creation. These models are used to design the internal schema of a database and often account for the database management system (DBMS)-specific properties and rules that include various tables, the columns on those tables, and the relationships between them. You’ll illustrate enough detail about data points and their relationships to create a schema or a final actionable blueprint since the physical data model will be directly translated into production database design.

Physical data models generally are used to design three types of databases: relational for traditional operational databases, document for NoSQL and JSON databases, and dimensional for aggregation and business intelligence data stores such as data warehouses and data marts.

Apart from the three main types of data modeling, organizations can choose from several different design and infrastructure methods in order to visualize their data model:

Entity Relationship Model

This is based on the notion of real-world entities and the relationships among them. It showcases data entities and demonstrates diagrams to show how they are connected with each other. While creating real-world scenarios into the database model, the ER Model creates entity set, relationship set, general attributes, and constraints to understand how your data should be connected in a database. The ER Model is based on entities and their attributes and the relationships among those entities.

Object-Oriented Data Model

This design method is based upon real-world situations that make complicated real-world data points more legible by grouping entities into class hierarchies. You’ll often find an object-oriented design in the early development stages of multimedia technologies. Here, the information is used in the form of objects. Both the data and relationship are represented in a single structure called an object.

Hierarchical Data Model

Hierarchical data models resemble a family tree layout. It represents the data in a tree-like structure in which your data entities look like “parents” or “children” and branch off from other data that shares a relationship with them, with a single parent responsible for each record.

Relational Data Model

This model is similar to the hierarchical data model but is more scientific. Instead of parent-child relationships, it maps out the connections among various tables of data. One of the most popular data models in DBMS, this model is based on first-order predicate logic and defines a table as an n-ary relation.

WhereScape Announces the Release of RED

WhereScape is pleased to announce the general availability of WhereScape RED This release is the culmination of man-years of effort. It confirms WhereScape’s commitment to continuing to develop new technologies and tools and its commitment to delivering the...

Effective AI through Data Modeling

As we journey deeper into the digital age, the importance of data modeling within the broader landscape of artificial intelligence (AI) has become more pronounced than ever. The success of AI-driven initiatives is tightly woven with the quality and structure of the...

Is Data Vault 2.0 Still Relevant?

TL;DR  Yes. Data Vault 2.0 Data Vault 2.0 is a database modeling method published in 2013. It was designed to overcome many of the shortcomings of data warehouses created using relational modeling (3NF) or star schemas (dimensional modeling). Speci fically, it...

Related Content