What Is Conceptual Data Modeling?

Conceptual data modeling is a way for project managers, business analysts, data architects, and software developers to visualize data workflows at an abstract level. Conceptual data models aren’t abstract for the sake of abstraction; they illustrate system organization plans as they relate to relevant business rules and client goals. 

Data professionals create conceptual models early in the project planning process. Such models resemble a family tree chart; conceptual data models show the interrelationship between relevant entities, occasionally making explicit certain cause-effect associations. Such high-level data models aren’t static and can undergo numerous iterations as new information emerges.

More and more industries rely on high-level conceptual data models to guide their decision-making process. Read on to learn more about conceptual data modeling, the types of jobs that use this knowledge, and the average salaries you might expect.

Types of conceptual data modeling

Conceptual data models are meant to help stakeholders understand data. As such, many different types of conceptual data models exist. Three common types are: 

1. Entity-relationship (ER) models 

Entity-relationship (ER) models depict entities—specific data values—and their relationships to one another. For example, an ER model might show the business relationship the entity known as Customers has to other entities such as Employers, Payments, and Locations.

In such a model, you can see how the entities relate to one another and how a change in an entity's value can affect others. For instance, if the value (i.e., number) of Customers changes, so too will other values such as Payments and Locations. This may call for re-modeling or even for the development of a new project plan.

2. Unified Modeling Language (UML) class diagrams

Many different modeling languages exist. In 1997, the Object Management Group (OMG) attempted to standardize things by creating the Unified Modeling Language (UML). Since then, data analysts often choose to create class diagrams in UML.

In data modeling, a class is a group of objects or entities that have both structure and behavior in common. That is, they are the same kind of thing and in terms of data, they do the same kind of thing. A class diagram allows you to open a drop-down menu under the class Customers; this menu then lists relevant customer data such as Name, Location, or Income. 

In short, UML class diagrams show the object-oriented relationships between classes while allowing data modelers to visualize additional, more specific attributes within those classes. 

3. Domain models

A domain is a concept or area of knowledge containing interrelated entities; you can think of domains as supersets of classes. Domain models comprise of interconnected class diagrams that allow you to show how concepts interrelate in a larger sense before examining specifics. 

Lines and arrows with text describe specific relationships between entities. For example, within the domain Customers you may have an entity called Jobs that's subdivided into classes that include Doctors; within that class are entities such as Pediatricians, Neurologists, and Psychiatrists. Within each of those are Income entities: Below $200,000; Between $200,000 and $300,000; and Between $300,001 and $400,000. 

This model allows you first to see how domains—such as Customers—relate to other concept domains, such as Vendors. Then, you can see how classes within the domain Customers interrelate: which types of Doctors earn which salaries, for example. 

What is conceptual data modeling used for?

The high-level overview that conceptual data modeling gives a development team is useful for various purposes. These include: 

• Clarifying data requirements: Conceptual data modeling helps you define and understand the data types you're working with. It also allows you to visualize their relationships per stated business needs. This tells you where to begin a project, what end goal to aim at, and how to go about doing things in a general way to achieve that goal most efficiently. 

• Facilitating communication: High-level data models allow you to create a visual schema that even technical non-experts can interpret. As such, a variety of stakeholders with differing domains of expertise can see what's going on and discuss it with each other. They can then offer helpful input. 

• Guiding system designs: Data modeling is a blueprint for developing future logical and physical data models. In other words, a conceptual data model is a larger plan that software engineers break down into multiple specific data models. From there, software developers will build out the more specific data models that help guide a project from one step to another.

Who uses conceptual data modeling?

Experts in a variety of industries and sectors use conceptual data modeling. Such experts include: 

1. Data architects

Average annual salary (US): $117,450  [1]

Job outlook (projected growth from 2023 to 2033): 9 percent [2]

Nearly every company leverages data to some extent, and data architects design data systems to meet specific organizational needs. They develop databases, establish data modeling and analysis procedures, and retrieve and analyze data. 

2. Business analysts

Average annual salary (US): $92,594 [3]

Job outlook (projected growth from 2023 to 2033): 11 percent [4]

Business analysts make data-driven decisions about how a business should operate. Data models help them communicate their decisions to various stakeholders. By modeling data, business analysts can visualize the outlook of different operational changes, such as those meant to improve efficiency, increase revenue, and realign personnel and client goals. 

3. Systems developers

Average annual salary (US): $96,176 [5]

Job outlook (projected growth from 2023 to 2033): 17 percent [6]

Systems developers are IT professionals who develop, implement, and repeatedly test and improve data systems. They determine the precise nature of a company's data systems, continually improve them, and troubleshoot issues as they arise. 

4. Project managers

Average annual salary (US): $89,470 [7]

Job outlook (projected growth from 2023 to 2033): 7 percent [8]

Project managers coordinate the entirety of a project from initial conception to client delivery. They ensure that data management strategies align with project goals. As such, they take an overall conceptual view of not only data modeling but also each individual project. 

Read more: What Is a Project Manager? A Career Guide

Pros and cons of using conceptual data modeling

Both pros and cons exist in conceptual data modeling. 

Pros

Conceptual data modeling allows you to: 

• Display data in an accurate and helpful way

• Simplify complex data systems into a digestible illustration

• Improve communication among stakeholders

Cons

However, conceptual data models may:

• Require extensive amounts of time and expertise to develop and refine

• Cause misunderstandings that can lead to flawed systems development 

• Lead to additional confusion if data model language preferences aren't universalized

How to get started in conceptual data modeling

Entry-level data analysis jobs exist in the financial, tech, marketing, and business sectors. Even the Federal Bureau of Investigation (FBI) wants data professionals. 

If you're interested in conceptual data modeling, start by learning the fundamentals. Familiarize yourself with key concepts and methodologies through online courses and resources, or consider getting a bachelor's degree in data analytics from an accredited university.

Getting started with conceptual data modeling on Coursera

Those with conceptual data modeling skills have many career options, and the demand for these skills is increasing. Find out how you can gain these skills to help your resume stand out; explore more on Coursera today. Google offers a course called The Path to Insights: Data Models and Pipelines. You can look into the University of Colorado Boulder's Data Analysis with Python Project from there.