Defining data modeling

Two professionals talking about data modeling

In this article about data modeling, you’ll learn:

What is data modeling?

Data modeling is the process of mapping out and visualizing different data sources into models — and then fitting those different data models together in a way that makes all the information easier to understand, manipulate, and analyze.

The goal of data modeling is to produce a clear picture of a company’s data through high-quality, consistent, and structured models. Using those models, business teams can clearly define and communicate the requirements for a computer system or database design, and the technical teams can build the design in response to those requirements.

Data modeling also helps businesses identify missing or redundant information. For example, a business can use data modeling to locate inefficiencies that may be leading to unnecessary spending. By modeling data from various departments, that business can analyze how resources are used across the organization, discover which areas have too many or too few, and reallocate them accordingly.

Types of data models

Types of data models

Depending on the complexity, requirements, and intended use of the data model, a company may choose one of three main types. These different types are related to the logical structure that controls how the data is stored, organized, and retrieved. Let’s explore each type:

Data modeling versus data analysis

While data modeling and data analysis are often thought to be the same, they’re different concepts that require entirely different skill sets.

Data analysis is what you do with the data you have access to. It’s about filtering data to find the most important insights in the form of reports, predictions, graphs, or other visualizations. Data analysis helps determine why aspects of a business are working the way they are.

On the other hand, data modeling is about creating the right conditions to make that analysis possible. Creating and fitting together a company’s data models needs to happen before teams can perform data analysis. It’s about determining which types of data to bring together — and in which ways — to get the results you’re searching for.

Model and store your data with the right platform

To understand and use your data effectively, it’s crucial to have a visual representation of how it all connects. That way, you can build better databases that provide the necessary insights for meeting business needs. Experience Data Model (XDM) from Adobe Experience Platform empowers you to use standard schemas and schema-based workflows to model data from any channel. And because it’s an open- source data model, you can extend those standard schemas with your own attributes and entities.

After you’ve gone through the data modeling process, the next step is to find the right data platform to house and store that information. Along with powering XDM, Adobe Experience Platform acts as a strong foundation for collecting, managing, and acting on customer data. From there, you can expand your platform with natively built applications like Adobe Real-Time Customer Data Platform (CDP), which securely unifies B2B and B2C data from across your organization into real-time customer profiles.

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