What is data modeling?

Data modeling is the process of analyzing and defining all the different data types your business collects and produces, as well as the relationships between those bits of data. By using text, symbols, and diagrams, data modeling concepts create visual representations of data as it’s captured, stored, and used at your business. As your business determines how data is used and when, the data modeling process becomes an exercise in understanding and clarifying your data requirements.

The benefits of data modeling

By modeling your data, you can document what types of data you have, how you use it, and the data management requirements surrounding its usage, protection, and governance. The benefits of data modeling include:

  • Creating a structure for collaboration between your IT and business teams.

  • Revealing opportunities for improving business processes by defining data needs and uses.

  • Saving time and money on IT and process investments through appropriate planning.

  • Reducing errors (and error-prone redundant data entry) while improving data integrity.

  • Increasing the speed and performance of data retrieval and analytics by planning for capacity and growth.

  • Setting and tracking target key performance indicators tailored to your business objectives.

it's not just about the results of data modeling, but how you get those results.

Data modeling concept examples

Now that you know what data modeling is and why it’s important, let’s look at the three different types of data modeling concepts as examples.

Conceptual data modeling

A conceptual data model defines the overall structure of your business and data. Used for organizing business concepts, your conceptual data model is defined by your business stakeholders and data engineers or architects. For instance, you may have customer, employee, and product data and each data bucket, known as entities, has relationships with other entities. Both the entities and the entity relationships are defined within your conceptual data model.

Logical data modeling

A logical data model builds upon the conceptual data model with specific attributes of data within each entity and the relationships between those attributes. For instance, Customer A buys Product B from Sales Associate C. This is your technical model of the rules and data structures as defined by data engineers, architects, and business analysts, helping drive decisions about what physical model your data and business require.

Physical data modeling

A physical data model is your specific implementation of the logical data model created by database administrators and developers. It is developed for a specific database tool and data storage technology, and with data connectors to serve the data throughout your business systems to users as needed. This is the “thing” the other models have been leading to—the actual implementation of your data estate.

How data modeling concepts impact analytics

Data modeling, data science, and data analytics all go hand-in-hand—you need a quality data model to get the most impactful data analytics for effectual business intelligence that'll inform your future decision-making. The process of creating a data model involves forcing each business unit to look at how they contribute to their holistic organizational goals. Plus, a solid data model means optimized analytics performance, no matter how large and complex your data estate is—or will become.

With all your data clearly defined, analyzing exactly the data you need becomes much easier. Because you’ve already set up the relationships between data attributes within your data model, it’s simple to analyze and see impacts as you change processes, prices, or staffing.

How to choose a data modeling tool

The good news is a quality business intelligence tool will include all the data modeling tools you need, other than the specific software products and services you choose to create your physical model. So, you’re free to choose the one that suits your business needs and existing infrastructure best. Ask yourself these data modeling best practice questions when evaluating a data analytics tool for its data modeling and analytics potential.

Is this data modeling tool intuitive?

The technical team implementing the data model might be able to handle any tool you throw at them. But your business strategists and everyday analytics users—basically, your business as a whole—aren’t going to get optimum value out of your data modeling tool if it’s not easy to use. You'll want a data modeling tool with an intuitive, straightforward user experience that can help your team with data storytelling and data dashboards.

How does this data modeling tool perform?

Another important attribute is performance—meaning speed and efficiency, which translate into the ability to keep the business running smoothly as users run analyses. The best planned data model isn’t really the best if it can’t perform under the stress of real-world conditions—which hopefully involve business growth and increasing volumes of data, retrieval, and analysis.

Does this data modeling tool require maintenance?

If every change to your business model requires cumbersome changes to your data model, your business won’t get the best results out of that model or its associated analytics. Look for a data modeling tool that makes maintenance and updates easy, so your business can pivot as needed while still having access to the most up-to-date data.

Will your data be secure with this data modeling tool?

Government regulations require that you protect your customer data, but the viability of your business requires protecting all your data as the valuable asset it is. Make sure the data modeling tools you choose have strong security measures built-in, including controls for granting access to those who need it and blocking those who don’t.

Get started with data modeling

Whichever data modeling tool you choose, make sure that it's high performing, intuitive, and easy-to-maintain so your business gets the full benefits of this vital business exercise. Now that you understand the importance of data modeling and what it can do for you, you’re ready for the next step. Find out how Microsoft Power BI—a leading business intelligence and data modeling solution—can help you optimize your use of data.

Learn more about Power BI

Frequently asked questions

What is the most important consideration in data modeling?

The most important consideration in data modeling is creating a foundation for a database that can rapidly load, retrieve, and analyze large data volumes. An effective data modeling concept requires mapping business data, connecting the relationships between that data, and understanding how the data is used.

How often should a data model be retrained?

The frequency with which a data model should be retrained varies between the model and the problem it solves—this could mean retraining daily, weekly, or more periodically, such as monthly or annually, based on how often training data sets change, whether data model performance has decreased, and other data science considerations.

What does it mean to validate a data model?

To validate a data model means to confirm the data model is structured properly and can perform its intended purpose. An effective data modeling tool facilitates the validation process with automated messages that prompt users to fix errors, sort queries, and optimize storage options for data reduction.

What are the three key concepts of data modeling?

There are three data modeling concepts: Conceptual data modeling, logistical data modeling, and physical data modeling. Ranging from the abstract to discrete, data modeling concepts create a blueprint for how data is organized and managed in an organization.