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What is Data Modeling? Everything you need to know

Table of Contents

Estimated reading time: 4 minutes

Data modeling definition

Data modeling is the process of analysing and defining all the different data your business collects and produces, as well as the relationships between those bits of data. The process of modeling your data creates a visual representation of data as it’s used at your business, and the process itself is an exercise in understanding and clarifying your data requirements.

Why data modeling is important

By modeling your data, you will document what data you have, how you use it, and what your requirements are surrounding usage, protection, and governance. Through data modeling, your organisation:

  • Creates a structure for collaboration between your IT team and your business teams.
  • Exposes opportunities for improving business processes by defining data needs and uses.
  • Saves time and money on IT and process investments through appropriate planning up front.
  • Reduces errors (and error-prone redundant data entry), while improving data integrity.
  • Increases the speed and performance of data retrieval and analytics by planning for capacity and growth.

So, it isn’t just what you get with data modeling, but also how you get it. The process itself provides significant benefits.

Data modeling examples

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


A conceptual data model defines the overall structure of your business and data. It’s used for organising business concepts, as defined by your business stakeholders and data architects. For instance, you may have customer, employee, and product data, and each of those data buckets, known as entities, has relationships with other entities. Both the entities and the entity relationships are defined in your conceptual model.


A logical data model builds on the conceptual model with specific attributes of data within each entity and specific 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 architects and business analysts, and it will help drive decisions about what physical model your data and business needs require.


A physical data model is your specific implementation of the logical data model, and its created by database administrators and developers. It is developed for a specific database tool, 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 impacts analytics

Data modeling and data analytics go hand in hand because you need a quality data model to get the most impactful analytics for business intelligence that informs decision making. The process of creating data models is a forcing function that makes each business unit look at how they contribute to holistic business goals. Plus, a solid data model means optimised analytics performance, no matter how large and complex your data estate is or becomes.

Choosing 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 questions when evaluating the data analytics tools for its data modeling and analytics potential.

Is it intuitive?

The technical folks implementing the model might be able to handle any tool you throw at them, but your business strategists and everyday analytics users and your business as a whole aren’t going to get optimum value out of the tool if it’s not easy to use. Look for an intuitive, straightforward user experience.

How’s the performance?

Another important attribute is performance speed and efficiency, which translate into the ability to keep the business running smoothly as your 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.

What about maintenance?

If every change to your business model requires cumbersome changes to your data model, your business won’t get the best out of the model or the associated analytics. Look for a 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 my data be secure?

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. You’ll want to make sure the 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.