What is data modelling?

Data modelling is the process of analysing 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 modelling 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 modelling process becomes an exercise in understanding and clarifying your data requirements.

The benefits of data modelling

By modelling 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 modelling 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 modelling, but how you get those results.

Data modelling concept examples

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

Conceptual data modelling

A conceptual data model defines the overall structure of your business and data. Used for organising 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 modelling

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 modelling

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 modelling concepts impact analytics

Data modelling, 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 organisational goals. Plus, a solid data model means optimised analytics performance, no matter how large and complex your data estate is—or will become.

With all your data clearly defined, analysing 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 analyse and see impacts as you change processes, prices, or staffing.

How to choose a data modelling tool

The good news is a quality business intelligence tool will include all the Data modelling 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 modelling best practise questions when evaluating a data analytics tool for its data modelling and analytics potential.

Is this data modelling 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 modelling tool if it’s not easy to use. You'll want a data modelling tool with an intuitive, straightforward user experience that can help your team with data storytelling and data dashboards.

How does this data modelling 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 modelling 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 modelling 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 modelling 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 modelling 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 modelling

Whichever data modelling 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 modelling 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 modelling solution—can help you optimise your use of data.

Frequently asked questions

What is the most important consideration in data modelling?

The most important consideration in data modelling is creating a foundation for a database that can rapidly load, retrieve, and analyse large data volumes. An effective data modelling 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 modelling tool facilitates the validation process with automated messages that prompt users to fix errors, sort queries, and optimise storage options for data reduction.

What are the three key concepts of data modelling?

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