What is data modeling?
Data modeling is the process of analyzing and defining all the different data your business collects and produces, as well as the relationships between those bits of data. Data modeling concepts create visual representations 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’ll document what data you have, how you use it, and what your requirements are surrounding usage, protection, and governance. Through data modeling, your organization:
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.
Sets and tracks target key performance indicators tailored to your business objectives.
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 it’s important, let’s look at the three different data modeling concepts as examples.
Conceptual data modeling
A conceptual data model defines the overall structure of your business and data. It’s used for organizing 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.
Logical data modeling
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.
Physical data modeling
A physical data model is your specific implementation of the logical data model, and it’s 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 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 optimized analytics performance, no matter how large and complex your data estate is—or becomes.
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, 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 questions when evaluating a data analytics tool for its data modeling and analytics potential.
Is your data modeling tool 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 that helps your team with data storytelling and data dashboards.
How does your data modeling tool perform?
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.
Does your 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 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 your 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.
How to get started with data modeling
Whichever data modeling tool you choose, make sure that it delivers high performance, is intuitive to use, and is 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.
Frequently asked questions
What is the most important consideration in data modeling?
The most important objective of data modeling is to create the foundation for a database that can rapidly load, retrieve, and analyze large data volumes. An effective data modeling concept requires mapping business data, relationships between data, and how the data is used.
How often should a data model be retrained?
The frequency with which a data model should be retrained varies with the model and the problem it helps solve. A model might need to be retrained daily, weekly, or more periodically, such as monthly or annually, based on how often training data sets change, whether model performance has decreased, and other considerations.
What does it mean to validate a data model?
The process of data model validation confirms that the 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, optimize queries, and make other changes.
What are the key concepts of data modeling?
Database modeling concepts fall into three categories: 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.