The importance of dimensional modeling

Why a well-designed dimensional model is crucial before you start building BI dashboards.

When organizations start building Business Intelligence (BI) dashboards, the focus is often on visualizations, KPIs and interactive charts. In other words, what users see. But underneath every good BI solution lies a foundation you don't see: the dimensional model. If you skip this step or don't do it carefully, problems arise over time, such as slow dashboards, inconsistent KPIs and difficult maintenance. And that is easy to prevent.

What is a dimensional model?

A dimensional model is a way of structuring data so that it is optimally suited for analysis and reporting. The model organizes data into:

• Facts: These are the measurable values from business processes, such as revenue, number of products sold or costs.
• Dimensions: These provide context to the facts. They describe who, what, where and when something took place. Examples of dimensions are time, product, customer, location and employee. Thanks to dimensions, you can filter, group and analyze data from different angles.

This structure is optimized for analysis and forms the basis for data warehouses and BI systems. A good dimensional model makes dashboards faster, easier to maintain and more reliable.

Common problems of building without a good dimensional model

It regularly happens that organizations have built useful and perfectly working dashboards, but after some time are confronted with:

• desired extensions that are not possible
• errors in dashboards after an extension
• measures that give a different result in another dashboard
• poor dashboard performance
• maintenance that takes a lot of time

In the vast majority of cases, the cause of these issues can be traced back to a poorly designed dimensional model. Choices are often made that are easier than adjusting the dimensional model. They are choices that seem quick and practical at first, but cause problems in the long run, such as:

• Poor performance: A wrong dimensional model or the use of transactional tables makes calculations unnecessarily complex and inefficient. This has a direct negative impact on the performance and response time of the dashboard.
• Dashboard complexity, data consistency and data integrity: Sometimes you can still get results from an unsuitable dimensional model by using complex calculations. Complex calculations make the dashboard hard to maintain and error-prone, and keeping calculations consistent across multiple dashboards is difficult. This creates the risk that different dashboards show different totals for the same measures. This undermines trust in the dashboards.

The benefits of a well-designed dimensional model:

1. Responsiveness and scalability
Dashboards often query large datasets. A well-designed dimensional model ensures that data is retrieved efficiently, avoiding long response times and, as a result, frustrated users.
2. One version of the truth
Without a clear model, concepts such as "Revenue" or "Number of customers" can be calculated in different ways in various dashboards. A dimensional model provides one version of the truth.
3. Flexibility
Business questions change. A robust dimensional model allows you to add new dimensions or measures without having to redesign everything.
4. Simplified dashboard design
When data is logically structured, dashboard developers can focus on visualization instead of struggling with complex formulas, inconsistent data and data retrieval.

Short-term results or long-term results?

To deliver a dashboard quickly, sometimes no dimensional model, or a poorly thought-out one, is chosen. After all, almost nobody sees it. If you want a dashboard that lasts, you have to consciously choose to spend time on the dimensional model. It is something that is not directly visible to the end user, but that they will ultimately be happy with.

For a proof of concept, it is fine not to fully work out the dimensional model yet. But as soon as it has been shown that the concept works, do the right thing: work out the model and let go of the proof of concept. Never carry a proof of concept over one-to-one into production.

Practical points of attention for dimensional modeling

Finally, a few points of attention when you are going to create a dimensional model:

• First identify the business processes (for example: the sales or inventory process).
• Clearly define the level of detail: what does one row in the fact table represent?
• Document everything; your future self will thank you.