The importance of dimensional modeling

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

When organizations start creating Business Intelligence (BI) dashboards, the focus is often on visualizations, KPIs, and interactive charts. That's 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 will eventually arise such as slow dashboards, inconsistency in KPIs, and difficult maintenance. And that's while this is actually 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: Giving 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 functioning dashboards, but after some time are confronted with:

• desired expansion that isn't possible
• errors in dashboards after expansion
• metrics that give a different outcome 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. These are choices that seem quick and practical at first, but create problems in the long term such as:

• Poor performance: An incorrect 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 ill-fitting dimensional model using complex calculations. Complex calculations make the dashboard difficult to maintain, prone to errors, and keeping calculations consistent across multiple dashboards is difficult. This creates the risk that different dashboards show different totals for the same metrics. This undermines trust in the dashboards.

The benefits of a well-developed dimensional model:

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

Short-term result or long-term result?

To deliver a dashboard quickly, sometimes no dimensional model, or a poorly thought-out one, is chosen. After all, almost no one sees it. If you want a dashboard that lasts a long time, a conscious choice must be made to spend time on the dimensional model. Something that isn't immediately visible to the end user but that they'll ultimately be happy about.

For a proof of concept, it's fine not to fully work out the dimensional model yet. But once it's been demonstrated 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're going to create a dimensional model:

• First identify the business processes (for example: 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.

Conclusion

Building dashboards without a solid dimensional model is like building a house without a blueprint. It can go fine for a while, but eventually cracks appear, or you have to run pipes in places you'd rather not. By investing in a well-designed dimensional model up front, you'll earn that back in performance, accuracy, and scalability.

Want to learn how to set up a dimensional model yourself?

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Written by Nico Does
Principal Consultant