Your Data Model Is Lying to You: 5 Design Choices That Distort Reporting

1. Combining Transactional and Analytical Models

Operational systems are built for speed and accuracy. Analytical models are built for interpretation and trend analysis. Confuse the two, and you end up with dashboards that act like operational logs; dense, duplicated, and impossible to aggregate.

I regularly see teams replicate source schemas because “it’s faster.” Yes, it speeds up ingestion, but it slows every decision afterwards.

Transactional structure in analytical reporting is like building a chocolate fireguard for your stove; technically possible, functionally useless!

Always design a data model for analytics first. De-normalised star schemas and business defined granularity should lead the model, not the source system.

2. Overloading Dimensions to “Save Time”

This is the classic shortcut:

“Marketing region? Sales region? Let’s just put them in one column, we know the difference.”

Until you don’t.

Overloaded dimensions create implied rules that only the original data modeller understands, meaning every new developer inherits a puzzle with pieces that do not quite fit. Wrong groupings, mismatched labels, and confused stakeholders are just some examples of misreporting.

The fix for this is actually remarkably simple. Dimensions should have a single meaning. When in doubt, create a new one.

3. Mixing Grain Levels in Fact Tables

A fact table with inconsistent granularity is one of the most deceptive forms of technical debt. One row represents a daily summary, another a transaction, another a product roll‑up; everything technically joins, yet nothing adds up correctly.

I have seen C-suite executives question entire BI teams over what was ultimately, a grain mismatch in a sales fact table. No one intentionally builds this; it creeps in over a period of time, as exceptions, imports, and “temporary patches.”

Always enforce a single, consistent granularity, and document it in plain human language. If the stakeholders define sales targets at item category but the sales orders are at item code, that is ok - just do not put them into one fact table! Use the Item Category dimension to roll up both facts for analysis.

4. Ignoring Slowly Changing Dimensions (SCDs)

Has the customer moved regions? A Product renamed? A Salesperson changed territories?

If your model rewrites history instead of preserving it, your reports will gaslight you. A dashboard might tell you that a region underperformed last year, but only because half its customers were reassigned incorrectly.

SCDs are not shiny and glamorous, but without them, you lose the ability to trust trends and insights.

This is not an article on SCDs and the various types, but as a piece of advice, implement Type 2 SCD’s as a minimum. Descriptive data (product name, customer address) never stands still, nor should your data model.

5. Modelling for the Tool, Not the Business Question

Tools come and go. Business logic is permanent.

When teams design models around what is easiest in Power BI, Qlik, Tableau, or Looker, they end up constrained by the tool’s “hidden features” instead of guided by the business question. The model becomes a technical artifact instead of a decision engine.

Avoid reports that require a dozen DAX or SQL “fixes” just to make basic metrics work. Sometimes having lots of measures is unavoidable, but when they are layered on top of each other, not only does the model perform badly, it becomes a spider’s web when trying to unpick any issues.

Try to start with business definitions first. Let the reporting tool adapt, not the other way around.

The Cost? Erosion of Trust

These mistakes rarely break reports or dashboards. Instead, they facilitate content that looks right but quietly leads people in the wrong direction. Once stakeholders stop trusting the numbers, the damage takes years to reverse.

It isn’t all doom and gloom, however. Most of these issues can be fixed with deliberate modelling practices and a willingness to revisit shortcuts made in the name of speed/convenience.

In analytics, the real danger isn’t dirty data. It’s clean data sitting on top of a deceptive model.

Ready to Fix the Foundation?

If your dashboards look right but feel wrong, the issue isn’t your reports; it’s your model.

At 5Y, our standard reporting models eliminate duplication, enforce consistency, and create a foundation that decision makers can trust. Built for Power BI and Fabric environments, they form the backbone of a governed, scalable analytics ecosystem.

Start by Uncovering the Hidden Flaws

With a Semantic Model Health Check, we diagnose the design choices that distort your numbers and provide a clear roadmap to restore accuracy and confidence across your reporting landscape. In just two weeks, you’ll understand exactly what’s holding your model back and what to fix first.

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