Power BI is not hard to use. It is hard to use well.
Power BI has become the go-to reporting tool at small and mid-sized companies. It is accessible, it fits well with the Microsoft stack, and it has a learning curve you can handle. The problem is not the tool. The problem is the way it gets implemented.
After working on several Power BI projects at companies of different sectors and sizes, the mistakes that cause the most trouble are not technical. They are about design and process. And they repeat with striking frequency.
Mistake 1: Connecting straight to the data source with no intermediate layer
The most common flow when someone starts with Power BI is to connect the report directly to the ERP, to Excel or to the production database. It is fast, it works in the demo, and it creates a huge problem in production.
When the report queries the operational database directly, every time someone opens the dashboard they are firing queries at the system the whole company uses to work. With few users it goes unnoticed. With twenty people pulling up dashboards at once during peak hours, the system slows down and the reports take minutes to load.
The fix is always the same: an intermediate data layer (a data warehouse, even a simple one) that refreshes on a schedule and from which Power BI consumes the data. The report runs faster, the operational system does not suffer, and the data gets a layer of cleaning and validation before it reaches the dashboard.
Mistake 2: A flat data model instead of a star schema
Power BI works better with a star schema data model: a central fact table (transactions, sales, orders) and dimension tables around it (customers, products, dates, geography). Many reports get built straight from the raw ERP tables, untransformed, and the result is models with 15 tables related to each other in inconsistent ways.
That kind of model is fragile, slow and hard to maintain. Any change in the data source breaks something. The DAX measures get needlessly complicated. And performance degrades over time as the data volume grows.
Restructuring the data model of an existing report always takes more time than having done it right from the start. It is the kind of technical debt that seems irrelevant until it stops working at the worst possible moment.
A well-designed star schema data model can cut a report's load time by 60-70%. That is not premature optimization, it is basic hygiene.
Mistake 3: One DAX measure for every variation of the same number
DAX is Power BI's calculation language. It is powerful, but it has a learning curve that leads to a very specific mistake: creating a separate measure for every variation of the same calculation. Total sales, previous-month sales, previous-year sales, percentage change, absolute change... each one as an independent measure, copied and pasted with small variations.
The result is models with 200 measures, half of which are duplicates with slightly different names. When you need to change the calculation logic, you have to hunt for it across every variant. And nobody is sure which is the correct version.
The right practice is to build reusable base measures and use parameters or filter context for the variations. Fewer measures, more maintainable, more reliable.
Mistake 4: Designing to impress rather than to decide
A dashboard with 12 different charts, bright colors and animations can impress in a presentation. Day to day, it is useless. The user takes longer to find the information they need than to make the decision.
The best dashboards I have seen in real use have few metrics, lots of space, and are organized around one specific question. A director who opens the dashboard has to be able to answer their question in under 30 seconds. If they cannot, the design is wrong, no matter how pretty it is.
Mistake 5: Documenting nothing
Power BI is not an app you install once and forget. Reports change, data changes, the people maintaining them change. Without documentation on where the data comes from, how it is transformed and what logic the measures follow, any future change is an archaeological dig.
You do not need a 50-page document. You need a clear record of the data sources, a description of the main transformations and an explanation of the key measures. Four hours of work at delivery time that save weeks of work later on.