Power BI makes it dangerously easy to build a dashboard that looks impressive but is quietly wrong. The charts render beautifully, the colours are on-brand, and your manager nods approvingly — but the numbers are misleading, the filters don't work properly, and the whole thing breaks when the data refreshes. Here are the five mistakes we see in almost every beginner's first dashboard.
The 5 Mistakes
- 1. Importing data without a proper data model — throwing all tables into Power BI without defining relationships creates cartesian products and inflated numbers.
- 2. Using SUM instead of SUMX — when you need row-by-row calculation, SUM gives the wrong answer. SUMX iterates over each row before aggregating.
- 3. Too many visuals on one page — more than 6–8 visuals on a single page slows rendering and overwhelms the reader. Split into focused pages instead.
- 4. Ignoring filter context — a measure that works on one visual may give a completely different result on another page because of hidden filters in the report.
- 5. Not setting up a Date Table — using the auto date hierarchy leads to incorrect Year-to-Date, Month-to-Date, and same-period comparisons. Always build a proper calendar table.
Pro Tip: Always verify your measure totals against your source data before presenting. A quick sanity check — does the grand total in Power BI match the SUM in your original Excel file? If not, you have a data model problem.
In our Power BI with AI course, we deliberately build broken dashboards and fix them live in class. Students learn to recognise these errors in the wild, not just avoid them on clean practice datasets.
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