Power BI · Chapter 51
Power BI: Data Types and Dirty Data — Every Raw Scenario and Cleanup
Every Power BI data type, locale traps, and a full dirty-data playbook with before/after cleanup — including how dirty data can really get.
Fictional sample data for learning. Brands belong to their owners. Numbers are made up for class.
In this chapter
- Name every common Power Query data type and when to use it.
- Fix locale pitfalls for dates, decimals and currency symbols.
- Clean blanks, placeholders, spaces, case, spelling and duplicates.
- Repair numbers-as-text, mixed dates, Excel serials and bracket negatives.
- Reshape merged headers, totals, crosstabs (unpivot), splits and merges.
- Normalise units and currencies; protect leading zeros and UTF-8 text.
- Handle orphans, folder combine, JSON, PDF junk, time zones and fiscal years.
- Use column profiling and a quality checklist before Close & Apply.