Applied Data Analytics for Impact

Introduction Many dashboards fail quietly because the transformation logic is scattered, manual, or undocumented. Analysts often clean data “just enough” to make visuals work, without designing transformations that are structured, repeatable, and auditable. In BI environments, this leads to fragile reports. A small schema change breaks refreshes. A new column introduces inconsistencies. Over time, confidence in the numbers erodes. Power Query sits exactly at this fault line between raw data and analytics. Power Query is not just a prep tool. It is an ETL layer embedded inside BI workflows . When transformations are well designed: data refreshes become predictable logic is transparent and reviewable models remain stable as data evolves downstream DAX stays simple When they are not, analysts compensate with complex measures and manual fixes, increasing technical debt. Structured transformations reduce that debt. Intermediate technical explanation: how to think abou...