Summary
In this chapter, you explored a range of advanced data cleaning and preparation techniques within Power BI’s Query Editor.
The chapter began by introducing the power of this tool and highlighted two critical techniques: fuzzy matching, which identifies and consolidates similar strings within your data, and fill down, which fills gaps in your dataset with values from the previous row. We also outlined some best practices for using these tools, emphasizing data backup, sensitivity adjustment, regular validation, documentation, and the iterative nature of data cleaning.
The chapter also introduced the concept of using custom data scripts in languages such as R and Python, illustrating their benefits for complex transformations, statistical analysis, third-party libraries, and data integration.
The machine learning capabilities within Power BI were explored, including fuzzy matching, AutoML, and AI Insights, which enable anomaly identification, automated data preparation...
