Power Query shines in data cleansing and normalization tasks. Here's how you can leverage it effectively:

Data Cleansing:

* Identify and Remove Inconsistent Values:
* Use the "Remove Duplicates" function to eliminate duplicates based on specific columns.
* Leverage conditional formatting to highlight inconsistencies and manually correct them.
* Apply text cleaning functions like "Trim()" to remove leading/trailing spaces or "Upper()" to ensure consistent capitalization.
* Handle Missing Values:
* Use the "Fill Down" or "Fill Up" functions to propagate values from previous/next rows for certain columns.
* Replace missing values with a specific value (e.g., "0" or "Unknown") using the "Replace Values" function.
* For complex scenarios, create custom functions to handle missing data based on specific conditions.
* Transform Data Formats:
* Use the "Text to Number" or "Number to Text" functions to convert data types as needed.
* Apply date/time parsing functions like "Date.FromText()" to ensure consistent date formats across your data.
* Leverage splitting and combining functions to manipulate text data for analysis.

Data Normalization:

* Identify Repetitive Data: Use the "Expand" or "Unpivot Other Columns" functions to break down tables with repeated information into separate dimension tables.
* Create Dimension Tables: After identifying repetitive data, use Power Query to create separate tables with unique identifiers (primary keys) for each category (e.g., Customer table, Product table).
* Establish Relationships: Define relationships between your main fact table and the dimension tables based on the foreign key linkages. This ensures data consistency and simplifies analysis in tools like Power BI.

Additional Tips:

* Use the Formula Bar: The formula bar displays the M code generated for each query step. Analyze this code to understand the transformations applied.
* Custom Columns: Create custom columns using formulas to manipulate data or derive new insights not directly present in the source data.
* Data Profiling Tools: Utilize Power Query's built-in data profiling tools to understand data types, identify missing values, and get a general overview of your data quality.

By following these steps and exploring Power Query's features, you can transform raw data into a clean and normalized format, ready for further analysis and visualization. Remember, practice makes perfect. The more you use Power Query for data cleansing and normalization, the more efficient and effective you'll become.