Book Image

Data Cleaning with Power BI

By : Gus Frazer
Book Image

Data Cleaning with Power BI

By: Gus Frazer

Overview of this book

Microsoft Power BI offers a range of powerful data cleaning and preparation options through tools such as DAX, Power Query, and the M language. However, despite its user-friendly interface, mastering it can be challenging. Whether you're a seasoned analyst or a novice exploring the potential of Power BI, this comprehensive guide equips you with techniques to transform raw data into a reliable foundation for insightful analysis and visualization. This book serves as a comprehensive guide to data cleaning, starting with data quality, common data challenges, and best practices for handling data. You’ll learn how to import and clean data with Query Editor and transform data using the M query language. As you advance, you’ll explore Power BI’s data modeling capabilities for efficient cleaning and establishing relationships. Later chapters cover best practices for using Power Automate for data cleaning and task automation. Finally, you’ll discover how OpenAI and ChatGPT can make data cleaning in Power BI easier. By the end of the book, you will have a comprehensive understanding of data cleaning concepts, techniques, and how to use Power BI and its tools for effective data preparation.
Table of Contents (23 chapters)
Free Chapter
1
Part 1 – Introduction and Fundamentals
6
Part 2 – Data Import and Query Editor
11
Part 3 – Advanced Data Cleaning and Optimizations
16
Part 4 – Paginated Reports, Automations, and OpenAI

Questions

  1. Why is it important to remove duplicates from your data before building a model in Power BI?
    1. To increase file size
    2. To enhance data accuracy in the analysis
    3. To speed up data loading
    4. To add complexity to the data model
  2. In the provided example with the products table, which column is selected for removing duplicates, and why is it crucial to choose the right column?
    1. Product ID, for simplicity
    2. Cost, for accurate financial analysis
    3. Product Name, as the main identifier
    4. Date, for chronological precision
  3. How can missing data, represented as null values, impact the analysis of your dataset?
    1. Enhances visual appeal
    2. Distorts analysis results
    3. Speeds up data processing
    4. Reduces data complexity
  4. When might you need to split columns in Power BI, and what example is given in the chapter?
    1. To increase data complexity – for example, splitting product codes
    2. To enhance visual appeal – for example, splitting financial data
    3. To gain desired dimensions for analysis – for example...