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. What is the aim of data cleaning in the data preparation process?
    1. Accumulating raw data
    2. Transforming data into a masterpiece
    3. Ensuring data is dirty for analysis
    4. Focusing on data quantity over quality
  2. Why is it essential to establish a framework and principles for data cleaning efforts?
    1. To speed up the data cleaning process
    2. To prevent a cycle of perpetual data cleaning
    3. To add additional steps in the process of data cleaning
    4. To create more documentation of data
  3. What does the process for cleaning data involve?
    1. Data visualization
    2. Data assessment, data profiling, data validation, data cleaning strategies, data transformation, data quality assurance, and documentation
    3. Data storage
    4. Data generation
  4. What does data profiling help identify in the data cleaning process?
    1. Patterns, distributions, and outliers
    2. Data storage mechanisms
    3. Data generation techniques
    4. Data transformation errors
  5. What is the significance of documenting the data cleaning journey?
    1. It demonstrates the importance...