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

What is data quality?

Firstly, before diving into how you can leverage Power BI to clean your data, it’s important to understand some key basics of what will affect your data quality.

Data quality is essential for accurate analysis, informed decision-making, and successful business outcomes. Understanding factors that affect data quality and recognizing the importance of data cleaning are crucial steps in the data preparation process.

In general, several factors describe and make up the quality of a dataset for analysis, which we will dive into further in the following list:

  • Data accuracy: Data accuracy means the extent to which data represents the true values and attributes it is intended to capture, indicating the degree to which it aligns with the true, real-world information it seeks to represent. Factors such as human errors during data entry, system glitches, or outdated information can compromise data accuracy.
  • Data completeness: This describes the degree...