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

Understanding data accuracy

Understanding the context in which data accuracy matters is crucial for data analysts and business professionals alike.

In this section, we will delve into two methods to validate data accuracy and detect errors or inconsistencies. By addressing data accuracy during the data import process, you will gain confidence in the integrity of your datasets, enabling you to make well-informed decisions based on trustworthy insights from Power BI.

Power BI provides several tools to assess data accuracy. One such tool is conditional formatting, where we can define rules to highlight data points that fall outside predefined accuracy ranges or thresholds. For example, we can set rules to flag unusually high or low values in our dataset.

Additionally, Power BI offers data profiling functionalities to examine data distributions and identify potential outliers. By identifying and addressing inaccurate data points, users can ensure that their visualizations and...