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Data Cleaning with Power BI

Data Cleaning with Power BI

By : Frazer
5 (7)
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Data Cleaning with Power BI

Data Cleaning with Power BI

5 (7)
By: 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)
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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

Replacing values

When connecting to and analyzing data, there are often times when we might find outliers within the data. If we identify that there are values skewing the data or showing incorrectly, it’s important for us to be able to replace the data with the correct values.

There are many scenarios where you might need to do this in Power BI. Here are just some example scenarios:

  • Replacing variations of N/A or Not Applicable with a consistent value such as Unknown.
  • You may need to rename or reclassify certain values in your dataset to align with your reporting needs or to create meaningful categories. For instance, you can replace abbreviations or acronyms with their full names or group similar values together.
  • You can replace numeric code with descriptive labels or convert coded values into meaningful text representations.
  • In cases where your data contains missing or null values, you can use the Replace Values function to replace them with a specific...

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