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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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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

Best practices for data quality overall

Of course, this book will delve deep into how you can actually clean your data with Power BI, but it wouldn’t be responsible for us to not provide some insight into implementing best practices to prevent dirty data.

As we discussed previously, dirty data can have a significant impact on business operations, decision-making, and overall success. To combat the challenges posed by dirty data, organizations must establish robust data cleaning practices. In this segment of the chapter, we will explore best practices that businesses can implement to effectively tackle dirty data and ensure data quality throughout their operations.

Establishing data quality standards

Define clear data quality standards that align with your organization’s goals and objectives. These standards should include criteria for accuracy, completeness, consistency, validity, and timeliness, as discussed next:

  • Developing a data governance framework...

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