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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

Understanding data consistency

Data consistency is the key to unlocking the full potential of Power BI as a powerful data analysis and visualization tool. However, achieving data consistency can be challenging, especially when dealing with multiple data sources and diverse data formats.

In this section, we will focus on understanding data consistency and its impact on Power BI reports and dashboards. By addressing data consistency proactively, you will be better equipped to harmonize and integrate disparate data, enabling you to create coherent and impactful visualizations in Power BI.

Power BI’s data modeling capabilities play a crucial role in assessing and ensuring data consistency. By creating relationships between tables and using data modeling best practices, such as defining proper data types and data categories, users can ensure that the data aligns seamlessly, leading to accurate and consistent analyses. You will learn more about this later in the book in Chapter...

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