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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

Summary

In this final chapter, we focused on how OpenAI, particularly ChatGPT, powered by the GPT architecture, can enhance data cleaning in Power BI, making processes more efficient and insightful.

The significance of data cleaning was emphasized. We showed you how to begin leveraging OpenAI and Copilot’s capabilities when enhancing the data cleaning processes, whether that was through query optimization in the chat playground or the use of Copilot within Dataflow Gen2. The chapter then introduced the concepts of natural language processing for identifying anomalies and outliers in your data.

We also explored specific use cases where OpenAI can be effective, including optimizing query plans, caching strategies, handling large datasets, dynamic query adjustments, guidance on complex transformations, and error handling strategies.

Lastly, you learned about some challenges associated with leveraging OpenAI in Power BI, including the risk of incorrect information, the...

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