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Data Cleaning with Power BI

You're reading from   Data Cleaning with Power BI The definitive guide to transforming dirty data into actionable insights

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Product type Paperback
Published in Feb 2024
Publisher
ISBN-13 9781805126409
Length 340 pages
Edition 1st Edition
Languages
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Author (1):
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Gus Frazer Gus Frazer
Author Profile Icon Gus Frazer
Gus Frazer
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Table of Contents (23) Chapters Close

Preface 1. Part 1 – Introduction and Fundamentals FREE CHAPTER
2. Chapter 1: Introduction to Power BI Data Cleaning 3. Chapter 2: Understanding Data Quality and Why Data Cleaning is Important 4. Chapter 3: Data Cleaning Fundamentals and Principles 5. Chapter 4: The Most Common Data Cleaning Operations 6. Part 2 – Data Import and Query Editor
7. Chapter 5: Importing Data into Power BI 8. Chapter 6: Cleaning Data with Query Editor 9. Chapter 7: Transforming Data with the M Language 10. Chapter 8: Using Data Profiling for Exploratory Data Analysis (EDA) 11. Part 3 – Advanced Data Cleaning and Optimizations
12. Chapter 9: Advanced Data Cleaning Techniques 13. Chapter 10: Creating Custom Functions in Power Query 14. Chapter 11: M Query Optimization 15. Chapter 12: Data Modeling and Managing Relationships 16. Part 4 – Paginated Reports, Automations, and OpenAI
17. Chapter 13: Preparing Data for Paginated Reporting 18. Chapter 14: Automating Data Cleaning Tasks with Power Automate 19. Chapter 15: Making Life Easier with OpenAI 20. Assessments 21. Index 22. Other Books You May Enjoy

Tackling challenges with AI

While Azure OpenAI opens new avenues for data cleaning in Power BI, challenges such as API rate limits and cost considerations should be navigated. Robust error handling, caching strategies, and efficient resource management are crucial aspects to address potential roadblocks.

While considering how these technologies can be leveraged, you should consider the following challenges:

  • Risk of incorrect information: One of the primary concerns when leveraging technologies such as OpenAI for data cleaning is the risk of generating incorrect information. Misinterpretation of user queries or inadequate context understanding can lead to suboptimal data transformations, potentially impacting the reliability of insights derived from Power BI reports.
  • Complexity of natural language understanding: Natural language understanding, a key feature of technologies such as ChatGPT, is inherently complex. Data cleaning instructions provided in natural language...
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