Data Cleaning with Power BI

Data Cleaning with Power BI

by Gus Frazer
Released February 2024
Publisher(s): Packt Publishing
ISBN: 9781805126409

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Book description

Unlock the full potential of your data by mastering the art of cleaning, preparing, and transforming data with Power BI for smarter insights and data visualizations

Key Features

  • Implement best practices for connecting, preparing, cleaning, and analyzing multiple sources of data using Power BI
  • Conduct exploratory data analysis (EDA) using DAX, PowerQuery, and the M language
  • Apply your newfound knowledge to tackle common data challenges for visualizations in Power BI
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

  • Connect to data sources using both import and DirectQuery options
  • Use the Query Editor to apply data transformations
  • Transform your data using the M query language
  • Design clean and optimized data models by creating relationships and DAX calculations
  • Perform exploratory data analysis using Power BI
  • Address the most common data challenges with best practices
  • Explore the benefits of using OpenAI, ChatGPT, and Microsoft Copilot for simplifying data cleaning

Who this book is for

If you’re a data analyst, business intelligence professional, business analyst, data scientist, or anyone who works with data on a regular basis, this book is for you. It’s a useful resource for anyone who wants to gain a deeper understanding of data quality issues and best practices for data cleaning in Power BI. If you have a basic knowledge of BI tools and concepts, this book will help you advance your skills in Power BI.

Table of contents

  1. Data Cleaning with Power BI
  2. Contributors
  3. About the author
  4. About the reviewer
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
    4. Download the example code files
    5. Conventions used
    6. Get in touch
    7. Share Your Thoughts
    8. Download a free PDF copy of this book
  6. Part 1 – Introduction and Fundamentals
  7. Chapter 1: Introduction to Power BI Data Cleaning
    1. Technical requirements
    2. Cleaning your data in Power BI
    3. Understanding Power Query
    4. Understanding DAX
    5. Where do we begin with data?
    6. Summary
    7. Questions
  8. Chapter 2: Understanding Data Quality and Why Data Cleaning is Important
    1. What is data quality?
    2. Where do data quality issues come from?
    3. The role of data cleaning in improving data quality
      1. Data integrity and accuracy
      2. Decision-making and business outcomes
      3. Data ownership and accountability
      4. A holistic view of the data ecosystem
      5. Early detection of issues
      6. Continuous improvement and learning
      7. Empowerment and collaboration
    4. Best practices for data quality overall
      1. Establishing data quality standards
    5. Summary
    6. Questions
  9. Chapter 3: Data Cleaning Fundamentals and Principles
    1. Defining data cleaning
    2. Who’s responsible for cleaning data?
    3. Building a process for cleaning data
      1. Data assessment
      2. Data profiling
      3. Data validation
      4. Data cleaning strategies
      5. Data transformations
      6. Data quality assurance
      7. Documentation
    4. Understanding quality over quantity in data cleaning
    5. Summary
    6. Questions
  10. Chapter 4: The Most Common Data Cleaning Operations
    1. Technical requirements
    2. Removing duplicates
    3. Removing missing data
    4. Splitting columns
    5. Merging columns
    6. Replacing values
    7. Creating calculated columns versus measures
      1. Calculated columns
      2. Measures
      3. Calculation group
      4. Considerations
    8. Summary
    9. Questions
  11. Part 2 – Data Import and Query Editor
  12. Chapter 5: Importing Data into Power BI
    1. Technical requirements
    2. Understanding data completeness
    3. Understanding data accuracy
    4. Understanding data consistency
    5. Assessing data relevance
    6. Assessing data formatting
    7. Assessing data normalization, denormalization, and star schemas
      1. Dimension modeling and star schema
      2. Denormalized data in dimension tables
    8. Summary
    9. Questions
  13. Chapter 6: Cleaning Data with Query Editor
    1. Technical requirements
    2. Understanding the Query Editor interface
    3. Data cleaning techniques and functions
      1. Adding columns
      2. Data type conversions
      3. Date/time
      4. Rounding
      5. Pivot/unpivot columns
      6. Merge queries
    4. Using Query Editor versus DAX for transformation
      1. Power Query Editor
      2. Data Analysis Expressions (DAX)
      3. Workflow
    5. Summary
    6. Questions
    7. Further reading
  14. Chapter 7: Transforming Data with the M Language
    1. Technical requirements
    2. Understanding the M language
      1. Structure of M
      2. Common use cases of M
    3. Filtering and sorting data with M
    4. Transforming data with M
    5. Working with data sources in M
      1. Creating parameters and variables
    6. Summary
    7. Questions
  15. Chapter 8: Using Data Profiling for Exploratory Data Analysis (EDA)
    1. Understanding EDA
    2. Exploring data profiling features in Power BI
    3. Reviewing column quality, distribution, and profile
      1. Column distribution
      2. Column quality
      3. Column profile
    4. Turning data profiles into high-quality data
      1. Recommended actions on column distribution
      2. Value distribution
    5. Summary
    6. Questions
  16. Part 3 – Advanced Data Cleaning and Optimizations
  17. Chapter 9: Advanced Data Cleaning Techniques
    1. Technical requirements
    2. Using Power Query Editor from within Dataflow Gen1 – fuzzy matching and fill down
      1. Fuzzy matching
      2. Fill down
      3. Best practices for using fuzzy matching and fill down
    3. Using R and Python scripts
      1. Benefits of using R or Python scripts
      2. Getting started with using R or Python scripts in Power BI
    4. Using ML to clean data
      1. Data cleaning with anomaly detection
      2. Data preparation with AutoML
      3. Data enhancement with AI Insights
    5. Summary
    6. Questions
  18. Chapter 10: Creating Custom Functions in Power Query
    1. Planning for your custom function
      1. Defining the problem
      2. Identifying parameters
      3. Setting clear objectives
    2. Using parameters
      1. Types of parameters
      2. Defining parameters
      3. Best practices for using parameters
    3. Creating custom functions
      1. Defining the function structure
      2. Writing M code
      3. Testing and debugging
      4. Documentation
    4. Summary
    5. Questions
  19. Chapter 11: M Query Optimization
    1. Technical requirements
    2. Creating custom functions
    3. Filtering and reducing data
    4. Using native M functions
    5. Optimizing memory usage
    6. Parallel query execution
      1. Using Table.Buffer and Table.Split
    7. Summary
    8. Questions
    9. Further reading
  20. Chapter 12: Data Modeling and Managing Relationships
    1. Understanding the basics of data modeling
      1. Importing versus DirectQuery
      2. Dimensional modeling
      3. Snowflake schema
      4. Intermediate tables
      5. Calendars and date tables
      6. Role-playing dimensions
      7. Aggregating tables
      8. Incremental refreshes
    2. Using bidirectional cross-filtering
      1. What is bidirectional cross-filtering?
      2. Best practices for bidirectional cross-filtering
    3. Understanding what’s the right cardinality
      1. Understanding cardinality
      2. Why cardinality matters
      3. Choosing the right cardinality
    4. Handling large and complex datasets
      1. Understanding big data
      2. Challenges of working with big data in Power BI
      3. Best practices for handling big data
    5. Avoiding circular references
      1. Understanding circular references
      2. Best practices for avoiding circular references
    6. Summary
    7. Questions
    8. Further reading
  21. Part 4 – Paginated Reports, Automations, and OpenAI
  22. Chapter 13: Preparing Data for Paginated Reporting
    1. Technical requirements
    2. Understanding the importance of paginated reports
    3. Connecting to data sources within Power BI Report Builder
    4. Data preparation
      1. Query
      2. Fields
      3. Options
      4. Filters
      5. Parameters
      6. Creating a dataset example
    5. Using filters and parameters
    6. Using row groups/column groups
      1. Organizing and structuring data
      2. Enhancing readability and presentation
    7. Summary
    8. Questions
  23. Chapter 14: Automating Data Cleaning Tasks with Power Automate
    1. Technical requirements
    2. Handling triggers for automation
    3. Automating notifications
    4. Automating refreshing of data
      1. Creating snapshots (temporary tables) of cleaned data
    5. Best practices with Power Automate
    6. Summary
    7. Questions
    8. Further reading
  24. Chapter 15: Making Life Easier with OpenAI
    1. Optimizing efficiency with OpenAI, ChatGPT, and DAX
    2. Using OpenAI for M queries
    3. Using Microsoft Copilot
    4. Tackling challenges with AI
    5. Summary
    6. Questions
    7. Further reading
    8. Putting it together
  25. Assessments
    1. Chapter 1 – Introduction to Power BI Data Cleaning
    2. Chapter 2 – Understanding Data Quality and Why Data Cleaning is Important
    3. Chapter 3 – Data Cleaning Fundamentals and Principles
    4. Chapter 4 – The Most Common Data Cleaning Operations
    5. Chapter 5 – Importing Data into Power BI
    6. Chapter 6 – Cleaning Data with Query Editor
    7. Chapter 7 – Transforming Data with the M Language
    8. Chapter 8 – Using Data Profiling for Exploratory Data Analysis (EDA)
    9. Chapter 9 – Advanced Data Cleaning Techniques
    10. Chapter 10 – Creating Custom Functions in Power Query
    11. Chapter 11 – M Query Optimization
    12. Chapter 12 – Data Modeling and Managing Relationships
    13. Chapter 13 – Preparing Data for Paginated Reporting
    14. Chapter 14 – Automating Data Cleaning Tasks with Power Automate
    15. Chapter 15 – Making Life Easier with OpenAI
  26. Index
    1. Why subscribe?
  27. Other Books You May Enjoy
    1. Packt is searching for authors like you
    2. Share Your Thoughts
    3. Download a free PDF copy of this book

Product information

  • Title: Data Cleaning with Power BI
  • Author(s): Gus Frazer
  • Release date: February 2024
  • Publisher(s): Packt Publishing
  • ISBN: 9781805126409