Book Image

Data Cleaning with Power BI

By : Gus Frazer
Book Image

Data Cleaning with Power BI

By: Gus 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)
Free Chapter
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

Building a process for cleaning data

The process of cleaning data involves several key steps that help to form a systematic approach to ensure comprehensive data cleaning.

While the specific steps may vary depending on the nature of the data and the organization’s requirements, the following general process provides a framework for effective data cleaning.

The effective steps to cleaning data follow this flow:

  1. Data assessment
  2. Data profiling
  3. Data validation
  4. Data cleaning strategies
  5. Data transformation
  6. Data quality assurance
  7. Documentation

Let’s go through these effective steps in detail next.

Data assessment

First of all, it’s imperative to assess the quality of data before we get started with cleaning the data. This may sound obvious; however, tracking this information will help you later down the line to ensure you have not missed any data transformations.

Equally, in the world of data analysis, it is always...