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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

Splitting columns

When working with data, particularly data extracted directly from source systems, there often comes a time when we will need to split columns to gain the desired dimensions for our analysis. This might be because the software or database of that source system might store that data in a particular format/encoding/arrangement. The most common example of this could be splitting a Date field in order to extract dimensions for [Day], [Month], and [Year].

In this example, we will connect and open the calendar.xlsx file. This Excel table includes one column of dates (as shown in Figure 4.8) and is to be used as a date table within Power BI. In this example, we might need to extract the individual date components for our analysis. Once connected, select Transform data to enter Power Query once again.

Figure 4.8 – The Date column within the date table in Power BI

Figure 4.8 – The Date column within the date table in Power BI

In order to split the columns, we will use the prebuilt function for splitting...

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