“`html

Data preparation is an essential phase in the process of data analysis, ensuring that unprocessed and intricate data is transformed into a neat and organized format that facilitates the creation of an insightful dashboard. Within Power BI, data preparation enhances reporting efficiency. Power BI provides robust tools such as the Power Query Editor, Data Profiling, and Column Quality indicators. This blog will cover the concept of data preparation, its various forms, step-by-step instructions for data preparation, and recommended best practices in depth.

Table of Contents:

• What is Data Preparation in Power BI?
• Types of Data Preparation in Power BI
• Advantages of Data Preparation in Power BI
• Steps to Prepare Data in Power BI
• Performance Optimization during Preparation
• Best Practices
• Conclusion
• Data Preparation in Power BI – FAQs

What is Data Preparation in Power BI?

Data preparation is the procedure of collecting, cleansing, converting, and arranging raw data into a functional format, which aids users in analyzing the data more effectively and supports decision-making by uncovering hidden insights. In Power BI, this process is invaluable and utilizes the Power Query Editor to format your data neatly.

Types of Data Preparation in Power BI

Power BI comprises four forms of data preparation:

• Data Cleansing: This involves eliminating inaccuracies and duplicates from your data.
• Data Transformation: This is the conversion of data into an appropriate format.
• Data Integration: This entails merging data from various sources into a unified view.
• Data Reduction: This process condenses data by excluding unnecessary details, enhancing efficiency.

Advantages of Data Preparation in Power BI

• Enhanced Data Quality: Preparing your data entails cleansing it, eliminating duplicates, and correcting errors.
• Accelerated Analysis: Organized and neat data can be analyzed more quickly, facilitating improved and faster insights.
• Superior Performance: Well-prepared data boosts the performance of data models and decreases data loading time.
• Improved Decision-Making: Organized data equips users to make informed decisions based on data trends.
• Enhanced Visualization: Cleaned and structured data are easier to interpret, providing better visualization.

Steps to Prepare Data in Power BI

Now, let’s delve into the steps necessary for preparing data in Power BI.

Step 1: Import Data: Select Home>Get Data>Text/CSV

Date	Product	Quantity	Price	Region
2023-01-01	iPhone	2	800	East
2023-01-02	Samsung TV	1	1200	South
2023-01-03	HP Laptop	1	1000	East
2023-01-04	null	3	300	West
2023-01-05	Office Chair	Null	150	North
2023-01-06	iPhone	1	null	South

Step 2: Transform the Data: Select Transform Data in the Home Ribbon.

Step 3: Eliminate Null or Blank Values

Null indicates missing data, whereas blank can signify empty cells in Power BI.

• Select the Product column.
• Navigate to Home>Remove Rows>Remove Blank Rows.
• Repeat these steps for removing null values in the Quantity and Price columns.

Step 4: Introduce a New Column

Add a new column that calculates total sales (Total Sales Quantity * Price).

Click on Model View and select Create column, then enter the following DAX formula.

Total Sales=[Quantity] * [Price]

Upon selecting ‘Create Column‘, a formula bar will be displayed.

Step 5: Extract Year and Month from Date

• Select the Date column.
• Navigate to Add Column>Date>year>year> Create a year column.
• Add Column>Date>Month>Name of the Month>Creates a Month Column.

Step 6: Verify Data Types: Ensure the data types for each column are correct.

• The Date column should be of the Date type.
• Product, Region, and Month should be of Text type.
• Quantity, Price, and Total Sales types should be of Numeric.

“““html
Whole numbers or Decimal numbers.

Step 7: Outcome: Once all procedures are executed, this is the appearance of your dataset.

Clarification: In this table:

• All Null entries have been eliminated.
• Year and Month details are extracted from the Date Column.
• All columns are formatted with the correct Data type.

Performance Enhancement during Preparation

• Filter out unnecessary rows and remove unused columns to minimize model size.
• Utilize appropriate data types in Power Query for precise results.
• Employ measures instead of calculated columns, as they are computed rather than stored.
• Transform your data prior to loading it into a model.

Recommended Practices

• After importing data to Power BI and adjusting it to your specifications with Power Query, ensure that there are no blank values present.
• During the execution of all data preparation tasks, consider recording these steps so you can track which were implemented.
• Assess if there are unnecessary columns and remove them; this will positively influence performance.
• Ensure that you are using appropriate names for the respective columns, contributing to a clearer understanding of the data.

Summary

Data preparation represents a crucial stage of data analysis, and executing it accurately ensures a well-organized dataset. The elimination of nulls and the creation of an additional calculated column enable you to convert your raw data into powerful information. Properly preparing your data conserves time and enhances performance, assisting you in making improved decisions. Power BI is a robust tool that aids in preparing your data. Simple tasks like data cleansing and column addition enhance your dataset, making it faster and more user-friendly.

To discover more about Power BI and its functionalities, explore this Power BI Course, and also review Power BI Interview Questions prepared by professionals in the industry.

Data Preparation in Power BI – FAQs

The post Data Preparation in Power BI appeared first on Intellipaat Blog.

“`