Power BI Data Cleansing Techniques: Raw Data to Insights

Data cleansing is the practice of identifying and correcting data that is inaccurate. The data is then erased, changed, and replaced with newly developed or relevant data. There are various data cleansing techniques available within Power BI.

Power BI provides a range of tools that facilitate the process of data cleansing. These tools include the ability to:

• eliminate duplicate entries,
• filter rows based on specified criteria,
• handle missing values,
• transform data types,
• manipulate text,
• standardize data,
• format data in a consistent manner,
• and merge and split columns as needed.

These methodologies enhance precision, uniformity, and preparedness for data analysis and visualization.

In this article, we explore data cleansing techniques. We begin by defining them and discussing why data cleansing is important. We then look at the benefits that data cleansing offers. We’ll also delve into some of the tools you can use.

We then learn how to perform data cleansing using Power BI. We’ll use the ChartExpo Sankey diagram as an example.

Table of Content:

1. What are Data Cleansing Techniques?
2. Why are Data Cleansing Techniques Important?
3. 7 Data Cleansing Techniques
   • Removing Duplicate Data
   • Handling Missing Values
   • Standardizing Data Formats
   • Correcting Inaccuracies
   • Removing Outliers
   • Validating Data Integrity
   • Converting Data Types
4. Tools & Software Used in Data Cleaning Method
5. Data Cleansing Example
6. Data Cleansing vs. Data Cleaning
7. Data Cleansing Steps in Power BI
8. How to Evaluate Data Cleansing in Power BI?
9. Challenges in Data Cleansing
10. Benefits of Data Cleansing Techniques
11. Tips For Using Data Cleansing Process
12. FAQs About Data Cleansing Techniques
13. Wrap Up

First…

What are Data Cleansing Techniques?

Definition: Data cleansing techniques are methods used to identify and rectify errors, inconsistencies, and inaccuracies within a dataset.

These techniques are essential for ensuring data accuracy, consistency, and trustworthiness during data analysis.

There are several data cleaning methods. These include:

• Handling missing data – this involves identifying and addressing missing or null values in a dataset.
• Removing duplicates – this process involves detecting and eliminating duplicate records or entries within a dataset.
• Standardizing data formats: This method ensures uniform formatting of data elements like dates, names, addresses, or measurement units.
• Handling outliers: Outliers are data points that significantly deviate from the rest of the data. Techniques may involve identifying and removing outliers or transforming or retaining them based on the analysis requirements.
• Data type conversion: This technique involves converting data from one type to another, for example, text to numeric data. This ensures data consistency and compatibility with analysis tools or processes.

Why are Data Cleansing Techniques Important?

Data cleansing techniques are critical to maintaining data quality, accuracy, and reliability. These factors are indispensable for organizations to derive meaningful insights and make informed decisions.

• Accuracy

The accuracy of data is ensured by having clean data. Incorrect data can result in incorrect conclusions, ineffective decision-making, and squandered resources. Through data cleansing, inaccuracies, duplications, and disparities are recognized and remedied, leading to more dependable insights.

• Completeness

Insufficient data can obstruct analysis and result in prejudiced outcomes. The procedure of data cleansing entails the identification and resolution of missing values or the elimination of deficient records.

This guarantees that the dataset is all-encompassing and appropriate for analysis. Additionally, understanding concepts like Power BI cross-filter direction is crucial, as it influences how data relationships are interpreted, ensuring accurate insights from the cleansed dataset.

• Data Integration

Data integration involves the incorporation of data from various sources, which often results in inconsistencies and discrepancies. Cross-tabulation can be a valuable technique in this process, allowing for a clearer comparison of different datasets. To ensure seamless integration and accurate analysis, it is essential to perform data cleansing to reconcile differences between these datasets.

• Enhanced Decision Making

Accurate data leads to better insights, which help with well-informed decision-making. Reliable data enables organizations to see opportunities, trends, and patterns more quickly, which improves their ability to make strategic decisions.

• Enhanced Data Consistency

Consistency is key when it comes to effective decision-making and data analysis. Data cleaning techniques help to standardize formats, spellings, and other variations within a dataset. They ensure everything is consistent and reliable.

With enhanced data consistency, such as that achieved through a Power BI income statement, you can trust the insights you gain from your analysis and make better-informed decisions. So don’t overlook the importance of data cleansing, it’s an essential step towards success.

7 Data Cleansing Techniques

Effective data cleansing ensures that data is accurate, consistent, and ready for analysis. Below are some key techniques used in data cleansing:

1. Removing Duplicate Data

Duplicate data can lead to misleading insights and errors in analysis. Removing duplicate data helps maintain data integrity and prevents inflated results. Using automated tools or Excel functions like “Remove Duplicates” ensures a clean dataset.

2. Handling Missing Values

Incomplete data can affect analysis accuracy. Handling missing values involves techniques like mean imputation, predictive modeling, or removing incomplete records. Choosing the right approach depends on the data type and its impact on analysis.

3. Standardizing Data Formats

Inconsistent formats can create confusion and errors. Standardizing data formats ensures that all data entries follow a uniform structure, such as consistent date formats, unit measurements, and naming conventions. This improves compatibility across different systems.

4. Correcting Inaccuracies

Incorrect data entries can distort analysis and lead to poor decisions. Correcting inaccuracies involves validating data against reliable sources, checking for typos, and ensuring all information is up to date. Automated validation tools help streamline this process.

5. Removing Outliers

Extreme values can skew results and reduce the reliability of insights. Removing outliers helps in maintaining data accuracy by identifying unusual values that do not align with expected patterns. Statistical methods like Z-score analysis can help detect and eliminate outliers.

6. Validating Data Integrity

Ensuring that data is accurate, complete, and reliable is essential. Validating data integrity includes cross-checking entries, performing audits, and using validation rules. This step prevents inconsistencies and ensures data is ready for analysis.

7. Converting Data Types

Mismatched data types can cause errors in calculations and analysis. Converting data types ensures that numerical values, text entries, and date formats are correctly assigned, making data processing smooth and efficient.

Tools & Software Used in Data Cleaning Method

Data cleaning is a crucial step in the data preprocessing stage of any data analysis. It involves identifying and correcting (or removing) errors and inconsistencies in data to improve its quality and ensure data integrity. Here are some common tools and techniques used in data cleaning:

• Power BI Query Editor

In Power BI, the cleaning of data is predominantly carried out using the Power Query Editor. The Power Query Editor is an efficient and user-friendly data transformation tool. It is seamlessly integrated into Power BI.

Users can link, clean, and alter data from various sources before importing it into the data model.

• OpenRefine

OpenRefine is an open-source tool that has been designed to effectively clean and transform data. The tool is commonly utilized for data-cleaning purposes.

OpenRefine supports the import of several data formats, so users can conveniently upload their datasets.

Upon importing the data, OpenRefine provides users with features like faceting, filtering, and sorting. These features help users comprehend the structure and quality of the data.

OpenRefine enables users to standardize data formats and values to ensure consistency across the dataset.

The tool provides tools for identifying and removing duplicate records based on user-defined criteria.

• TIBCO Clarity

TIBCO Clarity is a specialized platform designed for interactive data cleaning. The tool provides a user-friendly interface that streamlines data quality improvements, data discovery, and data transformation.

This tool is capable of processing various types of raw data and preparing it for various applications. Additionally, it facilitates deduplication operations and address verification before moving the information to its destination.

The cleansing process configuration can be reused for future raw data.

• DemandTools

DemandTools is an efficient data quality suite that is intended to assist organizations in enhancing their data.

It is compatible with Microsoft Dynamics 365 and Salesforce CRM.

DemandTools has a module called Cleansing Tools that is dedicated to improving data quality by:

• • Rectifying records and preventing duplicates.
  • Managing lead conversions without creating duplicate contacts.

The deduplication matching algorithm utilized in this module employs advanced techniques to identify more matches.

The Discovery Tools module enables you to validate CRM data by comparing it with external data sources.

The Maintenance Tools module streamlines CRM data management tasks, including loading, reporting, record reassignments, backups, and manipulation.

• IBM InfoSphere Information Server

IBM InfoSphere Information Server is a comprehensive data integration platform. It offers a range of top-notch data-cleaning tools.

This tool allows for various services like standardizing information, validating and classifying data, and deduplicating records.

The platform ensures the cleanliness and quality of your data through continuous monitoring. Moreover, it also offers address cleaning services.

IBM’s InfoSphere provides real-time integration, digital transformation, governance, data monitoring, and smooth scalability of data.

Data Cleansing Example

1. Salesforce Data Cleansing

Salesforce data cleansing is essential for maintaining high-quality CRM data. Over time, Salesforce databases accumulate duplicate contacts, outdated leads, and incorrect entries. A company using Salesforce for sales tracking may encounter:

• Duplicate customer records lead to confusion.
• Incomplete lead information affecting outreach efforts.
• Outdated email addresses cause communication failures.

2. B2B Data Cleansing

B2B data cleansing focuses on refining business contact databases for better marketing and sales outreach. Companies that rely on email campaigns, lead generation, and account-based marketing need clean and up-to-date B2B data. Common issues include:

• Inactive business emails lead to high bounce rates.
• Incorrect industry classifications cause targeting errors.
• Outdated company information reducing personalization.

Data Cleansing vs. Data Cleaning

Aspect	Data Cleaning	Data Cleansing
Definition	Removing errors, duplicates, and inconsistencies from data.	Standardizing, correcting, and enriching data for accuracy and usability.
Scope	Basic error correction.	More comprehensive, including validation and enrichment.
Focus	Fixing existing issues.	Improving overall data quality and consistency.
Techniques	Removing duplicates, fixing typos, and handling missing values.	Standardization, validation, deduplication, and data enhancement.
Outcome	Cleaned but not necessarily optimized data.	High-quality, structured, and reliable data for analysis.

Data Cleansing Steps in Power BI

Data cleansing in Power BI ensures accuracy, consistency, and reliability in reports and analysis. Follow these steps to clean your data effectively:

Step 1: Import Your Data

Start by importing data from Excel, databases, or other sources into Power BI. Use Power Query Editor to access the raw data and begin the cleansing process.

Step 2: Remove Duplicate Records

Duplicate data can cause inconsistencies in reports. In Power Query, select the relevant column, click Remove Duplicates, and ensure your dataset is free from redundant entries.

Step 3: Handle Missing Values

Missing values can impact data accuracy. Use the Replace Values function to fill in missing entries or the Remove Rows option to eliminate incomplete records.

Step 4: Standardize Data Formats

Ensure consistency in data, text, and numerical values. Use Transform Options in Power Query to change text cases, modify date formats, and ensure numeric consistency.

Step 5: Correct Errors and Inaccuracies

Check for incorrect spelling, formatting errors, and mismatched values. Use the Find & Replace tool to correct typos and maintain data accuracy.

Step 6: Remove Unnecessary Columns

Eliminate columns that are not needed for analysis. Select unwanted columns and click Remove Columns to keep only relevant data.

Step 7: Detect and Remove Outliers

Extreme values can skew results. Use Conditional Formatting or statistical functions to identify and remove unusual values that distort insights.

Step 8: Validate and Apply Data Cleansing Changes

Review the cleansed data, ensure it meets accuracy standards, and click Close & Apply to finalize the changes in Power BI.

How to Evaluate Data Cleansing in Power BI?

In this section, we learn more about how to clean data using Power BI. We’ll use the Sankey Diagram (Sankey Chart) as an example in Power BI Desktop.

Stage 1: Logging in to Power BI

• Log in to Power BI.
• Enter your email. Click the “Submit” button.

• Enter your password and click “Sign in.”

• Choose whether to stay signed in.

Stage 2: Cleanse the Data to Use in Your Sankey Diagram

• We’ll use the following sample data for this example:

Age	Gender	Marital Status	Occupation	Monthly Income	Educational Qualifications	Family size
20	Female	Single	Student	No Income	Post Graduate	4
24	Female	Single	Student	Below Rs.10000	Graduate	3
22	Male	Single	Student	Below Rs.10000	Post Graduate	3
22	Female	Single	Student	No Income	Graduate	6
22	Male	Single	Student	Below Rs.10000	Post Graduate	4
27	Female	Married	Employee	More than 50000	Post Graduate	2
22	Male	Single	Student	No Income	Graduate	3
24	Female	Single	Student	No Income	Post Graduate	3
23	Female	Single	Student	No Income	Post Graduate	2
23	Female	Single	Student	No Income	Post Graduate	4
22	Female	Single	Student	No Income	Post Graduate	5
23	Male	Single	Student	Below Rs.10000	Post Graduate	2
23	Male	Single	Student	No Income	Post Graduate	5
21	Male	Single	Student	No Income	Graduate	4
23	Female	Single	Self Employed	10001 to 25000	Post Graduate	5
24	Female	Single	Student	No Income	Post Graduate	6
28	Female	Single	Employee	25001 to 50000	Post Graduate	2
23	Female	Single	Student	No Income	Graduate	3
25	Male	Single	Student	No Income	Graduate	4
21	Female	Single	Student	Below Rs.10000	Post Graduate	1

• Once you access Power BI’s dashboard, choose “Import data from Excel.”

• Choose your dataset.
• It will be loaded into the Navigator pane.

• Choose the Excel sheet containing the data.

• As you can see, our data set contains null Values.

• Select “Transform Data” to remove null columns and rows.
• This opens the Power Query Editor tool dashboard.

• In the “Home” tab, look for the “Manage Columns” group. Click on the “Choose Columns” icon (it looks like a table with highlighted columns).

• The following window opens:

• You should see all the columns.
• Choose the columns to keep and click “OK.”

• To delete null rows, in the “Home” tab, look for the “Remove Rows” group. Click on the “Remove Rows” icon (it looks like a table with a row being removed).

• Choose “Remove Blank Rows.”
• You now have clean data that you can use to create your Sankey diagram.
• Choose “Apply” to save the changes you’ve made to your dataset.

• Open Power Query Editor by selecting the “Transform data” option on the home tab of Power BI Desktop.

The data in your selected query is displayed in the middle of the screen. To the left, the queries pane list is available and to the right, a list of your steps is available in the Query Settings pane.

Stage 3: Adding the Power BI Sankey Diagram Extension

• To finish creating our Sankey Diagram, we’ll use an add-in or Power BI visual from AppSource.
• Navigate to the Power BI Visualizations panel.
• Select the “Get more visuals” option.

• Enter “Sankey Diagram for Power BI by ChartExpo” in the highlighted search box.
• You should see the “Sankey Diagram for Power BI by ChartExpo” in the following image.

• Click the highlighted “Add” button.

• Power BI will add the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel.

Stage 4: Drawing a Sankey Diagram With ChartExpo’s Power BI Extension

• Select the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel.
• The following window opens in the report section of your dashboard:

• You can resize the visual as needed.
• Navigate to the right side of your Power BI dashboard. You should see “Fields” next to “Visualizations.”
• You’ll select the fields to use in your Sankey chart here.

• The ChartExpo visual needs to be selected, though.
• Select the fields in the following sequence:
  • Age
  • Educational qualifications
  • Family size
  • Gender
  • Marital Status
  • Monthly Income
  • Occupation

• You’ll be asked for a ChartExpo license key or email address.

• Select the ChartExpo visual. You should see three icons below “Build Visual” in the Visualizations panel.

• Select the middle icon, “Format visual.”
• The visual properties will be populated as shown below.

• If you are a new user,
  • Type in your email under the section titled “Trial ”
  • This should be the email address that you used to subscribe to the ChartExpo add-in. It is where your ChartExpo license key will be sent.
  • Ensure that your email address is valid.
  • Click “Enable Trial.” You’ll get a 7-day trial.

• You should receive a welcome email from ChartExpo.
• The Sankey Diagram you create under the 7-day trial contains the ChartExpo watermark.
• If you have obtained a license key:
  • Enter your license key in the “ChartExpo License Key” textbox in the “License Settings” section (see below).
  • Slide the toggle switch next to “Enable License” to “On.”

• Your Sankey diagram should now be ready (see screenshot). Note that it does not have a watermark.

• To add colors, expand the “Level Colors” properties and select a color.
• Do this to change the color of each node.
• All changes are automatically saved.

• Your final visualization will look like the one below.

Insights

The dataset above was gathered from an online based platform for ordering food. It includes different characteristics linked to occupation, family size, feedback, and more.

This dataset is used to examine how demographic and location factors relate to online food ordering habits.

• At Level 1, the sales are based on educational qualifications. Postgraduates account for 43.76%, graduates for 45.67%, and Ph.D. holders for 6.40%. School-educated individuals account for 3.52%, and uneducated individuals account for 0.65%.
• At Level 2, sales for females account for 42%, while sales for males make up 58%.
• At Level 3, the sales data is based on marital status. 65.38% of clients are classified as single, while 31.28% are considered married. The remaining 3.36% of the customers have not disclosed their marital status.
• At Level 4, sales are classified based on customer income. The customer segment with no income constitutes 45% of the total. Those earning between 25,001 and 50,000 constitute 19%. Customers with an income between 10,001 and 25,000 make up 12% of the total. Those with an income below 10,000 accounts for 6%. The remaining 18% are customers with incomes exceeding 50,000.
• At Level 5, sales are occupation-based. Students accounted for 49%, employees 33%, self-employed individuals 15%, and housewives 3%.

Challenges in Data Cleansing

1. Handling Large Datasets

Cleaning massive amounts of data requires significant time and computing power, making the process complex and resource-intensive.

2. Identifying and Removing Duplicates

Duplicate records often appear in different formats, making it difficult to detect and merge them without losing critical information.

3. Dealing with Missing or Incomplete Data

Missing values can lead to biased analysis. Deciding whether to remove, fill, or predict missing data is a major challenge.

4. Ensuring Data Consistency Across Sources

Merging data from multiple sources often results in inconsistencies in formats, naming conventions, and data structures, requiring additional cleaning efforts.

5. Maintaining Data Accuracy Over Time

Even after cleansing, data quality can degrade due to outdated information, requiring continuous monitoring and updates.

Benefits of Data Cleansing Techniques

In this section, we delve into the transformative benefits of data cleansing techniques. We explore how they empower organizations to extract actionable insights from their data assets.

Here are some key advantages of implementing data cleaning techniques:

• Boosting the Accuracy and Reliability of Your Data

Power BI provides a variety of tools and techniques to identify and rectify data errors. Whether it’s incorrect values, misspellings, or typos, these tools can help you get rid of them. This boosts the accuracy and reliability of your data.

• Better Data Integration

Clean data is easier to integrate across different systems and platforms. This facilitates seamless data exchange and interoperability between various applications within an organization.

• Aligned Decision Making

Teams can make informed decisions based on a shared understanding of clean and accurate data. This alignment ensures that everyone is working towards common goals and objectives.

• Improved Data Security

Data cleansing often involves identifying and removing redundant or obsolete data. This reduces the risk of data breaches and unauthorized access. By maintaining a clean data environment, organizations can enhance data security and protect sensitive information.

• Compliance and Regulatory Requirements

Many industries have strict compliance and regulatory requirements regarding data accuracy and privacy. Data cleansing helps organizations ensure compliance with these regulations by maintaining accurate and secure data.

Tips For Using Data Cleansing Process

1. Regularly Audit Your Data

Frequent audits help identify errors, duplicates, and inconsistencies before they impact decision-making. Schedule periodic reviews to maintain clean and reliable data.

2. Use Automation for Data Cleansing

Leverage AI-driven tools and scripts to streamline the cleansing process. Automated solutions help remove duplicates, standardize formats, and validate missing values efficiently.

3. Standardize Data Formats

Ensure consistency in data formats, such as dates, currencies, and text capitalization. This minimizes discrepancies and enhances data integration across platforms.

4. Remove Duplicate Entries

Duplicates distort analysis and confuse. Use built-in deduplication features in tools like Excel, Power BI, or Salesforce to eliminate redundant records.

5. Validate Data Sources

Cross-check data from multiple sources before using it for analysis. Implement validation rules to prevent incorrect or incomplete entries from entering your system.

FAQs About Data Cleansing Techniques

What are Data Cleansing Examples?

Data cleansing involves a range of tasks aimed at improving the quality and dependability of datasets.

Examples of data cleansing include:

• Elimination of duplicates
• Standardization of data formats
• Correction of typographical errors and misspellings
• Handling of missing values
• Validation of data for accuracy and integrity
• Identification and removal of outliers to prevent skewed analysis
• Elimination of redundant information
• Cross-referencing of data with external sources for validation
• Cleaning of textual data

What are the Three Points to the Cleansing of Data?

Data cleansing involves three critical points: accuracy, completeness, and consistency.

Accuracy entails the elimination of errors, inconsistencies, and duplicates. This ensures the data is reliable for decision-making and analysis.

Completeness ensures that a dataset has all the necessary information. This information includes the missing values to provide a comprehensive view of the subject matter.

Consistency focuses on standardizing data formats, resolving variations in entries, and promoting uniformity across different sources. This enables seamless integration and reliable analysis.

Addressing these aspects can improve the quality and reliability of the data. This enables organizations to gain valuable insights and make informed decisions.

Wrap Up

Data cleansing is an essential process for ensuring data quality, accuracy, and reliability.

Businesses increasingly rely on data-driven insights for critical decisions. The importance of clean and trustworthy data, therefore, cannot be overstated.

This article discusses data cleansing techniques that can be used to transform raw data into valuable assets. These include:

• Handling missing data,
• Removing duplicates,
• Standardizing formats,
• and Addressing outliers.

Power BI provides the intuitive Power Query Editor to help you streamline the data cleansing process.

After cleaning our data via Power Query, we then used it in a Sankey diagram.

We hope that these data-cleaning techniques will empower you to work with accurate and reliable data. Better still, make decisions based on high-quality data and keep everyone moving in the same direction.