Data quality is crucial for businesses that rely on accurate information for decision-making. However, raw data is often incomplete, inconsistent, or contains errors, making data cleansing a necessary step before analysis or reporting.
Power Automate, part of the Microsoft Power Platform, provides automation tools to clean, standardize, and transform data—without requiring complex coding. Whether it’s removing duplicates, formatting data, validating fields, or transforming records across systems, Power Automate ensures data accuracy and consistency.
In this guide, we’ll explore:
Common data cleansing challenges
How Power Automate helps clean and transform data
Step-by-step use cases for data cleansing
1. Why Data Cleansing and Transformation Matter
Challenges with Raw Data
❌ Duplicate records – Redundant entries create confusion
❌ Incomplete data – Missing values affect insights
❌ Inconsistent formatting – Differences in date formats, capitalization, etc.
❌ Incorrect values – Human errors or outdated records
❌ Data silos – Scattered data across different platforms
Benefits of Automating Data Cleansing with Power Automate
✔️ Saves time – Automates repetitive cleaning tasks
✔️ Improves accuracy – Reduces human errors in data entry
✔️ Ensures consistency – Standardizes data across all records
✔️ Enhances decision-making – Provides clean and reliable data
✔️ Integrates multiple data sources – Connects with Excel, SharePoint, SQL, Dataverse, and more
2. Common Use Cases for Data Cleansing in Power Automate
A. Removing Duplicate Entries from a Database
Example: A company collects leads in Excel or SharePoint but often has duplicate records.
Steps to Automate:
1️⃣ Select “When a new row is added to Excel/SharePoint list” as the trigger
2️⃣ Click “New Step” → Choose “List rows present in a table”
3️⃣ Use “Filter array” to identify duplicate email addresses
4️⃣ If duplicates exist, delete the duplicate row automatically
Impact: Keeps lead records clean and unique, improving CRM data quality.
B. Standardizing Name Formats in SharePoint or Dataverse
Example: A company stores employee records, but names are entered inconsistent (e.g., “john doe” vs. “John Doe”).
Steps to Automate:
1️⃣ Select “When an item is created or modified in SharePoint”
2️⃣ Use “Convert text to proper case” action (Power Automate Expression: toTitleCase())
3️⃣ Update the SharePoint list item with the formatted name
Impact: Ensures all names are properly formatted, improving record uniformity.
C. Validating and Cleaning Email Addresses
Example: A marketing team wants to validate email formats before adding contacts to a Mailing List (Excel, SharePoint, or CRM).
Steps to Automate:
1️⃣ Select “When a new record is added” in Excel/SharePoint
2️⃣ Use Power Automate Expressions to check if email contains “@”:
contains(triggerBody()?['Email'], '@')
3️⃣ If invalid, send an email notification to correct it
4️⃣ If valid, add it to the mailing list
Impact: Prevents incorrect email addresses from entering marketing databases.
D. Transforming Date Formats Across Systems
Example: Sales reports in Excel use MM/DD/YYYY format, but the database requires YYYY-MM-DD.
Steps to Automate:
1️⃣ Select “When a file is created in OneDrive/SharePoint”
2️⃣ Use Power Automate Expressions to reformat the date:
formatDateTime(triggerBody()?['Date'], 'yyyy-MM-dd')
3️⃣ Save the cleaned date to SQL/Dataverse
Impact: Ensures all date formats align correctly for analytics and reporting.
E. Automatically Filling Missing Data in a Database
Example: Customer records in Dataverse or SQL Server are missing phone numbers or addresses, which need to be filled in automatically.
Steps to Automate:
1️⃣ Select “When a record is created/updated” in SQL or Dataverse
2️⃣ Use “Get record from another database” (if the data exists elsewhere)
3️⃣ If a match is found, update the missing field
4️⃣ If no match is found, send an alert for manual review
Impact: Keeps customer data complete and updated, improving service efficiency.
F. Removing Special Characters from Text Fields
Example: A company exports data with unwanted special characters (@, #, !, etc.) in customer names or addresses.
Steps to Automate:
1️⃣ Select “When a new record is added”
2️⃣ Use Power Automate Expression to clean text:
replace(replace(replace(triggerBody()?['CustomerName'], '@', ''), '#', ''), '!', '')
3️⃣ Save the cleaned value back to the database
Impact: Keeps text fields clean for consistent reporting and analysis.