How to Archive Completed Records Automatically Using Power Automate

Introduction

As organizations generate more data over time, keeping SharePoint lists, Dataverse tables, or other data sources clean and efficient becomes increasingly important. Old or inactive records can impact performance, make searches more difficult, and clutter business applications.

An Archival Flow in Power Automate helps solve this problem by automatically moving old records from an active data source to an archive location while preserving the information for future reference. This approach improves system performance, simplifies data management, and supports compliance requirements.

What is an Archival Flow?

An Archival Flow is an automated process that:

Identifies records that meet archive criteria (such as records older than a specific date)
Copies the records to an archive location
Optionally deletes the original records
Logs the archive activity for auditing

Common archive locations include:

SharePoint Archive List
Dataverse Archive Table
SQL Database
Excel File
Azure Blob Storage

Why Use an Archival Flow?

Benefits include:

Improves SharePoint list performance
Reduces active data size
Keeps applications organized
Preserves historical information
Supports compliance and auditing
Eliminates manual archive work

Example Scenario

Suppose you have a SharePoint list named Employee Requests.

Employee	Request Type	Status	Completed Date
Emp101	Leave	Completed	15-Jan-2024
Emp102	Travel	Completed	10-Feb-2024
Emp103	Equipment	Open	—

Business Requirement:

Archive requests completed more than 180 days ago.
Move them to Employee Requests Archive.
Remove them from the active list.

How the Flow Works

Trigger
   ↓
Recurrence (Runs Daily)
   ↓
Get Items from SharePoint
   ↓
Filter Completed Records Older Than 180 Days
   ↓
Apply to Each Item
   ↓
Create Item in Archive List
   ↓
Delete Original Item
   ↓
Send Summary Email (Optional)

Step-by-Step Implementation

Step 1: Create a Scheduled Flow

Open Power Automate.
Select Scheduled Cloud Flow.
Configure:
- Runs once every day.
- Choose the preferred execution time.

Step 2: Get SharePoint Items

Add the Get items action.

Configuration:

Site Address
List Name: Employee Requests

Step 3: Filter Old Records

Example Filter Query:

Status eq 'Completed'

You can also filter using Power Automate conditions.

Example Condition:

Completed Date <= addDays(utcNow(),-180)

Step 4: Archive the Record

Inside Apply to each:

Add Create item

Destination:

SharePoint Archive List

Copy all columns:

Employee
Request Type
Status
Completed Date
Comments

Step 5: Delete Original Record

After successful archive:

Add

Delete item

This keeps the active list clean.

Step 6: Send Notification (Optional)

Send an email to the administrator.

Example:

Subject:
Employee Requests Archived

Body:
25 completed requests have been archived successfully.

Flow Diagram

Schedule Trigger
        │
        ▼
Get Items
        │
        ▼
Completed?
        │
   Yes ─┘
        │
        ▼
Older than 180 Days?
        │
   Yes ─┘
        │
        ▼
Create Item in Archive List
        │
        ▼
Delete Original Item
        │
        ▼
Log Success / Send Email

Best Practices

Always archive before deleting records.
Test the flow with a small dataset first.
Use pagination when processing large SharePoint lists.
Maintain an archive log for auditing.
Include error handling with Configure run after or Scopes.
Schedule the flow during off-peak hours.
Back up critical data before enabling automatic deletion.

Common Use Cases

Archive completed employee requests
Move closed help desk tickets
Archive completed purchase orders
Store historical project records
Archive old customer support cases
Move completed approval requests
Archive inactive asset records

Conclusion

An Archival Flow in Power Automate is an effective way to keep business applications fast, organized, and maintainable. By automatically moving inactive records to an archive location while preserving historical data, organizations can improve performance, simplify maintenance, and meet data retention policies with minimal manual effort.