Data Validation Flows - Rishan Solutions

Introduction

In today’s digital-first environment, organizations increasingly rely on automated workflows to handle everything from customer service to supply chain operations. While automation delivers speed and efficiency, it also introduces new challenges—chief among them is data integrity. Invalid, incomplete, or incorrect data can cause serious disruptions, especially when downstream systems depend on accurate information.

Data validation flows are automated processes that verify the correctness, completeness, and consistency of data before it enters or moves through a workflow. These flows help ensure that only valid data proceeds through the system, thereby preventing errors, improving decision-making, and maintaining compliance with data quality standards.

This guide explores the concept of data validation flows, their importance, how to design and implement them using tools like Power Automate, and best practices to ensure reliable, scalable, and efficient data management.

What Are Data Validation Flows?

Data validation flows are automated workflows designed to check whether data meets specific criteria before it’s accepted, processed, or stored. These flows can be implemented at various points in a business process—during data collection, ingestion, transformation, or transfer.

Validation can range from simple format checks (e.g., email address structure) to complex business rule enforcement (e.g., verifying that a purchase order amount does not exceed a user’s approval limit).

Common types of data validations include:

Format validation (e.g., is the date formatted correctly?)
Required field validation (e.g., is a mandatory field missing?)
Range or limit checks (e.g., is a value within acceptable limits?)
Cross-field validation (e.g., does the delivery date follow the order date?)
Lookup validation (e.g., does the value exist in a reference list or database?)

Why Data Validation Is Essential

Data validation flows play a crucial role in ensuring that automated systems work correctly and produce trustworthy results. Here’s why they matter:

Prevents Errors and Failures
Incomplete or invalid data can crash automation flows or cause inaccurate outputs.
Improves Data Quality
Validation flows filter out erroneous data, ensuring better analytics and reporting.
Ensures Compliance
Many industries (e.g., healthcare, finance) have strict data standards. Validation helps meet regulatory requirements.
Enhances User Experience
Catching mistakes early—such as missing fields in a form—prevents delays and rework.
Supports Integration
Data validation ensures compatibility when data is exchanged across different systems.

Key Components of a Data Validation Flow

A well-designed data validation flow typically includes the following components:

1. Trigger

Determines when the flow starts. Common triggers include:

Form submissions (e.g., Microsoft Forms, Typeform)
SharePoint item creation or modification
New rows added to Excel or SQL
API or webhook events

2. Data Capture

Retrieves data for validation. This might involve reading values from a form, Excel sheet, SharePoint list, or database.

3. Validation Logic

Performs checks using conditions, expressions, or scripts. This includes:

Required field checks
Pattern matching (e.g., regular expressions)
Reference lookups
Business rule evaluation

4. Decision Branching

Routes the flow based on validation results:

If valid → continue the process (e.g., save to database, trigger notification)
If invalid → send error message, flag item, or request correction

5. Error Handling

Includes actions like:

Sending email alerts to users or admins
Logging errors in a tracking system
Updating the status of a record (e.g., “Invalid Data”)

Data Validation Using Power Automate

Microsoft Power Automate is a low-code platform that allows you to build and manage automated workflows across Microsoft 365 and external applications. Power Automate offers multiple tools to implement validation logic in workflows.

Example Use Case: Validating Form Data from Microsoft Forms

Trigger: When a new response is submitted to Microsoft Forms
Action: Get response details
Condition: Check if required fields (e.g., name, email) are filled
Expression: Use expressions like empty(triggerBody()?['email']) to validate fields
Branching:
- If true → send a Teams message or email notifying the user of the missing information
- If false → continue processing, e.g., creating an item in SharePoint or sending approval request

Other Tools in Power Automate for Validation:

Condition Control: For simple IF-ELSE logic
Switch Control: For branching based on specific values
Expressions: Use built-in functions like length(), contains(), match(), or isMatch() for complex logic
Custom Connectors: To call external validation APIs
Apply to Each: For validating arrays or bulk data entries

Real-World Use Cases of Data Validation Flows

1. HR Onboarding Validation

When a new hire form is submitted, a flow validates whether all required documents (ID, resume, references) are uploaded. If any are missing, it sends a reminder to the applicant.

2. Invoice Submission Check

Accounts payable systems validate invoice uploads to ensure required fields (vendor ID, amount, due date) are filled, and values like the due date are in the future.

3. CRM Data Quality Check

Before adding new leads into a CRM system, a flow verifies the email address format, checks for duplicates, and confirms that the phone number has the correct number of digits.

4. Project Management Tracker

When team members update a task in SharePoint, a data validation flow ensures that the status, due date, and assigned resource fields are not empty, and that the due date is not in the past.

5. Inventory System Update

Before restocking inventory levels from supplier data, the flow checks that product codes are valid and quantities are numeric and positive.

Best Practices for Designing Data Validation Flows

Start Simple Begin with basic validations (e.g., required fields), then gradually add more complex rules.
Use Consistent Naming Label your conditions and actions clearly so others can understand and maintain your flows.
Avoid Hardcoding Reference environment variables or configuration lists instead of hardcoded values.
Validate Early Perform checks as soon as possible in the flow to avoid unnecessary processing of invalid data.
Provide Feedback Make error messages user-friendly and actionable (e.g., “Please enter a valid phone number”).
Log Everything Maintain logs of validation results for auditing, debugging, and performance tracking.
Use Lookup Lists or Tables Validate values against standardized reference data (e.g., product codes, department names).
Modularize Your Flows Use child flows or separate reusable flows for common validation patterns.

Challenges in Data Validation Flows

While beneficial, implementing data validation flows comes with challenges:

Complex Logic Handling Advanced validations (e.g., cross-referencing datasets) may require external scripts or custom connectors.
Data Format Inconsistencies Data from multiple sources may vary in structure or type, making validation tricky.
Performance Impacts Large-scale validation workflows can be resource-intensive. Optimize for speed and efficiency.
User Frustration If validations are too strict or unclear, users may get frustrated and abandon the process.
Maintenance Overhead As business rules evolve, validation logic needs regular updates to remain accurate.

Tools and Technologies Supporting Data Validation

Besides Power Automate, other platforms support data validation automation:

Power Apps: Add validation logic to form controls and input fields.
SharePoint: Use column validations and custom flows.
Azure Logic Apps: For large-scale or enterprise-grade validation workflows.
SQL Server: Use triggers and constraints for database-level validation.
Excel with Office Scripts: Validate data in spreadsheets using JavaScript logic.
Custom APIs: Call external services to validate complex data, like tax IDs or credit card numbers.

Future of Data Validation Flows

The future of data validation lies in intelligent, adaptive flows powered by AI and machine learning:

Predictive Validation: Using historical patterns to predict and prevent errors before they occur.
AI-Powered Cleaning: Suggesting or automatically correcting data inputs.
Natural Language Inputs: Validating inputs from voice or chat using NLP (Natural Language Processing).
Contextual Validation: Adjusting rules dynamically based on the user’s role or region.
No-Code Templates: Prebuilt validation templates for common business scenarios.