Data Migration Approaches

Empowering businesses with intelligent AI agents for efficient task management and innovative solutions.

AI Driven Approach

• Automated Data Profiling & Anomaly Detection

• Proactive Error & Inconsistency Corrections

• Detection of Complex Data Relationships

• End-to-End Validation Automation

• Compliance with Data Governance Policies

Conventional Approach

• Data Discovery & Analysis

• Defining Migration Scope & Strategy

• Manual/RPA-Driven Data Mapping & Transformation

• RPA-Driven Extraction, Loading & Testing

• Manual Data Quality Verification & Validation

The Data Migration Journey

Introducing Agentic AI

How we used in D365 Migration

AI Driven Processes

AI Agents in Profiling

AI Agents in Mapping

Data Migration Best Practices

Establish a Data Migration Strategy

Develop a comprehensive plan that outlines the project scope, timelines, roles and responsibilities, and key milestones.

Assess Data Quality and Integrity

Thoroughly analyze the source data to identify any issues, inconsistencies, or missing information that need to be addressed before the migration.

Design a Robust Data Mapping Process

Carefully map the fields and relationships between the source and target systems to ensure accurate data transformation and preservation of data context.

Implement Data Validation and Testing

Establish a rigorous testing process to validate the accuracy and completeness of the migrated data, including end-to-end testing and user acceptance testing.

Ensure Data Security and Compliance

Implement appropriate security measures and adhere to regulatory requirements to protect sensitive data throughout the migration process.

Develop a Detailed Backup and Recovery Plan

Create a comprehensive backup strategy to safeguard the data and enable a seamless rollback in case of any issues during the migration.

Provide Comprehensive User Training

Educate end-users on the changes, new processes, and any system updates to facilitate a smooth transition and adoption of the migrated data.