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What You'll Learn
Have you been thinking about any one of these? Moving from legacy systems to modern cloud platforms or consolidating fragmented data across business departments? Data migration is a critical initiative that can make or break your business transformation. And that’s exactly why you need a structured data migration project plan for your organization. It doesn’t matter whether you’re migrating to Snowflake, Salesforce, Informatica, Reltio, or other cloud platforms – you will require the right data migration checklist to successfully transition your data. In this guide, we break down the key phases and best practices to help you plan your next data migration move with confidence.
What is a Data Migration Project Plan?
A data migration project plan is a strategic document that outlines all the important elements involved in transferring data from one system or environment to another, safely and accurately. The elements are the necessary migration steps, migration timelines, data migration tools to be used, and roles and responsibilities for the data migration to take place effectively. If you are thinking of data migration as a technical task, you are mistaken. You can consider it a cross-functional effort. It requires business alignment, governance, and validation.
Your organization needs a structured data migration strategy if you are facing the following during your data migration process:
- Downtime or operational disruption
- Data inconsistencies or loss
- Regulatory non-compliance
- Stakeholder misalignment
When you follow a structured data migration approach, these risks are easily mitigated. All transitions such as migrating to a cloud data platform like Snowflake or shifting master data to an MDM platform like Informatica can be done smoothly and effectively.
6 Phases of a Successful Data Migration Project Plan
Let’s walk through each phase of a typical data migration lifecycle. In this section, you’ll find everything you might consider including in your data migration project plan.
#Phase 1 : Data Migration Project Initiation & Planning
Remember one thing – the foundation of your project is clarity! In this phase, you define the following:
- Business goals – Understand the “why” behind your project – do you want to just get rid of legacy systems or want to improve data access for your business teams or want to achieve some other goal?
- Source and target systems – Jot down your source and target systems for the migration project. For instance, On-prem would be source and Cloud platform would be destination.
- Migration type: Decide what type of migration plan you want to move ahead with. Do you want to do a full migration or hybrid migration?
One tip from our side is to involve data owners and business stakeholders early to prevent any friction later.
#Phase 2 : Data Discovery & Data Profiling
Before moving your data, you must understand what you’re working with.
- Data profiling: Assess data quality. Check for completeness of data. There should not be any data inconsistencies.
- Data classification: Tag all your sensitive or critical data elements.
- Metadata capture: Understand data lineage, how different data points are connected to each other.
This step is critical. It provides you with a fair idea about your data transformation needs, data cleansing priorities, and mapping logic.
#Phase 3 : Design & Data Mapping
This is the architectural phase of your data migration plan. This would have activities such as:
- Target schema design: Ensure it supports current and future business needs.
- Data mapping: Define source-to-target field relationships.
- Tool selection: Here you decide what data migration tools you plan to leverage. ETL tools, data integration platforms, or some other cloud-native capabilities.
#Phase 4 : Data Cleansing & Data Validation
Migrating bad quality data will only mean the failure of your data migration project. Data migration success is dependent on the quality of the data being moved. Hence, we recommend you to:
- Clean data: Remove duplicates. Fix all your data errors. And don’t forget to standardize data formats
- Ensure business rule validation: Apply logic relevant to finance, sales, HR, and other units.
- Get stakeholder approval: Confirm that the cleansed data meets your business expectations.
You can use tools like Informatica or dbt. They can help automate data cleansing and data transformation pipelines.
#Phase 5 : Project Execution & Testing
This is the phase where your plan becomes reality.
- Pilot migration: Migrate a small data set for testing.
- Full-scale migration: Execute your plan based on phased approach or a big-bang cutover.
- System integration: Verify if APIs, workflows, and dependencies work as they should.
- Data reconciliation testing: Compare pre/post-migration values for data accuracy.
#Phase 6 : Go-Live & Post-Migration Support
If you think migration ends with data transfer – you are wrong! The final phase includes:
- User acceptance testing (UAT)
- Performance monitoring
- Data backup validation
- Post-migration audit
- User training & documentation
Data Migration Checklist for You
Here’s a quick data migration checklist you can use to audit and successfully implement your plan:
Your Data Migration Project Plan Will Fail if You Avoid These Common Pitfalls:
-
You underestimate data complexity
- Legacy systems may hold undocumented fields or business logic. -
You skip data migration test cycles
- Migrating without iterative testing leads to rework and downtime. -
There’s poor communication between your teams
- IT, business, and compliance teams must be aligned throughout. -
You do not plan for downtime
- Especially critical for operational systems and customer-facing platforms.
| Data Migration Phase | What You Need to Do |
|---|---|
| Planning Phase | Decide the scope of your data migration project plan. Discuss roles, budget, and project completion timeline. |
| Discovery Phase | Profile your source data. Assess data quality and map sensitive data. |
| Design Phase | Design target schema. Document data mapping and choose your data migration tools for the project plan. |
| Cleansing Phase | This is where you standardize data formats, perform data de-duplication, and conduct data validation against business rules. |
| Execution Phase | Run pilots. Execute your data migration process and test for data integrity. |
| Go-Live Phase | Here you conduct UAT and monitor and document data migrate issues, if any. |
What Tools Can You Use for Enterprise Data Migration?
There are many on-premises data migration tools, cloud-based data migration tools, and open-source data migration tools that you can include in your data migration project plan. Some of these are Informatica, Fivetran, Snowflake Snowpipe, AWS database migration service, & more.
Use Informatica Cloud Data Integration for data mapping and ETL. Snowflake and Snowpipe are good options for scalable data warehousing, and Fivetran provides you with prebuilt connectors for fast data migration. You can also leverage dbt for effective data transformation.
LumenData’s Approach to Data Migration
At LumenData, we help enterprises navigate complex data migrations. We do it all – data migrations from legacy ERPs, cloud CRMs, or third-party applications. Our approach is a mix of data engineering, data governance frameworks, and AI-driven validation tools to ensure speed, scalability, and compliance.
We specialize in:
- On-premises/legacy to cloud migrations
- Cloud-to-cloud migrations
- Master Data Management migration to Informatica SaaS MDM, Reltio, & more
- Data quality checks and pre- and post-data migration support
- Change management and platform training
Ready to get started? Contact us to explore how we can help you plan your next project and get a free data migration ROI assessment for your organization.
About LumenData
LumenData is a leading provider of Enterprise Data Management, Cloud and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
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