5 tips for successful data migration
Every data migration is unique and complex. With several influencing factors including varying data formats, sizes and number of source systems to the type of environment and the impact of inflight transactions, there’s a lot that can go wrong.
Research shows that only 36% of data migration projects keep to the forecasted budget (Forbes, 2021). Furthermore, many data migration projects fail to deliver on their objectives, often due to poor planning, limited resources, and ineffective communication. The fallout can be significant, including lost revenue, reduced productivity, and regulatory fines.
These setbacks don’t just affect the project itself; they can also hinder broader digital transformation efforts and reduce the value delivered to customers and stakeholders.
So, where to start with data migration?
Planning is key. Evinact has developed a five-point checklist to assist businesses with planning for successful data migration.
#1 – Scope
Before engaging a migration partner, businesses should have a clear understanding of the migration scope. This should include:
- Source systems: The environments from which data will be extracted, including data formats, metadata, audit trails, workflows, and the overall data model. It should also cover an estimate of the volume of transactions to be migrated and an initial strategy for handling any unmigrated data.
- Target systems: The required data inputs, the target data model, key data relationships, and the business processes that will be supported in the new environment.
Having a high-level understanding of the scope, approach, timelines, and business requirements will form a strong foundation for developing a detailed and effective data migration plan.
#2 – Planning
A detailed Data Migration Plan should be developed. The migration plan should include:
- A detailed analysis of the source to target environments – identification of significant data transformations.
- A migration strategy including timelines, cutover, roles and responsibilities and impact analysis.
- Data archiving and decommissioning strategy.
- Assumptions, constraints and known risks / risk mitigation.
