SERVICES
MIGRATION PLANNING
Big data is what drives most modern businesses, and big data never sleeps. That means data integration and data migration need to be well-established, seamless processes โ whether data is migrating from inputs to a data lake, from one repository to another, from a data warehouse to a data mart, or in or through the cloud. Without a competent data migration plan, businesses can run over budget, end up with overwhelming data processes, or find that their data operations are functioning below expectations.
Why a Data Migration Strategy is Important
Regardless of the exact purpose for a data migration, the goal is generally to enhance performance and competitiveness.
But you have to get it right.
Less successful migrations can result in inaccurate data that contains redundancies and unknowns. This can happen even when source data is fully usable and adequate. Further, any issues that did exist in the source data can be amplified when itโs brought into a new, more sophisticated system.
A complete data migration strategy prevents a subpar experience that ends up creating more problems than it solves. Aside from missing deadlines and exceeding budgets, incomplete plans can cause migration projects to fail altogether. In planning and strategizing the work, teams need to give migrations their full attention, rather than making them subordinate to another project with a large scope.
A strategic data migration plan should include consideration of these critical factors
- Knowing the data โ Before migration, source data needs to undergo a complete audit. Unexpected issues can surface if this step is ignored.
- Cleanup โ Once you identify any issues with your source data, they must be resolved. This may require additional software tools and third-party resources because of the scale of the work.
- Maintenance and protection โ Data undergoes degradation after a period of time, making it unreliable. This means there must be controls in place to maintain data quality.
- Governance โ Tracking and reporting on data quality is important because it enables a better understanding of data integrity. The processes and tools used to produce this information should be highly usable and automate functions where possible.