DATA MIGRATION SERVICES
Due to the ever-changing business environment demanding technology refreshes, data migration services and tools are becoming more important to organizations. As their software applications continue to transform and evolve over time, their data continues to grow. Retaining redundant applications is similar to buying a new house before you’ve sold your old house, and you are paying two mortgages at the same time. If we add the facts that there is now simply less tolerance for downtime, and the much higher price tag associated with lengthy system conversions, the need for sophisticated data migration services becomes clear.
BayaTree has developed data migration expertise over a number of successful customer projects. We have put that expertise to work in creating robust tools and utilities that support every step of the data migration process. Our techniques are specifically designed to reduce risks, minimize common errors, and lower the overall cost of data migration projects. They include data conversion and mapping, and the ability to immediately migrate data across major hardware vendors and different disk capacities without application disruption.
We offer a best-practices implementation methodology specifically designed for data migration projects. This methodology encapsulates techniques that boost migration efficiency while reducing risks.
Introducing data into the migration process as soon as possible has several advantages.
We recommend an iterative approach to data migration. Traditionally, data migration has been conducted using a “big bang” approach. This often results in last-minute business and technical issues, which cause major delays in overall project implementation. An iterative approach allows for continuous tuning of the migrated data, until all issues are thoroughly addressed.
Use Real Production Data
We have developed a method for using production data that does not adversely impact production system performance. As a result, each migration iteration yields immediate benefits to the end-users. The results are more meaningful than if the process were undertaken with test or “dummy” data.