Data migration is essential in merger and acquisition (M&A) projects. When merging data landscapes, organizations often face challenges, including understanding the impact of the merger on their entire data landscape, dealing with different ways of measuring and calculating data, and overcoming cultural and language barriers.
A well-chosen data migration solution addresses these challenges by simplifying connectivity, supporting data transformation, and ensuring data resiliency. By leveraging standardized drivers, organizations can efficiently and securely migrate data, enhance agility, and reduce complexity. Early in the M&A process, it’s crucial to establish a thorough overview of the data landscape, including all its integrations, and ensure the final landscape adheres to a single type of logic.
During an M&A project, organizations might encounter surprises such as bad or outdated data, undocumented data relations, or other unexpected issues. To successfully merge landscapes, it’s essential to map individual data environments, understand the elements affected by the migration, and identify necessary changes for proper integration.
Automated solutions can help to quickly identify dependencies and reduce manual errors. The ROI in migrating data during a merger or acquisition project can be split between direct savings, such as reduced software and manpower costs, and indirect savings, including improved data quality.
To maximize ROI and project success, organizations should automate the mapping of current data landscapes and employ a documentation process that is up-to-date. Prioritizing data-driven decision-making and automation can help address the challenges that arise during data migration projects in mergers and acquisitions, ultimately leading to a more streamlined and successful integration of data landscapes.