Streamlining M&A Data Migration
A rapidly growing organization that had recently received a big infusion from private equity (PE) had plans to acquire more than 20 companies in a single year. To get there, the organization needed support to not only execute the transactions and the data migrations and integrations, but just as importantly, to develop scalable processes and modern techniques that would equip the company with the ability to perform these integrations in a timely manner. To get there, Clarkston simultaneously completed three unique and highly complex M&A data migrations and integrations, supporting integration and targeted data cleanup from acquirees with unsophisticated systems to the end state technology ecosystem.
The M&A Data Migration team was interested in finding opportunities to streamline and simplify the current migration processes with the goal of reducing the complexity and timeline for the targeted 20+ acquisitions that would require data migrations during the first year alone. This project was necessary to migrate clean data efficiently and improve the existing process by removing bottlenecks from the migration solution.
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Given that each migration was unique, Clarkston worked alongside the Clarkston Technology Solutions (CTS) team to deploy more than a dozen resources to understand the nuances across each of the customer data integrations to estimate and build data migration flows that properly cleaned, transformed, and enriched the data prior to migrating to the end-state data solution. Once the data flows were set up for the migration, further testing and validation showed traces of unclean data across inherently more complex acquisitions that required one-off alterations to the data – an atypical approach.
These complex migrations moved the client from an archaic excel based system to considering innovative solutions that reached the respective business and technology partners, yielding three successful data acquisitions.
Throughout the project, the team noted opportunities for improvement beyond the standard migration path previously taken. A more streamlined approach with fewer data versions, reviews, and approvals was identified as a key component to reducing the overall migration timelines.
