The largest integrated healthcare company in the United States, spanning pharmacy, health insurance, and retail clinics, was running its analytics on a legacy platform that could no longer keep up with the organization. A project it knew would be complex given the scale of data flowing through an organization of its size. The original estimate for the effort was three years and $30 million.
A year into that engagement, our client had already spent $10 million. And yet, not a single table had been migrated. For an organization of this scale, that wasn’t just a missed milestone, it was a signal that the approach itself wasn’t working. Every month of delay meant continued reliance on aging infrastructure, mounting costs with nothing to show for them, and a growing gap between the modernization the business needed and the modernization it was getting. The company needed more than a course correction. It needed a trusted partner who could step into a stalled, high-stakes project, rebuild confidence in the approach, and actually deliver, within budget, what a year of effort hadn’t produced.
Ascendion used its AAVA Data Modernization Studio to migrate the client’s legacy scripts to a modern cloud data warehouse through low-code automation, replacing the slow, manual, script-by-script approach a migration at this scale would otherwise demand. Docker images enabled automated deployment, streamlining operations across the migration.
The work went beyond moving data across. Datasets and labels were parameterized, and table partitioning and clustering were implemented for performance tuning, so the new platform was faster than the one it replaced rather than simply newer. Ascendion then developed and deployed lead-scoring models integrated with Python, putting the predictive capability the legacy stack couldn’t support directly into the modernized platform.
The migration was completed in 9 months for $9 million, 70% below the original estimate.
Tech Stack:
Delivered 60% TCO savings by moving to a modern cloud data platform.
Empowered 4,000+ engineers and data scientists with a scalable data platform.
Saved 5,000+ hours annually through automation and performance improvements.
Completed a legacy-to-cloud migration in 9 months for $9M, against a $30M estimate from a leading global consulting firm, 70% lower cost.
