Enterprise / Data & Analytics
Scalable Data Architecture for Multi-Acquisition Integration
Unifying fragmented systems from multiple acquisitions into one transparent, real-time analytics framework.
Problem Statement
Client's Problem
A US-based enterprise managing multiple acquisitions needed a scalable data architecture to unify disparate systems and give leadership transparent analytics across every acquired entity.
Technical Limitations
Each newly acquired company brought its own fragmented data systems, with inconsistent data models and reporting layers that didn't line up with each other. Integration timelines were slow and costly, and there was no way to get unified, real-time business insights across the group as a whole.
Key Challenges
- Fragmented data systems across newly acquired companies
- Inconsistent data models and reporting layers
- High operational costs and delayed integration timelines
- Need for unified, real-time business insights
Technology Stack
Platforms: Snowflake, Google BigQuery
ETL: Python
BI Tools: Looker, Domo
Cloud: GCP
Business Impact
Data consistency across entities improved significantly, with reporting now faster and more accurate through automated pipelines. Operational costs came down through optimized cloud-based solutions, and the client now has a modular framework in place to onboard data from future acquisitions without starting from scratch each time.
What Our Clients Say
"Arroact gave us a data architecture that actually scales with how we grow. Every acquisition used to mean months of untangling systems now we have a modular framework that gets new entities integrated fast, with reporting we can trust from day one."