How Did a POS SaaS Giant Unify Data From 20+ Acquisitions to Unlock Business Intelligence at Scale?
This case study explores how a leading POS SaaS company, operating across 15+ industries and supporting over 10,000 businesses, faced a critical data challenge following the acquisition of 20+ other POS platforms. Each acquired system brought its own tools, structures, and data silos, creating a fragmented analytics landscape that limited visibility and slowed decision-making.
To support strategic growth, stakeholders needed a unified solution that could integrate and normalize data across all platforms. Our objective was to build a scalable analytics infrastructure capable of consolidating this complexity and delivering real-time, actionable insights to drive planning, performance tracking, and executive alignment.
Key Results
The Challenge
The client's rapid expansion through acquisitions left them with siloed, inconsistent data systems across sales, marketing, payments, and customer experience:
- Over 20 acquired POS platforms, each with unique data models and tools
- Redshift Spectrum was underperforming at scale
- Lack of transformation layer caused inconsistencies and poor data quality
- Power BI environment had limitations with dashboard governance and scaling
- Stakeholders lacked visibility into key metrics across finance, CX, and marketing
- No unified view for strategic planning and operational oversight
Our Solution
We executed a full-stack analytics modernization by unifying data ingestion, transformation, and visualization under a governed and scalable architecture:
- Migrated data warehouse from Redshift Spectrum to Databricks for better scalability and processing power
- Developed custom PySpark extractors for Salesforce, Stripe, Google Ads, Adyen, and Amazon S3
- Implemented task dependency-driven workflows for daily data syncs and extraction reliability
- Used DBT Core for all transformations, cleaning raw data and implementing Kimball dimensional modeling
- Rebuilt BI layer using Tableau, delivering 80+ dashboards across functional domains
- Enabled advanced drill-down and filtering for granular analysis by business unit and vertical
Implementation Process
Architecture Redesign
Planned and deployed a modern data platform architecture using Databricks and DBT
Custom Data Extraction
Built PySpark pipelines for key sources: Salesforce, Stripe, Adyen, Google Ads, and S3
Transformation Layer
Implemented business logic and cleaned data using DBT Core with Kimball modeling
Migration & QA
Migrated Redshift Spectrum to Databricks, ensuring accuracy and performance gains
Dashboard Design & Development
Developed 80+ Tableau dashboards across Sales, Marketing, Finance, Payments, CX, and Onboarding
Stakeholder Enablement & Rollout
Trained internal teams, conducted feedback sessions, and rolled out role-specific views
Technologies Used
"We finally have a unified lens on our entire portfolio that brings 20+ systems, thousands of clients, and complex payments data into one place. This transformation didn’t just clean up our data; it changed how we run the business."
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