Cloud Software Company – Building a Unified Data Platform for Product Adoption, Consumption Analytics, and PE-Driven Carve-Out

Introduction

A fast-growing cloud software business needed a modern analytics foundation to understand customer adoption, product consumption, and revenue performance. At the same time, the company was undergoing a private equity–driven separation from its parent organization, requiring a clean and scalable data environment that could operate independently. With critical data scattered across multiple systems, the business needed a unified platform to support both day-to-day decisions and the upcoming transaction.

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The Problem (Gut-Based Decisions)

Before the engagement, the organization faced significant challenges:

• Customer, product, finance, and marketing data lived in disconnected systems.
• They depended on a centralized SQL Server data warehouse owned by the parent company.
• Important business logic was embedded in legacy SQL stored procedures that were not scalable or easily portable.
• Reporting was inconsistent, slow, and often contradictory.
• Leadership lacked a reliable view of adoption, feature usage, consumption, churn risk, and renewal health.
• Private equity stakeholders needed clean data separation, standardized metrics, and trustworthy analytics to support the transaction.

Critical decisions around customers, product strategy, and revenue forecasting were based on fragmented data rather than unified intelligence.

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The Solution (How Data Changes the Game)

We designed and delivered a modern data ecosystem that established full independence from the parent company while creating a future-ready analytics platform.

1. New Snowflake Enterprise Data Warehouse

• Built a clean, standalone Snowflake environment dedicated to the business.
• Established a layered architecture with source-aligned, integrated, and analytics models.

2. Migration Away From Parent Company Environment

• Extracted all relevant data from the parent’s SQL Server warehouse.
• Converted SQL Server stored procedures into Snowflake SQL, ensuring equivalent logic and improved performance.
• Fully eliminated dependence on shared infrastructure — a key requirement for the private equity carve-out.

3. Modern Data Ingestion via CRMA + ADF

• Used CRM Analytics (CRMA) to reliably extract CRM and custom object data.
• Used Azure Data Factory (ADF) to ingest data from:
  • Custom customer portals
  • Financial systems
  • Marketing platforms
  • Product usage repositories

4. Tableau Analytics Layer

Built executive-grade dashboards covering:

• Product adoption & feature-level usage
• License consumption & expansion signals
• Renewal risk indicators
• Customer segmentation & cohorts
• Revenue and KPI performance

5. Strong Architecture + Iterative Delivery Approach

• Implemented governance, documentation, and data quality processes.
• Released analytics in iterative cycles to deliver value quickly and refine with stakeholder feedback.
• Ensured long-term scalability and clarity through clean architectural patterns.

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Real-World Example (Specific Client Outcomes)

After implementation, the business achieved:

• Complete data independence required for the private equity transaction.
• Migration of hundreds of SQL stored procedures into modern, maintainable Snowflake logic.
• Unified visibility into customer adoption, consumption, and product health.
• Early identification of at-risk accounts and stronger customer success engagement.
• Consistent KPIs and executive dashboards replacing manual spreadsheet patchwork.
• Integrated insights across sales, product, finance, and marketing.
• A trustworthy, scalable data platform that improved both day-to-day execution and investor confidence.

The new analytics ecosystem became a cornerstone of the organization’s growth strategy.

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Conclusion

By replacing a legacy SQL Server dependency with a Snowflake-based enterprise data warehouse, delivering modern data pipelines via CRMA and ADF, and building Tableau analytics on top, the organization transformed its approach to product and customer intelligence. The new platform enabled accurate insights into adoption, churn, and consumption while supporting a private equity carve-out that required clean data separation and strong architectural rigor. With iterative implementation and solid data engineering foundations, the business entered its next phase of growth with clarity and confidence.