CASE STUDY

Creating Scalable Data Architecture

CLIENT’s Challenge

We helped a Software-as-a-Service (SaaS) provider create a scalable data architecture that improved trust using an automated, repeatable pattern-based methodology.

• Reduce risks in the analytic environment: Rapid organic growth and multiple acquisitions resulted in a fragmented analytic environment. Causing a lack of trust in analytics and insights reported across departments and confusion among executives and decision makers
• Insufficient operational controls, and limited data governance: Data inconsistencies drove operational inefficiencies, limited agreement on key KPIs and the creation of.
• Data is critical to managing important business processes: Quote to Cash; booking, billings, revenue recognition

OUR APPROACH

• Based on the previously delivered multi-year Data Strategy & Roadmap, designed, architected, and engineered the foundation for the new Enterprise Data Ecosystem
• Leveraged existing data stack of Snowflake, dbt and Fivetran to minimize cost and accelerate learning of client development team.
• Leveraged the Data Vault 2.0 methodology to integrate data from multiple transactional and historical data sources into the new ecosystem
• Applied an iterative, data driven approach to data modeling and continuous deployment. Automated the Extract, Transform, and Load processes and to enable continuous deployment.

skills & tech leveraged

Data Vault - Dbt - Fivetrain - Extract, Load, Transformation (ELT)

THE RESULTS

• Implemented historical, integrated, data structures, enabling the board to see how data changed month-to-month
• Enabled parallel processing aligned to the data vault methodology to allow easy scalability
• Leveraged the new platform to provide integrated enterprise data to support revenue recognition processing
• Established a platform to support continuous development by using agile sprints
• Trained existing staff on technologies and methodologies to empower client to continue the build