CASE STUDY

Creating Scalable Data Architecture

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

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.

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

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