Unified a fragmented data ecosystem into an Automated Analytics Platform for the Largest U.S. Scientific Society, enhancing data visibility and trust across the organization

About the client

Largest U.S. Scientific Society

Challenges

• Negative membership growth rate due to limited visibility and trust in enterprise data.
• Fragmented data across multiple silos, leading to cumbersome and manual reporting and analytics processes.
• High member acquisition costs coupled with elevated churn rates.

Approach

• Discovery & Analysis: Conducted in-depth workshops with stakeholders to map existing data processes, uncover inefficiencies, and identify critical data challenges.
• Design & Strategy: Developed a Federated Data Organization Model to balance centralized governance with flexibility for localized operations.
• Prototyping & Validation: Built proof-of-concept solutions to validate automation workflows and data quality frameworks before enterprise-wide deployment.
• Implementation Roadmap: Created a phased rollout plan for governance structures, automation initiatives, and the data catalog to minimize disruption and ensure adoption.
• Continuous Monitoring: Established KPIs and monitoring systems to track improvements in data quality, integration time, and acquisition rates.

Solution

• Centralized data governance through a federated organizational structure.
• Real-time data quality checks integrated during data creation.
• Automated data operations to reduce manual intervention and improve efficiency.
• Comprehensive data catalog implementation to enhance accessibility and usability of data assets.