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A state agency governing child services, programs, and policies was undergoing a significant transition and wanted to modernize their technology and data platforms to better fit their new goals. As the agency worked to transform some of their core agency functions, questions arose regarding their data landscape, specifically surrounding architecture for delivering traditional reporting and BI and analytics in a modern landscape.  

The agency was focused on one primary business case — enabling more proactive case management. Case management impacts stakeholders statewide and is highly regulated compliance with federally mandated timeframes. Their goal was to take their regularly updated raw data from their operational environment and bring desired analysis to life, resulting in insights they could use.  

The agency decided that they wanted to use Amazon Web Service (AWS) as a platform, but they had limited code developer resources skilled on the AWS platform. They were also dealing with significant bottlenecks created by process issues and backlogs at different levels of the agency and other departments involved.  

Fusion collaborated with Amazon to help the agency envision and showcase the capabilities of AWS to deliver a modern data platform that would integrate seamlessly into their ecosystem and build a foundation for the future.  



First, we completed an assessment of the current data landscape and processes to enter and analyze data. This included:

  • Identifying child support cases within the state that had exceeded federal timeframe compliance rules and cases that were approaching the federal deadlines  
  • Pinpointing bottlenecks in processing at the agency and caseworker levels that were leading to compliance issues  
  • Understanding where and how data was currently being entered and utilized throughout the agency  

We created a data model design that supported BI analysis of current cases and an analysis of trends over time. Given the limited developer resources for AWS within the agency, they needed a solution that would allow them to transition their current skills and extend additional skills capabilities effectively.

Based on the model design for their immediate use case (case management), we architected a solution to integrate different subject areas from their core systems and provided a modern data reference architecture to guide platform migration — all implemented in the AWS cloud environment.

The solution included standing up native AWS services including (IAM, VPC, S3, Lambda, Glue, Glue Catalog, Aurora Relational Data Service (RDS), Redshift, and QuickSight) to:

  • Provide an S3 landing zone for the semi-structured extracts of full and delta data from their operational systems
  • Establish raw (S3), curated (RDS Aurora-PostgreSQL), and enriched (Redshift) environments for the data as it is processed from a transactional arrangement of string inputs into a strongly typed and structured format for reporting
  • Design data models and build databases in each of the environments
  • Demonstrate additional AWS-specific capabilities for future use cases involving volume growth


This state agency can now produce a federal timeframe compliance report that gives them detailed information about cases that are out of compliance or nearing their compliance deadlines, allowing caseworkers to focus and resolve the issues quickly. The data was brought to life with a series of ideations, visualizing the defined metrics across a host of relevant dimensions, including maps of the state that showed counties with compliance issues and drill-down options that showed cases requiring immediate attention.  

The data architecture also allowed the agency to consider other BI technologies, such as Tableau, for future use cases and extend the architecture to support data growth and use cases at scale. 

Increased compliance with federal regulations
Increased compliance with federal regulations
Identified counties with high rates of incompliance
Identified counties with high rates of incompliance
Ability to scale data environment for growth and additional use cases
Ability to scale data environment for growth and additional use cases

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