A state agency governing child services, programs, and policies wanted to modernize their technology and data platforms to better fit their new goals related to enabling more proactive case management. Case management is highly regulated and must maintain compliance with federally mandated timeframes. To improve their processes and gain added insight to facilitate proactive case management, the agency wanted to take their regularly updated raw data from their operational environment and bring desired analysis to life, resulting in more concrete, valuable insights. As the agency worked to transform some of their core agency functions, they faced concerns about whether their existing data architecture could deliver reporting, analytics, and BI in a modern landscape.
To meet their objectives, they decided to use Amazon Web Service (AWS) as their cloud data solution, but they had limited code developer resources skilled on the AWS platform. Their other significant challenge was the significant bottlenecks they faced that were created by process issues and backlogs at different levels of the agency and other departments involved.
The agency reached out to Fusion to assist them in delivering a modern data platform that would integrate seamlessly into their ecosystem and build a foundation for the future.
First, we performed an assessment of the current data landscape and also considered the processes in place to enter and analyze data. This included:
- Identifying child support cases within the state that exceeded federal timeframe compliance rules or 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
Once we completed the assessment, we created a data model design for their immediate use case (enabling proactive case management) that supported BI analysis of current cases and and trends over time and would integrate different subject areas from their core systems. Our team also provided a modern data reference architecture to guide platform migration within the AWS cloud environment.
Our team used standing up native AWS services including:
- Identity and access management (IAM)
- Virtual private cloud (VPC) to launch resources in a logically isolated, defined virtual network
- Lambda, a serverless compute service that runs code in response to events and manages compute resources for the user
- Glue and Glue Catalog to analyze and categorize data during the extract, transform, and load (ETL) process
- S3, Aurora Relational Data Service, and Redshift to establish raw, curated, and enriched environments for the data as it is processed from a transactional arrangement of string inputs into a strongly typed and structured format for reporting
Our team designed data models and built databases in each of the environments and highlighted additional AWS-specific capabilities for future use cases involving volume growth.
By implementing this cloud data solution, 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.