Ensuring data architecture is robust, scalable, and efficient
Client
A mental healthcare provider offering a wide range of private psychological therapy and neurodevelopmental services, supporting adults, children and young people who experience emotional and psychological issues.
Background
A healthcare organisation’s existing data architecture was designed to integrate key systems including Proclaim and Dynamics while supporting future integrations. However, as the organisation expanded and additional systems were incorporated, challenges emerged around data accuracy, reliability, and accessibility.
Audacia was engaged to conduct a comprehensive technical review of the architecture, evaluating its suitability for the organisation’s scale and assessing whether the infrastructure could support robust, scalable, and efficient operations moving forward.
Solution
Our review encompassed multiple dimensions of the data ecosystem. We analysed the technology stack, which leverages different tools for data warehousing, data ingestion, and orchestration. While these technologies proved generally fit for purpose, we identified opportunities to optimise resilience and enhance data governance.
The architecture demonstrated strong scalability through cloud-based infrastructure and flexible data ingestion capabilities. However, the reliance on batch processing created latency in reporting, and manual processes in data validation and error handling limited efficiency.
Our assessment revealed opportunities in additional automation tools and advanced data governance. We identified key vulnerabilities around manual processes, inconsistent validation procedures, and coordination challenges with external systems.
A particular focus area was data quality, which emerged as the most significant challenge. The complexity of handling medical data, combined with limitations in input validation at external data sources, created downstream impacts on reporting and operations.
Results
The technical review delivered:
By delivering these recommendations, opportunities and roadmaps were provided to enhance the robustness and efficiency of their data operations while maintaining the security and integrity essential for handling sensitive medical information.
