Scalable Business Model Case Study
Modernizing Data Strategy for the Cloud
Sparkfish partnered with a healthcare startup to revolutionize its SaaS-based EHR software for ambulatory care specialty clinics. Through strategic architectural and management consulting, we transformed the client’s data strategy and development processes, reducing costs by 50% while improving scalability and data management capabilities.
The Problem
Our client, a promising healthcare startup, had developed robust SaaS-based electronic health records (EHR) software for ambulatory care specialty clinics. However, they were struggling with unsustainably high development costs that threatened their growth and scalability. The root causes included outdated approaches to data processing, monolithic architecture not designed for the cloud, inefficient development, and communication gaps between technical and non-technical teams.
The client’s data architecture was particularly problematic, as it was built on legacy systems that were difficult to maintain and scale. This not only hindered their ability to onboard new customers efficiently but also limited their capacity to provide meaningful data insights to their users.
Our Solution
Sparkfish implemented a comprehensive data strategy that addressed both technical and managerial challenges. We initiated a gradual transition from the monolithic architecture to a cloud-aware microservice design, focusing on modernizing their data processing. This allowed for more efficient data management, improved scalability, and enhanced reporting capabilities.
We also helped them diagnose and resolve key agile development missteps by streamlining communication processes between stakeholders, management, and the development team. By reorganizing teams and establishing clear lines of communication, we created a more efficient development lifecycle that reduced unnecessary bloat and accelerated the delivery of new features and improvements that complied with properly implemented data governance.
Challenges
- Lack of Comprehensive Data Strategy: The existing system lacked support for reporting needs, was not designed for change tracking, and suffered from continuous loss of historical data during updates
- System Inflexibility: The system’s rigidity made introducing new functionality extremely challenging, with resources constantly being directed toward maintenance and stability issues rather than innovation
- Inefficient Data Architecture: The existing data architecture was so inefficient that reporting and analytics capabilities required a complete redesign to meet business needs
- Infrastructure Limitations: The legacy on-premise hosting led to constant service disruptions, highlighting the absence of a modern cloud-based infrastructure
- Communication Issues: Significant communication gaps resulted in frequent misunderstandings, exacerbated by poor coordination between clinical experts and technical implementation teams.
Benefits
- Improved Data Management: The solution enhanced reporting capabilities and increased value for end users through better data handling and analysis.
- Robust Data Strategy Implementation: A comprehensive data strategy was put in place, enabling the production of accurate and timely reports while tracking data changes over time.
- Enhanced Scalability and Modernized Architecture: The gradual transition from a monolithic structure to a cloud-aware microservice design resulted in more efficient data processing, improved system flexibility, easier onboarding of new clinics, and better support for the SaaS model, significantly enhancing overall scalability.
- Accelerated Development Cycle: The streamlined processes resulted in faster implementation of new features and updates, significantly reducing time-to-market.
- Significant Cost Reduction: Development costs were reduced by more than 50% through improved processes and architecture, leading to more efficient and streamlined development teams.
- Improved Team Dynamics: Communication processes between stakeholders, management, and the development team were streamlined, with reorganized teams operating under clear lines of communication.
- Enhanced Development Lifecycle: The new approach reduced unnecessary bloat, accelerated the delivery of new features and improvements, and ensured better compliance with data governance standards.
