1. Demand for unlimited scaling possibilities
The company had previously relied on desktop-based and on-premise software for its developments and internal operations.
However, when the client sought to create a robust product that combined the features of a healthcare CRM with a Business Intelligence (BI) platform, it became clear that their traditional methods would fall short.
While the product was initially designed to manage data from just a few organizations, it was envisioned to eventually handle terabytes of data. The need for rapid scalability was a significant consideration in selecting the appropriate technical solution.
2. Utmost security for medical data
Managing both personal and medical patient data requires the maximum security, especially when safeguarding the company's proprietary information. In a cloud-based, multi-tenant architecture, where our Client offers the software to multiple distinct organizations, it's critical to ensure that the data of one organization remains isolated from others.
Failure to do so can lead to data breaches, regulatory infractions, operational challenges, and diminished trust from clients. Addressing this concern at the architectural level is of paramount importance.
3. Customized solutions for each tenant take too long
For a clear and intuitive presentation of patient data, each organization (or "tenant" from a technical standpoint) receives a set of Business Intelligence dashboards tailored to their needs. But requirements differ among tenants. For instance, one might manage data for 100 patients, while another oversees 5,000. Each client has a unique set and format of data as well.
Our client faced the challenge of efficiently crafting a unique solution for every tenant. Creating a single dashboard tailored to specific requirements could take 2 weeks to 1 month. If a client requires 20 distinct BI dashboards (such as one for diabetes analytics and another for cancer risk metrics), the cumulative period could extend from 40 to 80 weeks, or over a year.
Additionally, this approach requires a significant workforce, introducing the risk of human error — a significant concern when handling sensitive data.