Accurate & reliable Azure Data Platform for STIZON#Data Analytics
STIZON was looking for a future proof, data driven solution which would enable them to process the data extraction from multiple sources in a faster, more accurate, more secure and transparent way to support their business by implementing a new and reliable data platform.
A data-driven platform, build on Microsoft Azure, which enables STIZON to process data extractions of practitioners much faster and more accurate and secure.
STIZON collects medical data from patients to make it available for scientific research, both internally and externally. They receive these data through various channels, mainly from general practitioners but also from hospitals and pharmacies. They then clean, structure and enrich the data with various medical standards and reference sources. The goal is to support the healthcare suppliers with enriched data which can increase the insights in the treatment of patients. For this business model it is crucial for them that they can count on reliable & accurate data while guaranteeing privacy since they are ISO 27001 and NEN 7510 certified.
The primary triggers to start this project was their wish to replace their current legacy system and move from the private cloud to the public cloud to be much more cost efficient. This legacy system was not transparent when it comes to data transformation. It was some sort of a black box. Nobody exactly knew what was happening inside and what kind of transformations were applied.
Currently, the processing of the extractions is done manually by one person. It takes one FTE, about half a month, to collect, unpack, rename, structure, process and archive all extractions.
In short, the current way of working was very time-consuming, error prone, not transparent enough in according to new regulations and certainly not future proof. The solution we came up with a completely new developed data platform on Microsoft Azure brings them the following benefits:
- improved processing time of all data by 200%;
- Data processing time went from 2 weeks to 1 week;
- Reduced need for manual labor by 80%;
- Improved data quality by 60%.
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