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Refactoring: from a data warehouse built on SQL Server to AWS services: more quality for only 20% of the costs
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Jeugdgezondheidszorg (JGZ) is a division of ZuidZorg, an extramural care provider in Southeast Brabant. With over 150 employees and a few dozen practices in the region, JGZ offers children between the age of 0 and 3 years old first-line care such as vaccination, monitoring and support in collaboration with other healthcare providers.
In the past, all the divisions of ZuidZorg used a common data warehouse. In the course of 2018 Wijkzorg, ZuidZorg’s largest division, decided to switch to a different electronic health records system. This system also furnished the management information for the Wijkzorg, forcing JGZ to bear the full costs of the legacy data warehouse. JGZ could not shoulder these costs on its own.
The legacy data warehouse was built on top of Microsoft SQL Server. Data was collected from multiple sources and was converted into essential management information. Over the years, the functionality had expanded but maintainability and manageability had received too little attention. The consequences were fragility, poor performance and low data quality. As a result, it became increasingly difficult and costly to realize the periodic adjustments that are necessary in this sector.
Based on all these considerations, the JGZ concluded that the best way forward was to build a new data ETL process, a new data warehouse and new reports from scratch.
The new data warehouse was built in a few months. The ETL process was designed from scratch using AWS tools such as Glue, Dynamo DB and S3. The data warehouse was built in Redshift and the dashboards built with Quicksight.
With these AWS Platform-as-a-Service (PaaS) components, reliability, flexibility and scalability have been greatly increased. The security capabilities of AWS have been combined with tightened access management via Okta, which has greatly improved data protection and compliance with strict healthcare regulations.
Next to these tools, JGZ also uses a Cambrian tool to manage data quality. The tool evaluates the data that are processed during the ETL import and collects all anomalies in exception reports. By using our tool, users correct wrong or missing data in the source systems, exempt acceptable exceptions from future reports and identify user training needs.
The total annual operational costs – consisting of licenses, hosting and systems management – have been reduced by 80%. The investment in the transition was paid back in less than one year.