From scattered reporting to a more structural setup
As reporting needs continued to grow, Pameijer saw the need for a setup that would scale more effectively over time. In the existing environment, some transformations had ended up close to the reporting layer, directly inside Power BI. That works well in the short term, but it also makes it harder to maintain consistency and scale the environment as demand increases.
The ambition therefore became broader than solving today’s reporting needs. Pameijer wanted to build an environment that also supports self-service and creates the right basis for future AI applications.
A platform that fits Pameijer’s situation
Pameijer already operates fully within the Microsoft ecosystem. Fabric brings storage via OneLake, pipelines, modelling and Power BI reporting together in a single platform, which makes it a logical choice for an organization with this starting point.
In practice, data from different source systems comes together in Fabric. This includes administrative systems such as AFAS, client-related data, personnel data, billing data and financial data flows that have to align with multiple frameworks within healthcare. From that base, tables are modelled and made available for reporting and for teams that want to work with data more directly themselves.
Where architecture meets day-to-day practice
Fabric offers a wide range of options, and that is exactly what makes the setup important. The first months of the project focused on architectural choices and data modelling. How should the layers be structured so the environment remains reliable and maintainable? Where do transformations belong? And how do you prevent complexity from building up in the reporting layer over time?
CI/CD is a second area of focus. Together, Pameijer and Itility are introducing ways of working with version control, code reviews and quality safeguards that fit the scale and maturity the organization is aiming for. That matters, because architecture only creates value when it works not just in theory, but in daily practice.
The foundation that comes before AI
Pameijer’s AI ambitions are clear. AI tools can help users work more independently with reports, while AI-driven applications may eventually support employees in answering recurring questions faster and more consistently. That only works when the underlying data is reliable. If a chatbot or assistant returns incorrect figures, trust in the system quickly disappears. That is why the focus is on the foundation first. The next AI steps come on top of a base that can support them properly.
A working foundation, with the future in mind
With this approach, Pameijer is building towards even more consistent reporting, less manual work, more room for self-service and a stronger starting point for AI applications that add value in practice.
The gain is not in Fabric alone, but in the choices around it: how data is structured, how teams work with it and how the environment is designed to remain usable and maintainable over time. Ultimately, that creates better support for employees and more room to focus on what matters most: delivering good care to clients.
