Data & Analytics — Fabric & Power BI

Building a scalable analytics platform with Microsoft Fabric

Designing and implementing an end-to-end Microsoft Fabric and Power BI platform to support secure, high-performance analytics across a network of hundreds of pharmacies.

of pharmacies analysed on one platform: 100s
of records processed: 10s of millions
governed environments (Dev / UAT / Prod): 3

The challenge

Navi Group is a leading provider of technology and services to community pharmacies in Ireland, with a network spanning hundreds of pharmacies. Its portfolio includes DispenSense dispensary software, TillSense for point-of-sale, and a growing range of digital services for community pharmacy.

As the volume and breadth of data generated across its technology platforms continued to grow, Navi Group wanted a modern data and analytics architecture capable of supporting its existing scale and future growth.

The platform needed to bring together data from multiple sources, reliably process large volumes of information, provide secure access to pharmacy data and deliver responsive Power BI reporting over large, complex datasets — while giving Navi Group's own analysts and engineers a foundation they could manage and extend.

Our approach

MedModus worked with Navi Group to design and implement an end-to-end analytics platform using Microsoft Fabric and Power BI, establishing Fabric as the central analytical layer between operational systems and reporting.

The architecture combines automated ingestion and transformation, a central Fabric Data Warehouse, Direct Lake and Import-mode semantic modelling, Power BI reporting, security and monitoring. Separate Development, UAT and Production environments provide a controlled development lifecycle.

Rather than designing around individual reports or source systems, the focus was a reusable analytics foundation into which additional data sources and use cases can be incorporated over time.

The solution

Data from Navi Group's operational systems is automatically ingested into Microsoft Fabric using pipelines and dataflows. Large transactional datasets are processed and organised within a Fabric Data Warehouse, with incremental processing used where appropriate to handle new and changing data efficiently.

For high-volume datasets, Direct Lake lets Power BI work efficiently with data held in the Fabric platform, while Import mode is used for smaller datasets. A central semantic model provides consistent relationships, measures and business logic reused across reporting.

Security is implemented using row-level security, so the same platform serves individual pharmacies and pharmacy groups while users only see the information they are entitled to. Automated monitoring and alerting give visibility of data processing, freshness and potential failures, and Fabric capacity monitoring shows platform utilisation as usage grows.

Secure pharmacy benchmarking was a key requirement: pharmacies needed to compare performance against relevant peer groups without access to other pharmacies' underlying data. MedModus separated aggregated peer information from secured pharmacy-level data, so meaningful comparisons sit inside interactive Power BI reporting while security boundaries around commercially sensitive data are maintained.

Development, UAT and Production environments provide a controlled release process, and a GitHub-based CI/CD framework supplies source control and automated deployment between Fabric environments.

Reporting delivered on the platform

Power BI pharmacy dispensing dashboard showing items dispensed, dispensed value and dispensed sales with cumulative volume trends against previous year and network benchmark — A pharmacy-level Power BI report built on the Fabric platform: headline dispensing measures with year-on-year and network comparisons, and cumulative dispensing volume against the previous period and an anonymised network benchmark — peer comparison without exposing any other pharmacy's underlying data.

Outcomes

What changed for the organisation once the platform was live.

Hundreds of pharmacies
supported on one Fabric platform
Tens of millions
of records processed at scale
Central data layer
spanning multiple operational systems
Automated & incremental
data processing
Row-level security
for pharmacy and group analytics
Peer benchmarking
without exposing pharmacy-level data
Monitoring & alerting
for reliable ongoing operation
GitHub CI/CD
for repeatable Fabric deployments

Facing a similar challenge?

We will walk you through how this engagement was delivered and what the equivalent first phase would look like for your organisation.

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