Data Analytics
A leading global provider of advanced analytics, technology solutions, and contract research services in the life sciences industry, engaged Green Leaf to modernize and scale its Power BI data platform.
The client serves as a central clearing house for prescription and medical data, information used by pharmaceutical manufacturers, providers, insurance companies, and other healthcare organizations to guide commercial strategy, product development, and operational decisions.
To continue delivering high-value insights at scale, the client partnered with Green Leaf to design a scalable architecture capable of distributing key sales, prescription, and claims datasets through prebuilt Power BI reports for users spanning hundreds of customer organizations.
The client needed to scale Power BI to thousands of users while managing hundreds of customized datasets and reports, without driving up cost, complexity, or manual effort. They lacked the expertise, architecture, governance, and automation needed to deploy and maintain this environment efficiently.
Green Leaf designed a scalable Snowflake + Power BI Embedded Analytics architecture with dynamic report binding, Fabric Capacity scaling and optimization, and extensive automation through custom PowerShell scripts; eliminating report duplications and reducing operational overhead, essentially keeping the cost of operation at a minimum.
The new architecture will substantially reduce the customization and manual intervention required of their current solution. This will enable the client to operate their Data-as-a-Service platform more efficiently, accurately measure cost-per-customer, and support large-scale growth with minimal manual intervention.
While the client had existing Power BI reports, they needed a way to scale the platform to thousands of users in a cost-efficient, high-performing manner, without custom-building and maintaining separate reports for every customer. They engaged Green Leaf to define the architecture, governance model, and deployment framework required to optimize Fabric and Power BI capacity and operational costs.
The client also needed a scalable way to deploy and manage hundreds of Power BI semantic models across their customer base while also ensuring full isolation of data from one customer to another. Each of their customers received contracted datasets and Power BI reports that must remain continuously available and refreshed within strict SLAs based on source-data availability. The high degree of customization offered to each customer created significant manual overhead and made their existing operating model difficult to sustain at scale.
Green Leaf deployed a team, including a project manager, Power BI architect, and Snowflake architect, to design a scalable, cost-efficient architecture for the client’s Power BI Data-as-a-Service platform.
We began by gathering requirements, reviewing existing documentation, and aligning on the needs of the new system. The solution was built around a clear model: each end customer receives its own Snowflake database for raw and value-add datasets, and its own dedicated Power BI Workspace to ensure data isolation. Power BI semantic models fully import data from Snowflake, reducing Snowflake workload and runtime cost.
Reports are delivered through the client’s customer portal using Power BI Embedded Analytics (app-owns-data). This allows the client to control every aspect of the user consumption experience and eliminates the need for any customer users to have Power BI licenses whatsoever.
To avoid maintaining hundreds of duplicated Power BI reports, Green Leaf recommended Dynamic Report Binding, enabling a single report design to be reused across many customers by dynamically pointing it to the correct dataset at runtime.
The new platform leverages Fabric Capacities to host all Power BI artifacts, allowing controlled resource allocation and consumption. With predictable usage patterns, short weekly usage spikes, the client can scale capacities up before peak periods and down afterward to right-size computing power to meet demand and tightly manage costs.
Given the large scale (hundreds of customers, thousands of semantic models and reports), automation was critical. Green Leaf created comprehensive PowerShell scripts to automate tasks including:
Our team also validated approaches through discussions with Snowflake engineers, Microsoft documentation, experimentation, and best practices, and built a custom Fabric Cost Estimator tool in Power BI to help the client forecast capacity needs and cost impacts.
Green Leaf’s work enabled the client to clearly define their data-as-service solution’s technical requirements and future-state architecture. Through our questions and discovery, we helped refine their goals and expand their understanding of Power BI, Fabric, and the operational levers available to them.
The recommended architecture provides a scalable, cost-aware framework capable of supporting hundreds of customers, with clear controls for capacity planning and cost-per-customer measurement, enabling more accurate, data-driven contract and pricing decisions.
The automation framework significantly reduces manual overhead, allowing the customer to manage thousands of Power BI and Snowflake assets with consistent, repeatable processes. Early performance testing demonstrated PowerShell-based capacity operations (scale up/down, pause/resume) executing in under 2 minutes.
Beyond the technical outcomes, the project gave the client clearer visibility into feature limitations, workarounds, and best practices—equipping them with the knowledge and tools required to run their new solution efficiently at scale.
Overall, the project was a success, resulting in:
Are you a similar organization seeking to enhance your data infrastructure? Discover how Green Leaf Consulting Group can assist you in improving operational efficiency and making informed decisions with real-time data insights. Contact Neil McCole at neilm@greenleafgrp.com or (484) 535-4405.