Law firm implements the foundation for modern data platform on Microsoft Fabric

Client background

The company is a prominent law firm headquartered in Chicago, Illinois.

The business challenge

Baker Tilly initially conducted a data strategy assessment for the law firm. The organization faced the challenges of siloed datasets and a lack of visibility across their enterprise data. Their mix of cloud and on-premises data services did not meet their needs. Baker Tilly identified a clear path forward to enhance their data management capabilities in support of analytical and AI initiatives. This began by implementing the foundation of a modern data platform on Microsoft Fabric in Azure.

The primary objective was to establish a robust foundation for data ingestion, transformation and modeling to support the evolving analytical requirements of the business. The firm did not want to be locked into thinking about their business in any specific way. Instead, they wanted the flexibility to grow their analytics solution to meet increasing types of data from an increasing number of sources, in order to deliver a holistic understanding of their business operations.

Strategy and solution

In the first phase of the engagement, Baker Tilly collaborated with the client to modernize their data environment and directly support business functions across finance, HR and conflicts. The focus was on improving data access, streamlining reporting and laying the groundwork for future analytics initiatives. 

Key achievements include: 

• Established a modern cloud data platform on Microsoft Fabric: Provided a scalable and secure foundation that supports long-term data strategy and integration with emerging tools like Microsoft Copilot.
• Integrated data from multiple business systems: Unified siloed information from core operational areas, improving transparency across financials, workforce metrics and client matter tracking.
• Developed an enterprise data model for consistent reporting: Enabled one-version-of-truth Power BI dashboards to ensure teams are aligned around accurate, real-time insights.
• Standardized business definitions and metrics: Created clarity across departments, reducing misalignment and making performance discussions more consistent and actionable.
• Automated and accelerated data processing: Reduced manual effort and shortened the reporting cycle, freeing up business teams to focus on analysis and decision-making.
• Implemented DevOps practices to support scalable growth: Established a structured, repeatable process for deploying future enhancements and expanding data capabilities efficiently.

Business outcomes realized: 

• More informed, faster decision-making across finance, HR and conflicts 
• Greater confidence in reporting and performance tracking 
• Streamlined operations and reduced time spent on manual data tasks 
• Improved ability to identify and manage operational risks 
• A strong foundation for future AI-driven insights and self-service analytics 

What’s next: 

Phase two is now underway, focusing on incorporating new data sources (including payroll, HR and marketing), launching self-service tools for business users and building a sustainable data governance framework and analytics center of excellence.