Case Study
Data & Analytics
How a professional services firm transformed scattered data into predictive insights that drive strategic decisions.
The Challenge
A mid-sized professional services firm had data everywhere—CRM, project management tools, time tracking, financial systems, HR platforms—but no coherent view of their business. Every leadership meeting devolved into debates about whose numbers were correct. Strategic decisions were based on gut feeling because reliable data took weeks to compile.
The finance team spent days each month manually reconciling data between systems. The sales team couldn't see which leads were most likely to convert. Project managers couldn't predict resource bottlenecks until they hit. Everyone knew the data held answers—they just couldn't access them.
Our Approach
We began with a comprehensive data audit, mapping every source and understanding how information flowed—and failed to flow—across the organization. We interviewed stakeholders at every level to understand not just what data they had, but what questions they needed answered.
Rather than attempting a massive data warehouse project, we implemented a modern data lakehouse architecture that could ingest data from existing systems without disrupting them. AI models then sat on top of this unified data layer, providing insights that no single system could generate alone.
Analytics Capabilities Delivered
The Solution
We deployed a unified analytics platform that continuously ingests data from all five of their core systems. Data quality rules catch inconsistencies before they propagate. Automated reconciliation eliminates the manual work that consumed the finance team.
On top of this foundation, we built AI models tailored to their specific business questions. The revenue forecasting model was trained on three years of their historical data, learning the patterns specific to their industry and sales cycle. The resource optimization model understands their project structures and staffing constraints.
Perhaps most valuable is the natural language interface. Leadership can now ask questions like “How is Q3 pipeline trending compared to last year?” or “Which practice areas have the highest utilization this month?” and get immediate answers with supporting visualizations. No more waiting for analysts to build reports.
The Results
The transformation was dramatic. Monthly financial close that took a week now takes two days. Leadership meetings focus on strategy rather than debating which spreadsheet is correct. Sales closes deals faster because they focus on the leads most likely to convert.
The revenue forecasting model proved particularly valuable. With 85% accuracy six months out, leadership can make hiring decisions, office space plans, and strategic investments with confidence. They spotted a potential shortfall two quarters in advance—enough time to course-correct with targeted business development.
The compound impact on revenue exceeded $2 million in the first year: fewer lost deals from poor prioritization, better utilization from optimized staffing, earlier intervention on at-risk projects, and smarter strategic decisions.
“For the first time, we actually trust our numbers. We make decisions in minutes that used to take weeks of analysis. The AI doesn't just show us what happened—it tells us what's likely to happen next.”
— Chief Operating Officer
How We Can Help You
Most organizations are data-rich but insight-poor. The information exists, scattered across systems and spreadsheets, but extracting actionable intelligence is painful and slow. We bridge this gap with modern data architecture and AI-powered analytics.
Our approach starts with understanding your business questions—not the data you have, but the decisions you need to make. We then architect solutions that unify your data, ensure its quality, and surface the insights that matter. The result is a decision-making capability that compounds in value as you use it.