Overview
In this episode of The Dashboard Effect, Brick Thompson and Senior Account Executive Kate Eberle walk through how a billion-dollar, PE-backed organization broke its dependence on a fragile, manual reporting process and replaced it with a modern data platform built for scale. The company had grown through roughly 20 acquisitions, and its data infrastructure had not kept pace. The result was an FP&A team spending one to two weeks every month wrestling Excel files into a consolidated report that was already a month out of date by the time leadership saw it.
The episode covers how that changed, what it took to get there, and what became possible once the foundation was in place.
What This Episode Covers
A Fragmented Data Landscape (0:49 – 2:35)
With approximately 20 acquired entities operating across multiple ERPs, QuickBooks, Salesforce, and budgeting tools, consolidating data into a single, reliable view was a significant challenge. Every source had its own structure, logic, and quirks, and none of them talked to each other naturally.
The Cost of Manual Reporting (2:58 – 4:08)
The FP&A team was spending one to two weeks each month manually assembling a consolidated monthly pack in Excel. The process was brittle, prone to error, and offered limited ability to drill down into the numbers. It was also consuming the time of people who should have been doing analysis, not data entry.
Reporting a Month Behind (5:36 – 5:59)
Because reports were finalized in arrears, leadership lacked the real-time visibility needed to influence business performance while it was happening. By the time the data was ready, the window to act had often already closed.
Executive Alignment as a Success Factor (4:42 – 5:05, 10:32 – 11:10)
The project succeeded in large part because the CFO and a newly appointed CTO were aligned on both the problem and the vision. The CFO understood the pain firsthand. The CTO championed a clean, consolidated data foundation as the path forward. Without that alignment at the top, the project would not have moved.
Modern Infrastructure Built to Last (6:00 – 6:31, 10:56 – 11:06)
The company migrated to a Snowflake data platform with Power BI for visualization, using a medallion architecture to ensure clear data lineage and reliable business logic. The goal was not just to fix the immediate problem but to build something the organization could grow into.
Incremental Delivery Over Big Bang Rollout (7:09 – 7:37)
Rather than waiting for a complete overhaul before delivering anything, the team addressed individual data sources and reporting tabs incrementally, producing meaningful value within the first one to two months. Progress was visible early, which helped sustain momentum and organizational buy-in.
Outcomes and What Comes Next (9:15 – 13:04)
Within six months, the manual labor required to produce the primary consolidated report was eliminated entirely. General managers gained access to trusted, granular data and were empowered to operate as true owners of their individual P&Ls. With a robust data foundation now in place, the organization is positioned to layer in advanced analytics and AI-driven workflows to further scale operations.
Who It’s For
This episode is worth your time if you are a CFO or FP&A leader whose team is spending more time preparing reports than acting on them, an executive at a PE-backed or acquisition-heavy company struggling to consolidate data across multiple systems, a CTO or data leader making the case internally for a modern data infrastructure investment, or anyone who has inherited a reporting process built on Excel files that only one person fully understands.
Why It’s Worth a Listen
This episode works because it is a real story with a clear before and after. The problem is specific and recognizable, the solution is practical and well-sequenced, and the outcomes are measurable. It is not a theoretical argument for better data infrastructure. It is an account of what it actually took to build one inside a complex, fast-moving organization.
The point about incremental delivery is worth particular attention. One of the most common reasons data modernization projects stall is that they are scoped as all-or-nothing transformations. The approach described here, addressing individual sources and reporting tabs in sequence while delivering usable output along the way, offers a more realistic and sustainable model for organizations that cannot afford to pause operations while they rebuild.
And the closing discussion on AI readiness reframes the entire investment. The work done to clean and consolidate data for better reporting is the same work required to make AI useful. Companies that build that foundation now are not just solving today’s reporting problem. They are positioning themselves for what comes next.
