Turning Operational Data Into Decisions

Situation

The data was there — in the operational systems, in the financial systems, in the scheduling tools. But there was no reliable way to surface it, compare it across locations, or track it over time. Leadership knew some sites were performing differently than others, but couldn’t say which ones were drifting, in what direction, or how long it had been happening. Decisions about staffing, capacity, service mix, and pricing were happening on instinct because the data was effectively invisible.

Approach

We built a cross-location KPI layer that surfaced the metrics leadership had been operating without: utilization, revenue per site, service-mix concentration, throughput, and outlier detection across sites. Once the data was visible and comparable, the analysis itself was straightforward — the harder work was getting the data into a state where comparisons across locations were apples-to-apples. We also built simple ranking and drift views so leadership could see, at a glance, which locations were trending in the wrong direction and how that compared to the rest of the group. The reporting was designed to support decisions, not just describe them — every metric had a clear “what changes if this number moves” framing for the leadership team.