When Your Data Already Has the Answers — You Just Can’t See It

Overview

For operations leaders managing distributed field workforces, the most dangerous moment isn’t when performance falls short. It’s when performance falls short and nobody sees it coming.

That was the reality for a multi-market organization delivering physical records management services — destruction, retrieval, restaging, and vault operations — across a network of branches and facilities. The business had all the data it needed sitting across three systems: HR and workforce data in UltiPro, daily activity records in TotalRecall, and cloud analytics in Snowflake. What it didn’t have was any way to bring those systems together into a picture that operations managers and executives could actually act on.

The result was a workforce planning process that ran almost entirely on intuition and manual effort. Branch managers had no reliable way to know, mid-week, whether their teams were on track against forecasted workloads. Senior leadership had no consistent, trustworthy view of productivity performance — just fragmented reports that required hours of manual reconciliation to produce.

The Business Cost of Disconnected Data

Operations organizations routinely underestimate how much fragmented data costs them — not in IT budget, but in decision quality and response speed. In this engagement, the disconnection materialized in three compounding business risks:

• Reactive management replacing proactive planning. Without a mechanism to compare actual weekly throughput against expected workloads, performance gaps were invisible until after the period closed. Managers were always responding to variances they couldn’t have anticipated because the signal simply wasn’t visible in time.
• Workforce capacity allocated by feel, not by data. The concept of an Optimal Hours target — the precise number of hours a branch needs to meet its forecasted workload — existed in theory but had never been calculated systematically. Without it, resource allocation decisions were judgment calls rather than data-driven commitments.
• Productivity metrics that couldn’t be trusted. Activity data from source systems mixed operational work with project work, distorting the numbers managers relied on to evaluate team performance. When metrics aren’t clean, the decisions built on them aren’t reliable.
• Three systems, zero integration. UltiPro, TotalRecall, and Snowflake each held a critical piece of the workforce picture. Cross-system analysis required manual extraction and reconciliation — a process that introduced latency into every insight and consumed planning team capacity that belonged elsewhere.

The Transformation Bricklay Delivered

Bricklay’s engagement was built on a premise that most data projects miss: the goal isn’t to build a system. The goal is to change how an organization makes decisions. That required two things in parallel — a data foundation capable of integrating and sustaining live information from multiple sources, and an analytics layer that translated that data into actionable operational intelligence.

• From fragmented systems to a single source of truth. Automated data pipelines brought live information from UltiPro, TotalRecall, and Snowflake into a centralized data warehouse, updated continuously and available to the entire organization through a unified analytics layer. For the first time, a branch manager’s view of workforce hours and leadership’s view of market-level productivity drew from the same data, on the same schedule, with no manual reconciliation in between.
• From manual estimation to systematic forecasting. A forecasting engine calculates expected productivity targets — at both monthly and daily granularity — across the seven operational activity types that drive the business. This wasn’t a forecasting model built in a spreadsheet and emailed weekly. It was a live calculation, embedded in the data platform, updated automatically as conditions changed.
• From intuition to Optimal Hours. At the center of the production planning solution is Bricklay’s Optimal Hours methodology: a calculated target representing the precise number of hours a branch needs to meet its forecasted workload for any given period, accounting for a built-in efficiency buffer that reflects real-world operational variability. For branch managers, it became the primary planning anchor. For leadership, it became the standard for evaluating workforce utilization across markets.
• Clean metrics from day one. Strict separation of project activity from operational productivity data was embedded in the platform’s logic at build time — ensuring the metrics managers act on reflect genuine operational performance, not a distorted mix of apples and oranges.

What the Business Now Has

The platform delivered two purpose-built views of workforce performance, designed for distinct audiences but built on the same underlying data so every number is consistent and every comparison is valid.

Business Results

The impact of this engagement was most visible not in the platform itself, but in what the organization could do that it couldn’t before.

• Real-time forecast visibility. Operations teams now compare actual weekly and monthly productivity against forecasted targets without any manual data work — turning what had been an after-the-fact analysis into a live management tool.
• Data-driven capacity planning. Branch managers have a clear, objective Optimal Hours target for each period, giving resource allocation decisions a factual foundation instead of a subjective one.
• Zero manual reconciliation. The integration of three previously siloed systems eliminated the manual reconciliation burden that had been consuming planning team time and introducing latency into every cross-system insight.
• Utilization analytics that drive targeted action. Leadership now has structured visibility into how workforce hours are deployed across every market — enabling targeted interventions rather than blanket directives.
• Trustworthy metrics. The deliberate separation of project activity from operational productivity data ensures that the numbers managers are acting on accurately reflect operational performance.

How We Did It

Bricklay ran data engineering and analytics delivery in parallel — ensuring the business logic informed every technical decision from day one.

Technology Foundation

Every tool in the stack was selected to serve a specific business need — not to introduce complexity. The architecture is designed to absorb new markets, activity types, and data sources with minimal rework.