Redesigning SLA cost reporting so Accounting could refresh a pivot table instead of rebuilding a spreadsheet from scratch every month.
The New Business Partners team tracked operational effort across support functions — IT, Accounting, Credit, Legal, and others — directly in Jira, which Accounting then used to calculate SLA costs. But the reporting process itself relied on manually copying and consolidating that data every cycle, which was slow and left room for error. I redesigned the process to pull from Jira directly into a structured, pivot-table-based reporting model — turning a rebuild-from-scratch report into a refresh-and-go one.
The operational effort data Accounting needed already existed in Jira — support functions logged their time there as part of normal workflow. The problem wasn't data availability; it was that the reporting process hadn't been built to use that data directly, so every monthly cycle repeated the same manual consolidation work.
Before: Accounting manually extracted operational effort data, performed manual calculations, consolidated everything into reporting sheets, and prepared the final report — every single cycle, from close to scratch.
After: operational effort data flows from Jira directly into a structured pivot-table model. Accounting refreshes the report by replacing the latest Jira export and refreshing the pivot tables — the underlying structure and logic stay intact.
Primary source: Jira operational effort data. Supporting data: Accounting-provided hourly cost rates, the existing reporting template, and support-function metadata. The raw material was complete — the redesign was about restructuring it into a reusable analytical model instead of a one-off manual export.
The existing workflow was reviewed step by step to identify exactly which activities were manual and where they introduced risk: filtering exported records to the reporting scope, removing null or incomplete entries, validating support-function assignments, and consolidating data across multiple exports before any calculation could happen.
The report was rebuilt from scratch every cycle instead of being refreshed from a persistent data model — every month repeated the same manual consolidation work.
Reporting granularity was limited by the manual process itself. Breaking costs down by employee, support function, and country wasn't technically hard — it just wasn't practical to do by hand every month.
The root cause wasn't the data source or the cost-calculation logic — both were sound. It was the absence of a structured reporting model sitting between Jira and the final report. Without one, every cycle required rebuilding that link by hand, which capped both the process's efficiency and the level of detail Accounting could realistically request.
The redesigned process reduced the manual effort required for monthly SLA cost reporting, minimized the risk of reporting errors introduced by manual consolidation, and increased reporting granularity — all while keeping the underlying cost-calculation logic Accounting already relied on. The recurring reporting cycle went from rebuilding a report to refreshing one.
The deliverable here was the pivot-table-based reporting model itself — structured across Support Function, Employee, Country, Operational Effort (Hours), and Operational Cost — rather than a single fixed dashboard view. That structure is what let Accounting explore the data from multiple angles without rebuilding anything.