A month-end close that took eleven days
Finance rebuilt the same figures by hand every cycle. We modelled the ledger in the warehouse, defined forty metrics once, and automated the reconciliation.
- Days to close
- 11 → 3
- Metrics defined once
- 40
- Manual hours saved
- 320/mo
- Time to first value
- 7 wks
The situation
Ridgeline is a regional bank with about 1,900 staff. Finance closed the books each month using a chain of extracts that had grown over fifteen years: eleven source queries, four spreadsheets, and two people who knew the order they had to run in.
The close took eleven working days. Worse, the figures did not match what the business units reported, so the first two days of every month went on arguing about which version was right.
Constraints
The bank is regulated, so every reported figure had to trace back to a source record, and no data could leave its own tenancy. Finance had no engineers of its own, so whatever we built had to be operable by analysts.
What we did
We modelled the general ledger into the warehouse first, then layered the business unit sources on top so both read from the same tables. Forty metrics that had existed in four versions were defined once in the semantic layer, each with a named owner.
Reconciliation moved from spreadsheets into tested transformations that run nightly. Where a figure disagrees with its source, the pipeline now raises it before anyone opens a report.
Results
| Measure | Before | After |
|---|---|---|
| Working days to close | 11 | 3 |
| Manual hours per month | 410 | 90 |
| Metric definitions in use | 4 versions | 1 |
| Time to answer an audit query | Days | Same day |
They told us our second use case was not worth building. That one sentence bought them every project since.
Dana WhitfieldCFO, Ridgeline BankWhat we left behind
- A project in the client's own repository, with tests and CI
- Documented definitions with named owners
- Runbooks for every scheduled job, written for a non-engineer
- Two weeks of training for the team taking it over
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