The Business Complexity
A leading North American quad-play telecom provider grew into a complex business spanning fiber internet, 5G mobile, digital TV, and landline services. Acquisitions expanded the organization to 5 legal entities, 6 business units, 4 service lines, and 12+ source systems.
As the business expanded, so did the complexity of its finance data. Revenue, billing, customer payments, collections, and operational data sat across different systems. This left the finance team without a single trusted view of performance across the business.
The Finance Challenge
Before Finance could report on performance, teams had to assemble, reconcile, and validate data across 12+ systems to ensure the numbers were complete and accurate.
This slowed the entire finance cycle. Month-end close took 9 days, daily revenue visibility was limited, and gaps across systems made revenue and inventory leakage harder to identify.
Revenue recognition added another layer of complexity. Finance needed to apply ASC 606 logic consistently across revenue data coming from multiple products, services, entities, and systems.
The organization needed a way to bring this information together, apply finance logic consistently, and give Finance a trusted view across the business.
Building A Governed Finance Foundation
Midoffice Data deployed d4 over the existing technology stack, creating a common finance data foundation without requiring a costly rip-and-replace.
Data from 12+ systems across 6 business units was harmonized across customer, revenue, billing, payments, collections, and FP&A, while maintaining lineage back to source. This gave Finance a consistent view of the same data across entities, systems, and business units.
On this governed foundation, the finance team was able to automate cross-system reconciliation and apply ASC 606 logic to support daily revenue visibility. They could also draw on the same governed data for FP&A forecasting. Additionally, 26 anomaly-detection models were deployed to continuously surface revenue, inventory, and reconciliation exceptions for Finance investigation.
Together, these capabilities were brought into dedicated finance cockpits, giving teams a single place to access the data and insights relevant to their workflows.
The Impact: Faster Close & Earlier Visibility
With reconciliation largely automated, the finance team reduced month-end close from 9 days to 4. Daily revenue coverage reached 98%, giving Finance a more complete and timely view of performance across the business.
That visibility also made it easier to identify where value was being lost. The organization mitigated $4M in revenue leakage.
These improvements were delivered without replacing the existing technology stack. Within 14–16 weeks, Finance was able to reconcile faster, access more timely revenue insights, and identify potential leakage earlier.
Business Outcomes
- 9 → 4 days — Month-end close
- ~80% — Reconciliation automated
- 98% — Daily revenue coverage
- $4M — Revenue leakage mitigated
Additional impact
- 26 anomaly-detection models deployed
- 14–16 weeks to go live on the existing stack
The transformation gave Finance more than a faster close. It shifted the team from spending time assembling and validating data across systems to working with governed financial information, applying ASC 606 logic consistently, and investigating exceptions earlier. All while preserving the technology already running the business.
At Midoffice Data, we help organizations establish that foundation by connecting fragmented financial information, standardizing financial definitions, and governing the business context that gives enterprise data meaning. This enables finance teams to build the trusted Finance Intelligence needed to support reporting, planning, AI, and confident business decisions.