Finance Data & Analytics · Case Study
How a Telecom Provider Mitigated $4M in Revenue Leakage and Cut Month-end Close from 9 to 4 Days
Finance Data & Analytics | Telecom. See how a leading provider mitigated $4M in revenue leakage, cut month-end close from 9 to 4 days, and automated ~80% of reconciliation with Midoffice Data.
Finance assembled, reconciled, and validated data across 12+ systems before it could report. Month-end close took 9 days, and daily revenue visibility was limited.
Deploy d4 over the existing stack. Harmonize 12+ systems, apply ASC 606, automate reconciliation, and surface exceptions with 26 anomaly-detection models.
Close cut from 9 days to 4. ~80% of reconciliation automated. 98% daily revenue coverage. $4M in revenue leakage mitigated.
01 · The challenge
The finance team had no single trusted view of performance across the business.
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.
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. 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.
A working example
Month-end close took 9 days. The data is not the close.
Enterprise systems can hold the numbers. Before Finance can close - or act - it still has to determine whether those numbers are complete, consistent, and recognized correctly. None of the questions below are answered by source data alone.
The transformation gave Finance more than a faster close. It shifted the team from assembling and validating data to working with governed financial information.
Telecom · Finance Data & Analytics
02 · The insight
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.
03 · The framework
What the governed foundation made possible
Finance needed more than connected systems. It needed a consistent view of the same data, finance logic applied the same way every time, and a place to act on exceptions.
Harmonized finance data
Data from 12+ systems across 6 business units was harmonized across customer, revenue, billing, payments, collections, and FP&A - so Finance was no longer stitching those sources together at close.
Lineage back to source
Lineage was maintained back to source. That gave Finance a consistent view of the same data across 5 legal entities, 6 business units, 4 service lines, and the systems behind them.
ASC 606 applied consistently
On the governed foundation, Finance applied ASC 606 logic consistently across revenue data from multiple products, services, entities, and systems - supporting daily revenue visibility instead of waiting on close.
Exceptions Finance can investigate
26 anomaly-detection models continuously surfaced revenue, inventory, and reconciliation exceptions. Dedicated finance cockpits gave teams a single place to access the data and insights relevant to their workflows.
04 · The approach
How d4 was deployed on the existing stack
The work was practical: connect fragmented financial information, standardize how it is used, and give Finance a trusted view - without a rip-and-replace.
Deploy d4 over the existing technology stack
Midoffice Data created a common finance data foundation on the systems already running the business, without requiring a costly replacement of the stack.
Harmonize 12+ systems with lineage intact
Customer, revenue, billing, payments, collections, and FP&A data from 12+ systems across 6 business units was harmonized, while maintaining lineage back to source.
Automate reconciliation and apply ASC 606
On that foundation, Finance automated cross-system reconciliation and applied ASC 606 logic to support daily revenue visibility - and used the same governed data for FP&A forecasting.
Bring the work into finance cockpits
26 anomaly-detection models surfaced revenue, inventory, and reconciliation exceptions. Together, these capabilities were brought into dedicated finance cockpits so teams had a single place to work.
05 · The impact
Faster close and 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.
The team shifted from spending time assembling and validating data across systems to working with governed financial information, applying ASC 606 logic consistently, and investigating exceptions earlier - while preserving the technology already running the business.
Next step
Give Finance a trusted view across 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 - for reporting, planning, AI, and confident decisions.