Normalization and transformation rules replaced manual extraction and code adjustments.
MULTI-BILLION DOLLAR DIVERSIFIED HOLDING GROUP / FINANCE
Consolidated P&L reporting across four companies in under two months.
Each of the four operating companies ran its own P&L and ERP, with no single system for consolidated reporting. Consolidating and reconciling the four companies' monthly financial results took most of each month. PivotX built a working reconciliation foundation in under two weeks, covering business requirements, data consolidation, automated reconciliation, governance, and self-service reporting.
Industry
Diversified Holdings
Function
Finance
Entities
Four
Delivered In
Under two months
RESULTS
Automated data preparation
Faster consolidation
Automated aggregation improves comparison with the master file.
One reconciliation workflow
BigQuery replaced manual reconciliation across four ERP systems.
Four operating companies reconciled against a single master file, with Audit and other corporate functions asking to be onboarded next.
THE SITUATION
Four companies were closing their books across four separate systems.
Within one practice area of a group spanning hundreds of businesses, four operating companies each maintained their own P&L and ERP system. With no single system for consolidated reporting, the finance team spent most of each month consolidating and reconciling the results.
The work was also time-consuming and required teams to extract data and adjust codes manually while continuing to manage core financial operations.
01
Four operating companies, four separate ERP systems
02
No automated consolidation or ledger reconciliation
03
Codes readjusted manually for cross-entity consistency
04
Close absorbing most of the month, every month
THE WORK
What we built
01
Business requirements defined first
Before working with PivotX, the Finance team lacked a shared definition of the reconciliation and variance questions the new system needed to answer.
PivotX worked with Finance and other stakeholders to define the expected outcomes before engineering began. The first phase focused on reconciliation and variance analysis across four operating companies.
The focused scope gave the team clear requirements and a practical place to begin.
02
The approach tested on actual financial data
Before working with PivotX, teams could not automatically compare company-level results with the consolidated master file. Gap analysis and reconciliation remained manual.
PivotX built a prototype using the companies' actual financial data. The team transformed and consolidated the data, tested automatic comparisons with the master file, and identified discrepancies early.
The prototype validated the approach in under two weeks and gave the team greater confidence in its month-to-month comparisons.
03
A repeatable reconciliation pipeline
The prototype demonstrated that automated reconciliation could work, but the source files still needed a permanent home and a repeatable processing system.
PivotX built a Google Cloud data foundation that brings together files from Oracle and the master financial system and automatically runs the reconciliation process.
The organization gained a data foundation it owns in under two months. The resulting analysis now informs its biannual planning cycle.
04
Governance built into the data foundation
The new finance system needed clear controls for managing data quality, monitoring performance, and supporting additional business functions.
PivotX added data-governance standards, monitoring, and a data catalog to the new infrastructure. The company is also considering an interface that would allow Finance to add new codes and entities without engineering support.
Finance was the first function to use the foundation. Audit and other corporate teams have since asked to be added.
WHERE THIS TRANSFERS
Where this applies
Separate business units run separate systems, and reconciling them is somebody's manual job every month.
Your close consumes the time your finance team should be spending on analysis.
Previous attempts stalled because the programme tried to solve everything before delivering anything.
Proof
Pilots that became operating procedure.
PivotX has taken companies from initial assessment to production-grade AI in weeks, not quarters. The cases below aren’t proofs of concept. They’re actual production.
Which procurement workflow is creating the longest delay?
We can review the manual steps, handoffs, and systems involved and identify a practical place to begin.