Enterprise data reconciliation

98% reconciliation accuracy across SAP, Snowflake, and 12 analytics platforms

A global manufacturer was making decisions on numbers that did not agree between SAP, Snowflake, and a dozen analytics platforms. A continuous reconciliation program lifted accuracy to 98% and gave every team the same version of the truth.

98%

Reconciliation accuracy across systems

Analytics platforms brought into scope

Continuous

Validation instead of month-end checks

The challenge

Manufacturing operations generate data in many places: ERP transactions in SAP, operational metrics in Snowflake, and departmental reporting across a dozen analytics tools. Each system was internally consistent, but they disagreed with each other.

Month-end reconciliation caught some discrepancies, but by then the numbers had already reached planning, pricing, and executive reporting. Teams spent meetings debating whose extract was correct rather than acting on the result.

The approach

1. Define the critical data elements

Finance, supply chain, and commercial teams agreed which fields had to match — revenue, volume, cost, customer, and product identifiers — before any rules were written.

2. Reconcile at the source-to-report boundary

4DAlert compared SAP records with Snowflake models and each analytics platform's output, so breaks were caught where they originated rather than after aggregation.

3. Route exceptions by business domain

Discrepancies were classified and assigned to the owning team, with context on the systems and records involved. Analysts stopped triaging generic error queues.

4. Monitor continuously

Checks moved from a month-end batch to continuous validation, with thresholds tuned to the volatility of each data domain.

The results

Reconciliation accuracy reached 98% across SAP, Snowflake, and 12 analytics platforms. The bigger change was operational: teams now start meetings with a shared number and a short list of owned exceptions, not a debate about which report is right.

98% accuracy across all in-scope systems
12 analytics platforms reconciled to a common source of truth
Exception ownership defined by business domain

The client's identity is withheld under NDA. Scope and accuracy figures reflect the production deployment.

What made it work

The program succeeded because business owners agreed on critical data elements first. Technology came second. That sequencing kept the reconciliation rules defensible and made it clear who owned each break.

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