Centralizing general ledger data for accurate, audit-ready reporting

THE CLIENT

A Los Angeles-based asset management firm with over $4 billion in annual revenue needed to address recurring inconsistencies in how General Ledger data was categorized, tagged, and made available for financial reporting and audit compliance.

THE FOUNDATION

The organization maintained GL data across MongoDB, Oracle Fusion Middleware, and Azure Databricks, with Power BI used for downstream reporting. Core financial records were available, but the absence of consistent tagging rules and validation controls limited the reliability and usability of the data for reporting and audit purposes.

THE CONSTRAINT

Reliance on manual tagging processes introduced inconsistencies and errors across GL categorizations. Rigid manual workflows required significant rework when data structures changed, and the lack of standardized transformation rules made it difficult to maintain compliance with financial regulations and internal audit requirements.

THE MULTIPLIER

Logic Pursuits built a centralized tagging framework with robust databases, rules, and governance controls to enable consistent GL data classification, introduced automated reconciliation checks, business validation rules, and error detection to minimize manual intervention, leveraged Databricks Unity Catalog for end-to-end tagging, lineage tracking, and scalable data management, and created unified, standardized GL-tagged datasets integrated directly into Power BI reporting.

WHAT CHANGED

The organization moved from inconsistent manual tagging to a governed, automated GL classification framework. Reporting accuracy improved, manual rework was reduced, and leadership gained reliable, audit-ready financial data to support both operational and strategic decision-making.

WHAT MULTIPLIED