Structured data refers to information that is organized in a fixed, predefined format, typically rows, columns, and standardized fields that can be easily processed, queried, and analyzed by software systems. In finance, structured data underpins nearly every critical workflow: accounting entries, bank transactions, invoice line items, contract metadata, tax fields, GL codes, and operational KPIs. Because the format is consistent and machine-readable, structured data enables automation, reconciliation, analytics, and regulatory reporting with high reliability.
The challenge is that most financial information does not start out structured. PDFs, scans, emails, spreadsheets, supplier documents, and ERP exports vary widely in layout, naming conventions, and content quality. This fragmentation forces teams to manually reformat and clean data before it becomes usable, slowing down processes like invoice control, cash reconciliation, or margin analysis.
Structured data becomes especially powerful when combined with intelligent transformation. Phacet’s AI agents convert unstructured documents (PDFs, scans, emails) into precise, structured outputs by extracting fields, normalizing formats, labeling transactions, and applying business rules. Each extracted value is traceable back to its source, ensuring auditability and trust. Once structured, data can flow automatically into ERPs, BI dashboards, or financial reports without manual intervention.
For finance teams, structured data is the foundation that unlocks end-to-end automation: faster closings, cleaner reconciliations, richer analytics, and consistent reporting across entities or subsidiaries. It's not just a technical format, it’s the backbone of modern, scalable finance operations.
To see how structured data accelerates downstream workflows, explore Phacet’s 3-way matching automation, where clean, structured fields enable accurate and immediate document comparisons.
