Structured data refers to information that is organised in a predefined format, making it machine-readable, consistent, and easy to process across systems. Unlike unstructured formats (like PDFs or email text), structured data follows a fixed schema — often XML or JSON — where each field has a clear label and expected format.
In trade and finance, structured data plays a foundational role. It enables automation, real-time validation, and interoperability across banks, corporates, logistics providers, and regulators.
Why it matters:
Structured data allows digital documents to be understood and actioned by systems — not just stored or displayed. It’s what makes scalable digital trade possible.
Key benefits include:
- Instant verification of fields like amounts, dates, and counterparties
- Easier integration between trade platforms and banking systems
- Reduced operational risk from manual data entry
- Simplified compliance with ESG, Basel, and AML requirements
- A path toward true interoperability in trade ecosystems
However, to unlock these benefits fully, structured data must be paired with a trusted and transferable format — one that preserves integrity and legal value. That’s where technologies like MLETR-compliant digital originals come in.