Unstructured data extraction, explained for manufacturers and distributors
Unstructured data extraction is turning information with no fixed format, such as emails, PDFs, scanned forms, drawings and notes, into defined fields a database can store and a person can query. In a plant or a warehouse, most of that information arrives from outside: customers, suppliers, and the floor itself. This guide covers what counts as unstructured, the four ways to extract it and where each breaks, and how to run a project that ends in the ERP.
Written by Structify, which started out turning unstructured sources into structured datasets and now builds that extraction into the systems manufacturers and distributors run on.
Structured, semi-structured, unstructured.
The line is not the file type. A PDF can hold a clean table or a handwritten note. What matters is whether a system can find a field without a person reading the page.
| Kind | What it means | Examples in a manufacturer |
|---|---|---|
| Structured | Fixed fields in fixed places. A database can read it as is. | ERP tables, MES counts, a spreadsheet whose columns never move |
| Semi-structured | Labeled, but the labels and the layout drift from sender to sender. | EDI files, invoices from regular suppliers, CSV exports whose columns change between versions |
| Unstructured | The information is there for a person to read, with no format a system can rely on. | Customer emails, engineering prints, handwritten production sheets, scanned certificates, decades of archived reports |
Four ways to extract it, and where each one breaks.
- 01
Manual entry
- How it works
- A person reads the document and types the values into the ERP or a spreadsheet.
- Works when
- Volume is low and the person knows the business.
- Breaks when
- Volume grows. Hours go to retyping, errors reach the record, and the backlog decides how fast you can quote or close.
- 02
Templates and zonal OCR
- How it works
- OCR turns the page into text, and a template says where each field sits on the page.
- Works when
- A few senders whose forms never change.
- Breaks when
- A supplier moves a box, a customer sends a new PO format, or the scan is crooked. Each layout needs its own template.
- 03
Rules and patterns
- How it works
- Keyword rules and regular expressions find values near labels such as PO number or Qty.
- Works when
- The wording is predictable, such as order confirmations from one system.
- Breaks when
- Language varies. Emails, notes and drawings say the same thing a hundred ways.
- 04
Language and vision models
- How it works
- A model reads the page or the message like a person would and returns the fields you asked for.
- Works when
- Layouts vary, documents mix text, tables and handwriting, and new senders keep arriving.
- Breaks when
- Nobody checks it. Without validation against the system of record and a review step, a confident wrong value looks like a right one.
Six steps that end in the system of record.
The model is one step of six. The other five decide whether the data gets used.
- 01
Pick documents with a decision attached
Start where retyping slows something that matters: quotes waiting on RFQs, invoices waiting on matching, orders waiting on entry. A pile of PDFs with no question behind it is an archive project.
- 02
Write the target structure first
List the fields the ERP or the analysis needs, their types and the allowed values. The extraction is judged against this structure, not against the page.
- 03
Build a checked sample
Have the people who do the work today mark up a few dozen real documents, including the ugly ones. That sample is how accuracy gets measured, field by field.
- 04
Validate against what you already know
A part number should exist in the item master. A price should sit near the last one. A total should equal its lines. Checks like these catch more errors than any confidence score.
- 05
Keep a person on the exceptions
Clean documents flow through. Anything that fails a check goes to the person who owns it, with the reason, and their fix is kept for next time.
- 06
Land it in the system of record
Extracted data that sits in a spreadsheet is the same problem in a new file. The project is done when the fields are in the ERP, the quality system or a dataset people query.
15,000 boring logs, structured in two days.
The Idaho Transportation Department held decades of boring logs as PDFs: handwritten and digital, produced by seven different software tools. The engineering value was real and the data was unreachable.
Structify built a pipeline that reads each format and extracts both the header metadata and the interval-level geological detail into one standard structure. Roughly 15,000 logs across 20,000 pages were processed in two days, avoiding an estimated 3,750 hours of manual work. ITD now pulls a site's full boring history from a map, by location and date.
Where Structify fits.
For manufacturers and distributors, the documents worth extracting first are the ones someone retypes into the ERP: customer POs, supplier invoices, RFQs, prints and production sheets. See AI data extraction for documents and PDFs for how Structify reads them, checks them against the ERP, and posts them.
For enterprise and SAP transformation, Structify and SNP Group build Kyano Oros together, to make unstructured data a first-class citizen in a migration.
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