Unstructured Data
Unstructured data is information with no fixed schema: documents, emails, images, audio, chat logs, PDFs. Most of what a company knows lives in this form, but software must first parse, transcribe or embed it before it can be searched or counted.
No jargon, no math
In plain words, what is it?
A spreadsheet cell carries its own labels: this is a date, that is a price. A PDF carries nothing, just ink arranged on pages, and the meaning lives in the layout. That is unstructured data: contracts, emails, scans, photos, call recordings, maintenance notes. Roughly four fifths of what an organisation knows sits in this form, unreadable to anything that expects columns. To use it you run converters: OCR turns scans into text, transcription turns audio into text, and chunking plus embeddings turn text into searchable vectors. After that it can join the structured world, and you can ask across a decade of contracts the way you query a table. The boundary is honest: bad scans and mumbling audio produce bad text, so anything low-confidence needs a human glance before it counts.
Terms that live nearby.
Structured Output
Structured output is a mode where the model is forced to answer in a machine-readable format (JSON, a table, a fixed schema) instead of free text, so its answer can be validated, stored and passed to other software without a human retyping it.
ReadOCR
OCR (optical character recognition) is software that turns images of text, such as scanned pages, photographed delivery notes or exported PDFs, into machine-readable text that other programs can search, count and edit.
ReadChunking
Chunking is the way long documents are cut into pieces before indexing: the cuts decide what the search can find, so a good chunk is a self-contained passage that keeps its meaning when read alone.
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