Resource guide
Building a business dataset coverage matrix
Compare document types, sectors, languages, periods, and organizations without relying on a single record count.
← Business Datasets
Fiund / Quality & provenance
Keep the source, signal, and review connected.
A useful business datasets collection needs traceable context as well as usable files. Define what will be checked, how findings will be recorded, and which questions remain open.
Technical criteria
Category-specific acceptance starts here.
Record definition
What one document, row, or case represents; how related records are grouped.
Coverage
Countries, languages, industries, date ranges, document families, and layout diversity.
Data dictionary
Field meanings, units, missing-value conventions, stable identifiers, and provenance information.
Sensitive information
Required removal or replacement of personal, confidential, financial, and account-level details.
Review framework
Four separate questions for each collection.
| Review layer | What to establish | Evidence to request |
|---|---|---|
| Source lineage | Where this exact file or record came from and how it relates to other versions. | Stable identifiers, source references, and derivative relationships. |
| Technical fit | Whether the recorded fields and signal support the defined model task. | A representative inspection, file-level metadata, and issue records. |
| Annotation fit | Whether labels have the required meaning, timing, and consistency. | Written conventions, reviewed examples, and known limitations. |
| Permission scope | Whether the proposed use has been reviewed for the selected material. | Project-specific source documentation and agreed terms. |
Illustrative manifest
A manifest should describe the state of review.
Example structure only. These values do not describe an available collection or a completed permission review.
{
"record_id": "illustrative-record-001",
"source_reference": "to-be-established",
"category": "Business Datasets",
"specification_fields": [
"Record definition",
"Coverage",
"Data dictionary",
"Sensitive information"
],
"permission_review": "requires_project_review",
"technical_review": "not_yet_reviewed",
"accepted_exceptions": []
}Further reading
Read before choosing your acceptance criteria.
Resource guide
Creating an annotation schema for document AI
Define entities, evidence, ambiguity, and review rules before labeling business documents.
Resource guide
Planning sensitive-field review for business documents
Describe exclusions, transformations, and review evidence before business records reach a model workflow.