A dataset is a defined collection of data — rows in a spreadsheet, scanned documents, call transcripts, photographs, or sensor readings — gathered and organized so that an AI system can be built, tested or run against it.
Preparing a dataset is treated as a distinct stage of work in its own right, not something to skip past on the way to a model. The United States’ National Institute of Standards and Technology, in its AI Risk Management Framework, describes part of that early work as gathering and cleaning data, and “documenting the metadata and characteristics of the dataset” — a step that happens before anyone trains or runs anything against it. A dataset nobody has checked for what it actually contains is a liability, not an asset.
Statistics Canada’s own tracking shows how quickly the raw material behind these systems has started to matter across the country: “19.2% of businesses reported using AI to produce goods or deliver services over the 12 months preceding the survey” by the second quarter of 2026, and “this proportion has tripled since the second quarter of 2024 (6.1%)”. Every one of those businesses is running its tools against some dataset, whether that is a handful of spreadsheets or years of accumulated records.
Picture a small mortgage brokerage that wants to flag renewal files at risk of falling through. Its dataset might be nothing more than an export of past renewal files — the recorded outcome, the lender, the rate change, and whether the client renewed or walked. Before anyone builds anything with it, someone still has to decide which columns matter, remove the ones that do not, and check that the file does not quietly contain a client’s name or account number sitting somewhere it should not be.
See also: what is structured data, what is unstructured data, what is training data, what is machine learning.
A dataset only becomes useful once someone decides what it is for — custom-ai-solutions covers what a build actually needs from the data behind it, and who is responsible for keeping it fit for purpose.
