Datasets
Organized collections of data used to train, test, and evaluate AI models. The quality, size, and representativeness of a dataset directly determine how well a trained model performs in production. Bad data produces bad models — regardless of how sophisticated the training process is.
What is a Dataset?
A dataset is a structured collection of examples used to train or evaluate an AI model. In supervised learning — the most common approach for operational AI tasks — a dataset consists of labeled examples: an invoice image paired with the correct extracted fields, a support ticket paired with the correct category label, a demand history paired with the actual future demand. The model learns patterns from thousands of these examples and applies them to new inputs it has never seen.
The dataset defines the scope of the problem the model can solve — and the gaps in the dataset define where the model will fail.
What Makes a Dataset Good
Representativeness — covers the full distribution of inputs the model will encounter in production, not just the common cases
Label accuracy — the ground truth labels are correct; errors in labels teach the model wrong patterns
Class balance — rare categories have enough examples; a dataset with 10,000 approved and 50 rejected examples produces a model that almost never predicts rejection
Freshness — data that was accurate two years ago may not reflect current patterns; datasets need to be updated as processes evolve
Datasets in Operational AI
When deploying AI for the first time in an operational process, the first question to ask is: what data do we have, and is it labeled? Historical invoices in an ERP are a dataset — but only if the correct extraction fields are associated with each one. Building a useful dataset often requires going back through historical records and adding the labels the model needs to learn from. That work is not glamorous, but it is what determines whether the resulting model works.