Data Collection Methods

Every AI model starts with data, and how that data is gathered shapes everything that follows. The major sources include web scraping (crawling the internet for text, images, or code), licensed datasets purchased from data providers, user-generated data from products and platforms, sensor data from cameras and IoT devices, and purpose-built collection through surveys, interviews, or fieldwork. Each source comes with trade-offs. Web data is abundant but noisy, unstructured, and raises copyright questions that are still being tested in court. Licensed data is cleaner but expensive. User data is highly relevant to your specific context but carries privacy obligations under GDPR and similar regulations. Synthetic data - generated artificially by other AI models or simulations - offers scale and privacy benefits but risks embedding the artefacts of the generator. The choice of collection method also determines what's missing, and gaps in training data become gaps in model capability. A system trained mostly on English-language text will underperform in other languages. A dataset skewed toward certain demographics will produce biased results.