Training data is the information used to teach an AI model. For a language model it is vast amounts of text. For a forecasting model it might be years of sales records, and for an image model it is photos with labels.
During training the model finds patterns in this data and stores them as its internal settings. It cannot reliably know things that were absent from the data, and it can absorb mistakes, outdated facts and biases that were in it. A model also has a knowledge cut-off date after which it knows nothing.
For retailers and venues, training data matters in two ways. When you use a ready-made model, you inherit what it learned, so check it suits your market, including Australian products, spelling and regulations. When you train or tune something yourself, such as a forecast, your own clean, consistent sales and stock history becomes the key asset.
Ask vendors whether your data is used to train their models, how long it is kept, and whether you can opt out. Under the Privacy Act, personal information needs careful handling. Poor data in, poor results out: duplicates, missing stock counts and unrecorded promotions will all skew a model.
Good training data is often the difference between an AI project that works and one that disappoints, and tidy records are within every business's control.