Before an AI system can do anything useful, it needs examples to learn from. Those examples are called training data, and they are the single biggest factor in how well a model performs.
Consider a system trained to detect fraudulent bank transactions. The training data would be a large collection of past transactions, each labeled as either legitimate or fraudulent. The model studies those records and learns which patterns tend to appear before fraud occurs: unusual locations, odd amounts, rapid sequences of small charges. It does not know these patterns are meaningful in advance. It finds them by comparing thousands of fraudulent cases against thousands of clean ones.
The quality problem is where things get complicated.
If the historical data contains mostly fraud from one country, the model will be poorly calibrated for fraud patterns in other regions. If legitimate transactions from a certain demographic were incorrectly flagged in the past, the model will learn to repeat that error. The system has no way to question its own training data. It assumes the examples it was given reflect reality accurately.
This is why practitioners spend more time on data collection and cleaning than on selecting algorithms. A well-curated dataset with a straightforward model will outperform a sophisticated model trained on messy or biased data almost every time.
For anyone starting to study AI, the takeaway is simple: understanding data quality is not a secondary concern. It is the foundation the entire system rests on.