Training data quality is an important term in the fields of artificial intelligence, big data, smart data and automation. It describes how good and reliable the data is that is used to "train" an AI, an algorithm or a machine to learn certain tasks or recognise patterns.
The quality of the training data is crucial to how accurate and successful the end result is. Imagine a voice assistant that is supposed to respond to voice commands: If it is only fed with unclear or one-sided examples, it will not understand users well or recognise commands. If, on the other hand, clean, varied and representative data is used, the voice assistant works much better in everyday life.
Good training data quality therefore means that all data is error-free, up-to-date, diverse and as close to reality as possible. Training data quality is crucial for companies and decision-makers because it forms the basis for the reliable use of AI-supported solutions - whether analysing large amounts of data, automating processes or using intelligent systems. Poor data often leads to incorrect results and can even cause financial damage.
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