Data drift is a term from the fields of artificial intelligence, big data and smart data, as well as digital transformation. It describes a change in the data used for an automated system or artificial intelligence.
Imagine a company using AI to predict product demand. The AI has trained the system on past data, for example, customers buy more umbrellas during certain seasons. However, suddenly customer purchasing habits change, perhaps due to a prolonged summer bringing less rain. The old data no longer matches current conditions.
This is data drift: The data with which a system was originally trained differs from the current data over time. Consequence: The AI's predictions or decisions become unreliable or even incorrect.
Data drift is important to detect, as otherwise affected companies will be acting on outdated information. Monitoring and regularly adjusting algorithms ensures that artificial intelligence and big data solutions continue to work correctly even under changing conditions.
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