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data drift

What It Means

Data drift occurs when the real-world data your AI model receives changes over time compared to the data it was originally trained on. This mismatch causes the model's predictions to become less accurate and reliable, even though the model itself hasn't changed.

Why Chief AI Officers Care

Data drift can silently degrade business performance, leading to poor customer experiences, incorrect business decisions, and potential compliance violations. Without monitoring for drift, organizations may unknowingly rely on AI systems that are making increasingly inaccurate predictions, potentially causing significant financial or reputational damage.

Real-World Example

A credit scoring model trained on pre-pandemic data suddenly becomes less accurate during COVID-19 because customer spending patterns, employment rates, and financial behaviors changed dramatically. The model continues generating credit scores, but they no longer reflect actual risk levels, potentially leading to increased defaults or missed opportunities.

Common Confusion

People often confuse data drift with model degradation due to technical bugs or assume that if a model worked well initially, it will continue working indefinitely. Data drift is specifically about changes in the input data patterns, not problems with the model's code or infrastructure.

Industry-Specific Applications

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See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.

Healthcare: In healthcare AI, data drift commonly occurs when patient demographics, treatment protocols, or diagnostic equipment cha...

Finance: In finance, data drift commonly occurs when market conditions, customer behaviors, or economic environments shift from t...

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Technical Definitions

NISTNational Institute of Standards and Technology
"The change in model input data that leads to model performance degradation."
Source: Microsoft_Azure_documentation

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