Predictive analytics uses historical data and statistical or machine learning models to estimate what is likely to happen next. Common targets are customer churn, product demand and credit risk.
Machine learning is a branch of AI in which systems learn patterns from data to make predictions or decisions. The behaviour comes from training on examples rather than from rules written by hand.
Model drift is the decline in a machine learning model's accuracy over time. It happens when the data or real-world patterns seen in production move away from those it was trained on.
A data scientist uses statistics, programming and domain knowledge to find patterns in data, test ideas and build predictive models. Hiring one pays off when a team has reliable data and decisions that better evidence can improve.
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