Which data action helps identify unexpected patterns in data points that might skew forecasts?

Anomaly detection is a crucial data action that identifies unexpected patterns or outliers in data points. These anomalies can significantly impact the accuracy of forecasts, as they may represent unusual occurrences or errors in the dataset that deviate from normal behavior. By detecting these outliers, organizations can take corrective actions, ensuring that their predictive analyses are based on reliable and representative data. This process enhances the integrity of the forecasting models, allowing for better decision-making and more accurate future projections.

In contrast, data cleansing focuses on preparing and correcting data for analysis by removing inaccuracies and ensuring consistency, but it doesn't specifically target the identification of unexpected patterns. Regression analysis is a statistical method used to model and analyze the relationships between variables, primarily for prediction, rather than solely focusing on identifying anomalies. Currency conversion, on the other hand, deals with adjusting financial figures to different currencies and does not pertain to pattern recognition or forecasting accuracy.