Which data action is essential for improving the quality of data used in forecasting models?

Data cleansing is crucial for enhancing the quality of data used in forecasting models. This process involves identifying and correcting errors, inconsistencies, and inaccuracies within the data set. By ensuring that the data is clean, reliable, and well-structured, analysts can significantly improve the performance of forecasting models.

Clean data is fundamental because forecasting models rely on historical data to predict future trends. If the data contains duplicates, missing values, or incorrect entries, it can lead to skewed or unreliable forecasts. Data cleansing helps in standardizing formats, filling in gaps, and removing outliers that may negatively impact the model's accuracy.

This process lays the groundwork for effective analysis, enabling organizations to make informed decisions based on trustworthy insights. As a result, data cleansing is an indispensable action when it comes to training forecasting models.