What is the primary benefit of implementing data cleansing before starting a forecasting model?

The primary benefit of implementing data cleansing before starting a forecasting model is to ensure data integrity and accuracy. When data is cleansed, it involves identifying and correcting errors, removing duplicates, handling missing values, and ensuring consistency in the data set. This process is crucial because the quality of the data directly impacts the reliability of the forecasting model. If the data used for forecasting is flawed or inconsistent, the results can be misleading or incorrect, ultimately affecting decision-making processes that rely on those forecasts. High-quality, reliable data enables more accurate predictions and better insights.

While detecting anomalies, converting currencies, and performing regression analysis are important tasks in data analysis, they are not the primary benefits directly related to the foundational step of data cleansing prior to building a forecasting model. Data cleansing specifically targets the enhancement of data quality, which is essential for any subsequent analysis to be valid.