Algorithmic Forecasting for the Life Sciences Industry

For decades, forecasters have relied on Excel spreadsheets and cut-and-paste tools to collate data and run forecasting numbers. These old-school strategies are time-consuming, costly and face a high risk of error. They also limit the number of questions forecasters can ask, and how rapidly they can generate results to inform their future market strategies.

By replacing spreadsheets and manual number crunching with trained algorithms, forecasting technology can now run an increasing set of predictive analytics, including scenario comparison, bias detection across multiple forecasts, and outlier analysis to show where forecast assumptions are 'outside the norm', all with a high degree of speed and accuracy.

This white paper explores how algorithmic forecasting leverages real-time data feeds, automated algorithms, and an updated forecast workflow to accurately predict commercial performance

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