白皮书
生命科学行业的算法预测
如何为商业预测带来速度,准确性和控制
Download

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.

这份白皮书探讨了算法预测如何利用实时数据提要,自动化算法和更新的预测工作流程,以准确预测商业性能

Related solutions