Applying algorithmic analytics to forecasting in a digital world

Forecasting has traditionally been a manual process of gathering, compiling and manipulating data in spreadsheets. Many people also apply their own intuition and judgement, which often results in unconscious biases and conscious sandbagging. With more data available than ever, old forecasting methods are becoming a time-consuming process.

This session covers examples of how organisations are shifting to forecasting processes that involve people working symbiotically with data-fuelled, predictive algorithms. It’s all made possible by new technologies—advanced analytics platforms, in-memory computing, AI and machine learning. By modelling the potential impact of key decisions, Finance can now generate smarter insights and stronger business outcomes.