Predictive analytics, properly developed and applied, turns your data into key insights, and enables you to take action by making informed decisions about many areas of your business — based on extensive data. Greater accuracy in predicting future events is an advantage unto itself — in part because it can be applied to so many areas.

Sometimes the ultimate objective of a predictive model is the automation of certain business decisions. An example is an automated trading system that places real-time trades on your behalf, manages your portfolio (money and assets) and any financial leverage you may have.

The goal is to make the best decision as quickly as possible — automatically — taking into consideration the many complex factors that affect money management in response to existing market dynamics.

Business can also use predictive analytics to build a model that analyzes various aspects of not only a particular decision, but also its aftermath and possible scenarios — and then suggest the optimal decision for the circumstances.

Models’ outputs can help a company make decisions affecting many aspects of the business, from supply-chain management to identifying opportunities and budgeting.

Companies use predictive analytics models to identify effective strategies that are effective and optimized to handle future events automatically, their actions guided by strategies based on knowledge acquired from thorough and exhaustive analysis.

A functioning predictive model can lead to making informed decisions guided by data analysis. If the model does its job well, its results are reinforced through testing — and validated by the feedback generated in response to its deployment. Then, when faced with new events, the business can rely on models that were built to handle them — especially if the events are unprecedented and unfolding in real time.