A recent article by Eric Siegel of Predictive Analytics World argues that the greatest opportunities inherent in machine learning are also those that organizations are most likely to miss. As he elaborates, there is a significant potential for real-time predictive scoring to optimize companies’ largest-scale operations. But because this is relatively new and comes with such high stakes, enterprises often are overcome with analysis paralysis and lose the opportunity as a result.

According to Siegel, this is a mistake you can’t afford to make. He believes the move to real-time machine learning is happening, and it’s only a matter of whether you or the competition will get there first. With that in mind, the following are four critical things you must know to seize on the real-time machine learning opportunity:

1. Real-time predictive scoring is a business imperative

Your company’s highest-frequency operations are likely also to be the most abundant. As such, optimizing your largest-scale processes means that predictive scoring must take place in real-time, right at the moment of every single interaction.

2. You most likely already have the hardware

The system that is already currently running your high-volume transactions can also probably handle real-time scoring. According to Siegel, the scoring is much more lightweight than the training phase of machine learning, which won’t burden operational systems or any real-time system as training is typically conducted as an offline process.

3. Fine-tuning your model makes real-time scoring possible

Data scientists will usually need to test and tweak predictive models in order to meet performance requirements. This verifies the performance potential and eliminates any doubt about the feasibility of full-scale deployment.

4. Ultimately, you must take command

As with any strategic initiative, getting started with real-time machine learning requires strong leadership and the ability to drive cultural change. In addition, the practice requires cross-organizational collaboration between varied roles and disciplines. Companies must determine key project factors such as prediction goals, deployment, data and performance requirements, and more.

Siegel expands upon the above key points in his piece and makes the case that, while it’s a monumental leap, deploying predictive models in real-time nonetheless delivers monumental benefits. Head over to Predictive Analytics World for more information on how to make it a reality for your business.