With its latest release of Apama streaming analytics, Software AG has taken an exciting step forward by incorporating predictive analytics capabilities into the product. In this post, I’m going to look at is why this is such an important step for our customers.

Predictive analytics delivers the ability for organizations to predict, with a reasonable amount of certainty, an event that is likely to happen in the future, based on spotting patterns in, around and leading up to events in the past.

Predictive analytics allows organizations to build models that can be used to:

  • Reduce downtime by predicting mechanical and part failures
  • Reduce the risk of fraud by spotting unusual spending patterns
  • Increase customer lifetime value by being able to monitor lifestyle changes as they happen

Being able to deploy these models with the streaming analytic capabilities of Apama makes it possible not only to understand the events that might for example lead to a customer lapsing, or a fraudulent transaction, but it becomes possible to respond and react to these events in a way that allows you to influence the outcome before the main event has actually taken place. In other words, while it still matters.

In this way, organisations are able to increase efficiencies, reduce costs, and improve revenue while increasing customer satisfaction at the the same time.

The technology used to build these predictive models is not new. In fact, many of us already use the same or similar technologies when using the voice recognition capabilities of our phones. Or in our cars when we are driving and the car constantly monitors our behaviour for signs that we might be getting drowsy.

What is new is the ability to seamlessly combine predictive capabilities with the power of streaming analytics—and this is what we have delivered with the release of Apama 9.9.

By using proven and tested technology from Zementis, we have integrated the ability for organizations to take predictive analytics models from any of the tools they may be using—or plan to use in future—that support PMML (the industry standard for exchanging predictive analytic models) and to quickly and easily re-use these models within Apama.

Our inclusion of support for predictive analytics actually goes one step further, allowing organisations to refine their predictive models over time by looking at the effects of different interventions and building those results back into the models. That way Apama can determine the best way to influence different types of events, taking into account all the factors that might be relevant. This means that your predictive analytics solution becomes more intelligent the more you use it. Anticipate, influence, respond. This is the power of Apama 9.9

To find out more about how our predictive analytics capabilities can help your organization, please go to our predictive analytic pages.

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