“Imagine a world where it is possible to prevent problems or capitalize on opportunities before they even occur.”
In the movie “The Source Code,” the idea is to prevent a train wreck from happening. But what if computer code could do that?
Analytics has evolved from descriptive to predictive analytics, which shows the probability of certain events occurring. A third rung is prescriptive analytics, which shows why an event will happen and the outcome of certain decisions.
Analytics is growing increasingly sophisticated, however, with the ability to take in complex information, make decisions, and alter the future. That’s an analytic horizon we call “proactive computing.”
In this special report from RTInsights, you will learn:
- Examples of proactive, event-driven computing, including condition-based maintenance, prevention of traffic congestion, and disaster management
- How proactive computing uses predictive analytics to make operational decisions
- The four phases of a proactive system
- Schemes for various proactive computing scenarios
Want more? Check out our most-read content:
White Paper: How to ‘Future-Proof’ a Streaming Analytics Platform
Research from Gartner: Real-Time Analytics with the Internet of Things
E-Book: How to Move to a Fast Data Architecture
The Value of Bringing Analytics to the Edge
Three Types of IoT Analytics: Approaches and Use Cases
How AmEx Found Gold With Machine Learning
What’s Behind the Attraction to Apache Spark
John Bates, Plat.One: Enterprise IoT Doesn’t Have to Be Hard
Liked this article? Share it with your colleagues!