The two basic elements of predictive analytics are predictors and predictor groups. A predictor is a metric that can be compared against itself historically or against a pre-selected group of related metrics to predict future behavior problems. Groups of selected metrics are combined and analyzed as a single entity called prediction groups. When subjected to mathematical analysis, the prediction group can be used to forecast future events with a high level of reliability. In predictive modeling, data is collected, a statistical model using mathematical constructs is created, predictions are generated, and the output is analyzed and revised as additional data becomes available. This iterative process is required to continuously improve the algorithms used to generate the predictions.

Using predictive analytics to forecast key resource utilization patterns has the potential to change the IT world’s approach to problem prevention and capacity planning. Predictive Analytics allows IT departments to leverage historical availability and performance data to anticipate future events. It is the “crystal ball” or problem prevention, allowing IT departments to be truly proactive instead of reactive. Utilizing historical data as input, RDX’s forecasting engine constructs the mathematical algorithms needed to analyze and project the utilization of key availability and performance indicators weeks and months in advance.

Historical tracking allows our team to track past usage spikes to facilitate problem analysis activities. In addition, it allows RDX’s Proactive Monitoring and Response Center (PMRC) to forecast when performance will be good and provide a possible reason when performance degrades. The customer’s knowledge of usage spikes allows them to better understand why performance degrades during a particular time period and prevents them from trying to solve the same performance problem on a regular basis.