Predictive Analytics

RDX leverages historical data to predict when key resources will become over-utilized. Mathematical algorithms are required to filter the raw data points to allow a clearer picture of future behavior. RDX coined the term “Predictive Algorithm Engine” to name this new product architecture. Led by a Carnegie Mellon Mathematician, RDX’s Predictive Analytics team leverages historical data to forecast utilization of key computing resources.

  • Predictive Analytics Solutions
    Predictive analytics uses historical measurements to predict future trends, probabilities, and behavior patterns.
  • Analyzing the Past to Predict the Future
    By analyzing past occurrences within our customers’ environments, our DBAs can take a proactive approach rather than a reactive one.
  • Forecasting Benefits
    RDX’s monitoring architecture collects and stores key resource utilization indicators daily in a historical database repository.
  • Custom Metric Design
    RDX is able to work with customers to create a custom projection that shows anticipated growth and possible usage patterns that may affect them in the future.
Predictive Analytics Solutions

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.

Analyzing the Past to Predict the Future

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.

Forecasting Benefits

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.

Custom Metric Design

Representatives from RDX’s PMRC team meet with customers to analyze their needs and determine if the existing algorithms deployed in the Predictive Algorithm Engine can be used to accommodate the new custom projections. If new algorithms are required, PMRC mathematicians will analyze the metrics and customer requirements to determine the scope of the work required. The analysis will determine how much historical data is required, the algorithms utilized, and the quality of the projection.