Enterprise data is a strategic asset of an organization that provides valuable insight on the customer’s behavior, intent and actions. Predictive analytics reaches out to this rich data to derive predictive models that can maximize business performance, deliver products or services effectively and learn from the historic data to deliver another better model.

Predictive analytics for IT infrastructure is an integral part of the core enterprise practices, necessary to sustain the competitive advantage. This technology applies organizational learning derived from measuring, tracking and computing risk based on the IT infrastructure. It uses advanced and automated software to collect, analyze and correlate vast amount of real-time IT data to forecast IT performance issues before they affect the end users.

It ranks and scores accordingly so that enterprises can effectively turn risks into opportunities. Here the focus is on end user/customer satisfaction and their ability to perform their work despite an IT related event happening. As the analytic methods consumes almost 100% of the enterprise data, organizations can learn from their mistakes or negative outcomes that the predictive analytics provides.

Businesses can attain their full potential following the seven strategic reasons for employing Predictive Analytics for their IT infrastructure:

  • It’s all about availability of Big DataThe push towards cloud and service oriented IT has led to an unprecedented volume of data (structured and unstructured) from various sources. All of this brings complexity and availability issues, as new technologies and architectures are integrated into the IT infrastructure. Manual analysis of the data is impossible to accurately develop predictive models.
  • Continuous real time dataPredictive analytics for IT is focused on real-time data analysis. This real-time capability is made possible with emergence of Machine Learning or Behavior Learning technologies that analyzes and correlates real-time IT data to determine what the normal IT behavior, so that it detects anomalies and predict problems even before they occur.
  • Automation is the keyAll the predictive models are useless, if we don’t take preventive actions and act on them fast. The answer is automation, which is especially relevant in the case of distributed or remote IT infrastructures. It also helps to identify routine IT related tasks that can be automated.
  • Vendor IndependenceEmergence of multiple vendors in the analytics landscape has given organizations various options to select predictive analytics solutions that’s ideal for their IT needs. Plus, the inclusion of cloud based solutions, allows them cost effective solutions based on only the resources that they utilize.
  • Build your core IT capabilitiesWhether the business capabilities are in enterprise products or service offerings, organizations must deliver effectively. Predictive analytics improves product delivery, testing and maintenance by providing models that can drive IT related decisions to minimize loss ratio.
  • IT security and safety concernsEasy access to remote IT infrastructures in a distributed environment has raised security concerns. Utilizing predictive analytics, enterprises can take pre-emptive actions related to their operations, thus reducing the turn-around time for incident resolution.
  • Cost effective and better ROICompanies can leverage their predictive analytics solutions to deliver powerful, qualitative models based on their IT data repositories, and make informed business decisions that earn better ROI. It adds business value to their decisions as they can act fast rather than wait for a IT related incident to occur.