5 Practical Ways Predictive Analytics Can Support IT Posted on : Sep 27 - 2016

Predictive Analytics Can Support IT

With the proper analytical foundation, organizations now have the information needed to improve efficiencies, reduce costs and drive key enterprise changes. Perhaps benefitting the most from big data is IT. Here are five practical uses for predictive analytics

Endess possibilities

“As a department driven by service/change requests and often the primary supporter of key enterprise operations, IT possesses key data,” says Seng Sun, founder and CEO at SunView Software. “And if leveraged correctly, that data can help spur large enterprise changes, effectively transitioning IT from a responsive, tactically-driven department to a proactive, strategic enterprise leader. But where IT can really add value is discovering ways to use this data to create smarter ways for people to work in the digital workplace.” Here are five practical tips on how to use data and predictive analytics for more proactive IT.

Predicting Risk of an IT Change

“The volume and velocity of enterprise change is putting increasing pressures on IT, especially when it comes to risk and compliance,” Sun says. “While improving visibility has greatly helped IT teams reduce risks, incorporating data-driven predictive analytics into corporate change brings numerous advantages to IT, including reduced implementation times and errors. Typically, IT teams have access to massive amounts of data on enterprise asset history, implementation history (success rates) and more. Accessing this data prior to major change initiatives will allow IT to better prepare for any issues that could potentially arise. Once the changes are completed, IT should continue to evaluate incoming data and monitor the ongoing effects of any changes to make additional recommendations and adjustments as needed.”

Predicting Problems through Usage Patterns – Root Cause Analysis

“IT also needs to keep close tabs on enterprise usage patterns,” Sun advises. “Often, these produce key analytical data that can help IT anticipate, diagnose and solve problems before they arise. When evaluating usage patterns within your organization, consider distilling the data down into three categories: network, departmental and that of the individual user. Data for each of these categories provides a blueprint for common service requests, issues resolution processes and the larger enterprise impact of outages. This data can be evaluated to create individual predictive maintenance programs specific to each group. Ultimately, this leads to a reduction in issue resolution times and, more importantly, issues as a whole.”

Improving Workforce Optimization

“Because no two IT team members are the same, your workforce is made up of a collection of individuals that all bring unique skillsets to their respective roles,” Sun stresses. “Adding predictive analytics to your workforce management processes can shed light on the team’s skillsets by reviewing past performances, current workloads and availability and even their location. Using the right performance data can help you ensure that assignments are distributed to the tech best suited to solve the problem in the least amount of time. These practices improve worker morale through a more evenly shared workload while also promoting faster incident resolution for your end users.”

Faster, more Accurate Incident Resolution

“Within a progressive IT environment, analytics are key to building contextual relationships that provide real-time, accurate resolutions to user requests,” Sun says. “Specifically, analytics can help streamline issue resolution by analyzing similarities across case history, enterprise knowledge, and usage patterns to present suggested resolutions for problems. These contextual, data-driven relationships enable real-time resolution to user requests based on best practices. And best yet, they require minimal intervention from an IT professional.”

Augment User Self-Service

“Lastly, predictive analytics can be the secret sauce in your employee self-service strategy,” Sun notes. “For example, publishing analytically driven solutions to common issues within a company knowledge base or portal will help empower users to find self-service solutions to their problems. Furthermore, developing recommendation engines that can access usage patterns and case histories will ensure a self-service support approach that all but eliminates a personal interaction with a technician.” Source