We’ve all been through it. You arrive at the office in the morning and for some reason you can’t access the WiFi network. Or an application that you rely on is running extremely slowly. Or you’re having email problems or password issues.
That leads to a phone call to IT support or, even worse, the dreaded trouble ticket. After a few rounds of telephone tag or having your trouble ticket bounced around for a while, someone might stroll over to your desk and resolve the issue.
Of course, the situation becomes even more complex when you’re working from home or you’re a mobile worker visiting a client site or you’re trying to use a cloud-based application. In some cases, accessing IT support is so time consuming and the experience is so poor that some employees choose not to report the situation at all and just suffer in silence.
The cost to business productivity is considerable. And the erosion of trust between IT support and end users is harmful to the overall health of the company.
But what if the IT department had left you a voicemail or sent a text alerting you to the fact that your regular WiFi network was having performance issues? And what if they provided the name and password for a backup WiFi network you could use until the situation had been resolved?
Better yet, what if the IT department could have anticipated support service issues and remediated them before you even arrived at the office? That’s the promise of a new approach to IT support services that shifts from a reactive mode to a predictive mode through the use of advanced analytics.
Making your IT support services proactive and predictive
The first step in this two-stage process is moving from a reactive posture to a proactive one. Commonly used processes such as ITIL, service desk incident and log checking, and root cause analysis are all reactive.
In a proactive model, IT continuously monitors network services and devices. Alerts provide data about developing issues, and IT then reaches out to users before an issue occurs — with warnings, resolution or directions for prevention.
The next step is moving from proactive to predictive analytics, which entails the application of machine learning, data mining, predictive modeling, statistical analysis, and other methods to analyze recent and past outages and develop strategies for preventing recurrences.
Machine learning can identify where assets are underperforming and can find faults that could lead to downtime. Predictive analysis can identify patterns or anomalies across systems so as to predict when an issue is likely to occur. Over time, the predictive system accumulates more data, and its predictive capabilities actually improve.
The benefits of a predictive approach cannot be understated. It is estimated that even 10 hours of downtime can cost a small-to-midsize business $125,000 per year. For large enterprises, that cost reaches far into the millions.
Predictive IT support services can create happy business users who have uninterrupted, responsive, reliable access to the systems and information they need to do their jobs. And it also creates a happier relationship between end users and IT support.
Is your organization’s IT support service helping or hindering your employees? To learn more, check out the DXC paper, “Reactive to proactive to predictive — The new IT user support paradigm.”