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Machine Learning, IOT and ITSM: The Future of Service Requests

08/02/2016 by: The SunView Team

Machine learning may be on the razor's edge of technology at the moment, but the truth is many consumers are already well acquainted with its power, whether they know it or not. It's the engine behind recommendation lists on our favorite consumer websites and applications. It's the algorithms behind image search capabilities. Chances are, however, even if the average user cannot define machine learning in an academic sense, they've almost certainly experienced what it can do firsthand.

For businesses utilizing enterprise service desk and help desk software, machine learning represents a substantial shift away from human-to-human support. According to a recent Gartner prediction, the Internet of Things will automate support requests for users experiencing issues instead of relying on manual processing. How many? About 6 billion devices with support request capabilities by as soon as 2018. How does this deviation reshape IT service management?

What happens when robots rule requests
The IoT and big data revolution will increase the sheer volume of service requests a help desk receives on any given day. After all, if consumer devices and other kinds of equipment can communicate directly to product or service providers for support without any manual effort on behalf of the user, the chances of a support request reaching the said company are virtually 100 percent. This presents a problem to IT staff charged with addressing these concerns as their workload will spike dramatically, as will incidents of low-tier tickets.

Volume isn't the only factor when dealing with the IoT and ITSM. Velocity matters, too. Let's say a hypothetical software provider experiences a hypothetical server crash. Almost immediately, their help/service desk operations will be overrun with service requests. How can organizations expect to address a tidal wave of service requests all sent automatically by IoT smart devices? More importantly, how do businesses with help/service desk applications leverage these technologies effectively without spending exorbitantly on IT labor costs and operation complexity?

Fight robotic fire with robotic fire, that's how. More intelligent ITSM, powered by machine learning, will allow support desk managers and IT technicians to reduce workload, optimize resources and resolve service desk requests with greater depth and agility, even as IoT and big data transform service request creation.

Smarter classification of and response to service requests at the onset
When requests first reach and enterprise IT support network, the ticket must be classified by its severity. In a general sense, doing so suppresses the number of smaller claims requiring the one-on-one attention of ITSM staff.

That said, both users and businesses employing advanced help desk or service desk suites shouldn't necessarily liken this "checkpoint" to a mere filter where less pertinent requests fall to the bottom of the bucket never to be resolved and more important tickets rise to the top. In fact, deployment of intelligent ITSM means end users receive access to a constantly expanding supply of powerful self-service knowledge base resources generated by past resolved tickets. Yesterday's solutions become today's searchable answers to help desk queries. Depending on how IoT technology develops in relation to an advanced self-service support system, higher user adoption could provide help desk with a substantial value-add opportunity by simply rerouting a majority of service requests straight to these intelligent resources.

That door swings both ways, both toward the end user and to the support desk employee managing requests. Should a ticket transcend Tier 0 self-service resolution, ITSM software with machine learning may be able to draw from that same ever-growing knowledge base of historical data to recommend resolutions for IT personnel to employ. Machine learning recommendations not only rid the ITSM workforce of worrisome burdens common to innovative tech-driven spheres like siloed knowledge, but they supply IT workers with the most useful resolution up front to accelerate the process, again saving labor costs for higher-tier tickets.

Predictive analytics for heading off issues before they snowball
As we just mentioned, intelligent ITSM with machine learning technology not only supports end-user resolution but how technicians respond manually to requests. With greater speed and responsiveness, enterprise IT workers spend less time in the trial and error phase deciding how best to handle specific tickets.

However, to utilize machine learning as a method for enhancing how ITSM reacts to service requests does a disservice to the technology. Machine learning turns help and service desk processes into a proactive, predictive undertaking, capable of churning through data ambiently to address potential trouble before it evolves into a major catastrophe, thereby supporting sustained user uptime and minimizing labor.

First, machine learning algorithms can detect anomalies in usage based on historical data and begin to determine whether they constitute backend problems or simply changes in use patterns. Either way, this information can be sent through an automated notification directly to IT technicians who then analyze the data more closely.

Moreover, intelligent ITSM can also automate user-facing notifications, such as alerts as to schedule maintenance and other planned downtime events, as a way of keeping users abreast to bolster customer relations and preventing them from inundating IT workers with service requests unnecessarily. Predictive analytics, therefore, support both higher-level ITSM operations and reduce low-level manual tasks, as well as further unclutter the help desk request pipeline. In an age of big data and the IoT, a time when devices filing service requests can transfer large sums of data, intelligent ITSM utilizes each dataset as efficiently and effectively as possible to enhance customer experience and improve IT performance at the same time.

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