Today's technology advances have enabled end-users to operate more efficiently, and for businesses to more easily interact with customers and gather and store huge amounts of data that previously would be impossible to collect. In kind, IT departments can also collect valuable telemetry from their distributed enterprise devices to allow for many of the same benefits.

But now that all this data is within reach, how can organizations make sense of it all? Analytics solutions abound that can help companies sort through the data, identify patterns and trends and learn from the past to predict future outcomes. But these predictive capabilities have always relied upon the intelligence of humans to derive the most meaning out of the data.

If data-driven predictive analytics is the route to improved business growth, cognitive operations are fast becoming the Holy Grail to achieving it. Cognitive operations have the power to take the best of predictive analytics and act upon those predictions automatically.

For example, cognitive, or AI capabilities, will enable IT departments to reliably and accurately predict IT outages or performance problems before they happen and take corrective action, without a whole lot of human intervention.

According to a recent Forrester report, "the rise of AI-driven cognitive operations may put the Grail within reach for many organizations."

IT Departments Face the Torrential Downpours of Data

According to Forrester, "technology has grown too much and too quickly for humans to monitor and operate cloud-based analytics tools effectively." Consider the recent Hurricanes Irma and Harvey. The predictive models that were used to track and predict their pathways created enormous work for meteorologists, not to mention huge storage and processing requirements.

Enterprise IT teams have many of the same problems as these meteorologists. Massive amounts of data generated daily, coupled with emerging IT issues potentially connected to network faults, performance problems or mobile device adoption, are making it difficult for IT teams to both manage incoming data and address emerging issues. Current analytic techniques offer the ability to react quickly and reduce the impact of potential compromise, but this creates an all-too-familiar cycle: Break, fix, repeat.

The Cognitive Concept

AI-based intervention will soon be integrated into the IT department tool set. For example, new solutions will be able to monitor usage data or crash reports and automatically identify the source of a device malfunction, as well as determine the best course of corrective action, and then automatically take that action.

As we work toward full cognitive adoption, Forrester identified four critical benefits:

■ Reduced Effort — Intelligent discovery of potential issues by cognitive solutions reduces the effort and time required by IT to make basic predictions.

■ Improved Reaction — With access to predictive data on-demand, IT teams can quickly determine the best course of action before serious system consequences occur.

■ Proactive Prevention — Access to both end-user experience and underlying issue data helps achieve the "Holy Grail" of proactive IT: Problems solved before they impact the end user.

■ Business Meaning — The next step in IT analysis: Going beyond technology outcomes to assess the business impact of networking and device issues. This both solidifies the place of IT in the boardroom and provides C-suite members relevant, actionable data to inform ongoing strategy.

Improve Employee Experience – Make a Hero Out of IT

Adaptive, cognitive solutions help IT Infrastructure & Operation professionals monitor and manage larger, more complex environments with less effort.

So how do true cognitive operations benefit end-user productivity? If IT can detect issues on user desktops or mobile devices before end users submit a ticket or report an issue, both employee satisfaction and performance improves. As noted in the Forrester report, "the use of adaptive, cognitive solutions help IT Infrastructure & Operation professionals monitor and manage larger, more complex environments with less effort."

By shifting the burden of analysis and reporting workload onto autonomous, cognitive processes, it's possible to both increase the processing performance of network analysis and give IT the time and space it needs to determine the root of technology issues rather than simply managing their symptoms.

AI and cognitive operations offer the potential to improve IT operations performance, along with employee satisfaction and ultimately productivity. If data is the lifeblood of today's business, cognitive operations will soon be the delivery mechanism.