14 April 2020
In today’s digital world, a business’ success is inextricably tied to its ability to meet customer demands and rising expectations for quality, performance, responsiveness, and innovation. Digital transformation is no longer an optional add-on to help an organisation stand out in a saturated market.
It is a necessity for businesses hoping to keep up with fierce competition. In response to the hyper-competitive business climate, the pressure to quickly implement a new breed of digital services and workstreams has added further complexity and risk to an already challenging IT environment. The network required to adequately support this complexity while meeting customer expectations is unfortunately a distant reality for many enterprises. Network professionals are fully aware of this conundrum, but too often their days are consumed by monotonous, low-value tasks and the fight to keep the network running smoothly. They simply don’t have the time required for innovative thinking to drive digital transformation initiatives and to implement more effective technologies that can change the equation.
In fact, a recent Gartner study found that two out of three digital transformation projects fail to achieve the desired outcome. However, this doesn’t have to be the case. With new technologies like AIOps and automation, IT leaders can manage and mitigate challenges while delivering next-generation networks. Today’s networks are complex, vast environments. When things go wrong, it can be difficult to find the root cause as a single issue wreaks havoc throughout the ecosystem. Meanwhile, the myriad of network monitoring tools intended to keep things up and running have ironically created new challenges.
A recent study by Enterprise Management Associates found that almost 25 percent of large enterprises have eight or more network performance monitoring (NPM) tools installed, with some implementing as many as 25. All these monitoring tools mean that network professionals are faced with multiple data sources when troubleshooting. They also contend with thousands of alarms every day – most of which are false positives – making it hard to see the wood through the trees and identify true events that need attention. AIOps, or artificial intelligence for IT operations, cuts through the noise by applying machine learning to aggregate, analyse, and contextualise immense amounts of data from a wide variety of sources, including all of those monitoring tools.
Right out of the gates, AIOps offers a single pane of glass into the network environment by bringing data together in one place to facilitate analysis and eliminate the swivel-chair interface. Additionally, AIOps tools perform advanced event correlation and reduce alarm noise, highlighting real problems and intelligently grouping events, so Network Operations (NetOps) teams can take action. Further accelerating mean time to resolution (MTTR), these solutions use advanced analytics algorithms to pinpoint the root cause of network outages, eliminating the need for costly (and painful) IT war rooms, instead rallying only those resources required to fix the underlying issue behind all those alarms. AIOps solutions also offer auto-discovery and dependency mapping capabilities that deliver powerful visibility into network devices and their dynamic relationships with business critical applications. This enables NetOps teams to quickly evaluate the business impact of network device outages and to identify which business applications are at risk. Furthermore, infrastructure maps make it easy to visualise the root cause of issues within complex, hybrid environments. When an issue inevitably occurs, IT staff can spend less time hunting for the cause and move right to action while notifying business owners.
The ultimate potential of AIOps lies in its powerful predictive analytics. Once fully implemented, NetOps teams can rest assured that AI and ML are hard at work proactively identifying problems in the making, so they can be addressed before they impact end users, and in doing so, improving overall performance and customer experience. Insights derived from AIOps also improve IT decision making around operations and strategy by predicting periods of peak demand, optimising dynamic resource allocation, highlighting perpetual issues that need to be addressed, and identifying areas for cost reduction. The value that AIOps delivers is exponentially magnified when combined with intelligent automation.
By integrating these technologies, AI-driven findings can autonomously trigger automations when certain events or conditions occur. In many cases, this means that outages are avoided altogether, and in others, automation dramatically accelerates MTTR – oftentimes reducing the time required to resolve issues from hours to seconds. Together, AIOps and intelligent automation deliver a closed-loop system of discovery, detection, analysis, prediction, and automation, bringing enterprises closer to achieving the long-awaited promise of ‘selfhealing IT’ and autonomous IT operations. With AIOps and automation taking on much of the repetitive, task-based workload, NetOps teams can finally focus their valuable time and skills on upgrading the network to support strategic initiatives, improving end-user experience.
By Vijay Kurkal, CEO, Resolve