The ongoing digital transformation of our society has left no room for trepidation. As disruptive technologies flood the market, many governments and enterprises have remained at the forefront of adoption to benefit from the many advantages these solutions offer.
However, many legacy Wide Area Networks (WANs) are unable to keep pace with the unprecedented levels of traffic created by constant integration, which can lead to data vulnerability and loss across the network.
Intrinsic to enabling successful cloud adoption is SD-WAN, capable of providing dependable and consistent connectivity between end users and their respective cloud centers or platforms, the technology addresses many of the challenges that previously stressed WAN networks.
Despite the immediate success, the complexity and scale of networks have only continued to increasingly grow with companies constantly driving the integration of new devices entering the market to remain competitive. As a result, many IT teams have struggled to remain effective as they attempt to monitor and respond to issues across an ever-growing number of users and devices.
As these connections continue to grow more complex and unmanageable the importance of advanced tools that can reliably respond to the millions of generated alarms is insurmountable.
“The keys to successful wide area networking today, especially given the increasingly distributed nature of enterprise networks with more employees working from home or on the road, are automation and AI, which go beyond traditional network monitoring capabilities in the core but bring that intelligence all the way to the edge,” said Jeff Li, Senior Director, Partner Service Assurance at ConnX, a New Jersey-based global managed service provider.
Li explained the massive growth of AIOps: “There are more people working from more places using more devices than ever in history. It is literally impossible to ensure high-fidelity communications – whether voice, video, web collaboration, or messaging – without a deep level of intelligence built up and down the new network stack. AI, machine learning, and software-defined operational processes are what it takes today for enterprise networks to serve the business and the people and teams who make the business work.”
To combat the huge increase in IT data that has proved overwhelming for workforces to manage, ConnX has opted to leverage AIOps-based predictive analytics and machine learning capabilities within their solution. Critical to optimizing the myriad resources within a business, the AIOps platform market is projected to grow by $19.93 Billion, globally, by 2028 at 32.3% CAGR as it is increasingly used by organizations to monitor their entire networks, to identify and analyze issues, and to provide intelligent data that can be used for effective decision making before network performance is negatively impacted.
“As more and more connections are introduced, the need for AI-driven monitoring, diagnostics, and analytics insights is a top priority for maintaining the health and reliability of networks while supporting new devices required for companies to remain competitive at the forefront of their industries,” Li said. “This applies horizontally, across all industry types, as well as vertically. ConnX serves a broad range of the most connected industries in the world – retail, education, healthcare, manufacturing, scientific instrumentation, finance, banking, and more – and we are seeing important trends in designing super intelligent communications networks that consider the regulatory requirements, for example, that come into play. This has become urgent in many domains, like healthcare with HIPAA regulations must be adhered to, regardless of where a doctor or nurse is physically located. We simply need more data – actionable data – to ensure secure, compliant, reliable communications, especially when those communications can have an impact on health and safety.”
Li shared details on how ConnX, working with partners like Juniper Networks, designs, implements and manages premium services with “active assurance,” made possible through automation combined with highly predictive maintenance and highly productive troubleshooting.
“Our proactive AI/ML behavioral learning and automation assures detection and alerts to identify poor call quality causes and provides immediate notification of relevant network events,” Li said. “We leverage network and application layer instrumentation to deliver powerful, real-time performance data, presenting the data in an immediately actionable way. We use media fault isolation to quickly ‘bracket’ the source of performance problems, subsequently providing instant visibility and analysis to demonstrate the true end-user experience – this is truly a breakthrough in the IT industry.”
Li also explained the importance of insight into complex call flows, made possible through AIOps approaches, with full observability into the network path combined with event correlation.
“Today, we can instantly identify systemic vs. local failures, and fix issues with precisely the right solution; if one user has an issue, we can address that issue without wasting time having to figure out what’s behind the problem,” he explained.
The ConnX Maestro platform pulls together all diagnostics capabilities into a comprehensive, correlated “single pane of glass” view, “Which provides an easy-to-interpret presentation of all events and user experience metrics,” Li said. “By facilitating fault isolation and problem resolution by reducing the need for expert resources to analyze events and identify the root cause, our customers are more productive, and our managed services business is more profitable.”
Li predicts that without a strong AIOps platform, enterprise networks will continue to struggle unnecessarily.
“We have the tools now – thanks to incredible innovators like Juniper – so why not use them? We are excited about assembling software and hardware capabilities from across a world-class partner ecosystem and weaving those into an orchestrated service and consistently great experience. Anything other than hyper-intelligent enterprise communications is so last decade.”
