Keeping Your Networks Healthy With AI

Covid-19 introduced structural and operational challenges to communication networks. To build long-term resilience, react faster to crisis and even predict and mitigate service outages, network operators should embrace automation, intelligent connectivity and accelerated analytics at scale.

November 18, 2021

4 Min Read
Keeping Your Networks Healthy With AI

Earlier this year, Juniper Research published its "2021 Tech and Telco Megatrends" predictions, shining a light on challenges and opportunities telecommunication service providers are facing. As in most hindsight overviews and forward-looking predictions, when it comes to enabling technologies' effect on the world, trends are usually interwoven within other trends, affecting and being nurtured by each other.

The research company's spotlight on AI-based analytics acceleration, and, independently, its view on Intelligent network resilience are a good example of such relationship.

Juniper explains that the overall disruption caused by COVID-19, many industries were forced to adapt quickly to changing market conditions, as companies saw their users/customers changing their purchase/usage preferences, and experienced pandemic-derived changes to their workforce's headcount and overall operations processes.

These companies had to, and will continue to integrate automation into their business, to accelerate insights, increase efficiency and reduce their cost of operation – and this is expected to continue after the pandemic has ended, as "old business models are simply no longer suitable,” said Juniper.

AI is pretty much the most important building block of automation, so it's no wonder that AI became the go-to technology everyone's talking about, including telecommunications service providers, which saw the demand for their services explode as life moved from the physical realm to the digital.

Telcos have not kept pace with changing consumer data demands
Juniper predicted that this year we'll experience an increase in "Intelligent Connectivity," which it defines as "new solutions that make network-based services more resilient.”

"We do not believe that network operators have by and large dealt well with the shifts in demands for data," Juniper's analysts wrote, and named a few areas which will need to step up their evolution in order to keep up. Among them are using licensed and unlicensed spectrum, contemplating the ownership of network hardware, and exploring their options for network management.

The challenge is to not only keep the network alive during crisis, but maintain an efficient network throughout the year, with special consideration to the latency issue. As data has grown, latency has become an increasing burden, holding up the analysis of time-sensitive data and consequently, the delivery of potentially business-changing insights – both critically important in the age of interactive and collaborative work and play.

Telcos are having problems dealing with their own data demands
AI-based automation and acceleration can also come to the telcos rescue – here are just two of many examples of potential usage of AI within the journey for resilient networks providing intelligent connectivity and rapid response:

Network planning – when it comes to the placement of new cell sites, some mobile telcos are replacing their traditional key data sources – such as population size and traffic volume – with AI and ML-based predictions on profit margins and customer churn.

Network alerting, healing and optimization – implementing an AI-based fault detection system enables engineering crews to discern between false positives and actual network quality issues and deal with the latter. AI-based mechanisms can predict potential degradation of service, suggest what needs to be done to prevent them, alert the affected users, provide alternative connectivity options, and even apply configuration changes automatically.

In the age of abundant data derived 24/7 from man and machine, telcos are at the forefront of organizations facing challenges when it comes to growing and evolving data demands. Overcoming these challenges requires knowledge of how their networks and users operate. This can be achieved by analyzing the aggregated data, identifying patterns signaling potential operational problems or revenue opportunities, and reacting or pre-acting accordingly. The problem is that there's *too much* data, and *not enough* resources to analyze it rapidly enough to still make it relevant for action. We call this "The need to reduce Time to Insights.”

As with most things that sound amazing on paper, getting them to actually work is the biggest challenge. AI can help. Yet, a 2020 MIT-Sloan and BCG study found that 70% of companies surveyed reported minimal or no impact from AI at that time. Almost all (90%) of the companies made some investment in AI, yet fewer than 40% reported business gains from AI in the three years prior to the study. What are these organizations missing? How can telecom executives increase the chances of turning their AI potential into a tangible business result?

SQream recently published ‘The Telcom Executive's Guide to Powering Your Business with the Full Potential of Artificial Intelligence,’ an e-book which can help you get educated on potential pitfalls when trying to ingest some AI smarts into your operations. It provides critical advice on how to approach this challenge and actually utilize your data to its fullest, achieving a faster Time to Insight process, and ultimately, paving the way to a profitable AI future.

This content is sponsored by SQream.

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