• The telecoms industry needs machine learning to be able to process and regain control over what’s done with available data.
• The technology’s use cases in telecoms have shown great potential in assisting with anomaly detection, root cause analysis, managed services, and network optimization.

As technologies like artificial intelligence (AI) and machine learning (ML) become ubiquitous, it will be almost impossible to come across any industry not capitalizing on the benefits they can provide. The telecoms industry has traditionally navigated quite well through tech change.  Globally, they managed to transform from landline to mobile carriers, then move from voice calls to messaging and data-centric networks. In most of the developed markets, telecoms are creating ecosystems for the data-driven economy.

The reality on ground is that the telecoms industry is one of the fastest-growing industries as well as one that uses AI and ML in many aspects of their business from enhancing the customer experience to predictive maintenance to improving network reliability. ML in particular has numerous potential use cases since telecommunications companies deal with vast amounts of data and need to drive conclusions from it – an overwhelmingly difficult proposition to do manually. 

According to Ericsson in a blog post, “In the area of system monitoring, anomaly detection systems are crucial for identifying performance issues and problematic network behavior. Proactively predicting the degradation of key performance indicators, and identifying the likely root cause, can help reduce and prevent outages.”

As for the area of managed services, Ericsson said ML models can improve trouble ticket management by effectively classifying, prioritizing, and escalating incidents. Capacity planning and customer retention can be improved through explainable churn prediction.  “Furthermore, in the area of intelligent networks, the incorporation of ML tools can enable self-healing radio networks, which automatically detect issues and take corrective actions,” the report said, adding that new technologies such as deep learning and reinforcement learning can be used to automate the network design process and optimize network performance in real time.  

Common ML system components & use cases

Data is the lifeline of any ML system and telecoms data is complex, multimodal, and plentiful. It comprises numerical metrics and text-based logs collected from many thousands of devices. The computational and communication costs of processing the data, as well as the latency and performance requirements, determines how the data components should be designed and implemented. 

Another use case would be the offline and online predictions. ML predictions can be made in either periodically scheduled batches (offline), or in a dynamic streaming manner in real time (online) and batch prediction may be suitable when some delay is acceptable. In batch prediction, model prediction requests are accumulated over time, and the model responds to each batch of requests at an appropriate, predetermined time. 

The report stated that for mission-critical tasks such as predicting service outages, however, real-time predictions may be required. In this mode of operation, the ML model service immediately returns a prediction output upon receiving input data. This execution mode can have challenging requirements from an operational standpoint because real-time prediction may need to support a large and unpredictable number of requests, the model service may need to scale dynamically and provision more resources at peak request times. 

Then there’s the workflow management use case as well whereby to orchestrate the entire end-to-end ML pipeline, workflow management tools can help immensely. “An ML pipeline consists of a number of inter-dependent tasks including data collection, transformation, validation, training, and serving. Workflow management tools can help effectively chain these tasks together, such that unexpected delays or issues in one step do not break subsequent steps,” Ericsson said. 

While ML solutions are complex systems composed of several components that may differ from the existing infrastructure organizations have in place, the report said, depending on the particular use case, each of these sub-components may be implemented in a different manner.

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Companies today have to be strongly focused on customer service if they want to succeed in competitive markets. There is nothing new about this, but the addition of the Internet of Things (IoT) has had a huge impact on customer service in recent years. Increased connectivity leads to higher customer expectations, thereby enhancing demand for companies to meet these expectations. According to research, 42% of industries are spending more than $3 million annually on average on IoT, for instance, Charter Communications has recently invested in IoT to ensure that Spectrum internet customer service provides a smoother experience for the users. Here are some impacts expected from the development of the Internet of Things on customer care.

Customer service will become more Complex

The Internet of things will complicate customer service, starting with those companies that offer services of the IoT. Telecoms will also need to understand how the customer will best make use of the service, how the customer uses other IoT devices at home or in the office, and how products of the company interact with other products overlap.

Here’s how the field could look. Assume a malfunction of a wired thermostat device. Not only does this malfunction influence the thermostat, but also the HVAC system of the consumer and eventually the safety of the consumer at home. This small problem of software leads to a major problem in customer service, showing the complexity of our interlinked world.

Making Customer Service Smarter

In the customer service world, data is one of the advantages of the IoT. The information on these customers is constantly being collected from all these connected devices and technologies. Using this knowledge correctly, you can use data more easily for your clients and give the business an overview of what the consumers desire and how they utilize the services that you offer. You will see higher consumer loyalty as you use this data to deliver value-added offerings on the goods and services, currently delivered to the consumers. Diego Tamburini calls this “the secret sauce” that could boost your marketing efforts.

This kind of data gathering and processing has tremendous possible implications as more items are linked or equipped with sensors. Companies will be able to draw lessons about their products’ past uses on the manufacturing side and plan to improve future product models, to meet better customer requirements. The system is just as good as this, when a component of a device fails, a supplier will use the sensors to submit a new portion, or arrange a repair procedure appointment, to fix this component before it fails. Customers should also maintain predictive servicing of operating goods and this goes far further to improving the perspective of the customer on the commodity offered by an undertaking.

Automation of some areas

Automation is one of the major advantages of the Internet of Things, which is the same in many ways as it applies to customer support. Systems that can track and control connected devices in an IoT system and provide a dashboard for problem-solving are created. This can remove a human intervention layer and protect the contact center in situations, in which genuine intervention is required.

Tim Wilson from near Shore America identifies this as a big benefit, but still a responsibility for new customer support. He says that computers will be the first move in problem-solving if people cannot solve the issue. He warns, “Agents must step up to the table.” This also means that customer care personnel will respond to the data gathered on the IoT devices and contact the consumer if automatic systems cannot fix the issue.

Improved Security

Consumers are, understandably, very defensive of their details and it is necessary for mass implementation of IoT to maintain the highest degree of protection. There have been cases in the past where hackers have also stolen wired toys and devices. Companies need to focus on protecting users from external users seeking unauthorized access to their IoT devices – otherwise, customer services and brand backlash will occur.

Conclusion

How is IoT evolution in the field of customer service going to change? It will bring more expectations that can be met with more automation. It will still complicate the environment a little bit, but it will still assist with the increased data. All and all, once such resources are utilized properly, businesses will have more opportunities to find meeting their consumer requirements. The Internet of Things has been a real game-changer for customer engagement – robust infrastructure and a progressively technically savvy populace, combined with heavy rivalry and that consumer demands in most markets indicate that IoT-enabled companies are expected to enjoy the benefits of improved customer satisfaction and higher profits.

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On the surface, it would seem that artificial intelligence (AI) is widespread in the telecom industry. For years we’ve been familiar with voice-activated menu systems that respond to your verbal commands.

However, the potential for AI in the telecom arena goes much deeper than voice controls, albeit with some unique challenges. I chatted about the topic with Tom Footit, VP of Product Management at Accedian, a performance assurance solutions provider, Kailem Anderson, vice president of Portfolio and Engineering at Blue Planet, an intelligent automation provider and Eric Braun, chief commercial officer at MobiledgeX, an edge computing company.

Scott Matteson: What are the opportunities for AI in the telecom space?

Tom Footit: Telecom networks generate an enormous amount of data, and as a result there are a lot of opportunities for AI in this space. The opportunities break down into three main broad categories:

• Using data to understand and predict performance and security of networks and applications.
• Using data to understand and predict customer behavior. 
• Using data to assist customers when they encounter issues (customer support).

To get the most out of AI, telecom companies will have to leverage technology that is capable of providing the most granular, high-quality (and clean) data related to performance, end-user experience, and an organization’s security.

Eric Braun: The opportunities for AI in telecom are many. There are three opportunity spaces where AI in telecom can be transformational:

Telecom’s own operational and business systems maximize personalized customer experience opportunities while increasing efficiencies of delivery, performance and pre-emptive maintenance. Telecom operational and business systems generate large volumes of clean, valuable data–the ideal input to machine learning analysis and the use of the inference decision making that AI systems generate. Telecom today is already the largest operational Internet of Things (IoT) solution on the planet.

Due to telecom’s unique distribution, the infrastructure can be available to host third party purchase provider’s AI engines that can act on large volumes of third party publishing data in real-time that otherwise could not be used for third party purchase provider insights. This is the main opportunity that 5G and edge computing can capture from the third party purchase provider perspective, as all businesses become service providers in their own rights toward their own customers.  

In the future, there is also the opportunity to combine both telecom AI insights and third party purchase provider insights to further improve the experience and operations because of their presence inside the telecom edge network. One example is predictable radio performance where applications can pre-empt changes in connectivity capability and adjust their performance in advance of changes.

Kailem Anderson: AI is quintessential to operating and controlling not only today’s networks but future ones. As service providers continue to grow their networks to support the ever-growing demand for new products and services, they build them on top of existing systems–mixing old with new, vendor A with vendor B, and becoming increasingly complicated overall. 

Managing this network complexity is overwhelming network technicians and putting service providers on the back foot reacting to their network rather than being proactive. Bringing AI to networks presents an incredible opportunity to get in front of service issues, reduce manual tasks, and improve operational efficiency, which ultimately allows resources to be re-directed toward creating a better, more tailored customer experience. This is the end goal of a network that can adapt–AI-driven automation.

Scott Matteson: What can AI provide that current implementations cannot?

Tom Footit: Telecom networks traditionally were very static and very manual to design, deploy and maintain. More recent demands on operators’ networks have required them to become much more dynamic [leading to the rise of software-defined networks (SDN), network virtualization (NFV) and adjacent technologies] but the process of designing, deploying, and maintaining them have struggled to keep up with this rise in complexity, and largely remain manual processes for most telecom operators.

In a nutshell, AI offers the promise of being able to better automate some of the design/deploy/maintain lifecycle, allowing the increase in network complexity to occur without a corresponding increase in operational expenditures to run the network. 

Eric Braun: Historically, telecom systems have been carefully designed and implemented based on long experience with the nature of telephony calls. With 4G/LTE and 5G, the network loads are rapidly converting to data connections, which are entirely different in nature from voice phone calls requiring telecom service providers to quickly and dramatically change many of their design and operation principles as a result. Machine learning and data already available provide the means to do this, as well as the basis for network operation automation.

Machine learning based on this data is also a way by which network operators can gain invaluable insight into how their networks are being used and where the value is being generated.

Kailem Anderson: To put it simply, AI has the potential to improve every step of the network operations journey. AI can learn faster than humans can, so by leveraging the massive amount of data that these networks produce, AI solutions are able to identify patterns and potential network issues before they even materialize, find a solution, and implement it prior to a service disruption. For example, Blue Planet Proactive Network Operations solution leverages AI to foresee many unplanned network outages, pinpointing the cause and prescribing the necessary actions to preemptively resolve the issue.

Scott Matteson: How easy are these opportunities to implement?

Tom Footit: On one hand, these AIOps opportunities are easier to implement because we’re talking about networks that have traditionally generated a lot of data about how they operate. On the other hand, collecting that data and doing something intelligent with it is not a process that most telecom operators have started; they are largely using the data to generate alarms and operate in a break/fix mentality. Implementing systems and processes can be a challenge. 

Eric Braun: Changing design and business practices developed over a century of experience is difficult at best. These issues are compounded by the newness of ML/AI and the new knowledge required, and by the changes required to automate operations. The potential value is enormous, but capturing that value requires change at all levels of an operator from management and business structure down to the mathematical competence required.

If looking ahead at 5G and edge computing, and by using the 4 edge model explained here, then it is clear that telecom edge exists today but the only applications that take advantage of the presence of the distributed infrastructure are telecom functions themselves, such as radio controllers, packet cores and border network functions. The biggest challenge faced by telecom companies to fully capitalizing on enabling AI for itself and for third party purchase providers  is enabling programmable access to its infrastructure and systems and making such access as easy and “cloud-native” as possible.

Kailem Anderson: Leveraging AI in the network is not as simple as a snap of a finger but it is a wholly worthwhile endeavor. The first step for any service provider is defining what automation, and therefore AI, looks like in their network–where it’s needed and where it can best help provide value. From there, they can begin phasing AI into operations. This is crucial as, although they do so quickly, AI solutions still need time to learn, which means adopting AI is not immediate. 

In fact, providers will still rely heavily on human expertise, especially in early stages: Yes, the network operations team will use AI to identify potential root cause issues, but those team members won’t have the AI close the loop and automate the fix. Only when the AI solution has developed a track record and the operations team has a high degree of confidence in its recommendations and ability to prescribe and act appropriately should an AI solution be used to fully automate processes. 

Data is the underlying current that runs through this entire conversation, and it’s that data that will dictate the speed with which AI functionality is possible. These systems need data to learn. They rely on training and data tagging, which require investment. Service providers that embrace the importance of data early on will see pay-off in the long run as their AI capabilities come to fruition at an accelerated rate.

Scott Matteson: What’s causing these delays and impediments?

Tom Footit: The technology to leverage data for AIOps in telecom networks is there today and it is based largely on well-established open source technologies and well-established data science that has been developed in other fields and industries. The largest impediment that we see is change: The ability to get people and processes to adopt the changes they need to move from a largely manual way of working to one that is much more automated takes time. And organizations need time to learn to trust the data and trust that the AI can effectively automate multiple tasks, freeing humans up to do the more complex and higher value operations.

Eric Braun: Change of this magnitude must be driven top-down, based on a clear corporate strategic goal or mandated and with clear executive support to overcome the expectable resistance of “we don’t do it that way!” Adoption of cloud-native operations, processes, and talent is key, and discovery must move from academic architectures to intelligent learning in reality.  

As our CMO once said, clever telecoms are placing multiple bets. The good thing about edge computing is that it is going to happen because every single player has recognized it needs to happen. That includes hyperscalers, telecom mobile operators and all the vendors. So the only question is how should it happen. And it’s not a one-size-fits-all market.

What we are saying to all operators is that the only wrong strategy is to do nothing. Please do not academically talk about this for two years because in the next two years the market will have formed in the real word and you will have no leverage or understanding. If I were a CSO I would be very scared to choose one strategy when no one is really sure how this market will turn out. Place a couple of bets and learn from them.

Scott Matteson: What should organizations do to alleviate the roadblocks?

Tom Footit: AIOps is all about incremental change: Starting small and leveraging data to make better decisions, while still leaving the final decision in the hands of humans, is a good way to start. The technology is available to do “closed loop” automation without involving humans in the decision-making process; for example, detecting and fixing an issue in a network automatically–but that doesn’t have to be the first step.

Scott Matteson: What should providers do to alleviate the roadblocks?

Tom Footit: Stay in lock-step with their customers. If AIOps is about incremental change, then providers need to stop overstating the benefits of it and work with their customers to start implementing it. For most operators, change will come by evolution, not revolution. 

Scott Matteson: What opportunities will be available in the future?

Tom Footit: Because telecom networks are so data rich, there are many opportunities. The concept of using closed-loop automation for self-healing networks is the target everyone is aiming for, and it is something that is certainly attainable at scale in the future.

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The benefits of industry 4.0 have been well reported and the world of work has been revolutionized. Almost every organization operating today actively utilizes, or relies on, technologies that are becoming increasingly advanced. Both industry and society have adopted a data-driven culture in which information drives intelligent decision making and previously unheard-of efficiencies.

High-tech and telecoms organizations stand at the forefront of this transformation in a unique yet vulnerable position. In simple terms, technological development is outpacing the ability of many organizations to adequately address the subsequent risks it creates.

A gap has developed between the adoption of sophisticated technologies and protection against advanced threats. It is, therefore, important for organizations to assess the threat landscape to develop effective strategies and systems to minimize risk.

Below are six focus areas that represent significant threats to the high-tech and telecoms sectors.

Privacy and Data Protection

The high-tech and telecoms sectors are data-rich. Processing and storing extremely high volumes of personal information directly correlates to optimum service delivery and revenue generation.

As the regulatory landscape changes, the methods organizations use and how they protect customer data is being scrutinized. As various countries implement new and inherently different privacy regulations, regulatory compliance is now dependent on an ability to satisfy extensive and varied requirements. Failing to do so can have severe financial and reputational consequences.

Device Threats

In general, the risk appetite of high-tech and telecoms organizations and the people they employ is high. Internally, the collaborative and creative environments often cited when referring to high-tech and telecoms organizations pose a significant risk. For example, the latest mobile devices, apps and technologies celebrated by early-adopting employees are far more likely to have security flaws.

Vulnerable devices connected to the network by employees can introduce any number of malicious threats capable of causing limitless damage.

Cloud Security

Regardless of sector, cloud technology is increasingly relied upon for multiple business operations and is normally managed by external cloud service providers. Organizations have less control of these operations, and adequate threat response relies on effective contractual and service-level agreements, which dictate requirements and expectations.

The Internet of Things (IoT)

The wide-ranging adoption of IoT devices by both consumers and enterprise, as well as the exceptional volume of devices being produced, represents an increasingly high-impact threat. Many IoT-related threats are the result of poorly configured devices developed by manufacturers who, in some cases, may have had little regard for security. Unsecured devices connected to the networks of high-tech and telecoms organizations can make them vulnerable to attack.

The Human Element

When addressing information security, there is often a tendency to be drawn to technological threats and regulatory failures. As with many other sectors, high-tech and telecoms organizations must recognize human threats, which take many forms. Insider threat, social engineering and process failure all signify significant risks with multiple well-publicized incidents in the last year alone.

Supply Chain

High-tech and telecoms organizations have global supply chains that are extensive and complex. These supply chains inherit the vulnerabilities of their suppliers and are often exploited by attackers to get to their intended target. This threat places a focus on the enforcement of processes and controls designed to minimize the risks associated with third-party suppliers. In the high-risk, high-tech and telecoms environment, organizations must understand who they are doing business with and what needs to be done to minimize the risks they may pose.

For organizations operating in the high-tech and telecoms sectors, an effective information security management strategy and system that evolves with the threat landscape has never been more important.

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Few of us, if any, read user agreements prior to entering personal information or allowing applications to track our location. This is incredibly convenient for data corporations. The legal requirements for your explicit consent are camouflaged in microscopic print that seems to go on for pages and pages. As such, we are basically signing away our privacy rights, all to access the latest trend in smartphone applications.

A recent New York Times op-ed outlined how location tracking services embedded in smartphone apps are being archived and disseminated by data corporations. The thought of this occurring without users’ knowledge is both unsettling and enigmatic. An investigation led to the revelation that millions of Americans are having their locations tracked through their smartphones. And since the practice is legal in the U.S., the applied uses of location data are endless. Although the article is focused on the U.S. population, the same deceptive methods are carried out in Canada, as well. 

A 2018 report by The CBC names major Canadian telecoms complicit in the mining and selling of users’ location data. Any time you allow an app to use your location data, you are essentially “consenting” to having your location data collected and stored. Even if you don’t want your data distributed, once consent is given, these companies are legally permitted to obtain user locations — among other personal information — and sell that information to third parties.  

Although the laws in Canada and the U.S. demand different levels of oversight, the telecommunication companies operating in Canada are doing just enough to remain barely legal. The Office of the Privacy Commissioner of Canada says that “meaningful consent” must be obtained prior to gathering personal data. However, even if individuals do consent, are they aware of what they are consenting to?

Some of us here at SFU might be indifferent about this issue, saying, “Who cares if our locations are being perpetually tracked?” and “who cares what companies have access to this information?” It seems that we care about consent only when it is convenient, or when the potential for misuse is directly perceptible in our everyday lives. 

These data corporations are betting on us being too lazy to investigate exactly what we are consenting to. However, our growing reliance on technology and the rate at which it is being developed and distributed continues to blur the lines of cyber ethics. This makes government oversight over these companies increasingly difficult to legislate and enforce, thus delegating the task of trying to interpret “meaningful consent” onto the user.   

Aside from reading the entirety of the user agreement — which I don’t expect anyone is going to do — there are a few relatively easy ways of lessening the likelihood that your location will be tracked. Start by limiting the number of applications that operate using location services. Turn off location services for programs that are still able to operate without them. 

Even taking these precautions, there is evidence that your location is still tracked, notwithstanding users’ explicit instructions not to. There’s no reason why we should make this any easier on data miners. Educate yourself on what your phone is doing behind the scenes and protect yourself — and your location — from prying eyes.

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