opinion


Operators’ new ‘ways’ are creating a path towards service assurance utopia

Telecoms.com periodically invites expert third parties to share their views on the industry’s most pressing issues. In this piece Mark Geere, Product Marketing Manager at TEOCO, looks at how operator behaviour is influencing service assurance.

For operators worldwide, improving customer experience is always front of mind – happy subscribers equals loyal customers but keeping network performance in the green is no easy feat. This is especially true as networks become more complex and problems become harder to identify and resolve. The more complex the network, the more human power is required to manually investigate network faults before they significantly impact customer experience. There is only so much that human resources can achieve, especially when overwhelmed by an abundance of alarms, alerts, and their subsequent trouble tickets when each new issue occurs.

Quite simply, operators need new, more efficient, and less time-consuming ‘ways’ of managing network performance issues and faults. These new ‘ways’ are driving operators to put their most innovative foot forward by leaning on AI, machine learning, and analytics to bring automation to fault management.

A new approach for a new era

Today’s technology infrastructures and operations are going through a period of transformation as they try to keep pace with constant network innovation – as a result, the traditional approaches to service assurance are fast becoming redundant. The latest generation of platforms will need to take into account increasing interdependence across various domains and service offerings within its more dynamic technology environment, increased data volumes, and the acceleration towards autonomous operations.

One of the key functions in support of modern service assurance is trouble ticketing: the receipt, assessment, correlation, and resolution of incidents impacting the network, setting in motion both reactive and proactive processes – which if executed correctly – will maintain or increase network QoS for customers. In order to achieve an optimal trouble-ticketing process, operators need complete visibility of their network environment, both horizontally and vertically, with a timely and careful approach to decision-making to optimize process efficiency. Until now, operators have managed to cope with distinct service assurance platforms to serve varying domains. These legacy platforms and operations were intended to serve monolithic environments, a far cry from the complex and dynamic ecosystems that operators are faced with today. Now they are required to reconcile multiple in-house supplier and partner solutions and capabilities. Customization-led approaches are no longer sustainable when it comes to delivering the necessary levels of network efficiency and network quality expected today. At the same time conventional integration methods are expensive, inefficient, and fragile. The introduction of 5G networks is only contributing to this complexity which means traditional service assurance is no longer viable.

The gap is narrowing between service assurance requirements and capabilities

In light of this, operators must be primed to react quickly to evolving customer needs and new business opportunities, without the burden of inadequate service assurance systems. Operators are already moving towards more proactive, predictive, autonomous operations where decision making can be entirely automated which simultaneously removes human resource devoted to repetitive, prosaic tasks to focus on critical customer impacting issues.

Service assurance is a key element in this shift, operators are now looking at ways in which AI and machine learning insights can facilitate closed-loop automation to deliver new applications and services quickly and resolve network issues within a fraction of the time previously required.

As operators transition to leveraging AI and machine learning, we’ll see them move from reactive service assurance processes that fix existing problems, towards a predictive approach which pre-empts the emergence of problems before they can impact the network. The transformative power of AI will help address common underlying problems across multiple systems. Operators can use big data, machine learning and analytics to detect patterns in monitoring, capacity, and automation data across complex technology infrastructure, thus improving their overall service assurance processes. The use of technology in this service assurance context will see operators add another level to closed loop automation, whereby human involvement becomes minimal as they head towards zero-touch.

Service Assurance in the age of predicative analytics

One of the biggest contributors towards zero-touch closed loop automation will be predictive analytics paired with service assurance. The ability to predict network faults and failures before they even happen will transform how networks are managed. While such capabilities can be applied to real-world scenarios already, operators still need to evolve their processes to support the outcomes.

This starts at the beginning: a consensus must be established amongst internal teams as to what should be predicted. Once it is determined what merits a high priority issue, it can then be immediately addressed, though identifying and prioritizing such issues will be part of the learning curve. Service providers will need to adapt existing processes and competencies to address these AI-driven predictions which will require operations to be far more flexible and open to rapid change; one in which the industry is moving towards through the adoption of AIOps.

These new concepts in service assurance are already being addressed by, for example, applying unsupervised machine learning techniques to historical bodies of data held by operators: analysis of equipment alarm and failure records will lead to an understanding of incident patterns and its likely recurrence, allowing the definition and implementation of preventive maintenance processes. Similarly, the analysis of historical ticket data in terms of a current issue being investigated and the resolution that fixed the issue before can generate a knowledge base that powers a “next-best-action” recommendation engine. These applications of AI help operators manage their networks with greater efficiency, by cutting the human time spent in analyzing the problems and allowing them to focus on those customer experience impacting issues.

Innovation is the path to service assurance utopia

In a dynamic and rapidly evolving telecommunications landscape, service quality is key. Customer expectations are only set to rise with the introduction of 5G applications and services, while at the same time tolerance for disappointing performance, unreliable availability and slow reposes to problems is in decline. As the velocity of network provisioning increases through automation, operators must ensure that their service assurance tools and processes keep pace with these advances and become more automated as well. Emerging technologies including AI/ML hold the key to improved customer experience and by rapidly applying innovative approaches to operations process operators will ensure that the services consumed today and in the future keep running, even in the face of growing network complexity.

 

Mark is currently Product Marketing Manager at TEOCO and has spent the last 20 years creating and leading Telecoms product management and marketing teams. He is passionate about delivering product visions that delight customers and goes beyond organizational expectations, creating a win-win scenario for everyone involved.


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