Boost your bottom line with AI/ML-powered service assurance
Uncover the power of AI/ML in optimizing service assurance. Dive into the world of deep learning and its role in transforming data into actionable insights leading to quantifiable benefits for CSPs.
The past two decades have seen service assurance experience a remarkable overhaul. Evolving from siloed systems focused on collecting network events and measurements for engineers to achieving automation by leveraging network and service assurance data and AI/ML.
For our industry, this represents a true transformation fueled by two main drivers: the shifting needs of communication service providers (CSPs) and the rapid surge in technological capabilities.
Specifically, these needs encompass two main challenges. The first is financial – notably the plateauing or decline in average revenue per user (ARPU), which has catalyzed a push to optimize operational expenses. The second is technological, with advancements such as network softwarization through virtualization, cloud integration and disaggregation playing a vital role, while software evolution – and its integration with AI and automation – are creating the groundwork for streamlining manual processes.
It’s these strides in technological innovation, both in networks and software, that have substantially shaped the evolution of service assurance, thereby creating demand for more advanced functionalities.
Embracing automation in modern service assurance
Traditionally, service assurance systems focused on collecting network events and measurements, and then manipulating them to provide monitoring capabilities to the network operations center (NOC) and the service operations center (SOC). This provided engineers with information to do their work, albeit mostly manually. With the introduction of new technologies however, the focus shifted towards achieving automated self-maintenance and self-healing by leveraging assurance data and AI/ML techniques to increase the level of autonomy.
In the communication & media industry, this played out in a shift from standardized, static services to personalized, dynamic ones, a process which is now in its advanced stages. As a result, automation with AI/ML has today become a mandatory capability, empowering CSPs to cope with competitive pressures, customer expectations, operational efficiency and scalability.
As AI and machine learning become essential tools for automation and data analytics in service assurance, the potential for even more refined and effective solutions arises. This is where deep learning technologies come into play, offering more advanced analytics capabilities that enable the generation of actionable insights that can address complex challenges unmet by traditional methods. Let’s explore how deep learning is revolutionizing assurance analytics.
The role of deep learning in generating actionable insights
In their approach to assurance analytics, CSPs have, until recently, mostly embraced a siloed, tactical focus centered on domain-specific deployments and certain operational processes. However, this approach frequently fell short in addressing essential challenges such as root cause analysis, service impact analysis and effective anomaly detection. This is because operations teams mainly looked for algorithms that were constrained by their existing systems, usually relying on specific proprietary APIs and making minimal changes to their current assurance applications and processes.
To overcome these limitations, the industry has begun turning to advanced solutions, with deep learning technologies standing out as a game-changer. These new technologies introduced algorithms that not only addressed previously unmet challenges but also typically required larger data sets and more substantial hardware infrastructure.
Deep learning technologies use various types of neural networks, including feed-forward, recurrent (RNN) and convolutional neural networks (CNN). These technologies rely on two key components: the training model, which uses large data sets to improve operational insights, and the translation of these insights into operational data that can be operationally measured.
While common practice is to heavily focus on the training model, providing operational insights that can be translated into actions for self-maintenance and self-healing are equally vital. That’s because ultimately, the real value to operations comes from applying these actionable insights to achieve reduced operating expenses. In this context, service assurance has adopted new, richer and more sophisticated features that, when integrated, enable the exploitation of newly available data and knowledge to trigger insightful actions.
Lower opex, higher customer satisfaction
By implementing modern service assurance solutions that leverage the power of analytics and AI/ML, CSPs stand to benefit across multiple areas. These span all the way from employing automation to achieve improved operations efficiencies and opex reduction, to higher customer satisfaction by lowering the impact of network issues.
Such benefits can be easily quantified using well-known assurance KPIs, such as:
- Reduction of alarms
- Reduction of trouble tickets
- Reduction of mean-time-to-repair (MTTR)
- OpEx effort and cost reduction
Amdocs Helix Service Assurance Suite
As the industry progresses, the ability to convert data into actionable insights becomes increasingly important for CSPs seeking to optimize operational processes and customer experience. Advanced platforms are rising to the occasion with intelligent, integrated solutions. As an industry leading solution, Amdocs Helix Service Assurance Suite stands out in incorporating AI/ML and analytics into fault, performance and service quality management – not only enabling CSPs to navigate previously unmet challenges but also paving the way for superior business results.
To learn more about how these advancements could benefit your operations, visit Amdocs Helix Service Assurance Suite .
