AIOps: Predict & resolve the next outage before it occurs

Digital transformation is moving at a faster pace and shows no signs of slowing anytime soon.

With this growth, the demand for resilient, accurate, and timely IT operations (ITOps) is also increasing. As hardware and software become more powerful, they become more intricate, increasing the need for the ITOps teams responsible for managing them.

According to Gartner, the increasing complexity in IT environments and data management costs are becoming primary concerns for many service providers. Also, the proliferation of disparate monitoring tools has made it challenging to obtain end-to-end visibility across the service or application. Other pain points, such as the increased time spent on incident management, database replication issues, and outage of unknown origin, lead to huge revenue losses for service providers.

To overcome these challenges, service providers must adopt Artificial Intelligence for IT Operations (AIOps). AIOps is a software platform that uses Machine Learning (ML) to enhance a broad range of IT operations, including performance monitoring, event correlation, and analysis. AIOps can predict the next outage before it occurs and resolve it without human intervention. In addition, AIOps’ data collection and analysis capabilities can employ ML to current and historical data trends, creating highly accurate forecasts of future outcomes, thereby lowering the total cost of ownership and accelerating the return on investment.


Fig: AIOps implementation approach

Because of AIOps’ capability to intelligently collect and analyze IT operational data, it is an invaluable asset in a variety of actions and solutions. Here are the three key benefits of AIOps, delivered to enterprises:

  • Transition from a reactive to a proactive approach
  • Deliver superior user experiences with predictive analytics
  • Improve Mean Time to Identify (MTTI) issues and Mean Time to Resolve (MTTR) the incidents

Launching AIOps requires a unique approach depending upon your organization, its capabilities, and its needs. This insight provides a 3-step strategy to effectively implement AIOps, detect incidents before they impact users, automate the response, and prevent recurring issues.

AIOps employs ML to current and historical data trends, creating highly accurate forecasts of future outcomes

IT Agility

Speed up delivery of secure products

Leverage DevSecOps for proactive prevention of vulnerabilities

As service providers in the Connectedness industry strive to shorten product release cycles, they often deprioritize security. According to Cybersecurity Ventures, “More than 60% of enterprises experience breaches and increase in cyberattacks. Damages due to cybercrimes cost $6 trillion globally, and it is expected to compound annually by 15 percent for the next five years.

Traditionally, service providers implement security features late in the application lifecycle. In addition, the detection and fixing of security issues in the production phase lead to further delays in application releases and high OpEx. The traditional security approach also lacks mechanisms to address the increasing data breaches and rising vulnerabilities in open-source software.

To overcome these challenges and reduce vulnerabilities, service providers must look for ways to enable and prioritize continuous security at every stage of the software development lifecycle. Implementing DevSecOps enables automation, security, and continuous monitoring throughout the software lifecycle. This facilitates continuous integration, faster delivery of secure products, and reduction of compliance costs.


Fig: The four-step approach to implement DevSecOps and accelerate secure product releases

According to Cybersecurity Ventures, “The Cybercrime damages cost $6 trillion globally, and the cost is expected to increase by 15 percent per year over the next five years”.

Operational Excellence

Using robots to accelerate Mobile Number Porting

Leverage RPA for Mobile Number Porting with lesser workforce and time, ensuring resolution at the first instance

Mobile Number Portability (MNP) is a facility which allows subscribers to retain their phone numbers even after switching operators, services, or locations. MNP implementation started in the late 1990s in mature European markets like The Netherlands and UK. It offers favorable benefits to customers like lifelong ownership of the number, better services due to increased competition and efficient mobile number planning & administration. But this comes at a cost to the operator with minimal impact on customer – setup cost, maintenance cost, and call routing cost.

All Call Query is the most widely used mechanism of MNP and is the most efficient method for large, interconnected networks. It’s a direct routing scheme which makes use of a centralized number portability database (CNPDB). The process is highly efficient as the donor operator is nowhere involved in the entire scenario.

Figure 1: All Call Query- Mechanism of number porting

Service providers often have to fetch orders from multiple customer portals, making it one of the biggest challenges in MNP. As a result, the process becomes more cumbersome and complex. Moreover, the process requires high manual intervention, making it prone to errors and reducing efficiency. Service Providers must embrace RPA to overcome these challenges. Using RPA, they can:

  • Fetch orders from multiple portals
  • Search and aggregate data from various web and Citrix-based applications
  • Automate order entry in legacy applications

At least 9 out of 10 escalation requests are due to manual entry errors. RPA-based automation provides major cost savings by avoiding rework and penalties.

Operational Excellence

Robotic Process Automation: Rise of the machines

Automate processes and achieve faster ROI with full-featured RPA

Employing staff is the biggest operating expense for service providers in the Connectedness industry. Employees are also their greatest asset. Automation technology advances enable service providers with new ways to maximize employee productivity, revenue, and customer satisfaction while minimizing human error. One of those advances is robotic process automation (RPA), a broad, deep category of tools for automating business, network, and operational processes.

RPA can automate mundane processes which are tiring and boring for a human to do all day long – the kind of fatigue that results in mistakes and expensive turnover. By providing what customers seek faster than a live agent can, RPA eliminates one of their major complaints: wasting minutes on hold to speak with an agent or hours or days for a work order to be processed manually. The big advantages delivered by RPA are customer satisfaction, employee satisfaction, greater revenues and profits, security, and reliability. But the biggest obstacle to achieving these ends through RPA misunderstands how and where it can be deployed. This insight, sponsored by Prodapt and published by TM Forum, elaborates on how to embrace RPA across the business processes to achieve greater efficiencies, improved customer experience, and faster ROI.

RPA automates mundane processes which are tiring for a human to do all day long, thereby avoiding mistakes and expensive turnover

IT Agility

Get your products faster to market

From development to test to production, CI/CD over containers automates every phase

There have been significant changes in application development over the last decade. Organizations have seen huge benefits from moving from the traditional linear application development process to a DevOps-based Continuous Integration/Continuous Delivery model that supports frequent feedback mechanisms from development to operations. Furthermore, containerizing the CI/CD process greatly increases the agility, portability, and controllability in development, testing and deployment phases.

Figure 1: Solution benefits – CI/CD vs CI/CD over containers

CI/CD over containers facilitates the rapid deployment of secure and quality applications.

Operational Excellence

Enhancing your business operations with AI/ML

Leverage AI/ML to enhance the efficiency of your day-to-day business operations and improve customer experience

Today’s digital-savvy customers demand advanced and ultra-rich experiences. Therefore, factors like service availability, turnaround time, and quality of service (QoS) are becoming increasingly important. But the question remains how can Service Providers bridge the huge gap that currently exists between customer demands and their fulfillment?

Service providers’ current infrastructure and service delivery approach are unable to match up with customer expectations. There is a dire need to enhance their key operational areas such as customer service, service assurance and network automation.

Artificial Intelligence/Machine Learning (AI/ML) technology, with its recent advancements, is fast becoming the choice of service providers to bridge this gap and improve operational efficiency. AI/ML systems, together with Bigdata, can process huge amounts of historical and real-time data from various systems such as CRM, billing systems, NMS/EMS, and product catalogues to provide actionable insights and predictions.

Service providers must adopt AI/ML solutions in their infrastructure to offer next-generation services and experiences.

  • Intelligent software-defined approach for operations and delivery of services (virtualization, self-healing, and self-learning networks)
  • Automation of customer service and customer experience improvement (chatbots, virtual assistants)
  • Predictive maintenance and agile operations (automated problem detection, troubleshooting, and optimization of networks)
  • Innovation in subscriber profiling, usage analysis, and personalized offers

Approximately 63.5% telecom companies are committing investments on AI systems to improve their infrastructure and enhance operational efficiency – IDC.

IT Agility

Charge your customers in real-time using Online Charging Systems

Implementing a convergent and flexible charging for digital services

Digital services such as OTT platforms, video-on-demand, M2M communications, and IoT are experiencing tremendous growth in their subscriptions. To speed-up the delivery and management of these digital services, service providers in the Connectedness industry must leverage a real-time charging system. The Online Charging System (OCS) is a specially designed system that allows the service provider to charge an individual user for services in real-time. An OCS can handle the user’s account balance, correlation, and charging transactions. The OCS efficiently fills the current gaps by offering real-time charging for subscribers and flexibility in terms of service type, network technology, and payment method.

  • Charging methods – Content-based, Volume-& Time-based, Real-time service control
  • Service type – Voice, Data, Messaging, Video, Content
  • Network technologies – Fixed, Wireless, Broadband
  • Payment methods – Prepaid, Post-paid, and Hybrid

Although many service providers are embracing OCS, they cannot leverage its full potential and still face many operational challenges

Although many service providers are embracing OCS, they cannot leverage its full potential and still face many operational challenges. This insight discusses the typical challenges service providers face with OCS and recommends ideal solutions to overcome them.

Operational Excellence

Artificial Intelligence in Action

Adding intelligence to robotic process automation

Robotic Process Automation (RPA) is a low-code, low-cost option for the service providers in the connectedness industry to automate high-volume manual processes, delivering cost, efficiency, accuracy, and transparency. By automating a large part of day-to-day activities, service providers can drive accuracy, improve employee morale and productivity, and ensure reliability and consistency of operations. However, to drive the intended benefits from their RPA initiatives, service providers need to understand the difference between the three primary levels of RPA maturity: Basic RPA, Cognitive RPA, and Intelligent RPA.

The Basic RPA relies on easy-to-implement and understanding fundamental technologies such as macro scripts and workflow automation. It is rule-based, does not involve much coding, and uses an ‘if-then’ approach to processing. Cognitive RPA, on the other hand, is a knowledge-based approach. It uses complex technologies such as natural language processing (NLP), text analytics, and data mining to automate parts of the process that Basic RPA cannot. But service providers can primarily benefit from Intelligent RPA, which uses AI/ML technology for decision making. With AI/ML, Intelligent RPA can go beyond data processing (gathering, sorting, calculating, and reporting), automate processes based on continuous analysis of incoming information, and learn to act smarter over time. This is especially beneficial for service providers dealing with large volumes of unstructured data. Furthermore, Intelligent RPA can gather insights and improve them over time while working together for the best results.

The insight elaborates on the three maturity levels of RPA and how to adopt them across the customer engagement lifecycle to help build out and deliver high-value use cases.

With RPA, service providers can drive accuracy, improve employee morale and productivity, and ensure reliability and consistency of operations

Three main levels of RPA maturity

IT Agility

A comprehensive checklist to plan a successful migration

Leverage the migration strategy checklist for a quick, error-free, and resource-efficient migration

Migration allows service providers in the connectedness industry to get all the data they require in a single/centralized system. As a result, analysts and other employees have an easier time accessing the required data, which can be used to make better decisions, resulting in a faster time to insight.

Service providers opt for integration/migration programs for various reasons like integration post mergers and acquisitions (M&As), modernizing the legacy systems, changed compliance & regulatory requirements, and technology & feature enhancements. However, most service providers fail to migrate their systems effectively. According to Gartner, more than 83% of integration/migration programs either fail/exceed schedules and exceed budget by approximately 30%.

Leverage this insight for a quick and handy migration strategy checklist, covering both pre and post-migration activities. While the focus of the insight is on order management system migration, the same checklist can be used (with minor adaptations) for any transformation projects within the service providers’ environment.

According to Gartner, more than 83% integration/migration programs either fail/exceed schedules and exceed budget by approximately 30%.