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The Journey to Autonomous Networks: 6 Challenges to Overcome Before Putting Artificial Intelligence and Machine Learning to Work

Contributed by Yuval Stein, AVP Technologies at TEOCO

Advanced, zero-touch network operations – what it takes to create an ‘autonomous network’ – requires telecom network engineers to take the results from AI/ML algorithms and use them efficiently within a network’s operational processes. Up until now, much of the industry focus has been on the challenges developing the proper AI, yet sometimes forgotten, there are also challenges in putting the AI results to use in a way that contributes to the quality of the services and the network.  That’s what this blog post explores. But before I get ahead of myself, let’s discuss the purpose of autonomous networks.

Autonomous Networks – what they are, and why we need them

Building an autonomous telecom network is somewhat akin to building a living organism.  Just like our bodies can automatically self-regulate many of the functions that keep us alive – our stomachs digest, our lungs breathe, and our hearts beat – autonomous networks function in a similar way. The goal for Communication Service Providers (CSPs) is to create a fully automated environment that is self-configuring, self-healing, self-optimizing and self-evolving. A network that will hum along with minimal to no human intervention.

Why is this so important?  Yes, there are cost savings that can be achieved through automation, but that has become almost secondary. Let’s stick with the human body analogy for a bit longer. If we suffer a small cut or cold, our bodies automatically begin the healing process.  We don’t have to tell it what to do; it just happens automatically. If everyone had to rush to the hospital to seek the advice of a specialist for every sniffle, bump, and bruise, we would find it hard to get through the day. Job productivity would plummet, and there wouldn’t be enough medical personnel to deal with all the demand. That is the same issue for telecom networks.  Systems have become so complex and fast-moving that human intervention can’t be relied upon to fix every minor network error – there are simply too many to manage, and response times need to be immediate. Automation needs to be the default, and humans should only have to step in for the bigger issues – when absolutely needed.

It Takes a Village: Solving the Autonomous Service Assurance Stack

Communication Service Providers, companies like TEOCO, and groups like TM Forum, are working to create the software, systems and tools required to enable fully autonomous networks – and we are getting closer every day. As you can see in figure 1 below, there is a stack of service assurance steps that need to be achieved – each one building upon the one prior.


The 3 bottom steps in the diagram below are well established, with their own operational methodologies. CSPs know how to work with network messages, events, alarms and KPIs. However, the upper layers, including Analytics and Automation – are very different. They require incorporating and acting upon things like probabilities and forecasts, which are relatively new types of information that until now have only existed in separate silos across various departments. Now, it all needs to work together. This requires new methodologies (and APIs) for how to incorporate these new stages before networks can become truly ‘Autonomous’.

Figure 1- the Autonomous Network Assurance Stack

Six Human and Technical Operational Challenges for Managing AI/ML Data

Understanding how to best leverage the information and data being generated by machine learning and artificial intelligence, and how to ‘operationalize’ the ongoing use of this information, is the task at hand.

As the saying goes, the devil is in the details. In integrating these new layers, which as mentioned above work very differently than the previous layers, we’ve identified a ‘language gap,’ for lack of a better term – with both human and technical hurdles that need to be overcome.

Before we can address this gap, we need to understand it and identify exactly what challenges we are facing before we can fix them. My belief is that they are both human and technical. After all, even with automation there will always be people involved at some level. I’ve outlined some of these challenges below:

Human Challenges

  1. Lack of Trust: This is the main human challenge by far, as algorithms using deep mathematics are often not easily explainable.  The use of visual cues and explanations within the user interface, along with achieving proven results over time, helps build trust.
  2. Defining What is Actionable – and How to Act Upon It: AI and ML results are rarely black and white.  Like forecasting the weather, they often involve probabilities. But instead of predicting the chance of rain, the AI/ML results may show that there is an 80% probability for a network function failure in the next 12 hours. Network engineers need to decide- is this a high enough probability to require the system to automatically change a network configuration? And is there enough information to know what that change should be?
  3. Cost Benefit Analyses: Once an issue is predicted, are we able to compare the cost and impact of the expected failure to the cost and impact of the fix? To run a network in a cost-efficient manner, these types of decisions will be required on a regular basis. And what about future impacts? If a minor network error occurs you may decide to ignore it. But what if it could lead to a larger, more costly issue down the road– how do we predict and account for these?

Technical challenges

  1. Optimizing Data Size: When it comes to machine learning, there is always a delicate balance between getting enough data to generate good AI/ML results, but not so much that it takes too long to process.  Sometimes it is better to reduce the amount of data ingested so the algorithms can provide their findings closer to real-time. This needs to be done carefully though, to maintain quality results. Similarly, if the data output is too large, it becomes too complex and unwieldly for other systems to work with. Therefore, we need to reduce the results to those that are ‘operationally affective’. But how do we know which data to use and which to ignore?  Sometimes these efforts are complex enough that they require their own algorithms.
  2. Lack of standardization of APIs – Further standardization of APIs will eventually create a true ecosystem of best-of-breed systems that can work together seamlessly to create a truly autonomous network. The industry is still evolving in these efforts- with more work ahead. Currently, Automated Root Cause Analysis is the only widely adopted AI/ML API. There needs to be more.
  3. AIOps Challenges – Creating, analyzing, and working with AI and ML data is very different from traditional software. A typical software solution is ready to go upon implementation and no changes are required until the next upgrade, but that isn’t typically the case with AI and ML. These systems have shorter lifecycles and are best defined as a hybrid mix of both a product and a service. They require regular re-training and updates because they are constantly learning from new data all the time. Having the right support structure in place for the ‘care and feeding’ of these new systems will be critical to their success.

Aside from the operational challenges associated with creating an autonomous network, automation in general requires upfront investments in technology, skills, and services. These investments can be significant and are better shared across the whole enterprise. A hybrid approach, which involves selecting the most cost-effective tool for each scenario, may (in the short-term) enable more-rapid deployments. However, this approach can prompt expensive maintenance and vendor management issues in the long term.

Automation also has implications for staff and organizational change. Automation projects can be delayed or difficult to justify where redundancies or reassignment create cost implications. Automation is best achieved where there is a clear understanding of each end-to-end process, and each process is closely managed to prevent poor practices from being replicated through the automation.

Is it worth it?

Some may wonder if these challenges are worth the effort and expense.  The truth is that the telecom industry is at an inflection point, where for progress to continue we must address automation in a way to get both a positive return on investment in the long term, along with immediate results and efficiencies in the short term. What worked yesterday- the systems, processes, and skillsets – won’t work tomorrow. Telecom networks – and the future services they will enable – demand a new operational playbook.

TEOCO is at the forefront of this effort. We are working to address each one of these challenges by participating in research catalysts with groups like the TM Forum and investing in our own research and design; constantly exploring ways to help our customers manage the challenges ahead.


To learn more and hear how we are addressing some of these challenges, sign up for our FutureNet hosted webinar, Leveraging AIOps towards advanced zero-touch operations on the 15th of September at 4pm BST. Or contact us today.


Welcome to the Future of Network Automation: Juniper Paragon Automation as a Service

Contributed by Kanika Atri, Senior Director, Strategic Marketing, Juniper Networks.

Service Providers (SPs) don’t invest in automation for automation’s sake. They do it to achieve business outcomes. Faster time-to-market. Reduced operational complexity and costs. More reliable, higher-quality subscriber experiences. Now, as traffic volumes explode and operators introduce new business and consumer services, including 5G, edge cloud, Internet of Things (IoT), Fixed Wireless Access (FWA) and more, automation has become a top strategic priority for SPs. There is no place this matters more than where these growth trends and new service types converge: the Cloud Metro.

Figure 1: Drivers of Network Automation – Heavy Reading


However, when we survey the market, we see a need to fundamentally reinvent how network automation is deployed and consumed. In fact, market analysts suggest that more than 70% of “Do-it-Yourself” (DIY) in-house network automation projects fail. And when organizations rely on legacy vendor automation solutions, they don’t deliver real business outcomes. For example, in a recent Heavy Reading survey, 40% of SPs said that using a generic automation framework is actually a barrier to adopting automation in transport networks.

At Juniper Networks, we believe there is a better way, and it must be guided by three core principles:

  • Speed matters: The automation tools we use should have a maniacal focus on “time to first business outcome,” and that time should be measured not in years, but in days. Automation should let SPs move at the pace of business, not the pace of internal operations.
  • No overhead: It shouldn’t require a huge investment in capital budget, time and personnel to deploy, operate and continuously update automation software and hardware. Automation should let SPs focus on productivity, not production.
  • Easy button: It should be super easy for staff to use network automation—without needing extensive training or software development skills. Automation software should work for SPs, instead of SPs working for the software.

How can the industry deliver these requirements? Juniper believes the future of network automation is cloud-delivered and Artificial Intelligence (AI)-enabled. It’s been proven in other domains. Now, it’s time to bring this model to the Wide-Area Network (WAN).

Juniper is already an established player in WAN automation, with many of the world’s premier SPs and enterprises using Paragon™ Automation, particularly for closed-loop automation use-cases. Now, we are taking that value proposition further and doubling down on public cloud and AI.

Today, Juniper announced the launch of Paragon Automation as a Service. This solution is more than just a reimagined network automation suite – it’s a reimagined automation experience. And it paves the way for more sustainable business operations in the Cloud Metro and across the network.


Reimagining the Future with Paragon Automation as a Service  

With Paragon Automation as a Service, Juniper is reimagining the automation experience in the following ways:

  • It’s cloud delivered. Just sign up, log in, connect the devices and GO in minutes. There’s no need to worry about hardware/software installation overhead, and it’s much faster than trying to implement DIY automation that might take months or years.
  • It’s AI-enabled. The Paragon Automation cloud infrastructure comes with built-in AI and Machine Learning (ML) data and training pipelines, and WAN-specific AIOps use cases. SPs can spot WAN issues that would elude human analysis using conventional tools, identify root causes and fix them before they impact the service experience. And with AI/ML, the automation framework keeps learning every day and gets better at predicting such issues over time.
  • It’s assurance native. Paragon Active Assurance test agents are now natively embedded into Junos OS Evolved in every ACX7000 platform. These built-in “Experience Sensors” generate synthetic traffic to measure service/application quality, anywhere through the network. Coupled with Paragon Automation as a Service, SPs can detect and fix experience issues proactively. It’s yet another proof point of delivering on our vision of experience-first networking.
  • It’s trust verifiable. Paragon Automation as a Service helps SPs establish network trust at three levels. At the hardware level, it guarantees they are using genuine Juniper gear by validating a unique Device ID linked to TPM 2.0 chips embedded in our routers. At the software level, it then continuously checks software integrity and finally, calculates a network-wide trust score, providing them with actionable insights about potential risks.
  • It’s use-case based. That’s the value of cloud: use only what is needed and pay for only what is used. If SPs only need to automate one use case to solve an immediate business problem – then that’s where they start with Paragon Automation as a Service. There’s no need to go “all-in,” or “boil the ocean” by deploying and training legacy automation systems as one would with most vendor and DIY solutions. With Paragon Automation as a Service, SPs can start small, go fast. SPs can choose their own adventure across the lifecycle of Plan, Orchestrate, Detect and Assure and Optimize use cases.
  • It’s intuitively simple. We combined the art and science of UX design to make Paragon Automation as a Service so simple to use, it can feel like it’s reading your mind. The modern UI is built on a layered information architecture, recommendation engines and visual guides that flow with the user’s operational journey.


Why is the future of automation in the cloud?

Because it just makes sense—from both a business and technical perspective. There’s a reason most of the world’s software has moved to a cloud-delivered SaaS model. It’s why 61% of SPs told Heavy Reading that they want cloud-based network automation in a new survey on transport automation.

Furthermore, Analysys Mason quantified the benefits of SaaS-based automation compared to DIY/ legacy solutions, and the value of SaaS is just staggeringly obvious.

  • Business benefits:
    • The simplicity of a SaaS platform reduces deployment times by 50% for most projects, translating directly to faster time-to-market and increased revenues.
    • Once deployed, SaaS automation platforms allow SPs to add new use cases 70% faster than with traditional approaches.
    • SPs incur 40% lower costs with a SaaS-based automation approach compared to internal development. This owes to savings in hardware, staffing and operational costs for installing and maintaining your own solution.
  • Technical benefits: With cloud, innovation comes fast. Think weekly releases of software improvements, compared to months with traditional solutions. When it comes to AI, it makes sense to use cloud so SPs can leverage the collective intelligence from anonymized data, ML models and knowledge from networks around the world.

Figure 2: Benefits of Network Automation as a Service (vs. DIY) – Analysys Mason


Compared to today’s typical automation approaches, whether it’s a “big-bang” automation system from a vendor or an SP’s own DIY system, most of these efforts fail on the three guiding principles:

  • Slow time to first outcome: Applying a generic automation solution to all network domains requires a huge investment in time and effort. Often, they are just too broad and complex, making them difficult to get off the ground, evolve, scale and, most importantly, deliver business outcomes. SPs might, eventually, see some positive outcomes, but they won’t get there quickly or easily.
  • Extra overhead: The costs to build, deploy and manage the infrastructure grow quickly. Often, it ends up costing much more than budgeted, making it difficult to prove ROI.
  • No easy button: Traditional solutions require extensive training and skills to use effectively. This gets even harder when skilled employees are scarce and staff churn is high.

Bottom line: a cloud-delivered, AI-enabled network automation approach offers much faster, cost-effective, simpler and outcome-focused results.

Automating the Cloud Metro

As SPs adopt a Cloud Metro model, distributing more capacity and service intelligence out closer to subscribers, they can’t rely on yesterday’s approaches to metro network operations. What they want is built-in security, assurance and AIOps.

Figure 3: SP requirements for metro network automation – Heavy Reading


Coupling Juniper Paragon Automation as a Service with our Cloud Metro platform is a perfect fit, providing a complete, shrink-wrapped solution. SPs get on-box elements such as embedded active assurance sensors and built-in Zero Trust security, plus off-box elements from Paragon Automation as a Service. That’s how they can build sustainable Cloud Metro operations across the lifecycle.

  • Day 0: Authenticate and onboard thousands of Cloud Metro devices in minutes, not days, and launch new services more quickly with Paragon Automation as a Service.
  • Day 1: Natively enable the quality of Cloud Metro services with built-in Experience Sensors and establish network trust by verifying hardware and software integrity at scale.
  • Day 2 and beyond: Find, fix and predict Cloud Metro problems before they impact user experience, thanks to WAN AIOps built into Paragon Automation as a Service.

Service Providers are more than ready for automation. But most automation solutions haven’t been ready to deliver the outcomes SPs need—until now.

See the power of Paragon Automation as a Service for yourself with this sneak peek into just one example of the future of automation, our AI-enabled Device Onboarding as a Service and discover how we’re reimagining the onboarding process to make it instantaneous, virtually error-free and secured.

Cloud-first, AI-enabled automation is the future. Juniper’s initial offering will be available early 2023—and we’re just getting started. Throughout next year, we expect to roll out additional use cases and an AI-driven conversational assistant.

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Any SOPD Information within, or referenced or obtained from, this website by any person does not give rise to any reliance claim, or any estoppel, against Juniper in connection with, or arising out of, any representations set forth in the SOPD Information. Juniper is not liable for any loss or damage (howsoever incurred) by any person in connection with, or arising out of, any representations set forth in the SOPD Information.

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Presentation – The Future Telco: Autonomous networks

Watch On-Demand. Broadcast live at FutureNet World on the 11th of May 2022.

  • Dr. Muslim Elkotob, Principal Solutions Architect, Vodafone
  • Kevin McDonnell, Research Director and AN Architect, Huawei

Panel Discussion – The Cloud Native Telco – next generation operating models

Watch On-Demand. Broadcast live at FutureNet World on the 11th of May 2022.

  • Caroline Chappell, Research Director, Analysys Mason
  • Thomas van Briel, SVP Architecture & Strategy, Deutsche Telekom
  • Bastien Bianchi, Director Core Network and Transmission Solutions, Orange Belgium
  • Partha Seetala, President, Cloud BU, Robin.io, A Rakuten Symphony company

Panel Discussion – 5G Operational Transformation: How to leverage automation in the lower layers of the network

Watch On-Demand. Broadcast live at FutureNet World on the 11th of May 2022.

  • Gabriela Styf Sjoman, Board Member, TDC Net former Chief Strategy Officer, Nokia former Deputy COO & Head of Group Networks, Telia
  • Shahryar Khan, Head of Automation & Systems, Network Systems & Delivery , Telia Group
  • Alessandra Pavese, Head of Network Evolution and Automation, Vodafone Italy
  • Teresa Monteiro, Director Solution Marketing, Software and Automation, Infinera

BT Case Study – 5G Customer Cases- what new use cases can be monetized, and how

Watch On-Demand. Broadcast live at FutureNet World on the 10th of May 2022.

  • Neil McRae, Managing Director & Chief Architect, BT

Keynote Panel Discussion – Automation Now: Role of automation in running a network with disaggregation technology

Watch On-Demand. Broadcast live at FutureNet World on the 11th of May 2022.

  • Patrick Kelly, Founder, Partner, and Principal Analyst, Appledore Research Group
  • Neil McRae, Managing Director & Chief Architect, BT
  • Terje Jensen, Senior VP & Head of Global Network Architecture, Telenor
  • Franco Messori, Chief Product Strategy and Transformation Officer, Infovista

Opening Keynote Panel – Future Networks: Injecting intelligent automation into network and service operations

Watch On-Demand. Broadcast live at FutureNet World on the 11th of May 2022.

  • Amy Cameron, Principal Analyst, STL Partners
  • Juan Manuel Caro, Director Operations Transformation, Telefónica
  • Jean-Benoît Besset, VP IT & Network Strategy, Orange
  • Azhar Mirza, Group Vice President, Applications GBU, Oracle Communications

BT private 5G partnership buoys Ericsson, while declarative code gets a grip

Contributing Editor Annie Turner looks at some of the market and technology moves around network automation over recent weeks. 

BT and Ericsson (UK and Ireland) signed multi-million-pound partnership to provide commercial 5G networks. The two claim this is the first commercial 5G private network agreement of its kind in the UK. They also said it would “combine BT’s expertise in building converged fixed and mobile networks with Ericsson’s leading 5G network technology and enterprise solutions”.

Ericsson ended May on something of high, having had a torrid 2022 so far. In January it reported what Bloomberg called “stellar results”, with year-on-year profits up by 41%, but activist investor Cevian Capital, took the opportunity to complain about the Swedish vendor’s low share price, its $6.2 billion Vonage acquisition last November, and a lack of clarity about the enterprise market. Ericsson’s share price and reputation subsequently took a serious battering when allegations of payments to ISIS in Iraq surfaced.

BT’s Paul Murnaghan with Joe O’Neill from Belfast Harbour

Remedial measure

In the face of fierce criticism, in mid-May Ericsson announced it was restructuring to drive growth, which includes setting up a new division for Business Area Enterprise Wireless Solutions, comprising Cradlepoint and Dedicated Networks. George Mulhern is appointed head of the unit and will join the Ericsson executive team. The new structure will be in place from 1 June.

BT and Ericsson have worked together on major projects incorporating private 5G networks, including Belfast Harbour in Northern Ireland. They say their contract paves the way for BT to sell 5G products to enterprises in sectors from manufacturing to defence, education, retail, healthcare, transport and logistics.

Getting your ducks in a row

Sinéad Pillion, Head of Operations Ericsson Athlone, with Minister of State Robert Troy and Denis Dullea, Head of R&D Ericsson Athlone

Just before the contract was announcement, BT said it would invest almost £100 million over the next three years in its Division X unit which is supposed to accelerate the development of customer solutions which embed tech including 5G, IoT, edge compute, cloud and AI. Division X is led by Marc Overton.

For its part just ahead of the announcement with BT, Ericsson said it would recruit will hire 250 cloud native software developers, engineers and architects to its Athlone R&D centre in Ireland to work on 5G projects.

Ericsson says its Irish operation has grown 25% over the past five years and that it also intends to attract software developers, data scientists, architects, cloud and mobile communication engineers to the centre over the next three years.

Denis Dullea, Head of R&D at Ericsson Athlone said the moves are “to enhance our capability to deliver the benefits of cloud native technologies to our global customer base via our RAN, Management, Automation and Orchestration offerings.”

DIY declarative code gathers pace

There were some interesting findings in the Nemertes Network Automation Research Study 2022, published in May 2022. It looked at how organisations with a lot of Cisco kit in their infrastructure implement network automation. Turns out that fewer than 20% use its flagship DNA Center network controller and management dashboard to automate provisioning and change management.

By contrast, more than 40% of those surveyed provide their own automation solutions from a combo of imperative scripting or programming (Python in the main), while about half use a model other than imperative or in addition to it – declarative automation.

We explored the importance of declarative coding in our recent interview with Philippe Ensarguet, Group CTO of Orange Business Services. In short, declarative coding (such as HTML) describes the desired outcomes and achieves them using reconciliation loops to fix any deviations from the pre-set desired state.

Most programming is imperative – a series of ‘If this happens, do that and then if X happens do Y’. Accuracy is critical: it is not forgiving if the sequence isn’t correct or the coder includes more or less than is required.

The study found 33% of the organizations interviewed used Red Hat’s Ansible for network automation because of the increased use of DevOps and its infrastructure as code approach. It started out as imperative but then graduated to declarative around five years ago. Gluware is designed for network automation in Cisco-heavy environments.

As M. Ensarguet explained, a declarative approach can support full automation so that as data centres, networks and storage are softwarised, the people working in these areas make all employees affected by this shift “more effective, more productive,” He added. “If you are not able to automate longer or deeper than with the CI/CD [continuous integration/continuous delivery], then you have no lever to manage the scaling – and that’s critical to the whole [automation] thing.”

It looks like that message is increasingly well understood from the Nemertes research, and we’ll leave them with the last word from the study (see ‘Recommendations’ image).


Edge- Hype vs Reality

Watch On-Demand. Broadcast live at FutureNet World on the 10th May 2022.

  • Yesmean Luk, Principal Consultant and Practice Lead, STL Partners
  • Terje Jensen, Senior VP & Head of Global Network Architecture, Telenor
  • Joanna Newman, Global Edge Computing and 5G Principal Manager, Vodafone
  • Ari Banerjee, Senior Vice President, Strategy, Netcracker

Simplifying the Edge Opportunity with Automation

Contributed by Susan White, Head of Strategy and Portfolio Marketing, Netcracker.

Enterprises need edge compute for many reasons – enabling low latency for real-time IoT services, reducing backhaul costs for video processing and keeping sensitive data local – to name a few. It’s becoming a core strategy for many companies in vertical markets to boost productivity, performance and increase customer satisfaction.

However, most enterprises cannot deal with the complexity of putting all the piece parts together and running these mission critical services with strict SLAs. The choice and location of edge cloud platform, connectivity options, security solutions, distributed 5G core and MEC applications presents a hefty project that requires extensive design, testing, validation, deployment and continuous assurance.

Enterprises need simplicity.

This creates an ideal opportunity for CSPs to remove this complexity and at the same time deepen their engagement and value in enterprise and vertical markets. However, no CSP has all the piece parts that are needed to offer enterprise customers a successful edge solution. It’s a multi-partner play that requires a great deal of cooperation and integration across the ecosystem.

This is why automation is essential to build and monetize a successful edge business. Whether it’s part of a private or hybrid 5G network, automation is required at both the operational and business levels.


Unifying and Scaling Edge Operations

Operations automation of the edge domain is essential for three key reasons:

  1. Unification of operational processes within the edge cloud. Today, edge platform automation (on-premise or network-edge) is separated from the service lifecycle of VNFs and CNFs, which in turn is separated from high value MEC applications. This results in high complexity and manual intervention. These processes must be streamlined to commission the entire edge platform, services and even network slices with zero touch.
  2. E2E view across highly distributed edge locations, transport and core. Edge services are highly complex and may have stringent latency and performance requirements. Resources can reside on multiple edge hosts (CSP and hyperscaler) and multiple locations (on-premise, network-edge, regional, core). Edge service deployments extend beyond the edge. An E2E view is essential to automate edge services.
  3. The ability to scale to many customers. Silo private 5G and edge networks make replication and scale impossible. This in turn makes the business case harder to justify. A unified operations environment is needed that can start small and scale to many businesses.

Netcracker has built these capabilities into its Edge Orchestration solution. We are helping CSPs globally, including Etisalat, develop a successful edge business. It goes beyond the intelligent placement and lifecycle management of edge services to also ensure the RAN has the right QoS enabled, connectivity between all edge hosts is ready (CSP or hyperscaler) and the 5G UPF breakout function is enabled for edge hosts. With its modular architecture and multitenancy, CSPs can scale their edge services across public and hybrid 4G/5G deployments.


Giving Enterprises Visibility, Control and the Right Partner Solutions

Enterprises are demanding faster access to their services with the simplicity of selecting, activating and managing their services on-demand from an intuitive portal. This necessities automation at the business layer also. Back end processes such as product catalogue, customer management and revenue management need to be streamlined with the front-end portal to enable automation from customer order to activation.

Serving vertical markets with right edge service will require a dynamic ecosystem of partners to address their specific business needs. The resulting multi-party solution will require a new approach for partner management that automates the myriad of processes from on-boarding partners to cataloguing, product management, pricing, partner management, and settlements.

Netcracker’s Digital Marketplace solution brings all these capabilities together to help CSPs give their customers an easy way to purchase services with complete visibility and control. The integrated Partner Management solution, adopted by T-Mobile US for its wholesale MVNO and IoT business, helps CSPs to build a vibrant partner ecosystem that will be essential to compete in the edge market.

Edge is a complex business – but it has the potential to be a very lucrative one with the right automation in place that brings simplicity and value to enterprise customers.