Tag: A3

Network use metrics: Good versus easy and why it matters

Introduction

Telecoms, like much of the business world, often revolves around measurements, metrics and KPIs. Whether these relate to coverage of networks, net-adds and churn rates of subscribers, or financial metrics such as ARPU, there is a plethora of numerical measures to track.

They are used to determine shifts in performance over time, or benchmark between different companies and countries. Regulators and investors scrutinise the historical data and may set quantitative targets as part of policy or investment criteria.

This report explores the nature of such metrics, how they are (mis)used and how the telecoms sector – and especially its government and regulatory agencies – can refocus on good (i.e., useful, accurate and meaningful) data rather than over-simplistic or just easy-to-collect statistics.

The discussion primarily focuses on those metrics that relate to overall industry trends or sector performance, rather than individual companies’ sales and infrastructure – although many datasets are built by collating multiple companies’ individual data submissions. It considers mechanisms to balance the common “data asymmetry” between internal telco management KPIs and metrics available to outsiders such as policymakers.

A poor metric often has huge inertia and high switching costs. The phenomenon of historical accidents leading to entrenched, long-lasting effects is known as “path dependence”. Telecoms reflects a similar situation – as do many other sub-sectors of the economy. There are many old-fashioned metrics that are no longer really not fit for purpose and even some new ones that are badly-conceived. They often lead to poor regulatory decisions, poor optimisation and investment approaches by service providers, flawed incentives and large tranches of self-congratulatory overhype.

An important question is why some less-than-perfect metrics such as ARPU still have utility – and how and where to continue using them, with awareness of their limitations – or modify them slightly to reflect market reality. Sometimes maintaining continuity and comparability of statistics over time is important. Conversely, other old metrics such as “minutes” of voice telephony actually do more harm than good and should be retired or replaced.

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Looking beyond operator KPIs

Throughout the report, we make a semantic distinction between industry-wide metrics and telco KPIs. KPIs are typically generated for specific individual companies, rather than aggregated across a sector. And while both KPIs and metrics can be retrospective or set as goals, metrics can also be forecast, especially where they link operational data to other underlying variables, such as population, geographic areas or demand (rather than supply).

STL Partners has previous published work on telcos’ external KPIs, including discussion of the focus on “defensive” statistics on core connectivity, “progressive” numbers on new revenue-generating opportunities, and socially-oriented datasets on environmental social and governance (ESG) and staffing. See the figure below.

Types of internal KPIs found in major telcos

Source: STL Partners

Policymakers need metrics

The telecoms policy realm spans everything from national broadband plans to spectrum allocations, decisions about mergers and competition, net neutrality, cybersecurity, citizen inclusion and climate/energy goals. All of them use metrics either during policy development and debate, or as goalposts for quantifying electoral pledges or making regional/international comparisons.

And it is here that an informational battleground lies.

There are usually multiple stakeholder groups in these situations, whether it is incumbents vs. new entrants, tech #1 vs. tech #2, consumers vs. companies, merger proponents vs. critics, or just between different political or ideological tribes and the numerous industry organisations and lobbying institutions that surround them. Everyone involved wants data points that make themselves look good and which allow them to argue for more favourable treatment or more funding.

The underlying driver here is policy rather than performance.

Data asymmetry

A major problem that emerges here is data asymmetry. There is a huge gulf between the operational internal KPIs used by telcos, and those that are typically publicised in corporate reports and presentations or made available in filings to regulators. Automation and analytics technologies generate ever more granular data from networks’ performance and customers’ usage of, and payment for, their services – but these do not get disseminated widely.

Thus, policymakers and regulators often lack the detailed and disaggregated primary information and data resources available to large companies’ internal reporting functions. They typically need to mandate specific (comparable) data releases via operators’ license terms or rely on third-party inputs from sources such as trade associations, vendor analysis, end-user surveys or consultants.

 

Table of content

  • Executive Summary
    • Key recommendations
    • Next steps
  • Introduction
    • Key metrics overview
    • KPIs vs. metrics: What’s in a name?
    • Who uses telco metrics and why?
    • Data used in policy-making and regulation
    • Metrics and KPIs enshrined in standards
    • Why some stakeholders love “old” metrics
    • Granularity
  • Coverage, deployment and adoption
    • Mobile network coverage
    • Fixed network deployment/coverage
  • Usage, speed and traffic metrics
    • Voice minutes and messages
    • Data traffic volumes
    • Network latency
  • Financial metrics
    • Revenue and ARPU
    • Capex
  • Future trends and innovation in metrics
    • The impact of changing telecom industry structure
    • Why applications matter: FWA, AR/VR, P5G, V2X, etc
    • New sources of data and measurements
  • Conclusion and recommendations
    • Recommendations for regulators and policymakers
    • Recommendations for fixed and cable operators
    • Recommendations for mobile operators
    • Recommendations for telecoms vendors
    • Recommendations for content, cloud and application providers
    • Recommendations for investors and consultants
  • Appendix
    • Key historical metrics: Overview
    • How telecoms data is generated
  • Index

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A3 in open RAN

What is open RAN?

Open RAN (with a capital O) describes a set of standards defined by the O-RAN Alliance. The standard architecture of Open RAN is shown in the figure below, and the major parts of the architecture include:

  • The Service Management and Orchestration Framework (SMO), which will most likely form the umbrella RAN management system for all RANs going forward. It includes:
    • A design environment for rapid application development
    • A common data collection platform for management of RAN data and mediation for the O1, O2 and A1 interfaces
    • Support for licensing, access control and AI/ML lifecycle management
    • Existing OSS functions such as service orchestration, inventory/topology and policy control.
  • The RAN Intelligent Controller (RIC), which is responsible for controlling and optimising RAN functions. It has three main objectives: to receive a stream of data on which to make optimisation decisions, to ensure that services maintain the required performance levels, and to ensure RAN efficiency, balancing the needs of all users. The RIC has two components:
    • The Non-Real Time RIC (Non-RT RIC) offers closed-loop control functions which last for more than one second. Some of these functions are available today in C-SON. The placement of the Non-RT RIC in the SMO and not in the RAN is to secure access to contextual data and use it to optimise the RAN, something that the RAN nodes CU, DU and Near RT-RIC can’t do. rApps are developed for the Non-RT RIC and ingest radio environment data (e.g. device location, signal strength measurements), device data (e.g. positioning and trajectories, plus application-level information), cross-domain information (e.g. insights from the core) and external data (e.g. weather). There are a wide range of potential rApps being developed – including those involved in traffic steering, load balancing, capacity optimisation and energy optimisation. They also involve complex self-organising network functions, such as the dynamic orchestration of radio and transport domains; and various management functions, such as management of the cloud, slices and policy.
    • The Near-Real Time RIC (near-RT RIC) offers closed-loop control with functions lasting between 10ms and one second which support faster data streams and fast control of RAN functions. xApps are developed for the near-RT RIC and, unlike rApps, have access to data from specific CUs and DUs, and receive instructions from the rApps on actions to take. Use cases include network control, such as radio bearer management, load balancing, handover and interference mitigation, and mMIMO beamforming optimisation. Their development is challenging and requires specific knowledge of radio network  parameters and of currently vendor-proprietary APIs, as well as the ability to tightly interact with the vendor’s CUs and DUs.

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O-RAN overall logical architecture

Source: O-RAN Alliance White Paper, Feb 2020

Overview of the xApp/rApp market and opportunities for vendors

This section looks at the trends in xApp and rApp deployment over the next couple of years and provides some detail from analysis of their current availability.

As previously discussed, telcos have a wide range of requirements when upgrading the RAN: improved performance, cost reduction, improved spectrum and capital utilisation, as well as developing its future potential to underpin new revenues. This broad goal offers many opportunities for both RAN-specialist and other vendors to develop a range of simple to more sophisticated intelligence and automations. Opportunities will be dependent on market factors including:

  • The amount of telcos which will choose to convert, or ask vendors to convert, the already deployed capabilities of their existing C-SON into rApps/xApps. We expect this to be a popular option where the telco feels comfortable with current performance and capabilities
  • As the non-RT RIC can also interoperate with legacy RAN, which will help a smooth transition of existing capabilities into the open RAN, the number of new rApps needed in the short term might be smaller
  • Telcos, many of which will be Tier 1s with the ability to develop their own A3, will also impact the early market. Our interview discussions suggested that the majority of DIY telcos will solve specific network situations where there is no available vendor solution and that, therefore, the creation of home-grown solutions will reduce over time. However, there was also discussion of telcos wanting to become rApp developers in order to monetise their IP, which is likely to see a steady stream of app development from telcos.

Table of Contents

  • Executive Summary
    • The need for A3 within open RAN
    • A3 market development
    • Actions for telcos and vendors
  • Table of Contents
  • Table of Figures
  • Quick review: What is open RAN?
  • Overview of the xApp/rApp market and opportunities for vendors
    • rApp market
    • xApp market
    • Assessing the potential of rApps and xApps
  • A3 requirements in open RAN
    • Interference management
    • Channel estimation
    • RAN design and planning
    • Handover management
    • Load balancing
    • Traffic steering
    • RAN management
    • Beamforming
    • Service-related
    • Power management
    • The use of A3 in the SMO
  • Conclusion
  • Index

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Generative AI and beyond: Preparing for future A3

Generative AI and other technology changes

Previous work in 2020 (the basis of our report, Telco A3: Skilling up for the long term published in January 2021) uncovered four areas of A3 impact that will shape a telco into the mid and longer term. Since then, new internal and external consequences have emerged from both the telco’s and its customers’ adoption of A3, as well as changes around the underpinning technology that a telco will need to deploy – in addition to A3-induced shifts in organisational shape and focus.

 Four main areas of A3 impact

Source: Charlotte Patrick Consult, STL Partners

The figure below details the main A3 activities inside these four areas, shown against an approximate timeline which stretches from the short term into the longer term. This report addresses these activities, including thing as customer, decision intelligence, generative AI and digital immunity (as shown in the red boxes in the figure below), which we pay particular attention to due to the current high interest in the area and/or the significance of their expected future impact.

A3 activity areas for telcos

Source: Charlotte Patrick Consult, STL Partners

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Augmented customer experience

New A3 is used to provide support for unassisted (digital) and assisted (human agent) interactions between the telco, the telco’s customers or ecosystem partners and the telco’s supplier or partners. The figure below shows the increasing complexity of these interactions: the grey dashed lines show current interactions which are mostly human-to-human; the coloured lines show new machine interactions either for care purposes (orange) or for purchasing (red).

Entities in the new customer ecosystem

Source: Charlotte Patrick Consult, STL Partners

The newest area for telcos is the introduction of interacting with a “thing”. This is defined as a piece of user equipment (typically, a connected device or sensor or even a bot) that can interact with the telco to request care or make a purchase. The figure above shows the other entities within the environment.

  • Centralised purchasing bot: Designed to purchase goods and services on behalf of a company or individual.
  • Embedded intelligence: Intelligence added into a thing which takes it from being able to make simple requests (“I need help”) towards being able to collect data from multiple sources and create more sophisticated requests (the infamous smart refrigerator ordering groceries). Embedded intelligence in the telco network may also be able to receive more complex requests and prescribe/execute remedies in downstream systems.
  • General consumer bot: Amazon Alexa, for example.
  • Contact centre botand sales bot: These interact with humans or machines to provide help or take an order.

 

Table of Contents

  • Executive Summary
    • Developing A3 will significantly impact telcos in four areas
    • Preparatory actions for telcos
    • Activity streams: A summary
  • Introduction
  • Augmented customer experience
    • Main concepts
    • Thing as customer: The significance for telcos
    • Next steps for telcos in augmented customer experience
  • Augmented experts
    • Main concepts
    • Decision intelligence: The significance for telcos
    • The next steps for telcos in augmented experts
  • Intelligent automation
    • Main concepts
  • AI design
    • Main concepts
    • Generative AI: The significance for telcos
    • The next steps for telcos in AI design
  • Smarter customers
    • Main concepts
    • The next steps for telcos in supporting smarter customers
  • Increasing intelligence
    • Main concepts
    • The next steps for telcos in increasing intelligence
  • Trust, value generation and skills
    • Main concepts – trust
    • Main concepts – value generation
    • Main concepts – skills
    • Digital immunity: The significance for telcos
    • The next steps for telcos in trust, value generation and skills
  • Conclusion
  • Index

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The data-driven telco: How to progress

Becoming data-driven is an evolving journey

Telcos have started on the path to leveraging data more fully but techniques, technologies and their implications are continuously emerging and evolving – posing new opportunities and challenges for the teams responsible for plotting their course.

Although somewhat overused, the “data-driven” refrain provides a banner under which the Chief Data Officer (CDO) and other teams throughout the telco can remind the organisation of the importance of the work that they are doing.  As new technologies become available and capabilities such as automation progress in their sophistication, there will continue to be a steady stream of demands on the data team from across the organisation.  There will also be an increase in demand from outside the organization as telcos begin to play in multiple new ecosystems.

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STL Partners conducted primary and secondary research to determine the current priorities for telcos that have progressed some way down the data-driven track.  During our primary research, we spoke to four Chief Data Officers (CDOs) – or equivalent – at Orange, Zain, Telefónica and Vodafone and asked them about their core focus areas in the short- and mid-term and how they were driving forward the data-driven telco agenda. Topics for discussion included:

  • Their vision and expected future strategy
  • Their current focus areas
  • The work that they are undertaking to improve organisational structure and culture
  • Their priorities for future technology roll out.

As shown in the figure below, we note that some areas of priority remain unchanged from previous years and continue to be a focus in 2023, while new ones (shown in red) are appearing on the horizon.

Priorities for the CDO and their team

Roles of data-driven telco CDO

Source: STL Partners

Priorities are evolving from being focused specifically on accessing data and other relatively discrete A3 projects, to much more strategic and organisation-wide activities. As such, the scope of the CDO role is expanding.

Table of contents

  • Executive Summary
    • Recommendations
    • Vision and strategy
    • Organisation and culture
    • Technology
    • Next steps
  • Introduction
  • Priority 1: Select the right internal focus
    • How to select the most impactful projects
    • How to maintain a pipeline of successful projects
  • Priority 2: Create a joined-up organisation
    • A joined-up organization structure
    • Promoting the data culture
    • Skill sets of the Chief Data Officer (CDO)
  • Priority 3: Delivering a useable data set
    • Building a long-term data quality practise
    • Hybrid-cloud data deployment
  • Priority 4: Building data tools for all
  • Conclusion

Related research

 

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Telco digital twins: Cool tech or real value?

Definition of a digital twin

Digital twin is a familiar term with a well-known definition in industrial settings. However, in a telco setting it is useful to define what it is and how it differs from a standard piece of modelling. This research discusses the definition of a digital twin and concludes with a detailed taxonomy.

An archetypical digital twin:

  • models a single entity/system (for example, a cell site).
  • creates a digital representation of this entity/system, which can be either a physical object, process, organisation, person or abstraction (details of the cell-site topology or the part numbers of components that make up the site).
  • has exactly one twin per thing (each cell site can be modelled separately).
  • updates (either continuously, intermittently or as needed) to mirror the current state of this thing. For example, the cell sitescurrent performance given customer behavior.

In addition:

  • multiple digital twins can be aggregated to form a composite view (the impact of network changes on cell sitesin an area).
  • the data coming into the digital twin can drive various types of analytics (typically digital simulations and models) within the twin itself – or could transit from one or multiple digital twins to a third-party application (for example, capacity management analytics).
  • the resulting analysis has a range of immediate uses, such as feeding into downstream actuators, or it can be stored for future use, for instance mimicking scenarios for testingwithout affecting any live applications.
  • a digital twin is directly linked to the original, which means it can enable a two-way interaction. Not only can a twin allow others to read its own data, but it can transmit questions or commands back to the original asset.

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What is the purpose of a digital twin?

This research uses the phrase “archetypical twin” to describe the most mature twin category, which can be found in manufacturing, operations, construction, maintenance and operating environments. These have been around in different levels of sophistication for the last 10 years or so and are expected to be widely available and mature in the next five years. Their main purpose is to act as a proxy for an asset, so that applications wanting data about the asset can connect directly to the digital twin rather than having to connect directly with the asset. In these environments, digital twins tend to be deployed for expensive and complex equipment which needs to operate efficiently and without significant down time. For example, jet engines or other complex equipment. In the telco, the most immediate use case for an archetypical twin is to model the cell tower and associated Radio Access Network (RAN) electronics and supporting equipment.

The adoption of digital twins should be seen as an evolution from today’s AI models

digital-twins-evolution-of-todays-ai-models-stl-partners

*See report for detailed graphic.

Source: STL Partners

 

At the other end of the maturity curve from the archetypical twin, is the “digital twin of the organisation” (DTO). This is a virtual model of a department, business unit, organisation or whole enterprise that management can use to support specific financial or other decision-making processes. It uses the same design pattern and thinking of a twin of a physical object but brings in a variety of operational or contextual data to model a “non-physical” thing. In interviews for this research, the consensus was that these were not an initial priority for telcos and, indeed, conceptually it was not totally clear whether the benefits make them a must-have for telcos in the mid-term either.

As the telecoms industry is still in the exploratory and trial phase with digital twins, there are a series of initial deployments which, when looked at, raise a somewhat semantic question about whether a digital representation of an asset (for example, a network function) or a system (for example, a core network) is really a digital twin or actually just an organic development of AI models that have been used in telcos for some time. Referring to this as the “digital twin/model” continuum, the graphic above shows the characteristics of an archetypical twin compared to that of a typical model.

The most important takeaway from this graphic are the factors on the right-hand side that make a digital twin potentially much more complex and resource hungry than a model. How important it is to distinguish an archetypical twin from a hybrid digital twin/model may come down to “marketing creep”, where deployments tend to get described as digital twins whether they exhibit many of the features of the archtypical twin or not. This creep will be exacerbated by telcos’ needs, which are not primarily focused on emulating physical assets such as engines or robots but on monitoring complex processes (for example, networks), which have individual assets (for example, network functions, physical equipment) that may not need as much detailed monitoring as individual components in an airplane engine. As a result, the telecoms industry could deploy digital twin/models far more extensively than full digital twins.

Table of contents

  • Executive Summary
    • Choosing where to start
    • Complexity: The biggest short-term barrier
    • Building an early-days digital twin portfolio
  • Introduction
    • Definition of a digital twin
    • What is the purpose of a digital twin?
    • A digital twin taxonomy
  • Planning a digital twin deployment
    • Network testing
    • Radio and network planning
    • Cell site management
    • KPIs for network management
    • Fraud prediction
    • Product catalogue
    • Digital twins within partner ecosystems
    • Digital twins of services
    • Data for customer digital twins
    • Customer experience messaging
    • Vertical-specific digital twins
  • Drivers and barriers to uptake of digital twins
    • Drivers
    • Barriers
  • Conclusion: Creating a digital twin strategy
    • Immediate strategy for day 1 deployment
    • Long-term strategy

Related research

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The Future of Work: How AI can help telcos keep up

What will the Future of Work look like?

The Future of Work is a complex mix of external and internal drivers which will exert pressure on the telco to change – both immediately and into the long-term. Drivers include government policy, general changes in cultural attitudes and new types of technology. For example, intelligent tools will see humans and machines working more closely together. AI and automation will be major drivers of change, but they are also tools to address the impact of this change.

AI and automation both drive and solve Future of Work challenges

Futuore of work AI automation analytics

Source: STL Partners

This report leverages secondary research from a variety of consultancies, research houses and academic institutions. It also builds on STL Partners’ previous research around the use of A3 and future new technologies in telecoms, as well as organisational learning to increase telco ability to absorb change and thrive in dynamic environments:

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The Future of Work

We begin by summarising secondary research around the Future of Work. Key topics we explore are:

Components of the Future of Work

Future of work equation

Source: STL Partners

  1. The term Fourth Industrial Revolution is often used interchangeably with the technologies involved in Industry 4.0. However, this report uses a broader definition (quoted from Salesforce):
    • “The blurring of boundaries between the physical, digital, and biological worlds. It’s a fusion of advances in artificial intelligence (AI), robotics, the Internet of Things (IoT), 3D printing, genetic engineering, quantum computing, and other technologies.” 
  2. Societal and cultural change includes changes in government and public attitude, particularly around climate change and issues of equality. It also includes changing attitudes of employees towards work.
  3. Business environment change encompasses a variety of topics around competitive dynamics (e.g. national versus global economies of scale) and changing market conditions, in particular with relation to changing corporate structures (hierarchies, team structures, employees versus contractors).
  4. Pandemic-related change: The move towards homeworking and hastening of some existing/new trends (e.g. automation, ecommerce).

Content

  • Executive Summary
  • Introduction
  • The Future of Work
    1. The Fourth Industrial Revolution
    2. Societal and cultural change
    3. Business environment change
    4. Pandemic-related change
  • How will FoW trends impact telcos in the next 5 to 10 years?
    • Expected market conditions
    • Implications for telcos’ strategic direction
    • Workforce and cultural change
  • Telco responses to FoW trends and how A3 can help
    • Strategic direction
    • Skills development
    • Organisational and cultural change
  • Appendix 1
  • Index

Related Research

 

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A3 in customer experience: Possibilities for personalisation

The value of A3 in customer experience

This report considers the financial value to a telco of using A3 technologies (analytics, automation and AI) to improve customer experience. It examines the key area which underpins much of this financial value – customer support channels – considering the trends in this area and how the area might change in future, shaping the requirement for A3.

Calculating the value of improving customer experience is complex: it can be difficult to identify the specific action that improved a customer’s perception of their experience, and then to assess the impact of this improvement on their subsequent behaviour.

While it is difficult to draw causal links between telcos’ A3 activities and customer perceptions and behaviours, there are still some clearly measurable financial benefits from these investments. We estimate this value by leveraging our broader analysis of the financial value of A3 in telecoms, and then zooming in on the specific pockets of value which relate to improved customer experience (e.g. churn reduction).

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The diagram below illustrates that there are two parts of the customer journey where A3 will add most value to customer experience:

  1. The performance of the network, services, devices and applications is increasingly dependent on automation and intelligence, with the introduction of 5G and cloud-native operations. Without A3 capabilities it will be difficult to meet quality of service standards, understand customer-affecting issues and turn up new services at speed.
  2. The contact centre remains one of the largest influencers of customer experience and one of the biggest users of automation, with the digital channels increasing in importance during the pandemic. Understanding the customer and the agent’s needs and providing information about issues the customer is experiencing to both parties are areas where more A3 should be used in future.

Where is the financial benefit of adding A3 within a typical telco customer journey?

A3 customer experience

Source: STL Partners, Charlotte Patrick Consult

As per this diagram, many of the most valuable uses for A3 are in the contact centre and digital channels. Improvements in customer experience will be tied with trends in both. These priority trends and potential A3 solutions are outlined the following two tables:
• The first shows contact centre priorities,
• The second shows priorities for the digital channels.

Priorities in the contact centre

A3 Contact centre

Priorities in the digital channel

A3 Digital channel

Table of Contents

  • Executive Summary
  • The value of A3 in customer experience
  • Use of A3 to improve customer experience
  • The most important uses of A3 for improving the customer experience
    • Complex data
    • Personalisation
    • Planning
    • Human-machine interaction
    • AI point solution
  • Conclusion
  • Appendix: Methodology for calculating financial value
  • Index

Related Research:

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Driving the agility flywheel: the stepwise journey to agile

Agility is front of mind, now more than ever

Telecoms operators today face an increasingly challenging market, with pressure coming from new non-telco competitors, the demands of unfamiliar B2B2X business models that emerge from new enterprise opportunities across industries and the need to make significant investments in 5G. As the telecoms industry undergoes these changes, operators are considering how best to realise commercial opportunities, particularly in enterprise markets, through new types of value-added services and capabilities that 5G can bring.

However, operators need to be able to react to not just near-term known opportunities as they arise but ready themselves for opportunities that are still being imagined. With such uncertainty, agility, with the quick responsiveness and unified focus it implies, is integral to an operator’s continued success and its ability to capitalise on these opportunities.

Traditional linear supply models are now being complemented by more interconnected ecosystems of customers and partners. Innovation of products and services is a primary function of these decentralised supply models. Ecosystems allow the disparate needs of participants to be met through highly configurable assets rather than waiting for a centralised player to understand the complete picture. This emphasises the importance of programmability in maximising the value returned on your assets, both in end-to-end solutions you deliver, and in those where you are providing a component of another party’s system. The need for agility has never been stronger, and this has accelerated transformation initiatives within operators in recent years.

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Concepts of agility have crystallised in meaning

In 2015, STL Partners published a report on ‘The Agile Operator: 5 key ways to meet the agility challenge’, exploring the concept and characteristics of operator agility, including what it means to operators, key areas of agility and the challenges in the agile transformation. Today, the definition of agility remains as broad as in 2015 but many concepts of agility have crystallised through wider acceptance of the importance of the construct across different parts of the organisation.

Agility today is a pervasive philosophy of incremental innovation learned from software development that emphasises both speed of innovation at scale and carrier-grade resilience. This is achieved through cloud native modular architectures and practices such as sprints, DevOps and continuous integration and continuous delivery (CI/CD) – occurring in virtuous cycle we call the agility flywheel.

The Agility Flywheel

agility-flywheel

Source: STL Partners

Six years ago, operators were largely looking to borrow only certain elements of cloud native for adoption in specific pockets within the organisation, such as IT. Now, the cloud model is more widely embraced across the business and telcos profess ambitions to become software-centric companies.

Same problem, different constraints

Cloud native is the most fundamental version of the componentised cloud software vision and progress towards this ideal of agility has been heavily constrained by operators’ underlying capabilities. In 2015, operators were just starting to embark on their network virtualisation journeys with barriers such as siloed legacy IT stacks, inelastic infrastructures and software lifecycles that were architecture constrained. Though these barriers continue to be a challenge for many, the operators at the forefront – now unhindered by these basic constraints – have been driving a resurgence and general acceleration towards agility organisation-wide, facing new challenges around the unknowns underpinning the requirements of future capabilities.

With 5G, the network itself is designed as cloud native from the ground up, as are the leading edge of enterprise applications recently deployed by operators, alleviating by design some of the constraints on operators’ ability to become more agile. Uncertainty around what future opportunities will look like and how to support them requires agility to run deep into all of an operators’ processes and capabilities. Though there is a vast raft of other opportunities that do not need cloud native, ultimately the market is evolving in this direction and operators should benchmark ambitions on the leading edge, with a plan to get there incrementally. This report looks to address the following key question:

Given the flexibility and driving force that 5G provides, how can operators take advantage of recent enablers to drive greater agility and thrive in the current pace of change?

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Table of Contents

    • Executive Summary
    • Agility is front of mind, now more than ever
      • Concepts of agility have crystallised in meaning
      • Same problem, different constraints
    • Ambitions to be a software-centric business
      • Cloudification is supporting the need for agility
      • A balance between seemingly opposing concepts
    • You are only as agile as your slowest limb
      • Agility is achieved stepwise across three fronts
      • Agile IT and networks in the decoupled model
      • Renewed need for orchestration that is dynamic
      • Enabling and monetising telco capabilities
      • Creating momentum for the agility flywheel
    • Recommendations and conclusions

A3 for enterprise: Where should telcos focus?

A3 capabilities operators can offer enterprise customers

In this research we explore the potential enterprise solutions leveraging analytics, AI and automation (A3) that telcos can offer their enterprise customers. Our research builds on a previous STL Partners report Telco data monetisation: What’s it worth? which modelled the financial opportunity for telco data monetisation – i.e. purely the machine learning (ML) and analytics component of A3 – for 200+ use cases across 13 verticals.

In this report, we expand our analysis to include the importance of different types of AI and automation in implementing the 200+ use cases for enterprises and assess the feasibility for telcos to acquire and integrate those capabilities into their enterprise services.

We identified eight different types of A3 capabilities required to implement our 200+ use cases.

These capability types are organised below roughly in order of the number of use cases for which they are relevant (i.e. people analytics is required in the most use cases, and human learning is needed in the fewest).

The ninth category, Data provision, does not actually require any AI or automation skills beyond ML for data management, so we include it in the list primarily because it remains an opportunity for telcos that do not develop additional A3 capabilities for enterprise.

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Most relevant A3 capabilities across 200+ use cases

9-types-of-A3-analytics-AI-automation

Most relevant A3 capabilities for leveraging enterprise solutions

People analytics: This is the strongest opportunity for telcos as it uses their comprehensive customer data. Analytics and machine learning are required for segmentation and personalisation of messaging or action. Any telco with a statistically-relevant market share can create products – although specialist sales capabilities are still essential.

IoT analytics: Although telcos offering IoT products do not immediately have access to the payload data from devices, the largest telcos are offering a range of products which use analytics/ML to detect patterns or spot anomalies from connected sensors and other devices.

Other analytics: Similar to IoT, the majority of other analytics A3 use cases are around pattern or anomaly detection, where integration of telco data can increase the accuracy and success of A3 solutions. Many of the use cases here are very specific to the vertical. For example, risk management in financial services or tracking of electronic prescriptions in healthcare – which means that a telco will need to have existing products and sales capability in these verticals to make it worthwhile adding in new analytics or ML capabilities.

Real time: These use cases mainly need A3 to understand and act on triggers coming from customer behaviour and have mixed appeal to telcos. Telcos already play a significant role in a small number of uses cases, such as mobile marketing. Some telcos are also active in less mature use cases such as patient messaging in healthcare settings (e.g. real-time reminders to take medication or remote monitoring of vulnerable adults). Of the rest of the use cases that require real time automation, a subset could be enhanced with messaging. This would primarily be attractive to mobile operators, especially if they offer broader relevant enterprise solutions – for example, if a telco was involved in a connected public transport solution, then it could also offer passenger messaging.

Remote monitoring/control: Solutions track both things and people and use A3 to spot issues, do diagnostic analysis and prescribe solutions to the problems identified. The larger telcos already have solutions in some verticals, and 5G may bring more opportunities, such as monitoring of remote sites or traffic congestion monitoring.

Video analytics: Where telcos have CCTV implementations or video, there is opportunity to add in analytics solutions (potentially at the edge).

Human interactions: The majority of telco opportunities here relate to the provision of chatbots into enterprise contact centres.

Human learning: A group of low feasibility use cases around training (for example, an engineer on a manufacturing floor who uses a heads-up augmented/virtual reality (AR/VR) display to understand the resolution to a problem in front of them) or information provision (for example, providing retail customers with information via AR applications).

 

Table of Contents

  • Executive Summary
    • Which A3 capabilities should telcos prioritise?
    • What makes an investment worthwhile?
    • Next steps
  • Introduction
  • Vertical opportunities
    • Key takeaways
  • A3 technology: Where should telcos focus?
    • Key takeaways
    • Assessing the telco opportunity for nine A3 capabilities
  • Verizon case study
  • Details of vertical opportunities
  • Conclusion
  • Appendix 1 – full list of 200 use cases

 

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