Tag: ROI

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 Telco Cloud Manifesto 2.0

Nearly two years on from our first Telco Cloud Manifesto published in March 2021, we are even more convinced that going through the pain of learning how to orchestrate and manage network workloads in a cloud-native environment is essential for telcos to successfully create new business models, such as Network-as-a-Service in support of edge compute applications.

Since the first Manifesto, hyperscalers have emerged as powerful partners and enablers for telcos’ technology transformation. But telcos that simply outsource to hyperscalers the delivery and management of their telco cloud, and of the multi-vendor, virtualised network functions that run on it, will never realise the true potential of telco cloudification. By contrast, evolving and maintaining an ability to orchestrate and manage multi-vendor, virtualised network functions end-to-end across distributed, multi-domain and multi-vendor infrastructure represents a vital control point that telcos should not surrender to the hyperscalers and vendors. Doing so could relegate telcos to a role as mere physical connectivity and infrastructure providers helping to deliver services developed, marketed and monetised by others.

In short, operators must take on the ‘workload’ of transforming into and acting as cloud-centric organisations before they shift their ‘workloads’ to the hyperscale cloud. In this updated Manifesto, we outline why, and what telcos at different stages of maturity should prioritise.

Two developments have taken place since the publication of our first manifesto that have changed the terms on which telcos are addressing network cloudification:

  • Hyperscale cloud providers have increasingly developed capabilities and commercial offers in the area of telco cloud. To telcos uncertain about the strategy and financial implications of the next phase of their investments, the hyperscalers appear to offer a shortcut to telco cloud: the possibility of avoiding doing all the hard yards of developing the private telco cloud, and of evolving the internal skills and processes for deploying and managing multi-vendor VNFs / CNFs over it. Instead, the hyperscalers offer the prospect of getting telco cloud and VNFs / CNFs on an ‘as-a-Service’ basis – fundamentally like any other cloud service.
  • In April 2021, DISH announced it would build its greenfield 5G network with AWS providing much of the virtual infrastructure layer and all of the physical cloud infrastructure. In June 2021, AT&T sold its private telco cloud platform to Microsoft Azure. In both instances, the telcos involved are now deploying mobile core network functions and, in DISH’s case, all of the software-based functions of its on a hyperscale cloud. These events appear superficially to set an example validating the idea of outsourcing telco cloud to the hyperscalers. After all, AT&T had previously been a champion of the DIY approach to telco cloud but now looked as though it had thrown in the towel and gone all in with outsourcing its cloud from Azure.

Two main questions arise from these developments, which we address in detail in this second Manifesto:

  • Should telcos embarked or embarking on a Pathway 2 strategy outsource their telco cloud infrastructure and procure their critical network functions – in whole or in part – from one or more hyperscalers, on an as-a-Service basis?
  • What is the broader significance of AT&T’s and DISH’s moves? Does it represent the logical culmination of telco cloudification and, if so, what are the technological and business-model characteristics of the ‘infrastructure-independent, cloud-native telco’, as we define this new Pathway 4? Finally, is this a model that all Pathway 3 players – and even all telcos per se – should ultimately seek to emulate?

In this second Manifesto, we also propose an updated version of our pathways describing telco network cloudification strategies for different sizes and types of telco to implement telco cloud. We now have four pathways (we had three in the original Manifesto), as illustrated in the figure below.

The four telco cloud deployment pathways in STL’s Telco Cloud Manifesto 2.0

Source: STL Partners, 2023

Existing subscribers can download the Manifesto at the top of this page. Everyone else, please go here.

If you wish to speak to us about our new Manifesto, please book a call.

Table of contents

  • Executive Summary
    • Recommendations
  • Pathway 1: No way back
    • Two constituencies at operators: Cloud sceptics and cloud advocates
  • Pathway 2: Hyperscalers – friend or foe?
    • Cloud-native network functions are a vital control point telcos must not relinquish
  • Pathway 3: Build own telco cloud competencies before deploying on public cloud
    • AT&T and DISH are important proof points but not applicable to the industry as a whole
    • But telcos will not realise the full benefits of telco cloud unless they, too, become software and cloud businesses
  • Pathway 4: The path to Network-as-a-Service
    • Pathway 4 networks will enable Network-as-a-Service
  • Conclusion: Mastery of cloud-native is key for telcos to create value in the Coordination Age

Related research

Our telco cloud research aligned to this topic includes:

 

Leveraging insight: The neglected strategic capability

High quality insights are crucial for telcos

Each year telcos invest in external insights from strategic and tactical research houses, alongside primary research budgets. This investment is a response to the ever-evolving trends that are shaping the industry, the need to understand them and support decision-making. It is therefore critical that telcos develop the capability to leverage them well.

What drives the need for insight?

Learning and seeking evidence drive the insight needs across the business. This ranges from individuals drafting a one-off client proposal, to strategy teams developing the corporate response to an emerging opportunity. The breadth of the insight need has implications for research buying and funding practices, as well as how insight is distributed.

Being in the business of external insights, STL Partners is always keen to understand how telco customers use insights and what research management practices they deploy to derive more value from insight services. STL Partners asked Olga Holin, a seasoned research buyer with recent telecoms experience, to talk to a group of her peers and synthesise their perspectives on what “good” insight practice looks like.

The report examines the drivers for external insight acquisition and the types of insights typically acquired. It outlines the insight management approaches at four telcos (representative of Olga’s sample) and highlights the benefits and challenges of each. It then sets forth several guidelines for operators and other organisations to ensure insight quality and derive more value from insight acquisitions going forward.

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Why are external insights necessary?

Across all the organisations we spoke to, respondents agreed that research insights were necessary and valuable, chiefly to drive learning and aid business decision making. The stated reasons for acquiring research include:

  • To identify future growth opportunities and threats in order to plan and innovate accordingly,
  • To inform and educate employees, thereby complementing existing capabilities,
  • To validate internal assumptions and build a deeper understanding of the business and its environment,
  • To assess business performance in context and validate effectiveness.

While some of this insight could conceivably be generated internally, the value drivers of external research over an internal function are:

  • “We don’t know what we don’t know” – To gain access to topics and trends potentially not on the organisational radar. Drawing on the expertise from external sources allows organisations to capture insight more easily and assures no threats or opportunities are missed.
  • To remove blind spots in internal thinking – To challenge mental models, by providing objectivity to change the way an organisation might perceive a certain technology or topic.
  • To influence senior executives – To strengthen the credibility of business cases and market overviews. The insights of analyst houses with strong reputations make analyses more convincing to senior management. As one telco put it, “They don’t always listen to us, but they usually listen to external reputable sources.”
  • To increase the speed of internal knowledge acquisition and learning – To develop the knowledge of employees quickly (they don’t have to find the information, just contextualise it).
  • To secure quality information – To ensure information is robust, unbiased, consistent with industry definitions (external agencies validate information via multiple sources and have no vested interests to protect).
  • To supplement limited internal insight resources – To answer information einquiries more quickly and through experts versus having to recruit internal experts to understand an emerging area.
  • To get access to information that might otherwise be unattainable (e.g. competitor information).
  • To seed change – The outside and informed perspective of a research house can highlight a need for change that may not be recognised due to internal mindsets and environment.

The value to the organisation of having these insights will be influenced by the extent to which findings can inform learning or decisions in more than one part of the business – and the longevity of the findings (how quickly they go out of date) or whether they have a future focus.

External insights may only be required to address needs in a limited business area at a specific point in time, e.g. where a product team wants to know how a newly launched product is faring versus competitor offerings. This type of insight can be considered tactical insight, as it provides the information to enable quick adjustments and decision making in the shorter term, more likely the type of decisions taken by middle managers.

Strategic insights, on the other hand, can generally inform decision making across the organisation more broadly. The topics are relevant to more than one area (e.g. digital transformation) and over a longer period (they say something about the future).

Strategic insights are able to influence decision making at an executive level, equipping teams for discussions around larger investments and those concerned with long-term returns rather than immediate gains. This is illustrated below.

 Tactical versus strategic research

external insights

Source: STL Partners

The nature of the research has implications as to how it should be managed to maximise value.

Table of Contents

  • Executive Summary
    • Recommendations to maximise insight value
    • Telco insights challenge
    • Next steps
  • Introduction
  • Why are external insights necessary?
  • Insight management across the research lifecycle
    • Basic insight management process
    • Advanced insight management process
  • Telco insight management case studies
    • Telco 1
    • Telco 2
    • Telco 3
    • Telco 4
    • Set-up versus research type
  • How to increase the value of research in organisations
  • Index

Related research

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