Tag: network optimisation

End-to-end network automation: Why and how to do it?

Automation, analytics and AI: A3 unlocks value for operators

STL Partners has been writing about automation, artificial intelligence (AI) and data analytics for several years. While the three have overlapping capabilities and often a single use case will rely upon a combination, they are also distinct in their technical outcomes.

Distinctions between the three As

Source: STL Partners

Operators have been heavily investing in A3 use cases for several years and are making significant progress. Efforts can be broadly broken down into five different domains: sales and marketing, customer experience, network planning and operations, service innovation and other operations. Some of these domains, such as sales and marketing and customer experience, are more mature, with significant numbers of use cases moving beyond R&D and PoCs into live, scaled deployments. In comparison, other domains, like service innovation, are typically less mature, despite the potential new revenue opportunities attached to them.

Five A3 use case domains

Source: STL Partners

Use cases often overlap across domains. For example, a Western European operator has implemented an advanced analytics platform that monitors network performance, and outputs a unique KPI that, at a per subscriber level, indicates the customer experience of the network. This can be used to trigger an automated marketing campaign to customers who are experiencing issues with their network performance (e.g. an offer for free mobile hotspot until issues are sorted). In this way, it spans both customer experience and network operations. For the purpose of this paper, however, we will primarily focus on automation use cases in the network domain.  We have modelled the financial value of A3 for telcos: Mapping the financial value.

Request a report extract

The time is ripe for network automation now

Network automation is not new. In fact, it’s been a core part of operator’s network capabilities since Almon Strowger invented the Strowger switch (in 1889), automating the process of the telephone exchange. Anecdotally, Strowger (an undertaker by profession) came up with this invention because the wife of a rival funeral parlour owner, working at the local community switchboard, was redirecting customers calling for Strowger to her own husband’s business.

Early advertising called the Strowger switch the “girl-less, cuss-less, out-of-order-less, wait-less telephone” or, in other words, free from human error and faster than the manual switchboard system. While this example is more than 100 years old, many of the benefits of automation that it achieved are still true today; automation can provide operators with the ability to deliver services on-demand, without the wait, and free from human error (or worse still, malevolent intent).

Despite automation not being a new phenomenon, STL Partners has identified six key reasons why network automation is something operators should prioritise now:

  • Only with automation can operators deliver the degree of agility that customers will demand. Customers today expect the kind of speed, accuracy and flexibility of service that can only be achieved in a cost-effective manner with high degrees of network automation. This can be both consumer customers (e.g. for next generation network services like VR/AR applications, gaming, high-definition video streaming etc.) or enterprise customers (e.g. for creating a network slice that is spun up for a weekend for a specific big event). With networks becoming increasingly customised, operators must automate their systems (across both OSS and BSS) to ensure that they can deliver these services without a drastic increase in their operating costs.
    One  wholesale operator exemplified this shift in expectations when describing their customers, which included several of the big technology companies including Amazon and Google: “They have a pace in their business that is really high and for us to keep up with their requirements and at the same time beat all our competitors we just need to be more automated”. They stated that while other customers may be more flexible and understand that instantiating a new service takes time, the “Big 5” expect services in hours rather than days and weeks.
  • Automation can enable operators to do more, such as play higher up the value chain. External partners have an expectation that telcos are highly skilled at handling data and are highly automated, particularly within the network domain. It is only through investing in internal automation efforts that operators will be able to position themselves as respected partners for services above and beyond pure connectivity. An example of success here would be the Finnish operator Elisa. They invested in automation capabilities for their own network – but subsequently have been able to monetise this externally in the form of Elisa Automate.
    A further example would be STL Partners’ vision of the Coordination Age. There is a role for telcos to play further up the value chain in coordinating across ecosystems – which will ultimately enable them to unlock new verticals and new revenue growth. The telecoms industry already connects some organisations and ecosystems together, so it’s in a strong position to play this coordinating role. But, if they wish to be trusted as ecosystem coordinators, they must first prove their pedigree in these core skills. Or, in other words, if you can’t automate your own systems, customers won’t trust you to be key partners in trying to automate theirs.
  • Automation can free up resource for service innovation. If operators are going to do more, and play a role beyond connectivity, they need to invest more in service innovation. Equally, they must also learn to innovate at a much lower cost, embracing automation alongside principles like agile development and fast fail mentalities. To invest more in service innovation, operators need to reallocate resources from other areas of their business – as most telcos are no longer rapidly growing, resource must be freed up from elsewhere.
    Reducing operating costs is a key way that operators can enable increased investment in innovation – and automation is a key way to achieve this.

A3 can drive savings to redistribute to service innovation

Source: Telecoms operator accounts, STL Partners estimates and analysis

  • 5G won’t fulfil its potential without automation. 5G standards mean that automation is built into the design from the bottom up. Most operators believe that 5G will essentially not be possible without being highly automated, particularly when considering next generation network services such as dynamic network slicing. On top of this, there will be a ranging need for automation outside of the standards – like for efficient cell-site deployment, or more sophisticated optimisation efforts for energy efficiency. Therefore, the capex investment in 5G is a major trigger to invest in automation solutions.
  • Intent-based network automation is a maturing domain. Newer technologies, like artificial intelligence and machine learning, are increasing the capabilities of automation. Traditional automation (such as robotic process automation or RPA) can be used to perform the same tasks as previously were done manually (such as inputting information for VPN provisioning) but in an automated fashion. To achieve this, rules-based scripts are used – where a human inputs exactly what it is they want the machine to do. In comparison, intent-based automation enables engineers to define a particular task (e.g. connectivity between two end-points with particular latency, bandwidth and security requirements) and software converts this request into lower level instructions for the service bearing infrastructure. You can then monitor the success of achieving the original intent.
    Use of AI and ML in conjunction with intent-based automation, can enable operators to move from automation ‘to do what humans can do but faster and more accurately’, to automation to achieve outcomes that could not be achieved in a manual way. ML and AI has a particular role to play in anomaly detection, event clustering and predictive analytics for network operations teams.
    While you can automate without AI and ML, and in fact for many telcos this is still the focus, this new technology is increasing the possibilities of what automation can achieve. 40% of our interviewees had network automation use cases that made some use of AI or ML.
  • Network virtualisation is increasing automation possibilities. As networks are increasingly virtualised, and network functions become software, operators will be afforded a greater ability than ever before to automate management, maintenance and orchestration of network services. Once networks are running on common computing hardware, making changes to the network is, in theory, purely a software change. It is easy to see how, for example, SDN will allow automation of previously human-intensive maintenance tasks. A number of operators have shared that their teams and/or organisations as a whole are thinking of virtualisation, orchestration and automation as coming hand-in-hand.

This report focuses on the opportunities and challenges in network automation. In the future, STL Partners will also look to more deeply evaluate the implications of network automation for governments and regulators, a key stakeholder within this ecosystem.

Table of Contents

  • Executive Summary
    • End-to-end network automation
    • A key opportunity: 6 reasons to focus on network automation now
    • Key recommendations for operators to drive their network automation journey
    • There are challenges operators need to overcome
    • This paper explores a range of network automation use cases
    • STL Partners: Next steps
  • Automation, analytics and AI: A3 unlocks value for operators
    • The time is ripe for network automation now
  • Looking to the future: Operators’ strategy and ambitions
    • Defining end-to-end automation
    • Defining ambitions
  • State of the industry: Network automation today
    • Which networks and what use cases: the breadth of network automation today
    • Removing the human? There is a continuum within automation use cases
    • Strategic challenges: How to effectively prioritise (network) automation efforts
    • Challenges to network automation– people and culture are key to success
  • Conclusions
    • Recommendations for vendors (and others in the ecosystem)
    • Recommendations for operators

Request STL research insights overview pack

Telecoms data analytics – Where’s the real value?

Why telecoms data analtyics matters

Telecoms data analytics matter because telcos currently face big challenges. As connectivity services are increasingly commoditised, telcos are seeing a steady decline in core revenues. They are at risk of becoming seen as providers of basic utilities, rather than offering innovative services to their customers.

Improved analytics fuels benefits in multiple layers. Initially in the (human) management of operational performance, and then increasingly through using analytics and managed AI to control automation. This can apply to existing and new services.

Future-proofing: why data telecoms analytics are essential to today’s business, 5G and beyond

There is a lot of noise from the telecoms industry around fifth generation (5G) mobile networks and how 5G may provide a renewed source of revenue growth. There is no doubt that 5G will unlock new vertical opportunities for telcos, however, if telcos do not invest in developing additional services, revenues will primarily still come through connectivity. While some may gain a first-mover advantage, over time, 5G will experience the same diminishing returns per user that we have seen with previous generations (see Figure 1). 5G, through connectivity alone, is therefore likely to make only a short-term impact on telco revenue streams.

Figure 1: The effect of increasing 4G subscriber penetration on ARPUs

Source: Data from company filings, analysis by STL Partners

STL Partners has been writing about the commoditisation problem for many years and has seen that operators increasingly accept it as inevitable. Most, in one form or another, are looking beyond connectivity to improve the bottom line. Telcos are adopting two main strategies:

  • build or acquire new revenue streams outside of connectivity
  • cut costs.

The first of these is increasingly popular. Telcos worldwide have accepted the idea that they must develop new capabilities outside of their core service area and find ways to make money from them. These capabilities, and how well they link back to existing connectivity offers, vary widely. For example:

  • Some, realising telcos’ technical expertise, are developing end to end solutions based on new technologies such as multi-access edge computing and 5G. Although technologies such as 5G may not bring sustained growth through connectivity alone, they do offer the potential for telcos to access new areas of the value chain and derive new growth opportunities.
  • Some are developing new services in specific verticals. For example, TELUS in Canada and Telstra in Australia are both building service platforms in the healthcare sector, primarily through acquisitions of health-tech companies.

Unfortunately, due to heavy capex constraints and debt regulation, many telcos face challenges in investing in innovative technologies and only some have shown real success in building new offerings outside of traditional telecoms. All telcos are, however, implementing the second strategy, focussing on cutting costs and driving efficiencies throughout their organisations. Although exploring new verticals and areas of opportunity outside of connectivity is a must to drive sustained growth, in order to defend their territory against the likes of Amazon (who operate on razor thin margins), it is essential that telcos look internally and cut costs across their businesses.

While we see many variants and combinations of these two core strategies across the industry, there is one key element that ties them together. Operators are increasingly taking the view that the key to success – both in building new revenue streams and keeping costs down – is finding ways to make better use of data.

Through their networks and customer interactions, telcos collect a broad array of data. This data comes from both internal (for example data on network performance) and external (for example customer location data and usage data) sources. Telcos can extract and leverage insights from this data more accurately and more quickly through advanced analytics, informing key business decisions, creating efficiencies for internal processes, and unlocking data-enabled new service areas including the facilitation and adoption of technologies like 5G.

Building an advanced analytics capability

High ambitions: data and the AI continuum

When we talk with operators globally about data analytics, a key point of discussion is artificial intelligence (AI). “AI technology” is often cited as a powerful way to cut costs, increase ARPUs, and reduce churn – across an operator’s business. Indeed, at STL Partners we have written extensively about how this could be achieved. However, much of the discussion around AI in the industry is just that – discussion. Many AI solutions are still in their nascent phases and there is a lot more talk than live implementations that deliver measurable business value.

We raise two points to help cut through this hype and understand the real-world value for operators, both in the long and short-term.

  1. “All AI is equal, but some AI is more equal than others”. It may seem out of place to paraphrase George Orwell, but the truth is that operators and vendors alike market an increasingly broad set of solutions to customers and the analyst community under the blanket term “AI” (“all AI is equal”). This is often misleading, if not erroneous. “AI” can mean different things depending on who you speak to, ranging from computers following simple instructions or rules set by humans, to more complex fully autonomous computer systems that learn and improve with limited human interaction (“but some AI is more equal than others”). These examples differ strongly – but both fit within a generic definition of “artificial intelligence”.

Agnostic of what you include in your definition of AI, there are clearly tiers of AI solution which are based on the algorithm’s complexity, its ability to implement decisions independently (in terms of rights/permissions and integration with automated processes), and the level of human interaction or guidance necessary. At STL Partners, we have written previously about how we see advanced data analytics and AI as a continuum, with stepping stones on a journey towards the fully autonomous telco (Figure 2) The detailed explanation and formulation of this continuum is more thoroughly explained in a previous instalment of our AI research series.

  1. Most live and scaled deployments fall under our definition of rules based automation. Operators speaking to us about AI tend to want focus on innovative AI use cases that fall in the right-hand side of Figure 2. Examples include automated and self-improving chatbots that can solve any customer query and translate a complaint into a sale, or self-healing networks that fix themselves with no need for engineers to intervene. It’s true that these use cases will deliver high-value for telcos and help to answer the big questions set out above. However, should telcos be prioritising these if their data systems cannot yet tell them which customers are having a poor experience, or give them a full, real-time view of network performance?

Where are operators compared to their AI aspirations

Source: STL Partners

In terms of real progress, we have seen only a handful of leading Tier 1 operators deploying telecoms data analytics solutions that truly fit under the ML/AI banner within our framework. Most operators are still much earlier on in the journey towards automation. Even those pioneer operators have deployed only in specific geographical regions and in specific parts of their business. They face problems in deploying more complex solutions at scale and deriving measurable value.

At STL Partners, we believe that too much focus on a poorly defined end-goal risks stalling necessary work that must be done up-front. Operators should strive for and research innovative uses of data, but we believe the focus in the short-term, for Tier 1 and 2/3 telcos alike, should be on laying the necessary groundwork to ensure that data is accessible and clean, with a clear governance structure, as well as building the analytics capabilities necessary to make full use of it.

Laying the groundwork: stepping stones toward data analytics

There are three key components to building even the most basic data analytics capabilities:

  1. Clean, unified data
  2. The skills and tools to process and analyse it
  3. The ambition and drive to do so – data-centricity

This may seem straightforward but telcos globally (including even the most advanced operators) have faced challenges in meeting these requirements. For example, 77% of the operators we have spoken to stated that data collection and management was a key issue for them in implementing an analytics strategy. Furthermore, over a third of the operators we spoke with mentioned a lack of both internal and external skills with regards to advanced analytics (see Figure 3).

Figure 3: Top 4 issues faced by telcos looking to make use of data

Source: STL Partners research programme, October 2018

In order to overcome the issues listed in Figure 3, and to build future-proof telecoms data analytics capabilities, telcos must develop the three components mentioned above. Without doing this in the short-term, operators will lack the underlying platform from which to springboard into developing innovative solutions that leverage AI or ML.

Contents:

  • Executive Summary
  • Future-proofing: what to do?
  • Building an advanced telecoms data analytics capability
  • High ambitions: data and the AI continuum
  • Laying the groundwork: stepping stones toward data analytics
  • In practice: Assessing real analytics use cases
  • Improve business as usual
  • Monetise user data
  • Enable next-generation services
  • Conclusions
  • Key recommendations
  • Conclusion

Figures:

  • Figure 1: The effect of increasing 4G subscriber penetration on ARPUs
  • Figure 2: The journey to AI and telco automation
  • Figure 3: Top 4 issues faced by telcos looking to make use of data
  • Figure 4: Telefónica’s data management structure across multiple opcos
  • Figure 5: What is your biggest challenge in leveraging analytics?
  • Figure 6: The opportunity areas for telcos in advanced analytics
  • Figure 7: A comparison of Iliad against the leading Italian operators
  • Figure 8: A graphical representation of KPN’s Data Services Hub
  • Figure 9: Where operators are compared to their AI aspirations

The ‘Agile Operator’: 5 Key Ways to Meet the Agility Challenge

Understanding Agility

What does ‘Agility’ mean? 

A number of business strategies and industries spring to mind when considering the term ‘agility’ but the telecoms industry is not front and centre… 

Agility describes the ability to change direction and move at speed, whilst maintaining control and balance. This innate flexibility and adaptability aptly describes an athlete, a boxer or a cheetah, yet this description can be (and is) readily applied in a business context. Whilst the telecoms industry is not usually referenced as a model of agility (and is often described as the opposite), a number of business strategies and industries have adopted more ‘agile’ approaches, attempting to simultaneously reduce inefficiencies, maximise the deployment of resources, learn though testing and stimulate innovation. It is worthwhile recapping some of the key ‘agile’ approaches as they inform our and the interviewees’ vision of agility for the telecoms operator.

When introduced, these approaches have helped redefine their respective industries. One of the first business strategies that popularised a more ‘agile’ approach was the infamous ‘lean-production’ and related ‘just-in-time’ methodologies, principally developed by Toyota in the mid-1900s. Toyota placed their focus on reducing waste and streamlining the production process with the mindset of “only what is needed, when it is needed, and in the amount needed,” reshaping the manufacturing industry.

The methodology that perhaps springs to many people’s minds when they hear the word agility is ‘agile software development’. This methodology relies on iterative cycles of rapid prototyping followed by customer validation with increasing cross-functional involvement to develop software products that are tested, evolved and improved repeatedly throughout the development process. This iterative and continuous improvement directly contrasts the waterfall development model where a scripted user acceptance testing phase typically occurs towards the end of the process. The agile approach to development speeds up the process and results in software that meets the end users’ needs more effectively due to continual testing throughout the process.

Figure 5: Agile Software Development

Source: Marinertek.com

More recently the ‘lean startup’ methodology has become increasingly popular as an innovation strategy. Similarly to agile development, this methodology also focuses on iterative testing (replacing the testing of software with business-hypotheses and new products). Through iterative testing and learning a startup is able to better understand and meet the needs of its users or customers, reducing the inherent risk of failure whilst keeping the required investment to a minimum. The success of high-tech startups has popularised this approach; however the key principles and lessons are not solely applicable to startups but also to established companies.

Despite the fact that (most of) these methodologies or philosophies have existed for a long time, they have not been adopted consistently across all industries. The digital or internet industry was built on these ‘agile’ principles, whereas the telecoms industry has sought to emulate this by adopting agile models and methods. Of course these two industries differ in nature and there will inevitably be constraints that affect the ability to be agile across different industries (e.g. the long planning and investment cycles required to build network infrastructure) yet these principles can broadly be applied more universally, underwriting a more effective way of working.

This report highlights the benefits and challenges of becoming more ‘agile’ and sets out the operator’s perspective of ‘agility’ across a number of key domains. This vision of the ‘Agile Operator’ was captured through 29 interviews with senior telecoms executives and is supplemented by STL analysis and research.

Barriers to (telco) agility 

…The telecoms industry is hindered by legacy systems, rigid organisational structures and cultural issues…

It is well known that the telecoms industry is hampered by legacy systems; systems that may have been originally deployed between 5-20 years ago are functionally limited. Coordinating across these legacy systems impedes a telco’s ability to innovate and customise product offerings or to obtain a complete view of customers. In addition to legacy system challenges, interview participants outlined a number of other key barriers to becoming more agile. Three principle barriers emerged:

  1. Legacy systems
  2. Mindset & Culture
  3. Organisational Structure & Internal Processes

Legacy Systems 

One of the main (and often voiced by interviewees) barriers to achieving greater agility are legacy systems. Dealing with legacy IT systems and technology can be very cumbersome and time-consuming as typically they are not built to be further developed in an agile way. Even seemingly simple change requests end in development queues that stretch out many months (often years). Therefore operators remain locked-in to the same, limited core capabilities and options, which in turn stymies innovation and agility. 

The inability to modify a process, a pricing plan or to easily on/off-board a 3rd-party product has significant ramifications for how agile a company can be. It can directly limit innovation within the product development process and indirectly diminish employees’ appetite for innovation.

It is often the case that operators are forced to find ‘workarounds’ to launch new products and services. These workarounds can be practical and innovative, yet they are often crude manipulations of the existing capabilities. They are therefore limited in terms what they can do and in terms of the information that can be captured for reporting and learning for new product development. They may also create additional technical challenges when trying to migrate the ‘workaround’ product or service to a new system. 

Figure 6: What’s Stopping Telco Agility?

Source: STL Partners

Mindset & Culture

The historic (incumbent) telco culture, born out of public sector ownership, is the opposite of an ‘agile’ mindset. It is one that put in place rigid controls and structure, repealed accountability and stymied enthusiasm for innovation – the model was built to maintain and scale the status quo. For a long time the industry invested in the technology and capabilities aligned to this approach, with notable success. As technology advanced (e.g. ever-improving feature phones and mobile data) this approach served telcos well, enhancing their offerings which in turn further entrenched this mindset and culture. However as technology has advanced even further (e.g. the internet, smartphones), this focus on proven development models has resulted in telcos becoming slow to address key opportunities in the digital and mobile internet ecosystems. They now face a marketplace of thriving competition, constant disruption and rapid technological advancement. 

This classic telco mindset is also one that emphasized “technical” product development and specifications rather than the user experience. It was (and still is) commonplace for telcos to invest heavily upfront in the creation of relatively untested products and services and then to let the product run its course, rather than alter and improve the product throughout its life.

Whilst this mindset has changed or is changing across the industry, interviewees felt that the mindset and culture has still not moved far enough. Indeed many respondents indicated that this was still the main barrier to agility. Generally they felt that telcos did not operate with a mindset that was conducive to agile practices and this contributed to their inability to compete effectively against the internet players and to provide the levels of service that customers are beginning to expect. 

Organisational Structure & Internal Processes

Organisational structure and internal processes are closely linked to the overall culture and mindset of an organisation and hence it is no surprise that interviewees also noted this aspect as a key barrier to agility. Interviewees felt that the typical (functionally-orientated) organisational structure hinders their companies’ ability to be agile: there is a team for sales, a team for marketing, a team for product development, a network team, a billing team, a provisioning team, an IT team, a customer care team, a legal team, a security team, a privacy team, several compliance teams etc.. This functional set-up, whilst useful for ramping-up and managing an established product, clearly hinders a more agile approach to developing new products and services through understanding customer needs and testing adoption/behaviour. With this set-up, no-one in particular has a full overview of the whole process and they are therefore not able to understand the different dimensions, constraints, usage and experience of the product/service. 

Furthermore, having these discrete teams makes it hard to collaborate efficiently – each team’s focus is to complete their own tasks, not to work collaboratively. Indeed some of the interviewees blamed the organisational structure for creating a layer of ‘middle management’ that does not have a clear understanding of the commercial pressures facing the organisation, a route to address potential opportunities nor an incentive to work outside their teams. This leads to teams working in silos and to a lack of information sharing across the organisation.

A rigid mindset begets a rigid organisational structure which in turn leads to the entrenchment of inflexible internal processes. Interviewees saw internal processes as a key barrier, indicating that within their organisation and across the industry in general internal decision-making is too slow and bureaucratic.

 

Interviewees noted that there were too many checks and processes to go through when making decisions and often new ideas or opportunities fell outside the scope of priority activities. Interviewees highlighted project management planning as an example of the lack of agility; most telcos operate against 1-2 year project plans (with associated budgeting). Typically the budget is locked in for the year (or longer), preventing the re-allocation of financing towards an opportunity that arises during this period. This inflexibility prevents telcos from quickly capitalising on potential opportunities and from (re-)allocating resources more efficiently.

  • Executive Summary
  • Understanding Agility
  • What does ‘Agility’ mean?
  • Barriers to (telco) agility
  • “Agility” is an aspiration that resonates with operators
  • Where is it important to be agile?
  • The Telco Agility Framework
  • Organisational Agility
  • The Agile Organisation
  • Recommended Actions: Becoming the ‘Agile’ Organisation
  • Network Agility
  • A Flexible & Scalable Virtualised Network
  • Recommended Actions: The Journey to the ‘Agile Network’
  • Service Agility
  • Fast & Reactive New Service Creation & Modification
  • Recommended Actions: Developing More-relevant Services at Faster Timescales
  • Customer Agility
  • Understand and Make it Easy for your Customers
  • Recommended Actions: Understand your Customers and Empower them to Manage & Customise their Own Service
  • Partnering Agility
  • Open and Ready for Partnering
  • Recommended Actions: Become an Effective Partner
  • Conclusion

 

  • Figure 1: Regional & Functional Breakdown of Interviewees
  • Figure 2: The Barriers to Telco Agility
  • Figure 3: The Telco Agility Framework
  • Figure 4: The Agile Organisation
  • Figure 5: Agile Software Development
  • Figure 6: What’s Stopping Telco Agility?
  • Figure 7: The Importance of Agility
  • Figure 8: The Drivers & Barriers of Agility
  • Figure 9: The Telco Agility Framework
  • Figure 10: The Agile Organisation
  • Figure 11: Organisational Structure: Functional vs. Customer-Segmented
  • Figure 12: How Google Works – Small, Open Teams
  • Figure 13: How Google Works – Failing Well
  • Figure 14: NFV managed by SDN
  • Figure 15: Using Big Data Analytics to Predictively Cache Content
  • Figure 16: Three Steps to Network Agility
  • Figure 17: Launch with the Minimum Viable Proposition – Gmail
  • Figure 18: The Key Components of Customer Agility
  • Figure 19: Using Network Analytics to Prioritise High Value Applications
  • Figure 20: Knowing When to Partner
  • Figure 21: The Telco Agility Framework