Four goals for the data-driven telco

Becoming a data-driven telco

There have been many case studies over the last five years demonstrating the disruption caused by “data-driven businesses”, i.e. those using insights to understand customers, automate processes, change their business models and drive new revenues. In the future, this concept will become an integral part of what it takes to compete successfully, allowing organisations to understand and run all parts of their operations, work with their customers and partners and take part in external activities in new ecosystems. This applies to telecoms operators as much as any other industry.

This research builds on a range of reports STL Partners has previously published on strategic topics related to telcos’ use of data, including:

This research turns to the practical topics of delivering on these strategic goals. The diagram below offers an overview of the drivers and barriers affecting delivery areas such as telco data management and machine learning (ML) in the short and longer term.

Drivers and barriers to being a data-driven telco

Source: STL Partners

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What capabilities should telcos develop?

Telcos are reasonably sophisticated users of data, but their particularly complex web of legacy systems requires a good deal of work around data management and governance to enable the extraction of data sets to give 360-degree view of the customer – and increasingly to provide training data for algorithms.

In the mid-term, telcos that are successful in selling IoT and becoming ecosystem players will require new A3 to deal with the increasing number of services, devices, price points and parties involved in providing service to a customer. Our research suggests that there is a range of new A3 technologies that can provide the automation and intelligence for this, as well as for the underlying data management processes.

In the longer-term, A3 will speed up decision making, impacting company strategy, new product and service creation, and customer experience. Humans will increasingly be supported by AI-, ML- and automation-powered tools in their decision-making. A similar progression will occur among competitors in telecoms, and in adjacent markets, increasing the complexity and speed of doing business. Besides integrating A3 into human workflows, working at increasing speed will depend on getting richer insights out of the available data with techniques such as small data and creation of synthetic data.

Capabilities for a data-driven telco

Source: STL Partners

 

Table of contents

  • Executive Summary
    • Capabilities telcos should develop over the medium term
    • What will telcos focus on in the mid-term?
    • Next steps
  • Becoming a data-driven telco
    • Short term drivers
    • Barriers in the short term
    • Long term drivers
    • Barriers in the long term
  • Availability of data
    • Use of data fabrics
    • Better data labelling
    • Rise of synthetic data
    • More intelligent data selection
    • Telco strategies for cloud usage
  • Equipping people
    • Augmented analytics and business intelligence
    • Decision intelligence
  • Work on governance
    • Governance across the telco
    • Agility in governance
    • Governance for AI and machine learning
    • Ethical governance
    • Improved measurement of governance
    • Governance in ecosystems
  • Index

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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DataSpark: Lessons on building a new telco (data) business

Data analytics as a new business

This case study looks at DataSpark, an autonomous business unit of Singtel (www.dsanalytics.com) and evaluates the benefits of creating a separate organisational structure within a telco to provide technology and support for the development of analytics, AI and automation as a new business. It is created after conversations with Shaowei Ying, Chief Operating Officer of DataSpark. The company’s activities include both the creation of internal capabilities and data monetisation capabilities for external customers.

DataSpark was formed in 2014 at a time when not many telcos were actively exploring new data business opportunities. The unit consisted of a small group of data professionals with skills around, particularly, location data. Singtel’s CEO was a strong supporter of leveraging telco data to establish competitive differentiation and therefore tasked them with looking at various location-related external monetisation opportunities. It was considered natural to create internal use cases for the data to defray the cost of the data preparation. In particular, the same mobility intelligence was of use to radio network planners optimising their network roll out using not just congestion, but now subscribers’ mobility patterns, too.

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DataSpark’s progress to date

Telcos’ external monetisation units, such as DataSpark, are not yet large enough to split out the revenues in their reports and accounts. However, in the 2018 and 2019 Management Discussion and Analysis DataSpark’s progress was reported to include:

  • Activity to bring mobility data to sectors such as transport and out-of-home media in Singapore and Australia
  • Partnership in out-of-home advertising with large players taking a data-as-a-service solution to optimise their assets
  • Provision of insights including first party enterprise data in the consumer goods sector to deliver new use cases in advertising and retail store inventory optimisation
  • Recent support for governments in predicting spread of Covid-19, including understanding the socio-economic impact of the virus.

Service example: COVID-19 insight for the Australian local government

COVID-19 data analytics innovation

Source: DataSpark

Table of Contents

  • Executive Summary
    • Two diverging strategies for a small, independent data unit
    • Scaling up the data business as an integrated unit
  • Introduction
    • DataSpark’s progress to date
  • DataSpark’s approach to building a data unit
    • What services does it offer?
    • Go-to-market: Different approaches for internal and external customers
    • Organisational structure: Where should a data unit go?
  • How to scale a data business?
    • The immediate growth opportunities
    • Following in others’ footsteps
    • Building new capabilities for external monetisation
  • Assessing future strategies for DataSpark
    • Scenario 1: Double down on internal data applications
    • Scenario 2: Continue building an independent business

 

Read more about STL Partners’ AI & automation research at stlpartners.com/ai-analytics-research/

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AI is starting to pay: Time to scale adoption

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AI adoption yields positive results

Over the last five years, telcos have made measurable progress in AI adoption and it is starting to pay off.  When compared to all industries, telcos have become adept at handling large data sets and implementing automation. Over the last several years the telecoms industry has gone from not knowing where or how to implement AI, to having developed and implemented hundreds of AI and automation applications for network operations, fraud prevention, customer channel management, and sales and marketing. We have discussed these use cases and operator strategies and opportunities in detail in previous reports.

For the more advanced telcos, the challenge is no longer setting up data management platforms and systems and identifying promising use cases for AI and automation, but overcoming the organisational and cultural barriers to becoming truly data-centric in mindset, processes and operations. A significant part of this challenge includes disseminating AI adoption and expertise of these technologies and associated skills to the wider organisation, beyond a centralised AI team.The benchmark for success here is not other telcos, or companies in other industries with large legacy and physical assets, but digital- and cloud-native companies that have been established with a data-centric mindset and practices from the start. This includes global technology companies like Microsoft, Google and Amazon, who increasingly see telecoms operators as customers, or perhaps even competitors one day, as well as greenfield players such as Rakuten, Jio and DISH, which as well as more modern networks have fewer ingrained legacy processes and cultural practices to overcome.

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Telecoms has a high AI adoption rate compared with other industries

AI pays off

Source: McKinsey

In this report, we assess several telcos’ approach to AI and the results they have achieved so far, and draw some lessons on what kind of strategy and ambition leads to better results. In the second section of the report, we explore in more detail the concrete steps telcos can take to help accelerate and scale the use of AI and automation across the organisation, in the hopes of becoming more data-driven businesses.

While not all telcos have an ambition to drive new revenue growth through development of their own IP in AI, to form the basis of new enterprise or consumer services, all operators will need AI to permeate their internal processes to compete effectively in the long term. Therefore, whatever the level ambition, disseminating fundamental AI and data skills across the organisation is crucial to long term success. STL Partners believes that the sooner telcos can master these skills, the higher their chances of successfully applying them to drive innovation both in core connectivity and new services higher up the value chain.

Contents

  • Executive Summary
  • Introduction
  • Developing an AI strategy: What is it for?
    • Telefónica: From AURA and LUCA to Telefónica Tech
    • Vodafone: An efficiency focused strategy
    • Elisa: A vertical application approach
    • Takeaways: Comparing three approaches
  • AI maturity progression
    • Adopt big data analytics: The basic building blocks
    • Creating a centralised AI unit
    • Creating a new business unit
    • Disseminating AI across the organisation
  • Using partnerships to accelerate and scale AI
    • O2 and Cardinality
    • AT&T Acumos
  • Conclusion and recommendations
  • Index

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