How video analytics can kickstart the edge opportunity for telcos

Processing video is a key use for edge computing

In our analysis and sizing of the edge market, STL Partners found that processing video will be a strong driver of edge capacity and revenues. This is because a huge quantity of visual data is captured each day through many different processes. The majority of the information captured is straightforward (such as “how busy is this road?”), therefore it is highly inefficient for the whole data stream to be sent to the core of the network. It is much better to process it near to the point of origin and save the costs, energy and time of sending it back and forth. Hence “Video Analytics” is a key use for edge computing.

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The edge market is evolving rapidly

Edge computing is an exciting opportunity. The market is evolving rapidly, and although still fairly nascent today, is expected to scale significantly over the next 2-3 years. STL partners has estimated that the total edge computing addressable market was worth $10bn in 2020, and that this will grow to $534bn in 2030. This is driven by the increasing number of connected devices, and the rising adoption of IoT, Industry 4.0 and digital transformation solutions. While cloud adoption continues to grow in parallel, there are cases where the increasingly stringent connectivity demands of new and advanced use cases cannot be met by cloud or central data centres, or where sending data to the cloud is too costly. Edge answers this problem, and offers an alternative option with lower latency, reduced backhaul and greater reliability. For the many enterprises who are adopting a hybrid and multi-cloud strategy – strategically distributing their data across different clouds and locations – running workloads at the edge is a natural next step.

Developments in the technologies enabling edge computing are also contributing to market growth. For example, the increased agility of virtualised and 5G networks enables the migration of workloads from the cloud to the edge. Compute is also developing, becoming more lightweight, efficient, and powerful. These more capable devices can run workloads and perform operations that were not previously possible at the edge.

Defining different types of edge

Edge computing brings processing capabilities closer to the end user or end-device. The compute infrastructure is therefore more distributed, and typically at smaller sites. This differs from traditional on-premise compute (which is monolithic or based on proprietary hardware) because it utilises the flexibility and openness of cloud native infrastructure, i.e. highly scalable Kubernetes clusters.

The location of the edge may be defined as anywhere between an end device, and a point on the periphery of the core network. We have outlined the key types of edge computing and where they are located in the figure below.

The types of edge computing

It should be noted that although moving compute to the edge can be considered an alternative to cloud, edge computing also complements cloud computing and drives adoption, since data that is processed or filtered at the edge can ultimately be sent to the cloud for longer term storage or collation and analysis.

Telcos must identify which area of the edge market to focus on

For operators looking to move beyond connectivity and offer vertical solutions, edge is an opportunity to differentiate by incorporating their edge capabilities into solutions. If successful, this could result in significant revenue generation, since the applications and platforms layer is where most of the revenue from edge resides. In fact, by 2030, 70% of the addressable revenue for edge will come from the application, with only 9% in the pure connectivity. The remaining 21% represents the value of hardware, edge infrastructure and platforms, integration, and managed services.

Realistically, operators will not have the resource and management bandwidth to develop solutions for several use cases and verticals. They must therefore focus on key customers in one or two segments, understand their particular business needs, and deliver that value in concert with specific partners in their ecosystem. As it relates to MEC, most operators are selecting the key partners for each of the services they offer – broadcast video, immersive AR/VR experiences, crowd analytics, gaming etc.

When selecting the best area to focus on, telcos should weigh up the attractiveness of the market (including the size of the opportunity, how mature the opportunity is, and the need for edge) against their ability to compete.

Value of edge use cases (by size of total addressable market by 2030)

Source: STL Partners – Edge computing market sizing forecast

We assessed the market attractiveness of the top use cases that are expected to drive adoption of edge over the coming years, some of which are shown in the figure above. This revealed that the use cases that represent the largest opportunities in 2030 include edge CDN, cloud gaming, connected car driver assistance and video analytics. Of these, video analytics is the most mature opportunity, therefore represents a highly attractive proposition for CSPs.

Table of Contents

  • Executive Summary
  • Introduction
    • Processing video is a key use for edge computing
    • The edge market is evolving rapidly
    • Defining different types of edge
    • Telcos must identify which area of the edge market to focus on
  • Video analytics is a large and growing market
    • The market for edge-enabled video analytics will be worth $75bn by 2030
  • Edge computing changes the game and plays to operator strengths
    • What is the role of 5G?
  • Security is the largest growth area and operators have skills and assets in this
    • Video analytics for security will increasingly rely on the network edge
  • There is empirical evidence from early movers that telcos can be successful in this space
    • What are telcos doing today?
    • Telcos can front end-to-end video analytics solutions
    • It is important to maintain openness
    • Conquering the video analytics opportunity will open doors for telcos
  • Conclusion
  • Index

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Innovation leader case study: Telefónica Tech AI of Things

The origins of Telefónica Tech AI of Things

Telefónica LUCA was set up in 2016 to “enable corporate clients to understand their data and encourage a transparent and responsible use of that data”.

Before the creation of LUCA, Telefónica’s focus had been on developing assets and making acquisitions (e.g. Synergic Partners) to build strong internal capabilities around data and analytics – with some data monetisation capabilities housed within their Telefónica Digital unit (a global business unit selling products beyond connectivity, which was disbanded in 2016). Typical projects the team undertook related to using network data to make better decisioning for the network and marketing teams, and providing Telefónica Digital with external monetisation opportunities such as Smart Steps (aggregated, anonymised data for creation of vertical products) and Smart Digits (provision of consent-based data to the advertising industry).

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Creating the autonomous LUCA unit made a statement that Telefónica was serious about its strategy to offer data products to enterprise customers. Quoting from the original press release, “LUCA offered three lines of products and services:

The Business Insights area brings the value of anonymous and aggregated data on Telefónica’s networks for a wide range of clients. This includes Smart Steps, which is focused on mobility analysis solutions for more efficient planning. For example, to optimise transport networks and tourist management in cities, or in the case of a health emergency, in helping to better understand population movements and in limiting the spread of pandemics.

The analytical and external consultancy services for national and international clients will be provided by Synergic Partners, a company specialized in Big Data and Data Science which was acquired by Telefónica at the end of 2015.

Furthermore, LUCA will help its clients by providing BDaaS (Big Data as a Service) to empower clients to get the most out of their own data, using the Telefónica cloud infrastructure.”

The following table shows a timeline from the origins of LUCA in the Telefónica Digital business unit through to its merger into the Telefónica Tech AI of Things business in 2019 – illustrating the progression of its products and other major activities.

Timeline of Telefónica’s data monetisation business

Telefonica-data-monetisation-luca-AI-IoT

Source: STL Partners, Charlotte Patrick Consult

Points to note on the timeline above:

  • Telefónica stood out from its peers with the purchase of Synergic Partners in 2015 (bringing in 120 consultancy headcount). This provided not only another leg to the business with consulting capabilities, but also additional headcount to scope and sell their existing product sets.
  • Looking at the timeline, it took Telefónica two years from this purchase and the establishment LUCA to expand its portfolio. In 2018, a range of new, mainly IoT-related capabilities, were launched, built up from existing projects with individual customers.
  • Telefónica has added machine learning to its products across the timeframe, but in 2019 the development of NLP capability for use in Telefónica’s existing products, and an internal data science platform, were then productised for customers (see below discussion about its Aura product set).
  • As the number of products has expanded, the number of partnerships has also expanded, bringing specific platforms and capabilities which can be combined with Telefónica’s own data capabilities to provide added value (examples include CARTO which creates geographic visualisations of Telefónica’s data).
  • Looking at changing vertical priorities:
    • Telefónica has always been strong in the advertising sector, starting with products from O2 UK in 2012. The exact nature of what it has offered has changed over time and some capabilities have been sold, however, it still has a strong mobile marketing business and expects it data to become of more interest to brands/media agencies as the use of cookies diminishes across the next few years.
    • The retail sector offers opportunity, but has been challenging to target over the years. Although Telefónica has interesting data for retail companies, creating replicable products is challenging as the large retailers each have differing requirements and working with small cell data in-store can be expensive. The product set is therefore currently being simplified, as the pandemic has also reduced demand from retailers.

One of Telefónica’s key capabilities which is not clearly displayed in the timeline is the provision of services to the marketing teams of the various verticals it targets. These include analytics products which Telefónica has developed from its internal capabilities and other functionality such as pricing tools.

The formation of Telefónica Tech

In 2019, Telefónica LUCA became part of the newly formed, autonomous Telefónica Tech business unit. The organisation is split into two business areas: cybersecurity & cloud, and the assets from Telefónica LUCA combined with the IoT unit. The goal of Telefónica Tech is to:

  • Enable the financial markets to clearly see revenue progression. Telefónica’s stated aim is for sustained double digit growth, which it achieved with year-on-year growth of 13.6% in 2020, although the IoT and Big Data segment only grew 0.8% y-o-y in 2020, due to the impact of COVID-19 on IoT deployments, especially in retail. Showing signs of recovery, in H121 revenue growth in the IoT and Big Data segment rose to 8.1% y-o-y, and to 26% y-o-y for the whole of Telefónica Tech.
  • Coordinate innovation, particularly around post-pandemic opportunities such as remote working, e-health, e-commerce and digital transformation
  • Take advantage of global synergies and leveraging existing assets
  • Ease M&A and partnerships activity (it already has 300 partners to better reach new markets, including relations with 60 start-ups across products)
  • Build relationships with cloud providers (it has existing relationships with Microsoft, Google and SAP).

To better leverage existing assets, Telefónica LUCA was integrated with Telefónica’s IoT capabilities to create a more unified set of capabilities:

  1. IoT is seen as an enabling opportunity for AI, which can bring added value to Telefónica’s 10,000 IoT customers (with 35 million live IoT SIMs worldwide). Opportunities include provision of intelligence around “things” (for example, products to analyse sensor data) and then the addition of Business Insight services (i.e. analysis of aggregated, anonymised Telefónica data which adds further insight alongside the data coming from IoT devices).
  2. AI is now often a commodity discussion with C-Level prospects and Telefónica wishes to be seen as a strategic partner. Telefónica’s AI of Things proposition offers an execution layer and integration experts with security-by-design capabilities.
  3. Combining capabilities provides sales teams with an end-to-end value proposition, as the addition of AI is often complimentary to cloud transformation projects and the implementation of digital platforms.

There is a growing ecosystem in IoT and data which will generate more opportunities as both IoT solutions and ML/AI solutions mature, although it is not a straightforward decision for Telefónica on how to compete within this ecosystem.

Table of contents

  • Executive Summary
    • How successful has Telefónica been in data monetisation?
    • Learnings from Telefónica’s experience
    • Key success factors
    • Telefónica’s future strategy
  • Introduction
    • The origins of Telefónica Tech AI of Things
    • The formation of Telefónica Tech
  • Vision, mission and strategy
    • Scaling the business
    • Building a product set
    • Learnings from Telefónica Tech AI of Things
  • Organisational strategy
    • Where should the data monetisation team live?
    • Structure of Telefónica Tech AI of Things Team
    • External partnerships
    • Future plans
  • Data portfolio strategy
    • Tools and infrastructure
    • AI Suite
    • Vertical strategy
    • Product development beyond analytics
  • Conclusion and future moves

Related research

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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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