Tag: supply chain

The Economy of Things: Unlocking the true value of IoT data

Download this report for free by clicking here

Progressing the Economy of Things (EoT)

The Internet of Things (IoT) has rapidly gained traction in the last decade. Many billions of IoT devices and machine-to-machine (M2M) applications have been developed creating efficiencies and enabling more intelligent, informed and automated decision-making in industries as diverse as manufacturing, healthcare, and transportation. Despite this, telcos are struggling to unlock significant IoT revenues today .

The unfulfilled potential of the IoT

The true value of IoT data today is unrealised and not business enabled. Today, data insights generated from IoT are typically focused on improving internal efficiencies within one organisation. In the future, IoT should both drive internal efficiencies and create new revenue opportunities through making some of the data available for external organisations to purchase.

The fact that the IoT data generated cannot be shared across different IoT devices and systems, is missing a great opportunity to unlock wider collective value across a broad network of connected devices. Most IoT devices are closed command and control solutions where only the device and the manager of the device can communicate. This siloed approach means that opportunities are missed to combine data sources to create more contextualised insights with deeper value.

economy of things

For example, while a coffee company may know what coffee you order (data collected from your connected coffee machine), without sharing that data across a broader network (such as data also collected from your connected smart metre, fridge and car), they will lack the wider context of your other habits/likes/dislikes which limits the targeted advertising they can achieve. Device owners are also often unwilling to share their IoT data with other businesses citing concerns around data security and authorisation and the difficulty in providing an immutable track record of each transaction.

So, how can data generated from IoT devices be monetised and shared across the wider ecosystem?

Economy of Things: The natural next step

The answer could lie in EoT. The term was coined by the IBM Institute for Business Value and represents the ”liquification of the physical world” where physical assets (the ”things”) in IoT become participants in digital markets . EoT signifies a network of participating connected “things” that can interact and communicate with each other to trade and transact autonomously. EoT offers the ability to anchor an identity to an IoT device to be able to transact autonomously. EoT provides true interoperability that can redefine the limits of a traditional IoT ecosystem.

Driving the transition from IoT to EoT relies on creating a platform that creates open participation and collaboration between a cross-industry ecosystem of partners. This interoperable infrastructure helps bring EoT into reality, providing the fundamental brokerage of data products, services and IoT data across the platform.

We are expecting to see the inflection point by 2028 as businesses look towards the EoT to enable the monetisation of their IoT data. This inflection is partly being driven by the sheer number of connected IoT devices that exist today within close proximity to each other. Each are capturing transactional data that could be of value to the other, rather than from larger data sets from distributed sources.

We forecast that the number of EoT devices will grow at a compound annual growth rate of nearly 70% from 2024 to 2030, representing up to 10% of total IoT devices by 2030. Of these EoT devices, up to 20% will be cellular connected devices by 2030.

Economy of Things

Table of Contents

  • Executive Summary
  • Introduction
    • The unfulfilled potential of the IoT
  • Economy of Things: The natural next step
    • Transitioning to the Economy of Things
  • Enter Vodafone DAB platform
  • Initial EoT use cases focus on mobility
    • Vodafone debut use case: EV charging
    • Supply chain monitoring is another leading EoT use case
    • There are endless potential use cases
    • Primary revenue stream revolves around data monetisation
  • Recommendations for enterprises
  • A message from our sponsor

Related research

Download this report for free by clicking here

How to embed sustainability across a telco

Why telcos must embrace sustainability

On a macro level, the need to focus on sustainability is clear. We need to use the world’s finite resources more efficiently. They are depleting, and this is an existential threat to us and the planet. Governments and businesses are beginning to understand that the onus is largely on them to bring about the necessary changes. Telecoms operators have a vital role to play in this effort, as outlined in our vision for the Coordination Age.

For businesses, the need to embrace sustainability is no longer abstract, and the consequences of not doing so are now material. Telcos are acknowledging that their future success is linked closely to their ability to be credible and resilient with regards to sustainability. Increasingly, a more sustainable company is going to be a more valuable company. We can already see this; companies that are focusing more of their efforts on sustainability are performing better financially. Things will continue to shift in this direction. Each year sustainability is moving higher up the global agenda and climate action is becoming ever more imperative.

Enter your details below to download an extract of the report

All telecoms’ stakeholders have a vested interest in sustainability:

  • Customers – primarily enterprises – but also some consumers, want to purchase sustainable products so they can demonstrate progress towards their own net-zero targets or rest assured that they are taking responsibility and contributing towards a sustainable future. Nearly all operators we spoke with for this research reported rising demands to prove sustainability credentials in customer request for proposals (RFPs).
  • Employees want to work for a company that is sustainable and gain a sense of purpose from contributing to their company’s sustainability A recent survey from IBM found that 67% of respondents are more willing to apply for jobs with environmentally sustainable companies, and 68% are more willing to accept positions from such companies.
  • Governments are increasingly more prepared to help companies that are sustainable in the form of tax incentives, grants, loans and subsidies. The US government recently announced nearly US$400 billion in federal funding as part of its Inflation Reduction Act, much of which is aimed at tackling climate change. The European Commission has also adopted a package of proposals labelled The European Green Deal, and there are talks of further measures being adopted in response to US legislation.
  • Regulators will also increasingly favour companies that are sustainable and hurt companies that are not. Governments have their own ambitious net-zero targets, for instance the UK targets net-zero by 2050. They are likely to begin enforcing stricter regulations as they try to meet these targets.
  • Ultimately, all of this means that shareholders and investors are beginning to put pressure on companies to be sustainable, because the consequences of avoiding it will be too costly to a business over the long term.

There may be some very short-term gains to be made by sidestepping and ignoring sustainability, but these will quickly disappear. Even in the medium term, companies that cannot demonstrate concrete progress on sustainability will struggle to compete.

As Figure 1 demonstrates, getting to net-zero is not straightforward. Telcos that still have low hanging fruit to capture, such as AT&T and T-Mobile, can make faster progress, but those that are further along in their journeys such as BT and Telefónica must now work towards more incremental gains. Other operators risk facing rising challenges in sustainability depending on their strategies, as illustrated by Softbank which has pursued an aggressive M&A strategy to expand beyond telecoms since 2019. This reinforces the importance of ensuring buy-in and commitment at the C-suite and across the whole organisation.

Comparing carbon emissions of major telcos

Source: STL Partners

This report focuses on how to embed sustainability across key telco areas, including the sustainability team, the C-suite, network operations and IT, procurement, the consumer and enterprise units and the finance unit. Each section identifies key actions that these units can take and associated KPIs they can adopt in order to catalyse and measure progress. The research is based on interviews with eight telecoms operators globally as well as extensive analysis of telecoms sustainability initiatives.

Table of contents

  • Executive Summary
  • Why telcos must embrace sustainability
  • Sustainability team: Direction and agenda
    • Developing sustainability targets and agenda
    • Working towards sustainability targets
    • Facilitating and coordinating change
  • C-suite: Vision and structure
    • Vision building
    • Structure
    • Incentives are crucial to delivery on commitments
  • Sustainable network operations and IT
  • Sustainable procurement
    • Circular economy
    • Identifying sustainable suppliers and educating SMEs
    • Fair working practices
  • Sustainability in enterprise and consumer units
    • Delivering services in more sustainable ways
    • Sustainability-enabling products for enterprise
    • Helping consumers become more sustainable
  • Sustainability is now integral to telco finance and investment
    • Future proofing telcos
    • Green finance
    • Appealing to ESG investors
  • Index

  • Related research

  • Driving sustainability in telco metro networks
  • Telecoms sustainability scorecard
  • Net-zero enablement use case directory

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.

Enter your details below to download an extract of the report

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

Enter your details below to download an extract of the report

Reliance Unlimit: How to build a successful IoT ecosystem

Reliance Unlimit’s success so far

Unlimit, Reliance Jio’s standalone IoT business in India, established in 2016, understood from the start that the problem with the IoT wasn’t the availability of technology, but how to quickly pull it all together into a clear, affordable solutions for the end customer. The result is that less than four years later, it has deployed more than 35,000 end-to-end IoT projects for a prestigious portfolio of customers, including Nissan Motor, MG Motor, Bata, DHL, GSK and Unilever. To meet their varying and evolving needs, Unlimit had built a IoT ecosystem of almost 600 partner companies by the end of 2019. Of these, nearly 100 are fully certified partners, with which Unlimit co-innovates solutions tailored to the Indian market.

Enter your details below to download an extract of the report

The state of the IoT: Balancing cost and complexity

In 1968, Theodore Paraskevakos, a Greek American inventor and businessman, explored the idea of making two machines communicate to each other. He first developed a system for transmitting the caller’s number to the receiver’s device. Building on this experiment, in 1977 he founded Metretek Inc, a company that conducted commercial automatic meter reading, which is essentially today’s commercial smart meter. From then, the world of machine to machine communications (M2M) developed rapidly. The objective was mainly to remotely monitor devices in order to understand conditions and performance. The M2M world was strongly telecommunications-oriented and focused on solving specific business problems. Given this narrow focus, there was little diversity in devices, data sets were specific to one or two measurements, and the communications protocols were well known. Given this context, it is fair to describe first-generation M2M solutions as a siloed, with little – if any – interaction with other data and solutions.

The benefits and challenges of the IoT

The purpose of the Internet of Things (IoT) is to open those silos and incorporate solution designers and developers into the operating environment. In this evolved environment, there might be several applications and solutions, each delivering a unique operational benefit. Each of those solutions require different devices, which produce different data. And those devices require life cycle management, the data needs to be analysed to inform better decisions, and automation integrated to improve efficiency in the operational environment. The communication methods between those devices can also vary significantly, depending on the environment, where the data is, and the type of applications and intelligence required. Finally, all this needs to run securely.

Therefore, the IoT has opened the silos, but it has brought complexity. The question is then whether this complexity is worth it for the operational benefits.

There are several studies highlighting the advantages of IoT solutions. The recent Microsoft IoT Signals publication, which surveys over 3000 decision makers in companies operating across different sectors, clearly demonstrates the value that IoT is bringing to organisations. The top three benefits are:

  • 91% of respondents claim that the IoT has increased efficiency
  • 91% of respondents claim that the IoT has increased yield
  • 85% of respondents claim that the IoT has increased quality.

The sectors leading IoT adoption

The same study highlights how these benefits are materialising in different business sectors. According to this study – and many others – manufacturing is seen as a top adopter of IoT solutions, as also highlighted in STL Partners research on the Industrial IoT.

Automotive, supply chain and logistics are other sectors that have widely adopted the IoT. Their leadership comes from a long M2M heritage, since telematics was a core application of M2M, and is an important part of the supply chain and logistics process.

The automotive sector’s early adoption of IoT was also driven by regulatory initiatives in different parts of the world, for instance to support remotely monitored emergency services in case of accidents (e.g. EU eCall). To enable this, M2M SIMs were embedded in cars, and only activated in the case of an accident, sending a message to an emergency centre. From there, the automotive industry and mobile network operators gradually developed a broader range of applications, culminating in the concept of connected cars. The connected car is much more sophisticated than a single emergency SIM – it is an IoT environment in which an array of sensors is gathering different data, sharing that data externally in various forms of V2X settings, supporting in-vehicle infotainment, and also enabling semiautonomous mobility. Sometime in the future, this will mature into fully autonomous mobility.

The complexity of an IoT solution

The connected car clearly represents the evolution from siloed M2M solutions to the IoT with multiple interdependent data sources and solutions. Achieving this has required the integration of various technologies into an IoT architecture, as well as the move towards automation and prediction of events, which requires embedding advanced analytics and AI technology frameworks into the IoT stack.

High level view of an IoT architecture

Overview of IoT architecture

Source: Saverio Romeo, STL Partners

There are five levels on an IoT architecture:

  1. The hardware level includes devices, sensors, gateways and hardware development components such as microcontrollers.
  2. The communication level includes the different types of IoT connectivity (cellular, LP-WAN, fixed, satellite, short-range wireless and others) and the communication protocols used in those forms of connectivity.
  3. The middleware software backend level is a set of software layers that are traditionally called an IoT platform. A high-level breakdown of the IoT platform includes a connectivity management layer, a device management layer, and data management and orchestration, data analytics and visualisations layers.
  4. The application level includes application development enablement tools and the applications themselves. Those tools enable the development of applications using machine-generated data and various other sources of data –all integrated by the IoT platform. It also includes applications that use results of these analytics to enable remote and automated actions on IoT devices.
  5. Vertically across these levels, there is a security layer. Although this is simplified into a single vertical layer, in practice there are separate security features integrated into IoT solutions at each layer of the architecture. Those features work together to offer layer-to-layer and end-to-end security. This is a complex process that required a detailed use of security-by-design methodology.

The IoT architecture is therefore composed of different technological parts that need to be integrated in order to work correctly in the different circumstances of potential deployment. The IoT architecture also needs to enable scalability supporting the expansion of a solution in terms of number of devices and volume and types of data. Each architectural layer is essential for the IoT solution to work, and they must interact with each other harmoniously, but each requires different technological expertise and skills.

An organisation that wants to offer end-to-end IoT solutions must therefore make a strategic choice between “in-house” IoT architecture development, or form strategic partnerships with existing IoT technology platform providers, and integrate their solutions into a coherent architecture to support an IoT ecosystem.

In the following sections of this report, we discuss Unlimit’s decision to take an ecosystem approach to building its IoT business, and the steps it took to get where it is today.

Table of contents

  • Executive Summary
    • Four lessons from Unlimit on building IoT ecosystems
    • How Unlimit built a successful IoT ecosystem
    • What next?
  • The state of the IoT: Balancing cost and complexity
    • The benefits and challenges of the IoT
    • The sectors leading IoT adoption
    • The complexity of an IoT solution
    • The nature of business ecosystems
  • How Unlimit built a successful IoT business
    • So far, Unlimit looks like a success
    • How will Unlimit sustain leadership and growth?
  • Lessons from Unlimit’s experience

Enter your details below to download an extract of the report

The Industrial IoT: What’s the telco opportunity?

The Industrial IoT is a confusing world

This report is the final report in a mini-series about the Internet for Things (I4T), which we see as the next stage of evolution from today’s IoT.

The first report, The IoT is dead: Long live the Internet for Things, outlines why today’s IoT infrastructure is insufficient for meeting businesses’ needs. The main problem with today’s IoT is that every company’s data is locked in its own silo, and one company’s solutions are likely deployed on a different platform than their partners’. So companies can optimise their internal operations, but have limited scope to use IoT to optimise operations involving multiple organisations.

The second report, Digital twins: A catalyst of disruption in the Coordination Age, provides an overview of what a digital twin is, and how they can play a role in overcoming the limitations of today’s IoT industry.

This report looks more closely at the state of development of enterprise and industrial IoT and the leading players in today’s IoT industry, which we believe is a crucial driver of the Coordination Age. In the Coordination Age, we believe the crucial socio-economic need in the world – and therefore the biggest business opportunity – is to make better use of our resources, whether that is time, money, or raw materials. Given the number of people employed in and resources going through industrial processes, figuring out what’s needed to make the industrial IoT reach its full potential is a big part of making this possible.

Three types of IoT

There are three ways of dividing up the types of IoT applications. As described by IoT expert Stacey Higginbotham, each group has distinct needs and priorities based on their main purpose:

  1. Consumer IoT: A connected device, with an interactive app, that provides an additional service to the end user compared with an unconnected version of the device. The additional service is enabled by the insights and data gathered from the device. The key priority for consumer devices is low price point and ease of installation, given most users’ lack of technical expertise.
  2. Enterprise IoT: This includes all the devices and sensors that enterprises are connecting to the internet, e.g. enterprise mobility and fleet tracking. Since every device connected to an enterprise network is a potential point of vulnerability, the primary concern of enterprise IoT is security and device management. This is achieved through documentation of devices on enterprise networks, prioritisation of devices and traffic across multiple types of networks, e.g. depending on speed and security requirements, and access rights controls, to track who is sharing data with whom and when.
  3. Industrial IoT: This field is born out of industrial protocols such as SCADA, which do not currently connect to the internet but rather to an internal control and monitoring system for manufacturing equipment. More recently, enterprises have enhanced these systems with a host of devices connected to IP networks through Wi-Fi or other technologies, and linked legacy monitoring systems to gateways that feed operational data into more user-friendly, cloud-based monitoring and analytics solutions. At this point, the lines between Industrial IoT and Enterprise IoT blur. When the cloud-based systems have the ability to control connected equipment, for instance through firmware updates, security to prevent malicious or unintended risks is paramount. The primary goals in IIoT remain to control and monitor, in order to improve operational efficiency and safety, although with rising security needs.

The Internet for Things (I4T) is in large part about bridging the divide between Enterprise and Industrial IoT. The idea is to be able to share highly sensitive industrial information, such as a change in operational status that will affect a supply chain, or a fault in public infrastructure like roads, rail or electricity grid, that will affect surroundings and require repairs. This requires new solutions that can coordinate and track the movement of Industrial IoT data into Enterprise IoT insights and actions.

Understandably, enterprises are way of opening any vulnerabilities into their operations through deeper or broader connections, so finding a secure way to bring about the I4T is the primary concern.

Enter your details below to download an extract of the report

The proliferation of IoT platforms

Almost every major player in the ICT world is pitching for a role in both Enterprise and Industrial IoT. Most largescale manufacturers and telecoms operators are also trying to carve out a role in the IoT industry.

By and large, these players have developed specific IoT solutions linked to their core businesses, and then expanded by developing some kind of “IoT platform” that brings together a broader range of capabilities across the IT stack necessary to provide end-to-end IoT solutions.
The result is a hugely complex industry with many overlapping and competing “platforms”. Because they all do something different, the term “platform” is often unhelpful in understanding what a company provides.

A company’s “IoT platform” might comprise of any combination of these four layers of the IoT stack, all of which are key components of an end-to-end solution:

  1. Hardware: This is the IoT device or sensor that is used to collect and transmit data. Larger devices may also have inbuilt compute power enabling them to run local analysis on the data collected, in order to curate which data need to be sent to a central repository or other devices.
  2. Connectivity: This is the means by which data is transmitted, including location-based connectivity (Bluetooth, Wi-Fi), to low power wide area over unlicensed spectrum (Sigfox, LoRa), and cellular (NB-IoT, LTE-M, LTE).
  3. IoT service enablement: This is the most nebulous category, because it includes anything that sits as middleware in between connectivity and the end application. The main types of enabling functions are:
    • Cloud compute capacity for storing and analysing data
    • Data management: aggregating, structuring and standardising data from multiple different sources. There are sub-categories within this geared towards specific end applications, such as product or service lifecycle management tools.
    • Device management: device onboarding, monitoring, software updates, and security. Software and security management are often broken out as separate enablement solutions.
    • Connectivity management: orchestrating IoT devices over a variety of networks
    • Data / device visualisation: This includes graphical interfaces for presenting complex data sets and insights, and 3D modelling tools for industrial equipment.
  4. Applications: These leverage tools in the IoT enablement layer to deliver specific insights or trigger actions that deliver a specific outcome to end users, such as predictive maintenance or fleet management. Applications are usually tailored to the specific needs of end users and rarely scale well across multiple industries.

Most “IoT platforms” combine at least two layers across this IoT stack

graphic of 4 layers on the IoT stack

Source: STL Partners

There are two key reasons why platforms offering end-to-end services have dominated the early development of the IoT industry:

  • Enterprises’ most immediate needs have been to have greater visibility into their own operations and make them more efficient. This means IoT initiatives have been driven primarily by business owners, rather than technology teams, who often don’t have the skills to piece together multiple different components by themselves.
  • Although the IoT as a whole is a big business, each individual component to bringing a solution together is relatively small. So companies providing IoT solutions – including telcos – have attempted to capture a larger share of the value chain in order to make it a better business.

Making sense of the confusion

It is a daunting task to work out how to bring IoT into play in any organisation. It requires a thorough re-think of how a business operates, for a start, then tinkering with (or transforming) its core systems and processes, depending on how you approach it.

That’s tricky enough even without the burgeoning market of self-proclaimed “leaders of industrial IoT” and technology players’ “IoT platforms”.

This report does not attempt to answer “what is the best way / platform” for different IoT implementations. There are many other resources available that attempt to offer comparisons to help guide users through the task of picking the right tools for the job.

The objective here is to gain a sense of what is real today, and where the opportunities and gaps are, in order to help telecoms operators and their partners understand how they can help enterprises move beyond the IoT, into the I4T.

 

Table of contents

  • Executive Summary
  • Introduction
    • Three types of IoT
    • The proliferation of IoT platforms
    • Making sense of the confusion
  • The state of the IoT industry
    • In the beginning, there was SCADA
    • Then there were specialised industrial automation systems
    • IoT providers are learning about evolving customer needs
  • Overview of IoT solution providers
    • Generalist scaled IT players
    • The Internet players (Amazon, Google and Microsoft)
    • Large-scale manufacturers
    • Transformation / IoT specialists
    • Big telco vendors
    • Telecoms operators
    • Other connectivity-led players
  • Conclusions and recommendations
    • A buyers’ eye view: Too much choice, not enough agility
    • How telcos can help – and succeed over the long term in IoT

Enter your details below to download an extract of the report