Feb 11, 2021

IoT Platforms and Digital Regulation

A couple of recent and seemingly unconnected publications provide food for strategic thought on the topic of IoT platforms. 

Platforms are an important topic for the following reasons. As businesses deploy Internet of Things applications, many will turn to the service provider market for affordable, feature-rich, and well-engineered platforms. Platforms also represent an important topic for the large Cloud-providers, such as Amazon Web Services, Google Cloud and Microsoft Azure, who understand the importance of platform strategies and data. 

The first publication that caught my attention is a short article on the IoT Agenda site. It outlines that issues of IoT technology fragmentation and discusses the trend towards concentration in the IoT platform market [1]. The second is a study by a group of economists with an expertise in platform economics and competition policy. They studied the EU’s Digital Markets Act (DMA) and its regulatory implications for large and dominant digital platforms [2]. 

Jan 8, 2021

2020 in Review: Corporates Adapt Their IoT Business Models

This review of 2020 corporate initiatives in the IoT market builds on a history of tracking strategic industry developments for over a decade. Two sets of corporate events that bookended the start and end of 2020 provide instructive examples of the roadmap and dead ends that characterize today’s IoT market. In the intervening months, organizations in different parts of the industry ecosystem bolstered their IoT strategies. Some developed complementary capabilities through M&A while others addressed go-to-market issues through business reorganization and product-innovation initiatives. For many organizations, however, there remain challenges in balancing short term imperatives with strategic positioning goals. There is a degree of comfort in embracing the familiar. The risk is that this leads to an under-investment in properly integrating new business approaches and complementary technologies.

Nov 13, 2020

Where the IoT Market is Heading

I delivered a presentation some weeks back at an online conference for the managed-services industry. My talk was about the implications of IoT for digital transformation [1]. To prepare for the presentation, I began by looking back over the past decade of market developments, joining a sequence of past and present developments to see into the future of IoT. This exercise provided useful insights into the evolving pattern of customer needs, consequences for where the market is heading and, implications for strategy and business innovation.

Jul 7, 2020

Opportunities to Apply AI and ML in IoT Systems

My last article [1] introduced a framework to explain the basic elements of an IoT system with the aim of highlighting where Artificial Intelligence (AI), Machine Learning (ML) and Digital Twin (DT) components are typically added. 

The aim of this article is to explore the longer-term opportunities for AI/ML technologies and how these will shape mobile operator and technology provider business strategies. There are two developments to consider in drawing out this roadmap. One is the tighter integration of IoT and AI/ML technologies vertically along the technology stack. Think of this as a way of improving how well different components interact to improve reliability and service quality. The second concerns a new set of requirements that users and regulatory agencies will expect from AI/ML systems. As an illustrative example, consider an AI application that issues an alarm that a machine is about to fail with some probabilistic context such as “greater than 75% chance of failure in the next month”. Is it enough to stop a production line based on this read out? In practice, there is likely to be a higher-level requirement that determines the trustworthiness of this alarm based on its performance over time. Like the boy who cried ‘wolf’, does a sequence of alarms point to a deteriorating piece of equipment or a faulty sensor? The judgement required here involves a different set of data and potentially the involvement of other, supervisory AI/ML sub-systems.

May 30, 2020

A Framework for AI and IoT

Early in my career, I had the good fortune to work on several research and innovation projects on the topics of AI and ML. These project used mathematical techniques to model the dynamic behavior of machines and to work out if they were developing faults. This are known as ‘condition monitoring’ and ‘predictive maintenance’ procedures. These projects involved data from real machines, not simulations. They included an industrial scale diesel generator, a gas-turbine used for ship propulsion and, black-box data from military aircraft [1].

In modern terminology, these projects involved the creation of digital twins from IoT data. They began by collecting time series data around events such a change in operating speed. This is important because systems do not provide dynamically rich data under static operating conditions. Think of a lightbulb with a hairline crack in its filament. Unless you have incredible eyesight, it is impossible to tell if the lightbulb is work on not. However, if you gently tap the lightbulb, you will hear the filament vibrate. That is what reveals that the broken is bulb. In addition to the signal processing aspects, this diagnostic and testing process relies on our mental model of how filament lightbulbs work.