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Challenges shifting IoT spending

With this post, I am pleased to join TechTarget IoT Agenda as a contributor; I hope to post to the community monthly. My background is in IT operations as a practitioner for 17 years, then moving to an analyst role at Gartner for three years. Currently, I’m helping to drive vision, strategy and roadmap at AppDynamics, an application intelligence software company focused on application performance monitoring and analytics.

This post analyzes some key data and predictions for the IoT market, and helps frame segmentation and IoT spending in IoT project phases.

The Internet of Things has some traits in typical applications, but also introduces some significant new challenges. With typical applications, users interact directly and data is collected inclusive of the specific actions the user is taking as they engage with the app. This means the app is only emitting data for small, more definitive timeframes. IoT systems, on the other hand, often emit data when they are at rest — especially in the case of consumer systems.

IoT spending (in US $ millions)

2015 2016 2020
Connectivity 10 12.6 31
Consumer 4.9 7 39.2
Professional 178 215.2 411.6

Forecast Analysis: Internet of Things — Services, Worldwide, 2015 Update (04 January 2016) Analyst(s): Peter Middleton | Thilo Koslowski

Gartner segments IoT spending (you must be a subscriber to access) between connectivity of “things” — typically via mobile networks or Wi-Fi — and two major market use cases: consumer and professional. As the data shows, the professional market is over 50 times larger than the consumer market.

 

Gartner predicts that “manufacturing and natural resources” is the largest segment by total spend, estimated at $95 billion in 2016 and projected to grow to $136 billion by 2020. By comparison, the entire consumer segment is estimated to be $7 billion in 2016 and projected to grow to $39 billion by 2020. In many cases, these industrial systems do not rest often, and hence provide a larger opportunity — and significant challenge as well — due to both the size of the data and the value locked inside. Among these typically complex and often widely dispersed systems could be sensor-connected machines in factories, construction vehicles, mining equipment, oil and gas equipment, and robotics in manufacturing.

Gartner has done additional interesting analysis, looking at how IoT is likely to evolve. As an IoT project gets underway and is in development, Gartner estimates that 20% of the project spend is focused on design and consulting, 35% on implementation and 45% on operations.

As IoT evolves over the next four years, the ratio of that spending will shift more heavily towards operations. Gartner predicts that in 2020, the ratio will move towards 18% spend on design and consulting, 30% on implementation and 52% on operations.

Operational costs are tied to analytics use cases, which are applied to the operations of IoT assets, including not only hardware or cloud services, but also software and services to enable infrastructure management, application management, device management, performance monitoring, remote diagnostics, authentication, billing and support. The purposes of these functional areas are to collect and analyze the data generated from IoT devices and create insights from it with algorithms and other views of the data.

The reason for this shift is that the high value of IoT is within the analysis and insights derived from the collected data. Newer, more sophisticated algorithms and analysis require more computing and associated resources. The operational cost of increasing the number of devices, customers and ultimately users means more IoT spending on more storage, traffic and raw processing power needed to scale the IoT business.

As the use of IoT projects ramps up, there are economies of scale at play. But the current models analysts are predicting still show that the cost of these services will be passed onto end users in the form of premium services and offerings. This will increasingly become a normal upsell tactic within IoT that has yet to take hold.

Thanks for reading this post! Next time I’ll offer insights around device and data management, along with emerging IoT platforms.

Please leave comments here or via twitter: @jkowall.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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