When I ask people what they think the Internet of Things (IoT) is all about, the majority say, “smart homes”, probably based on personal experience with Alexa or Siri. If I say that it’s also about industries making using of sensor data, most think of manufacturing. Sensors have been used for a long time in manufacturing, and the concept of using data generated at the edge to monitor and run automated processes is well understood. But this is underestimating the potential of IoT. In practice, IoT can be applied anywhere.
The Artificial Intelligence of ‘things’
It’s easy to identify the use cases for industries with ‘things’ to monitor.
Manufacturing has the most obvious examples. Connected sensors monitor and manage the health of manufacturing equipment, identify root causes of defects and improve quality.
Healthcare equipment generates digital information about how patients’ bodies are working (e.g., pulse, temperature) and what they look like (e.g., x-rays, scans). There are numerous opportunities to monitor people’s health more closely and accurately to catch signs of disease early, or even avoid it altogether. Data from the equipment can also help eliminate “alert fatigue” in hospitals to ensure proper care is administered at the right time.
The insurance industry is using telematics to monitor driving behavior and assess the risk posed by individual drivers. Telematics also improves the claims process by providing information leading up to a crash that can indicate who is at fault and Image Analytics can assess photos of the damaged vehicle to automatically determine whether the car should be written off or repaired.
Many automotive manufacturers and suppliers are seizing new opportunities to connect with the consumer, tap into new intelligent vehicle capabilities and harness the power of an IoT ecosystem. For example, with IoT and embedded AI, leaders are now able to identify and leverage the most valuable IoT data generated at the edge (analyzed and filtered on-board the vehicle) to create exciting services that generate new revenue streams.
IoT also has potential in industries that, on the face of it, do not really have “things,” such as financial services. Banks and other financial providers are extremely interested in IoT, focusing on “things” which do not belong to the banks themselves, but to customers: mobile phones and payment cards, for example. Banks can improve fraud detection by notifying customers each time their cards are used – in real time – while also checking that the customer is with the card at the time. That, clearly, is a huge service for customers: no more cloning and no more fraudulent transactions.
Game changers enabled by IoT analytics
A fundamental shift in business model is being enabled by IoT analytics: a move from products to services. For example, Rolls-Royce is traditionally considered an engine manufacturer. The company made and sold engines, then sold services to maintain those engines. Now, however, rather than pay for maintenance, airlines can choose to pay an hourly rate for the time that the engine is propelling the aircraft. In other words, it can pay for the outcome it wants: the plane in flight at particular times. Increasingly individuals, too, are choosing to pay for a service, rather than goods, such as access to a car-sharing service, rather than owning a car.
However, this shift presents challenges for the service providers. If you are providing a service that includes a physical asset, you do not want to have to spend time and resources inspecting that asset. Instead, you want it to run itself as much as possible. IoT allows providers to remotely monitor and collect data on all the important aspects of each asset – how it is performing, how it is being used and environmental factors, for example – and therefore automate much of its management.
The data collected from IoT is only really useful when you can derive useful intelligence from it, and preferably in an automated way. This automation requires intelligence -- artificial intelligence (AI).
The importance of AI – and innovation at the edge
This is one of the biggest reasons why IoT is really taking off now: AI algorithms are becoming more usable. But, there’s still a problem. Most AI algorithms need huge amounts of data and computing power. They rely on powerful servers and central data storage.
In computing terms, we humans perform most of our computation and decision making at the edge (in our brain) and in the moment, referring to other sources (internet, library, other people) where our own processing power and memory will not suffice. This is more or less the complete opposite of the current AI algorithms, which tend to perform most of their calculations far from the data source, in servers, drawing on stored data.
To enable timely decision making in the world of IoT, you need to be able to deploy some of the cleverness (predictive models and decisioning rules) at the edge, closer to the “things” that you are managing. Some businesses are already doing this, while many others are still trying to figure out how to organize and make sense of the deluge of data available to them. Those at the forefront of combining AI and IoT have a huge opportunity to steal a march on their competition.
In my personal view, this is the biggest change in business models since the dot-com boom. And, as in the 1990s, there will be some big winners, and there will also be those who don’t quite get it right and fall by the wayside.
About Your Guest Blogger:
Jennifer Major is a pre-sales consultant at SAS where she has worked across a wide range of sectors, including energy utilities, telecommunications, pharmaceuticals, media and services. In early 2018, Jennifer became head of the IoT practice for SAS UK & Ireland. In this role, she is focused on helping organizations to realize the value of IoT data through the use of AI and Machine Learning techniques. Jennifer holds a bachelor’s degree in Mathematics and Drama.