Designing Business Models for IoT

Cheng Jang Thye, Chief Architect & Head, Digital eXperience Center, Fujitsu Singapore

Cheng Jang Thye, Chief Architect & Head, Digital eXperience Center, Fujitsu Singapore

The Internet of Things (IoT) is an approach to applying information technology solutions to areas traditionally outside of information processing. We attach sensors or wearables to things, living or not, to collect the data, analyze them and with the insight, take some actions to improve or innovate our operation of the things around us. This approach can be used across all industries and there is increasing interest in building business models around the adoption of IoT technologies.

According to Fujitsu’s Global Digital Transformation Survey in early 2017, 89 percent of over 1,600 business leaders across 15 countries are adopting digital technologies such as artificial intelligence (AI) and IoT. Among them, 34 percent of the digital transformation projects in place had already delivered positive outcomes.

Today’s IoT business models often show common stages of technology adoption, on how data and information processing contribute towards the capability of the system, from simple visibility of the data around the things in IoT to complete intelligent automatic orchestration of the entire system of things.

These stages offer solution providers increasing levels of business values. By understanding the five discrete incremental stages of adoption level of IoT technologies, an IoT business model that takes into account the cost of resources and implementation for each stage can be constructed.

In addition, there are certain considerations to note when customizing IoT business models to different needs. Stages can be combined to offer a meaningful package of business benefits. More advanced stages with higher business value can cost more than the basic stages.

The 5 stages are: Asset, Aggregate, Activate, Augment, and AI

Data collection

In the Asset stage, sensors are used to collect data (or integrated with operational systems to generate data), and are sent over to an IoT cloud to store the data. The data is typically captured as a time series and further analysis as well as alert action could be executed in the IoT cloud to provide rules and constraint checking with SMS/email notification. The fundamental concept in this stage is the ability to uniquely tag an identifier with the source of data. The sensors are wired up to gain visibility and track data in real time.

Creating a digital dual

In the Aggregate stage, some thought is put into looking at the data collected from different sensors and sensor types, and creating a model to represent the Thing that we are monitoring. We now have a digital dual of the Thing that allow us to do simulation, analysis, and optimization using the data collected in the IoT cloud and other knowledge about the Thing.

IoT business models will no longer be a value proposition, but a basic requirement

With this increased ability to model the physical object (thing or human), many more types of analytics can be done, from trending analysis to prediction to anomaly detection.

Improving existing business operations

The Activate stage offers the ability to change or control the process the Thing operates. Two-way communication with the Thing is needed, which may require a significant upgrade for the network to connect the sensors. We are able to change the way the Thing works at this stage and even improve its business or operational functions.

Creating new insights

The Augment stage is often taken for granted, as some businesses stop after building the capabilities in the earlier stages. Augmentation uses other additional tools and further human input to improve operations beyond its original operational perimeters. Simulation and visualization tools and possibly other data sources are employed to consume and process the real-time data from the sensors to provide new insights and new boundaries. Human decision may sometimes be required, and Augmented Reality technology in conjunction with the simulation and visualization tools could be employed in the human decision-making process. For example, a head-mounted display with a camera allows live images/video to be captured and viewed within the head-mounted display augmented with overlay information.

Automating decision making

The next step is to replace human decision making with artificial intelligence. We may augment the sensor data and gather more inputs, and add more processing for the data. Whether this stage is possible would depend on policy and ethical considerations.

Building business models around the 5 stages

The initial Asset stage is essential to get things going and fortunately, the technologies in sensor hardware, network connectivity and cloud capacity are well understood. Subscription packages in units of sensor deployments can serve as the base business model. Cloud application for analyzing data can be built in cloud native fashion (i.e. scalable horizontally) and priced in terms of the value of visualizing the data from the sensors.

The next two stages, Aggregate and Activate provide improved operations over visibility. These stages are heavily dependent on software technologies, so further subscription upgrades could be packaged for the units deployed.

The AI stage is slightly tricky with its fair share of debate on ethical grounds. It is then important to understand the possibility and limits of AI. A computer can collect and analyze huge amount of data, and produces a statistically correct answer, humans cannot catch up with this speed. Humans learn from the communication and experience using our body and brain. It is thus imperative to combine the data analysis skill of AI with the creativity of human, where people can give common sense and an objective to AI.

Business leaders around the world are now keenly aware of the importance of digital transformation in enhancing business growth. IoT business models will no longer be a value proposition, but a basic requirement. However, digital transformation does not happen just by the adoption of new technology–design thinking, leadership, and co-creation with partners are crucial as well. Developing an organization culture that supports new transformation initiatives will also be integral for future business success.