The recent pandemic crisis has shaken many industries worldwide. With the rise of flexible work models and remote monitoring solutions, the pandemic accelerated the digital transformation of entire ecosystems.

From building occupancy to indoor air quality monitoring, the demand for IoT-based solutions has increased dramatically.

Moreover, the commoditization of IoT hardware and sensors is constantly driving down the infrastructure costs of new IoT installations.

These are positive signs toward a broad market application of IoT. However, a substantial barrier to IoT adoption still exists.

Launching an IoT project is complex.

Out of our experience, a majority of new IoT endeavors fail, most in the proof of concept stage. This situation is partly due to the misalignment between the project’s expectations and the technical expertise required to execute it.

The successful implementation of an IoT solution requires the interconnection of several building blocks that do not always offer standardized interfaces. A typical system contains end nodes in the form of sensors or actuators that communicate via different connectivity technologies, possibly reaching a gateway that would then forward the messages to a network server and finally to an application server in the cloud.

But this is not the end.

In the application server, the sensor payload needs to be decoded into usable data, which can then be processed into the final end-user application (a web app, a visual dashboard, or any other cloud application).

Interconnecting all these building blocks and processing the IoT data is usually done by technical specialists. These IoT engineers, developers and data scientists have the IT expertise to connect the dots between all these different technologies and deliver the formated data to the final application.

More often than not, the project’s initiator will have domain knowledge but not enough expertise in the technicalities of the IoT. He will then have to externalize the implementation of the IoT middleware to a system integrator or other expensive digital consulting companies.

This means that the people with domain knowledge and who have a clear idea of the business benefits generated by the smart solution tend to be somewhat disconnected from the application
development process. This can lead to high coordination costs, delays, and other points of friction. In the worst scenario, this can kill an IoT project in its infancy.

This is why we have seen a shift towards low-code and self-service tools in the IoT ecosystem in recent years. These tools make it more accessible for innovation and R&D teams to participate directly in the IoT development process.

The low-code movement dramatically accelerates the path from idea to proof of concept (PoC) and is impacting not only the IoT sphere but the whole IT industry. Research from Gartner shows that “by 2024, low-code application development will be responsible for more than 65% of application development activity.”

The IoT evolution needs low-code functionalities.

This is why, at akenza, we have developed numerous low-code features into our IoT platform. Data parsing of IoT device payload, no-code definition of the data processing chain and advanced rule engine are some of the functionalities that make it easy to start an IoT project in one afternoon.

Early IoT adopters need a solution to quickly and effortlessly create IoT use cases. By providing a low-code, self-service IoT platform, we enable an agile IoT solution creation process.

Source: Plato Data Intelligence: PlatoData.io