If you’ve read our article about the differences between GPS tracking, Telemetry and IoT, you’ve probably realised that the market bandies the term ‘IoT’ around a lot.
IoT stands for ‘Internet of Things’. And anyone selling sensors or connectivity solutions (or software programs, for that matter) will tell you that they’re delivering IoT.
The truth is that these are all components of what makes an IoT-enabled digital evolution project. But none of them individually deliver on the promise of IoT. That’s for two reasons:
- Sensors alone don’t do the trick, you still need to see the data in some form.
- Connecting sensors to a visualisation layer delivers a use case. This is good enough for proof of concepts, but few operations employ someone whose job is to look at sensor reading visualisations.
What makes IoT projects successful?
To be truly successful, an IoT project needs to extract sensor data from its silo. Then it needs to merge that data with data from other sources that exist across an operation. And then it needs to analyse the combined data and make it available to decision-makers (or better yet, automatically make decisions where possible).
I was reminded of this at IoTAA’s recent Australian IoT State of the Nation event. One of the many challenges that participants identified in scaling up IoT projects was the difficulty in understanding what was required to deliver on those projects.
The CEO of the IoT Alliance, Frank Zeichner, referred to the ISO/IEC definition of IoT as:
“An infrastructure of interconnected objects, people, systems and information resources together with intelligent services to allow them to process information of the physical and virtual world and react.”
The market has mostly focused on the ‘interconnected objects’ component of that definition. However, the companies that most successfully leveraged IoT in their operation have fully embraced the other components too.
These companies have recognised that a successful IoT project isn’t just about monitoring ‘things’. Instead, it’s about monitoring how those ‘things’ interact with the people in the operation who use them, and the other systems those people use. And, more than just monitoring, it’s about actively using the data to enable the people in the operation to do their jobs faster, more cost-effectively and more safely.
IoT use cases in the consumer space
It’s not just large industrial operations that can fail to truly realise the benefits of IoT, however. A couple of examples in the consumer space that come to mind when comparing IoT successes and failures include…
Smart thermostats, not-so-smart integrations
A few years ago, I decided to look at smart home appliances. One thing I wanted to understand was what, exactly, the value of a ‘smart’ (read, ‘connected’) thermostat was.
After I’d installed the equipment in my home and the app on my phone, I could remotely control my A/C unit. But that was no different to what I’d been able to do with my remote control.
Next, I tried to connect the thermostat through to my smart speaker… which is when things started to go south. In theory, integrating the system with my speaker was straightforward. But actually using the thermostat through the speaker was much less so.
This meant that the integration failed for me because it didn’t fit with my way of working. As a result, this IoT use case was of little value to me.
Intelligent video surveillance using geofencing
At the other end of the spectrum is my video surveillance system. I also manage this system through an app – in this case, one that controls the video cameras. But the designers for this system have thought more about how the system’s users will interact with it.
Something I really appreciate is that they’ve removed one of the most frustrating things about surveillance: notifications of your own movements around the cameras. They’ve done this by creating a geofence around the property that effectively ‘turns a camera off’ when my phone is within a certain range.
This is great operational thinking: recognising that I don’t want to have to manually change the camera’s status before I walk into its range.
Together, these two examples show that unless developers take other components of an operation into account, many IoT use cases are really just gimmicks.
Getting the most from IoT in the commercial space
Imagine a fully-fledged commercial operation that wants to incorporate IoT. For the sake of argument, let’s say it involves cold chain logistics.
Done manually, one part of the logistics process might look something like this:
- The driver of a refrigerated truck checks the temperature of the produce they’re delivering, using a gun thermometer.
- They write this temperature onto a printed Bill of Lading.
- Finally, they get the customer to manually sign the Bill to confirm delivery.
This is a fairly widespread process that is the norm today across a range of industries requiring cold chain traceability.
The logistics company could make many things across this process more efficient. In particular, focusing on this customer interaction:
- They could use a sensor to visualise the temperature of the product at every point from warehouse to delivery – however, this is telemetry, not IoT.
- They could integrate the data from the sensor readings into a pre-filled digital form that the customer can then sign digitally. Then the system could automatically record the transaction in a centralised platform that triggers the invoicing process… and this is IoT.
In the latter example, the driver doesn’t need to manually take the reading. Instead, the workflow is already streamlined. Then, to create a really strong value proposition, the logistics company could add in:
- advanced delivery warning, by integrating the route management software
- compliance checking, by providing the customer with a report of the temperature before delivery.
Where could your organisation benefit from true IoT?
In our cold chain logistics example, the sensor is simply a data source. The bulk of the IoT value delivery lies in the sensor’s integration with the other systems that make the use case possible.
When you start a project to digitise your workflows and make them more efficient, there’s a little value in sensor data ingestion pipelines. However, there’s a lot more in ingesting and making sense of data from other sources.
This is where a platform like Perspio™ outshines any other. It operates as a flexible middleware layer that transforms your siloed data into contextual information, ready for automation.
Want to know more? Reach out to the team!
