An IoT solutions aims at creating new information links in the system it integrates into, by regularly measuring data on the field and making it available to system actors (be they human users or other machines) to consume. Its success is determined by its ability to influence the system for the best, in particular by reinforcing desirable loops and weakening nefast ones.
An IoT dashboard is an information software which communicates the IOT gathered data to human users. In order to fullfill the IoT solution's goal, it should aim at making users learn about the system (how does it behave), identify point of actions (where should I act upon it, what can I do), and support the decision making process with necessary and relevant information (why and how can I do it).
Learning:
- Identify typical behaviour (and not only average behaviour)
- Understand dynamics and relationships between system components
- exploration, pattern identification
Identify point of actions:
- Spotting outliers
- Spotting unexpected change from the normal
- Identifying positive and negative dynamics: metrics correlated with goal metrics
- Showing things to do now
Support decision making:
- Action points s should stand out
- Show why is it worth doing, or checking further
- Bring as much supporting context as possible
- Keep action points relevant to the context
How does your IoT service helps your clients? Are your dashboard(s) designed with these goals in mind? Or did you manage to reduce the dashboard to its minimum (like the Nest thermostat), using minimal user input to let the system act on its behalf? That is anticipatory design. While anticipatory design has so far only been practical in simple contexts (e.g. room temperature control), it should still be possible to bring context sensitivity even when dealing with more complex systems (like healthcare).