3 min read The ability to leverage data to automate the behaviour of IoT devices [...]
The ability to leverage data to automate the behaviour of IoT devices is one of the most compelling features of the IoT smart home automation industry.
But to work properly, analytics-driven IoT devices need to be based on useful data. Many IoT-enabled devices continue to rely on poor (or siloed) data sources that skew the devices ‘learning’ process. The end result is a market full of shiny products that confuse the user and disrupt the home automation experience
Christopher Tozzi wrote a compelling article about his experiences with one of the most sought after home automation IoT products, the Nest. While the device is aesthetically pleasing and delivers on its promise for remote monitoring and control, the functional benefits are limited. One of its biggest shortcomings is its inability to accurately learn from and adapt to the surrounding environment. But Nest is not the unique example of this problem. In fact, it’s a recurring problem faced by many home automation device manufacturers.
At CES, the annual electronics show, thousands of vendors occupy the shop floor hoping to penetrate the consumer electronics space. Last year we saw an increased focused on Artificial Intelligence (AI) and self-learning smart homes. But almost a year later, we continue to see product manufacturers unable to properly learn from their device data. In order to enable artificial intelligence, and truly automate the behaviour of IoT devices, manufacturers are tasked with the unique objective of looking at their device data both in aggregate and individually.
Where individual data limits the ability to provide a complete, end-to-end, automated experience, aggregate data provides the complete picture required to construct actionable product insights.
When we have aggregate data, we can drill down to see interactions between products and customers, or products and products. Device manufacturers can determine when there is a problem and the relevant context around that problem allowing them to :
Reduce the number of product faults
Provide feedback back to the product roadmap
Improve aftermath services
Uncover new insights that were not conceived during the initial project planning
In his article, Christopher highlighted the devices limited interface as a burden to the user experience. Given that new devices cannot display as much information as their traditional counterparts, product managers must determine beforehand which information to make readily available. But how can users find the default information useful or not once your devices are deployed?
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One simple way is through the interactions with the device and the application. User interaction with the application is one way to determine if someone is continuously seeking out additional information because it is not available on the device itself. In his case, Christopher had to open the application to get the current temperature because he wasn’t interested in the projected temperature that was displayed on the device. Overtime, you can learn from the user interactions what information is important to them and adapt the default options.
The availability of data is transforming the IoT. To progress beyond obtrusive products with limited learning functionalities, manufacturers will need to focus on the quality of their data. With it, IoT analytics can empower product manufacturers with a number of invaluable insights such as:
And much more…
Find out how mnubo is tapping into the benefits of product data by transforming raw sensor data into actionable business insights. Read more here!
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