Bob wakes up to an alarm sound based on an ambient song at around 80bpm, the alarm system’s choice of genre based on his optimal heart rate for early morning starts. The alarm system varies genres according to Bob’s listening habits. Later, during his early morning jog, he picks up the pace at the post office, just past his convenience store, where an advertisement for coffee flashes on his smartphone. Data logs from his workout monitor show that his performance usually drops by 15% at this point, spurring him on to make that additional effort. Noting the busy day ahead, breakfast is accompanied by half a cup of medium- strength Java. His new brain sensing headband indicates that 75 milligrams of caffeine stimulates Bob’s mental performance by around 28% on an average, without a cortisol spike, just enough for that report due in at 11am. Bob is one of several people living a data driven physical life – logging, monitoring and analyzing aspects of his ‘quantified self’, to make constant changes and improvements.
Sensors – motion sensors, accelerometers, heart rate monitors, breathalyzers, electric hormone sensors, and the list goes on – are progressively getting cheaper to produce, are smaller and are more easily available. Permeating consumers’ lives, more recently through their smartphones and wearable devices, sensors are set to be an intrinsic part of the digital native’s interaction with the world. This is paving the way for predictive appliances through data capture. Potential is increasingly seen in bundling sensors in order to receive more comprehensive data. Combining the data received from sensors in various parts of a phone, for instance, to obtain exhaustive information can make the device contextually aware. These ‘aware’ appliances can determine how and where they are being utilized, ushering in the age of predictive applications, shifting from static to dynamic at will, transforming the patterns of technology consumption. The economic and strategic implications of this development for organizations are significant.
As you would expect from devices that are used ubiquitously and for various purposes, sensors generate a staggering amount of data. By logging real-time activity and behaviour, this data could be the most diverse and accurate source of information available yet. Much like Big Data, however, the potential of sensor data will be unlocked not by volume, but by the quality of analytics applied across data sets. From embedding biometric chips in soldiers to assess battle-readiness, to assessing employee engagement through sentiment analysis, organizations are constantly on the hunt for inventive ways to gather, assimilate and analyze data driven by sensor deployment. For example, in the retail industry, companies can employee dynamic pricing based on sensor data to maximize sales and reduce wastage.
Perhaps the sixth sense was never meant to be a capability driven by evolution, but by enterprise and innovation. Organizations that dedicate their efforts to deploying and adapting a ‘proprietary blend’ of sensors, alongside accurate and optimized data analytics, for every instance of user behaviour will have the best chance of becoming intrinsic to consumers in this hyper-connected, hyper-personalized world.
What are your thoughts on the potential of sensor-based data? Will sensor data analytics be an inherent feature of your company’s future IT strategy? Share your views with us in the comments section below.