Every second of the day, social networks, smartphones, point-of-sale devices, medical records, financial transactions, automobiles, energy meters, and other digital sources generate a wealth of data.
These streams of data show a real-time reflection of where people go, what they do, and what they think is important. The accumulation of real-time data, when combined with environmental data, can be used to provide a type of proactive intelligence.
Our phones are a major recipient of personal data, and services like Aviate (recently acquired by Yahoo) or Cover make smartphones smarter by learning what apps and settings are used throughout the day. Depending on whether the owner is at home, at work, or in the car, Aviate and Cover automatically update the user’s home screen to the desired setting, removing the need to scroll through endless apps and settings for each aspect of the day.
Along similar lines, Google Now is an intelligent, albeit much more robust, personal assistant. It is trained to predict when a person is about to take certain actions, and it offers help accordingly. It can also learn about an individual to fine-tune the assistance it offers. Google Now’s algorithms use the data in a person’s Gmail and calendar accounts as well as web searches to offer proactive cards that help a user manage his day, stay connected, or, in Google’s words, be a local. Popular cards include real-time public transit information, sports scores, and meeting reminders.
Other services like Ginger.io and Behav.io, take the idea of proactive intelligence to the next level and use our mobile devices as a proxy to our physical and mental well-being. Ginger.io turns mobile data into health insights by tracking how users interact with their phones. By analyzing how they interact with their phones, where they go, whom they call and when, and what’s happening in the background, Ginger.io can identify changes in behavior that may be warning signs for people with chronic illnesses. If a user’s habits and patterns deviate, doctors or caregivers can be notified and changes can be made to behavior. Behav.io, which joined Google in 2013, also uses data recorded by smartphone sensors—movement, texting, call activity, and location—to predict when owners will become sick, even before they show any symptoms.
In the same ways that our phones generate massively useful data, the ways in which we inhabit our homes can also be used to improve our lives. The Nest Learning Thermostat and recently introduced smoke detector, Nest Protect, automatically learn inhabitants’ schedules and use this behavioral information to program themselves. Similarly, Canary is a home-security device—with built-in sensors capable of detecting changes in motion, temperature, humidity, and air quality—that individuals can place anywhere in their houses. When the device detects spikes in activity, such as a rise in temperature or unexpected movement, it sends an alert to the user’s smartphone. The more alerts that are sent, the more the device can learn the behavior and habits of its user and respond more effectively to certain changes.
How smart are these devices? By analyzing consumer data and proactively adjusting temperatures, Nest helped users save 1.2 billion kilowatt hours in 2013, enough to power 1.3 million homes. This collective savings shows the potential power of Nest and other connected devices when connected to a larger network. This power is best evidenced by Nest Energy Services, where the company is able to link its device to the collective, cloud-based knowledge of utility companies. When consumers link their individual devices to their utility companies, Nest can automatically limit energy consumption during peak periods. Users who participate in the program save money while helping to prevent brownouts, and the service is indicative of the larger trend of using personal data to power proactive, collective intelligence.
The accumulation of real-time data, when combined with environmental data, can be used to provide a type of proactive intelligence.
–JS & AW.