Connecting the Dots
Bringing order to the Internet of Things
From Data to Understanding
We don’t have to look to far to see that everything around us is now collecting data about us, our behaviors and our context.
As I type this on my connected, location-aware laptop computer connected to a open access-point in the coffee shop in which I sit, with my location and calendar-aware iPhone sitting next to me, and the activity-aware Misfit Shine on my wrist, I’m transmitting countless data points through the aether.
The second I stand up and start walking back to my apartment, my Misfit Shine will be quantifying my movement and sending the data to my iPhone. My iPhone will constantly be getting updates from my calendar and to-do list that I sync with my different Google accounts, and be prompting me through out the day with notifications to keep me on time for my appointments and scheduled tasks.
If I get in my car to drive home, the Automatic adapter I have connected to my vehicle will record and report on my driving behavior, whether I’ve sped up too fast, breaked too hard or drove over the prescribed 70MPH threshold for best miles-per-gallon performance for my car.
We are sending out millions of data points every day, from when we get up in the morning to when we go to sleep at night. And if you’re me, you even track what’s going with your body WHEN you sleep!
Mary Meeker points out the potential of all the devices transmitting data about us in her 2014 Internet Trends presentation:
“[The] Fastest growing segment of valuable data comes from Internet of Things (IoT) — billions of sensors / intelligence systems capturing / sending data, increasingly in real- time… “
According to Meeker, right now it only accounts for 7% of the data collected about users, but rapidly growing and immensely valuable.
These are all just “dots” of data representing our physical context and behavior at that specific moment. In physics, this is referred to as a “vector” — a construct depicting momentum as a product of the mass and velocity of an object.
For instance, if I get up out of my comfy chair at the coffee shop, my devices register my position as in the coffee shop and heading for the door. I could go left out the door and head to the Whole Foods down the street to get a salad or I could go right back towards my apartment to take a nap.
My behavior, captured by my devices previously, has been to go left and head to Whole Foods to get a salad to satiate my appetite so I don’t snack on processed junk food at home.
So, why doesn’t my device, and the services it connects to, analyze these disparate data points and advise me on the best course of action? Why do I not have a “vector” or “vector(s)” for the best opportunities ahead of me?
Two reasons: disparate data points and limited analysis.
Disparate Data Points: My Misfit Shine only knows that I’m walking or not, while my iPhone knows where I am, based on my GPS or Swarm Check-In, and my Visa card knows that I’ve bought a salad at Whole Foods 3 days out of the last 7 for lunch. My calendar & to-do list knows that I’ve gotten more done after I’ve had my salad, versus if I went home and had a nap, so it has a history of my productivity.
Limited Analysis: While the Misfit Shine may say “you need to walk more today to hit your goal”, it doesn’t offer me an option of what to do and when based on my particular behaviors that have been successful in the past. NO ONE has correlated my habitual behavior and created the appropriate recommendations of what should happen next in my life to make me happier, healthier and more productive.
Sure, you can be easy to dismiss this as a exercise for the techno-elite. Of course I have access to all the greatest technology in the world, and have been applying different hardware, software and cloud technology to my problems of learning about myself and making behavioral changes for the better.
But today it starts with Smartphones, Gaming Stations, Visa cards and Check-Ins, which are accessible to all. Then it extends to more data points with “smart cars”, “smart watches” or “smart-whatevers” that are capturing every movement, activity or engagement in the physical world.
What will really blow your mind, is when the physical world connects to the digital world.
Take Commuting. Digital projections are getting so good; traffic patterns can be anticipated based on time of day, weather and even transitory events, that we could be getting a recommendation when to leave for work on the night before. This isn’t the future, this can happen today.
Yet, here lies the crux of the problem: the dots are NOT connected.
Each product or service lives in its silo, and we, as humans, are asked to make sense of all the disparate data points, concocting our own analysis. “I need to walk to Whole Foods because I haven’t hit my daily goal for 10,000 steps, and I need to get a salad because I tend to get more done once I’ve eaten healthy instead of napping over lunch.”
This seems asinine, as we as humans are not built to do this complex analysis and seems like the perfect job for computational technology.
To improve the state of any individual, we must optimize the “vectors” of behaviors by connecting these dots.
How do we accomplish this? Here’s my recommendations:
- Build a single, user-owned Database of the data points collected from ALL connected devices and relevant, other databases (i.e. — my calendar)
- Write the numerous routines that correlate the disparate data against each other.
For example:
My eating habits from what I purchase at Whole Foods based on my Visa transactions correlated against my activity level coming from my Misfit Shine, to understand what foods may give me more energy to be active versus the ones that make me more lethargic.
This should be a platform, where anyone can contribute routines. And the routines receive up/down votes based on effectiveness for the audience, bubbling up the ones that prove to be better at predicting and recommending good behaviors. - Present this data in a “GPS for your life” that displays options for next steps based on historical data and predictive analysis.
(note: I stole the term “GPS for you life” from a very well-respected CTO and leader at a major consumer company).
Its already beginning. Researchers at Georgetown University have put more than 250 million global events into the cloud for anyone to analyze to create predictive models:
Its my hope that this simple formula will lead to a service that makes positive behavioral change from the disparate data points and complex computer analysis that happens “behind the scenes”, maximizing the potential for our lives.
