One Way to Overcome a Limit of the Quantified Self - Using Notifications

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I love data. As an Operations Researcher, the absence of hard information drives me crazy. But there is a reason QS is limited in its application or use by the average person.

Rather than busy executives or housewives, QS has (stereotypically) attracted young techies with lots of time on their hands. They are the ones with the bandwidth to sift through new storehouses of personal data, looking for unforeseen clues to improvement opportunities.

The average busy person, suffering from sharp limits to their time and attention, needs a shortcut. One clue I’m exploring in my work comes from an insight I gained from working as a contractor at McKinsey. On their assignments, they resist the temptation to “boil the ocean” - their term for doing unlimited data mining.

Instead, from the onset of a project, they set up a small number of hypotheses and only then do they look for the data to prove/disprove them.

From their success, it’s safe to conclude that this approach works. It gives them a way to focus their attention on the most pressing issues, reducing the cost and time of assignments, making for happy clients paying very high fees.

I think the average person operates in the same way. For example, imagine that someone notices that they have been feeling tired for the past few weeks, wondering why. “Maybe it has something to do with my sleep?” he/she asks. To prove the hypothesis, data is needed… the kind that’s he/she has does not have or has ever seen.

Enter QS. Until quite recently, most people never imagined having the power to gather their own data at low cost. Now, they can answer their hypotheses and maybe even solve the overall personal problem in record time.

Once they do so, they put away their wearables and archive their spreadsheets, perhaps never to be used again. But they aren’t being ungrateful, just busy. They may quietly thank the inventor of the device, QS and their luck to be born at this time, but they move on.

This happy outcome is rare, but its extraordinary nature has little to do with QS. Instead, it’s related to the limits of human capacity.

The problem is that the average person has neither the time nor attention to set up sensors in order to data mine i.e. boil the ocean. Instead, I think the best they can do is achieve an end-point I have named “The Notified Self.”

It’s your own individual vision in which you are perfectly notified of all interruptions, alerts, beeps, popups, updates and notifications, even when they occur on different devices and platforms. At this end-point, your iPhone, wearable, laptop and Amazon Echo all act in perfect concert.

In the future, it may look something like the picture presented in this 2 minute video I just found on the HyperVoice website:

It’s awesome and inspiring… and you can see where the outcomes it portrays absolutely needs QS working in the background.

But for now, I’m just hoping to bring order to the chaos that exists for most people today. They have given up: either shutting off all their notifications or simply ignoring them. It’s a problem I describe in this rather long article - “How to Organize Your Notifications to Accomplish the Perfectly Notified Self” -

In the article I argue that there are ways to approach The Notified Self today, without adding more technology. In fact, doing so is a must for busy professionals who could benefit from all the work being done in QS, wearable devices and other emerging innovations.

I welcome comments or questions, here or on the Medium site… plus leads to anyone else interested in this area.

Francis / @thenotifiedself

Hi Francis, There’s something interesting here but it’s a bit concealed by the hype in your post, which makes lots of assumptions about what everybody else is doing wrong and how you’ve fixed it awesomely. I’ve noticed that this approach is usually greeted with dead silence on the QS Forum, even though there are some very expert developers and pioneering users here who are eager to try out new things. Your mileage may vary, but I give you this feedback in case it is helpful.

I just wanted to point out something about the statement that it is better to “set up a small number of hypotheses”. Hypothesis based research is now performed as a second step in the research process now that we have Big Data and machine learning algorithms and the cloud. Now, it is about first discovering “patterns” in the data without an hypothesis.

However, on a personal level we are not there yet. But it will come. Take microbiome sequencing, if you do it, you get DNA/RNA sequence data in the Gigabytes and if you do it repeatedly you end up with Big Data. You want to squeeze out all the patterns out of it, and then run some hypothesis based studies.

If you collect fitbit data over years and years, you will also be able to apply machine learning to it and discover meaningful patterns.

Regarding notifications or the article in the link,I read it but still don’t understand how that has anything to do with collecting data and studying it using a hypothesis.

Thanks Gary,

I wondered about the tone after I wrote it… I see your point now, in retrospect. So your comment is much appreciated.

While I have done some beginning work in this area and feel strongly about it, I’m not close to solving anything. In fact, we need all hands on deck, in a coordinated way. That’s clear from what I have seen from my research because no single company has the means to create “The Notified Self.” If anything, it appears to be harder and bigger than I first imagined.

At best, I hope to find others interested in solving it, and perhaps persuade a few more… but I don’t want to get in the way of the message… It’s far bigger than my post indicates. I think solutions would empower the Quantified Self in ways we can only imagine.

Tx again.

Hi Pat,

Let me take a stab at addressing your last statement: > still don’t understand how that has anything to do with collecting data and studying it using a hypothesis.

The underlying premise of my article is that we are all a bit either time-pressured, busy or maybe a bit lazy.

If you are feeling poorly you may think it has something to do with an irregular heartbeat, because your family has a history of arrhythmia. You decide to do something about it.

Deciding to buy a heart rate monitor to track some data is a decent first step, and the decision to do so comes from your initial hypothesis.

You gather the data and notice that there are some mornings when you heart rate is above normal - it’s those nights when you have had little or no sleep. You decide to sleep another 30 minutes when this occurs.

But how do you discover when this has happened so early in the morning? You need a notification of some kind… a warning light that sets itself off when you have had a bad night, perhaps within five minutes of your waking up. It would lead you to consider turning off the lights and going back for more sleep.

Oftentimes, most of us need two kinds of help to use QS data effectively…

  1. Help in converting the concept “I have a problem and I need to find a solution” into a testable hypothesis (and then finding a means to test it)
  2. Help in turning “the answer” into a sustainable change in behavior - using notifications informed by QS data

There is not necessarily a link between the two, but I think it helps the average person to see the entire change process from end-to-end, so that they don’t think they are wasting their time by running out to purchase a wearable device.

I agree that hypotheses can/do arise from data. I have some data on productivity skills gathered from my training that I believe has some powerful answers locked inside it.

But, like most people, I’m also time-stressed, busy and a bit lazy… so it sits there un-mined. (I keep waiting for someone else to take it on as a project!)

However, the point I try to make in the article is that the average person isn’t going to think about data mining for a number of reasons. They won’t buy a device to collect data just so they can see what they find… It takes too much bandwidth on their part.

At best, they may try to solve a known problem, following the path we have been discussing.

In other ways, the potential of QS will be unlocked when people see a pathway to its use. At that point, they may not even know what QS is all about, but that’s OK. They will be solving their immediate problem, which is what they really want anyway.

Does that help at all?

Thanks Mike. I have a similar background and remember showing clients simple flowcharts that would leave them mystified… They just weren’t used to seeing lots of data condensed in an unusual way.

We all would benefit from having lots of skilled translators who can help bridge these gaps. And software of course.

I suspect that manufacturers of wearables are aware of this fact. Hopefully they are doing some things to help.