If we live in a data-centric world, it is still true that data are more immediate for some than for others.  To use one individual as an example: by the fifth grade, Ben was an excellent data analyst.  His life depended on it.

Ben went to grade school with my younger son and Ben has type 1 Diabetes.   I remember him explaining to his mother one day how he had, that morning, manipulated his intake of carbs, monitored his blood sugar and activity levels, and carefully calculated what amount of insulin he should take.

He was extra careful and deliberate so he could have one of the cupcakes that a classmate had brought in for her birthday.  Not only did Ben’s life depend on his ability to collect and analyze data, the normalcy of his life depended on it.  (Still does: Ben is in High School now.)

As his mom describes it:

“Until fifth grade, he had an adult guiding him to make decisions about how much insulin he needed based on his blood glucose number or the amount of carbs. Also, until about fifth grade, we put a note in his lunch saying how many grams of carbs each item was.

He did, though, do his own checks, and had to enter that number into the [insulin] pump sometimes. He also had to enter the correct amount of insulin into the pump (which he could read on the screen as he pushed buttons, up or down).

About First or second [grade], we set up the “Bolus Wizard” on his pump. That meant he had to enter the total amount of carbs eaten and the blood glucose number and the pump did the math and suggested an amount of insulin… The analysis that he always had to do, and that has always been hard to train other adults to do, is to consider what was eaten and how recently, in combination with what type of activity he has been doing, and how much he is about to eat.

All this information is what my dad called in frustration, “fuzzy math”. This is the part of D[iabetes] management that is an art, and not easily programmed or put in a chart. He became his own Bolus Wizard in this regard by about 6th grade. Also, by about 5th, I’d say, he was noticing patterns of highs and lows and talking about how to make adjustments to the programmed basal rate and bolus ratios in his pump.”

For every other kid in the class, the only thing required to have a cupcake was to say “thank you” when it was handed to them.

When data relate to people, the subject of each data point is a unique individual.  Even if the data point is the result of analysis, the raw data (the source observations) that went into the analysis illustrate the uniqueness of the subject.  The subject is the only actor whose participation with the data point must be simultaneous with the initial observation that creates that data point.

When you’re moving quickly through space, things close up are hard to see clearly when you pass them.  Things farther away are easy to focus on.  When you’re sitting still, you can choose to focus on the details of what’s in front of you or in the distance.

The data-centric world, the digital world of observed events, as seen by the subject is the opposite of that.  Individual transactions, the immediate foreground of experience recorded as data, are easy to spot.  It’s the background , the pattern or the aggregate that is hard to make out.   Likewise, you can always step back from the transaction and choose to focus on the foreground or the background.

Economists have used this characteristic of perception to help divine people’s motives.  Their goal has been to determine how people make significant decisions, some of which might be against the individual’s long term best interest.  They describe the impulse to see only the immediate transaction/decision as being motivated by a desire for “instant gratification”:

“When making decisions with immediate consequences, economic actors typically display a high degree of impatience. Consumers choose immediate pleasures instead of waiting a few days for much larger rewards. Consumers want “instant gratification.”

However, people do not behave impatiently when they make decisions for the future. Few people plan to break their diets next week. Instead, people tend to splurge today and vow to exercise/diet/save tomorrow. From today’s viewpoint, people prefer to act impatiently right now but to act patiently later.” (“Impatience and Savings”, David Laibson, Economics professor, Harvard University)

Dr. Laibson goes on to point out that how different brain systems are invoked in making decisions that he labels “short-run” or based in the present or “long-run” or based in the future:

“Data from neuroscience experiments provide a potential explanation for these observations: short-run decisions engage different brain systems from long-run decisions. Using functional magnetic resonance imaging (fMRI), Samuel McClure, George Loewenstein, Jonathan D. Cohen, and I have shown that decisions that involve at least some short-run tradeoffs recruit both analytic and emotional brain systems, whereas decisions that only involve long-run tradeoffs primarily recruit analytic brain systems. These findings suggest that people pursue instant gratification because the emotional brain system – the limbic system – values immediate rewards but only weakly responds to delayed rewards.” (same web posting, next paragraph, emphasis mine)

While this argument may be valid for describing how people make certain kinds of decisions, it also makes clear that the mind’s perception of the single data point that relates to the present (what I will eat now) is different from the perception of the aggregate (how my diet is going).

There is also no reason to believe that these two different ways of seeing an event or action is limited to only what economists might wish to measure.  In other words, the difference in perceiving actions is not just in how people ultimately judge them when deciding, but how they see them in the first place.

Security professionals recognize this concentration on the present action coupled with a lack of vision of  a bigger picture (in this case a policy framework) as one of the strongest explanations for why social engineering schemes succeed.  But that’s not my focus here.

Here I am attempting to differentiate between data creation and data persistence.  People’s actions create recordable data continuously.  And whether making a purchase or taking a blood test, the subject of the data is at the immediate center of the creation of the data point. It is what occurs at that moment that data points are created by being recorded that give the data-centric world its most unique characteristic.

The data point is persisted, stored, recorded.  It is completely removed from the sensory experience of the subject it refers to and so it is completely removed from its original context unless, like Ben’s record of his blood glucose level, it is only recorded and used by the subjects themselves.

Otherwise, the record of the blood test results does not refer to the needle or how easy or hard the blood draw was (or thousands of other circumstances of collecting the blood sample).  The record of the purchase does not refer to which decision making mechanisms of the purchaser’s brain were more or less engaged.  Not yet at least.

It is this removal from the subject that accounts for people’s discomfort when arguing about who “owns” the data.   It helps explain why, for example, public health officials and privacy advocates do not always agree on who should have access to health records even though both favor healthy populations.  It accounts for the transformation of data points to analysis sometimes with the subject’s consent but  almost always without the subject’s participation. It begs the question of bias, of what kind of knowledge is beginning to monopolize our perceptions and what are the consequences of that.

Leave a Reply