I recently tried to apply the same data analytics rationale that I use at work to my personal life. This is not a rant, it is more like an data storytelling of an actual use case I would like some input on.

I set a goal - gotta thin up a bit and calm down my ticker - and got a (almost unreasonably expensive) field expert consultant to yell at me about it for a couple hours.

I unravel the metrics - there is like a million weight-related KPIs and most say nothing at all. I have never seen an non-infrastructure measurable subject that could not be resumed to 2-5 performance metrics. I got overall weight, how well my nine-years-old business suit fits me, heart rate, and day-after relative muscle pain (it will make sense soon).

Then its data-pipeline time. I bought a cheap weight scale and smartwatch, and every morning I input the data in an app. Yes, I try to put on the suit every morning. It still does not fit.

After establishing a baseline, I tried to fit different approaches. Doing equipment-free exercises, going to the gym, dieting. None was actually feasible in the long run, but trying different approaches does highlight the impacts and the handling profile of each method.

Looking at the now-gathered data, one thing was obvious - can't do dieting because it is not doable to have a shopping list and meals for me and another for the family.
Gym is also off the table - too much overhead. I spend more time on the trip there and back than actually there.
And home exercise equipment is either super crappy or very expensive. But it is also the most reasonable approach.

So it is solutions time. I got a nice exercise bycicle (not a peloton), an yoga mat (the wife already had that one) and an exercise program that uses only those two resources. Not as efficient without dieting, not as measurable and broad as the gym, but it fits my workflow. Deploy to production!

A few months pass and the dataset grows. The signal is subtle but has support - it works! The handling, however, needs improvement, since I cannot often enough get with the exercise program. Some mornings are just after some hard days.

I start thinking about what else I can improve in the program, but it is already pretty lean and full of compromises.
So I pull an engineer and start thinking about the support systems and draft profile. What else could be draining my willpower and morning time?

Chores. Getting the kids ready for school, firing up the moka pot, setting the off-brand roomba, folding the overnight-dried clothes, cooking breakfast, doing the dishes, cleaning the toilets. All part of my morning routine. It might benefit from some automation.

Last month I got that machine our elders call "wasteful" and "useless crap lazy entitled Americans invented because they feel oh-so-insulted for simply doing something by hand like everyone always did" - a "dish-washer".
Heh, I remember how hard was to convince my mother-in-law that an remote-controled electric garage door would not make she look like an spoiled brat.
Still to early to call, but I think that the dishwasher just saved me about 25 mins every morning. It might be enough to save willpower for me to do more exercise.

This is all so reflective of all data analytics cases really are out in the wild - the analytics phase seems so small compared to the gathering and practical problem-solving all around. And yet d.a. is what tells you that you are doing the wrong thing all along. Or on what you should work next.