Day 31/35: What Does the Data Say?

Some stats from Day 32, Wednesday, 4 November, 2015.

745–850AM Hip treatment and exercises at PT. Much needed leg traction to release the hip joint. Psoas massage.

  • 2 sets 10x standing skaters
  • 2 sets 20 seconds mountain climbers on bosu
  • 2 sets 5–7x single leg hamstring rolls w/stability ball
  • 2 sets 10x single leg standing deadlift, no weight
  • 2 sets 10x single leg functional reach squats w/TRX
  • 2 sets 5–7x single leg side to side jumps w/TRX

517–601PM Run

  • 3.79 miles
  • 12:19 minute splits
  • 424 calories
  • 1 lost house key and sentimental key chain

Hours spent reading about inclusion and diversity in tech

  • Til my eyes bled

So, what do those numbers mean?

In our present iteration, we are all obsessed with data. Anything that can be quantified, we attempt to prove quality, whether it is positive or negative. If it is the latter, we will intend to use the data to create new systems to make change. But how much change are we willing to make to get positive quality outcomes? Who defines what a positive quality outcome is? Won’t there be consequences for some who do not want, or cannot be a part of the change? Who decides what data matters most?

These are the things I think about, often while on a run, in my current obsession with A.) finding a great new job, and B.) the tech industry here in the Bay Area. I do not work in tech, but I have applied to tech companies in my recent job search, and I am very concerned about the problems with inclusion and diversity for people of color, women, and people older than 39. Because I am not a quant, and my attention to data pretty much extends to miles run and split times, I don’t think I am qualified to comment on the process of data collection and analysis, but I sure know how I FEEL about it. I am worried. But, more importantly, I feel that we are about to win some rounds.

What I would like to explore more this month as I run and write, and apply to jobs and go on interviews, are these feelings and concerns, because that seems to be a valid data point that gets lost, especially in the discussion about hiring people of color, women, and those of us over 39. Shouting “meritocracy!” ever more loudly is not going to drown out the quality people who need to be hired, and allowed to fail, and supported as they level up.

More to come.

cc/thank you: Julie Ann Horvath for getting way up in my brain on inclusion and women in tech, and Steven Levy for naming and facing the age discrimination topic.

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