Category: fun

Experimenting with One on Ones: A Tale of a Team Lead

One of the best things to come to the forefront of the business world through the Startup superhighway is the commitment to an experimental approach to business. This goes by many different names, “iterative mindset,” Lean Startup, and even Agile in some ways is a particular application of the experimental approach.

Businesses and organizations taking an experimental approach to their work and products is not without its drawbacks, but there is a really neat philosophical argument for the larger picture, which is, if the experimental framework fails to produce the results you’d like, that is itself an acceptable outcome of that framework, as itself can be used as an experiment.

Look, trying new things, tracking their outcomes, and relentlessly pursuing small improvements over time is a great way to build or run a business. One of the very coolest features of working remotely for a results-oriented company is that you’re able to apply this same mindset to yourself, and to your own work and work processes, and even your life in general.

It’s pretty rad.

When I first started working remotely, I stuck to a pretty consistent schedule, around 8A – 5P from Monday through Friday. Over time, I’ve made small changes here and there, found ways to make my schedule more conducive to my work and my life.

 

When Mango (the toddler that is currently terrorizing my house) was first born, I worked four longer days per week, taking a day off to spend with her (and save some cash on daycare).

Since then I’m back to five days per week, but they’re unusual days; I really pack Mondays and Wednesdays, with five or six one on one sessions on each day plus other meetings, etc. Batching that kind of really conversational and personal work helps me to stay focused, to stay in the right mindset.

How do I know that batching that work helps me stay focused? Because I experimented with it, of course!

At first, I tried doing all on my one-on-ones on one single day – that was suboptimal because by the end, I was pretty fried and wasn’t giving my very best to the folks on my team. As a team lead, nothing takes precedence over serving the folks on my team, and accepting that meant that I had to keep experimenting. That took about two weeks.

I tried to spread it out, to have 1-2 one on one sessions per day; this was suboptimal because, as I learned about myself, I have to be pretty intentional about these kinds of personal and professional relationships.

My natural state is to assume that everyone and everything is OK, and that an alarm will go off somewhere, somehow, if things are not in top shape. This is the wrong approach for one on ones, and at least for me, being a leader in general. Bad things often have a long runway, but you have to know where to look, and you have to take the time to look.

So, for me, it takes some effort to get into the right personally curious and empathetic mindset that one on one preparation and execution require. Recognizing that this effort exists and was a cost meant that for me to get into that mindset every day was costing me efficiency elsewhere.

This took probably another four or five weeks. I had identified two points on a larger line that were both suboptimal for different ways: doing all of my one on ones on a single day wasn’t going to work (and was not really scalable), and spreading my one on ones across the week had its drawbacks as well.

What I needed was what Aristotle called the Golden Mean!

(As a sidebar, I think comparing Aristotle’s idea here to Goldilocks is inherently flawed: Goldilocks identified her preference as a nearly-perfect halfway between two points, whereas Aristotle allows for the much more interesting idea that the ideal decision may be closer to one incorrect outcome than another. The ideal point between being a headstrong fool and a coward may be closer to headstrong fool.)

I didn’t come up with anything revolutionary. I followed the Operations Management 101 playbook and tried batching the work to minimize setup costs. It worked, and I’m more productive and (I hope!) better at these small but incredibly important conversations.

This also lets me keep my calendar on Tuesdays and Thursdays relatively open, so I can schedule big blocks of time on projects that require more sustained focus to really find success.

Of course, in time, this might change, or opportunities to improve it may appear, which will require ongoing experimentation.

What I’m saying is, keep experimenting, you crazy kids. Keep on hypothesizing, you wild star people. It’s the way we get better.

Munging NASA’s Open Meteor Data

Munging NASA’s Open Meteor Data

In snooping around the US Government’s open data sets a few months back, I found out that NASA has an entire web site dedicated to their publicly available data: https://data.nasa.gov/

Surely, you understand why that would excite me!

I dug around a bit and pulled out some information on meteor landings in the United States, with tons of information, mass, date, lots of stuff.

To simplify the data set and make things tidy for R, I wrote a quick Python script to strip out some columns and clean up the dates. Here’s the gist if you want to have a go at the data as well.

I ended up looking to see if there was a trend between date and meteor mass, to see if maybe there were obvious cycles or other interesting stuff, but some super-massive meteors ended up shoving the data into pretty uninteresting visualizations, which is too bad.

We can do some simpler stuff, even with some super-massive meteors. For instance, here’s a log(mass) histogram of all of the meteors:

Screen Shot 2016-01-05 at 7.49.24 PM.png

Check it out! It results in a somewhat normal, slightly right-skewed distribution. That means we can use inferential statistics on it, although I am not sure why you would want to! The R code is a super quick ggplot2 script.

It’s pretty amazing how easily we can access so, so much information. The trouble is figuring out how to use it in an actionable and simply explained way. The above histogram is accurate, and looks pretty (steelblue, the preferred default color of data folks everywhere), but it isn’t actually helpful in any way.

Just because we can transform a dense .csv into a readable chart doesn’t mean it’s going to be useful.

2015: The Good Stuff

2015: The Good Stuff

This is the third chunk of my 2015 wrap up series. Here are my broad goals for 2016, and here are the things that I think I could have done better in 2015. 

My vision statement for 2015 was “Send value into the universe, selflessly.” Looking back today, in January 2016, I think that I stayed true to that vision statement. Both at work and at home, I spent a lot of time and brain power finding ways to create value, create connections between people, and unlock the treasures that were trapped inside their own little silos.

Like my list of things that could have gone better, I’m going to try to focus on things that I directly created or did, rather than simply thought about. These are the things that count, I think.

Continue reading “2015: The Good Stuff” →

7 Weeks of Hop Growth Data

7 Weeks of Hop Growth Data

Since the very end of May, I’ve taken weekly measurements of the height of all of the first year hop bines in my test yard. Here are the results, by location and height:

Screen Shot 2015-07-12 at 12.11.12 PM

Like any pile of data, we come away with more questions than answers: are there significant differences between the locations that grew better and those that grew worse? Is there a variable at play that isn’t described by the graphic? In this case, I can tell you I hope not; they’re all watered automatically and at the same rate – I tested! They also all have nearly exactly the same amount of sunlight per day, due to the location and alignment.

However, it is neat to notice how the different variety of hop plant are growing differently: you can see that B2 and B3 are far outgrowing the others (at 85″ and 93″ respectively, versus a yard average of 41″ for this week) – these plants are both of the Chinook variety, described by my friends and yours at Hopunion as “A high alpha hop with acceptable aroma.”

We can also see that the two laggards (A1 and B1) are both Centennials (“Very balanced, sometimes called a super Cascade.”) – while I know that the first year’s growth is not necessarily indicative of any plant or variety’s long term success, it will be interesting to see how these trends correlate to yield in future years – it’s possible that the Centennial plants are pushing out more substantial root stock than the others, which may make this apparent first-year laziness in fact an investment in greater long term success.

Ain’t data fun?