As far as I can tell, I’ve never really been a morning person. I don’t know if that will ever change, but maybe one day I’ll figure it out. Mornings for me, are all about getting aligned for the day. I take my shower, I eat my breakfast, have my coffee, feed the pets, queue my podcasts, read the headlines and I’m out the door.
However, more often than not, while at the office I feel most productive after 2 pm, and before 6 pm — that’s problematic because I’m of course expected to be productive during the entire day. But other days, I’m most productive from 10 am to 3 pm. So what’s going on here?
Let’s pause for a moment. Productivity isn’t linear, and neither is creativity. When I think about tracking productivity, I think about probability densities. The knowableness of the Hydrogen electron exists in a quantum state, and the viewer can know the movements of an electron or the speed of such a body — but not both. The sheer act of measuring one property seems to affect the certainty of the other. Seriously cool stuff.
Back to our Newtonian realm, we can track time worked on a project and drill down just how much each team member contributed. But now what? I suppose that data will serve its purpose in future estimates. But, what if it was a bad timeline proposal to begin with? What if the deliverables took more time to produce than the actual comps? Were there too many hands on deck? Should there have been two teams instead of one? What if this project was just uniquely complex?
After a project is checked-off, the uncertainty remains. It now seems that productivity is a quantum problem.
Furthermore, tracking time at work can be a miserable affair. This is mostly due to the fact that it isn’t passively done. A user has to actively log entries or worst, someone logs them for you. No one enjoys this. It can take mental effort and increases your cognitive load. Yuck.
I believe the best tools for time-tracking lie unbuilt and undiscovered. Ideally, I’d like to see a machine-learning passive bot that observes, tracks and tags my work, keystrokes, and time-to-complete. Wouldn’t that be nice? Obviously training this bot to understand individual habits would be unique to an organization, and therefore training the bot, in the beginning, will take time. A film studio editor and say a museum director, work in two completely different ways and probably use vastly different tools to get work done. Perhaps the bot idea would track all the wrong things? Perhaps, a bot is a bad solution here…
A machine learning bot has a high barrier to entry for any sort of software tool, even today. It would likely take a several years to build, model, train, and test.
But there’s a simpler solution. If you use project management software like Basecamp or Asana that’s a good start. But, when you complete a task, that’s it. It’s just… over. But there’s a missed opportunity here.
Enter The Post-mortem
Project post-mortems are incredibly useful. No doubt about that. However, I have yet to see productivity software that employs one following a completed project. I mean, wouldn’t it be nice to know:
- How was this project for you?
- Could the process be improved?
- How much time did you spend working on this?
- How were time estimates compared to actual time worked?
- How complex was this project?
- Did we miss or meet expectations?
- Did you feel like your contribution was worth your time?
- Do you feel your talents are being used to their full potential?
Credit goes where credit is due — Basecamp has a feature called Automatic Check-ins, which is just about the closest you’ll get to an end-of-project post-mortem.
Obviously, these conversations happen anyways albeit in Slack or at the water cooler or in a Basecamp check-in — but why not survey and collate them at the ceremonial completion of a project? Especially while it is fresh on everyone’s minds. Even if you don’t use project management software, you should be asking these questions as often as possible to get a pulse on your team.
Getting back to the linear problem at hand — why is my productivity all over the place? I won’t claim to know exactly why (apart from my penchant for distraction), but I do know my routines help me stay organized, and reduce my cognitive load during the workday.
Ben Orenstein wrote a fantastic little piece on his morning routine and even wrote a shell script to jumpstart his day. I thought it was cool. But I really enjoyed his perspective on routines:
I don’t always start my day with a checklist, but the days where I do seem to go better.
If I want my productivity to begin earlier and last longer, I’ll need to integrate a task with my morning routine. For example, after decluttering my email inbox, I’ll move forward with hot items, then the lesser items, and so forth. Working my way backward in priority.
I also think I’m going to take a page from Ben’s playbook and start a morning checklist for when I arrive at the office. Maybe even go a step further and make an additional after-lunch, and evening checklist too.
The Full Orbit
Measuring productivity doesn’t have to be cumbersome or annoying. I really think the current state of time-tracking software is horrible, unproductive and provides a select few with actionable insights. But the actual time it takes to track, tag and log — is a complete waste of time (oh the irony).
I say, stick to two principles: checklists and post-mortems. Stick to a routine, and understand why (or how) something worked (or didn’t work). Time tracking is, well, a waste of time.