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The Mystery of the Miserable Employees: How to Win in the Winner-Take-All Economy - The New York Times

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This article connects the analytical approaches used in highly data-driven sports like baseball, with a quantitative approach to understanding what makes people successful in their jobs. This specifically dives into a large team at Microsoft where people expressed dissatisfaction with work-life balance. The exploration of instinctive reasons for this proved wrong. Through survey data they found some good reminders (emphasis added).

One of their findings was that people who worked extremely long work weeks were not necessarily more effective than those who put in a more normal 40 to 50 hours. In particular, when managers put in lots of evening and weekend hours, their employees started matching the behavior and became less engaged in their jobs, according to surveys. Another finding was that one of the strongest predictors of success for middle managers was that they held frequent one-on-one meetings with the people who reported directly to them. Third: People who made lots of contacts across departments tended to have longer, better careers within the company. There was even an element of contagion, in that managers with broad networks passed their habits on to their employees.

The more interesting data came when they looked at data that quantified the teams experiences (emphasis added).

The two kept iterating until something emerged in the data. People in Mr. Ostrum’s division were spending an awful lot of time in meetings: an average of 27 hours a week. That wasn’t so much more than the typical team at Microsoft. But what really distinguished those teams with low satisfaction scores from the rest was that their meetings tended to include a lot of people — 10 or 20 bodies arrayed around a conference table coordinating plans, as opposed to two or three people brainstorming ideas.

The issue wasn’t that people had to fly to China or make late-night calls. People who had taken jobs requiring that sort of commitment seemed to accept these things as part of the deal. The issue was that their managers were clogging their schedules with overcrowded meetings, reducing available hours for tasks that rewarded more focused concentration — thinking deeply about trying to solve a problem.

I have my own bespoke system for tracking calendar analytics. I track a handful of dimensions on the data in addition to just frequency and scheduled v. unscheduled time. I track wether I scheduled the meeting or if someone else did, to get a sense of how much of my time I am directing. I also track special flags for meetings with just one other person, a one-on-one meeting, as well as a flag for large meetings with a lot of attendees. Most recently I’ve also started attaching an emoji to each event to gauge a qualitative rating of the meeting. I’m now using that data to both correlate my own level of excitement about various parts of my work as well as insure that I’m putting my most precious resource, time, towards my most important objectives.

Posted on June 19, 2019








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