Maker's Schedules Versus Manager's Schedules and Why it Matters for Data Scientists

Paul Graham of Y-Combinator wrote a fascinating article in July 2009 about the "Maker's Schedule" versus the "Manager's Schedule." In it, he describes the differences in how managers and makers (software programmers) define and schedule their time and the implications this has for meetings, productivity, and possible tensions between these two groups.

While I will be the first person to point out the large differences between software engineering and data science, the scheduling mentality of the maker is pretty similar to that of the data scientist; large blocks of uninterrupted time (think half or full day) are required to do work and work is defined as the creation of something, be it an analysis, a new methodology, a visualization, etc.  In contrast, managers think in hourly blocks with the meeting being the actual product created or unit of work. Thus, for the data scientist, a single meeting at 10am can completely destroy a half-day block of potential productivity.

Paul's insights strongly ring true from my personal experiences and he advocates several different strategies to mitigate this potential conflict. Simply put, I highly recommend reading this article for both data makers and data managers alike.