Philosophy on meetings: a data scientist’s perspective

One thing I’m currently struggling with is balancing two very different types of workloads.

On the one hand, there are items that are more maintenance, budgeting, planning – basically, the not-fun parts of a data scientist’s job, but stuff that kind of has to be done (especially when you start managing people).

On the flip-side, there are the tasks that other people would more readily associate with data science: digging into the data, communicating with users, making cool visualizations, finding insights – basically being innovative.

It’s not really a time-balance issue.  Although currently my time is pretty constrained, that’s not the core issue.

For me, it’s an energy issue – specifically; the huge amount of energy needed to keep context switching back and forth between one type of thinking (maintenance) vs another (innovation).  It’s just a completely different way of looking at the world.

Accompanying those less-fun maintenance tasks is every data scientist’s ‘favorite’ way to spend time: meetings.  They are a necessary evil, so below are some philosophical thoughts:

 

The larger your team grows, the more time you’ll have to spend in meetings

This…is something I’m struggling to accept, but it’s seeming more and more like it’s my new reality.

The fact is, as you add more and more people on our team, working on interconnected items, you will need more coordination to keep everyone on track.

For me, my biggest concern is to make sure that I’m not the bottleneck.  For that reason alone, status meetings are helpful – especially when I’ve been so overwhelmed with other items that I might have lost track that someone is waiting on me for something.

However…something else to consider, especially when switching between ‘maintenance’ and ‘innovative’ modes of thinking:

Intermittent meetings can cause huge context-switching costs

There’s a major difference in mentality between exploring data to find insights that no one else has ever seen – vs the mentality needed to explain to another team why their maintenance estimates were just way too optimistic, and now we need to discuss escalating the issue to upper management.  And how we’re going to handle the ‘messaging.’

All while playing nice, keeping everyone happy, and not seeming like you’re out here just making excuses and being a Debbie Downer.

Some people are really good at that maintenance perspective, while some are better at the innovative perspective – but when you have to actively straddle between both (as many of us do), just a couple intermittent meetings could absolutely kill your productivity for the rest of the day.  We just can’t afford the energy sink.

 

It can be tempting to start scheduling meetings at the first sign of ambiguity

Someone in my little industry group was recently telling me how they were getting flooded with half-hour meeting requests involving 4+ people – when in reality, these things could probably have been resolved in three-minute conversations involving only two people.

Why is this happening?  One theory: especially for people who maybe come from the more ‘maintenance’ background, they just don’t recognize the cost of having an additional meeting – because, dealing with maintenance and meetings is pretty much all they do, all day.

To them, scheduling another meeting is not big deal.  For others (like data scientists), it is a huge deal – but we have a hard time conveying that message while still playing nice.

 

Especially for data scientists, some meeting types are way more draining than others

Not all meetings are bad – in fact, I’ve been in multiple meetings recently that were a net positive, in that I had more energy after the meeting than before.  Some traits and commonalities I noticed:

Uplifting: presenting my analysis, having working sessions to discuss a visualization, discussing next steps with end-users, discussing strategy.

Draining: having conflict about deliverable dates, explaining why an initial estimated timeline was too optimistic, coordinating work between multiple groups, gently trying to explain to a confident person why they are mistaken.

 

Conclusion

Unless you’re somehow doing data science while living under a rock, you’re going to have to spend substantial time and energy in meetings.  At least for me, I’m currently working on having a better relationship with meetings – but it’s of course a work in progress 😀

 

The views expressed on this site are my own and do not represent the views of any current or former employer or client.

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