I’m the only person at Basecamp with “data” in their job title but data is by no means a monopoly and it takes a village to maintain and evolve our data infrastructure.
A lot of people at Basecamp have the ability to pull and analyze data. I never get offended when other people do data write-ups. In fact, I think it’s cool there’s such a high baseline level of data fluency at Basecamp. When I think about how I can add the most value to Basecamp, I consider where I have more unique abilities to contribute.
On the infrastructure side, our Operations and Security, Infrastructure, & Performance (SIP) teams do the heavy lifting. They both maintain our data infrastructure and monitor operational data: performance, throughput, etc.
In terms of work processes, I do plan out my work to some extent in six-week periods but with far more flexibility to respond to changing priorities and contexts. (View Highlight)
Our default is to not add any extra measurement. Basecamp is proudly not “data-driven.” Before late 2010, there was no data analyst at Basecamp. Since then, the company has considered data as one ingredient to inform decisions. We consider a variety of factors including the triangle of data, anecdotes, and intuition when making decisions.
When it comes to features, we’re mostly interested in what is the uptake? That generally can be queried from the application database. We focus more on high-level indicators to tell us when our customers like something we’ve released like signups and trial conversion rates.
So in order for us to rig up specific measurement for a particular feature or customer behavior, we really need to need it. It takes a pitch to instrument, it’s not a given. (View Highlight)
There are three primary sources of information that should influence any given decision:
Quintessential instinct: i.e. your gut, developed from years of experience and building internal values frameworks
Quantitative data: aggregated information that tells you about signals within a crowd that you might have missed if you only asked a few folks
Qualitative data: much more rich information about a select few experiences
When these three sources point to the same decision, rejoice! You’re probably on the “right” track — at least based on what you know. When the sources point in different directions though, it means it is probably worth digging in a little more on why they aren’t aligned. Is it differences in assumptions? Biases? A mistake? Even after you figure out the why (or if there isn’t a way to really suss it out), then determine what the conscious trade-offs you are making and make your decision with awareness.
I’ve got an essay I want to write on the concept of data never having gotten its driver’s license; whenever I write that, I’ll send it along. :) (View Highlight)
I spend a lot of time thinking about how to not analyze data. When we need to analyze data, we can and do but more often we ask ourselves: do we really need it? (View Highlight)
There’s tension around the soul of analytics. That age-old question of not “can we?” but “should we?” I’m heartened to see more people asking the latter and advocating that data practitioners see that our jobs are not neutral. So often, a data analyst holds the choice on whether to guide an initiative down a generative or destructive path. We need to take ownership of that. We have to teach that the first step to a project isn’t figuring out which cool new algorithm we want to use, but what are the human implications of this question? (View Highlight)
Another trend I see is an increasing awareness of privacy, and the fact that “free” generally means “paid with my personal data.” It’s becoming mainstream, which is both exciting and poses a new challenge: how to establish different financing models for essential digital services in an equitable way. Most people can’t afford to pay cash for 100 subscription services a month, which is part of how we got where we are now with the data-fueled Internet. (View Highlight)
