The push to execute quickly--to "move fast and break things" is easy to vilify. It thrashes the team, leads to accumulating tech debt, and tends to be motivated by poorly weighed risks-reward tradeoffs. But IMO, speed is not the enemy as much as a lack of humility is
If you're moving fast to test hypotheses and figure out what works in practice vs. theory, you're going through a helpful exercise of reducing risk. It prevents you from investing a ton of effort into scaling something that leads you straight into a dead end
If you DON'T have a hypothesis or you're not willing to accept that you may be wrong, that's when speed is the enemy
I get that there's a certain amount of bluster necessary in business. Sometimes you gotta put on a brave face to instill confidence in ideas that seem crazy but have the chance to work. They might not, but it's easier to convince people of things if you seem like you believe them
But even if this IS what you're doing, if you plan to just rush through to the end without any thought for how you'll evaluate your progress as you're going... well, there's a good chance you'll get what you deserve
A circular, iterative workflow might actually end up being faster if done right, at least in that it will help you course correct earlier and more reliably steer towards a good outcome
The hardest part about that, it seems, is valuing that outcome above your own ego
So by all means, move fast! But if you do, you better pay attention and have a destination in mind other than "I was right"

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More from @imightbemary

13 Jun
It's fun to speculate about what direction DS as a profession is going, but it's also instructive to dig into how it grew into its modern form. This paper's an example of that, a snapshot from a few years before the term itself was coined circa 2008 projecteuclid.org/download/pdf_1…
The author Leo Breiman (who you may know from such greatest hits as bagging and random forests) talks about going from stats academia to working in industry as a statistical consultant. When he eventually returned to academia, he experienced a sort of reverse culture shock
He frames academia and industry as different cultures of statistical modeling. Both share the goal understanding the relationship of some input variables X an output variable Y. The true relationship is unknown, a black box, but statisticians in both camps aim to approximate it
Read 14 tweets
13 Feb
The surest sign that a company has no idea how to work with Data Science is requesting Insights™ as its primary output. You can tell just from the word--it's vague and sort of mystical, which is not exactly how you want to describe your quantitative teams
Yes, data analysis is about convincing someone (perhaps yourself) that something is or isn't true, but it takes a special kind of talent to do it consistently. It's easier to remember your analyses that changed someone's mind because it's easier to remember unusual events
That's definitely been the case for me, at least. I've done a lot of analysis over the course of my career, and I can only think of a handful that meaningfully changed the conversation around a subject
Read 9 tweets
28 Jul 20
I remember the first time I made an important decision at work. It was defining a metric a sales team would be using, and I didn't even realize I'd done it until it had already happened.
It was bizarre. I'd always thought people should be listening to what I thought and taking my advice, but this felt very abrupt. They were just going to take my word for it? Wasn't someone going to check my work? What if I was wrong?
This was the first time that I felt like there might be real consequences to me being wrong, and it intimidated me. The fact that people WOULD just take my word for it meant I had responsibility to not say things lightly.
Read 5 tweets
5 Jul 20
What is the difference between an engineering manager and a data science manager? It's a question I find myself ruminating over almost constantly. There's tons of good thinking and writing about eng management out there, but I don't find that it always translates to the DS world.
Granted, "Data Science" is still a broad cover term, so depending on your flavor of data science, the leap is shorter. One way of segmenting the DS world that relate to is the type A vs. type B DS, where A is for "analysis" and B is for "building"). medium.com/@rchang/my-two…
My suspicion is that most eng management advice can apply to Type B DS management pretty readily. Their work is more purely engineering. But what does that mean for Type A DS management? What makes it different and thus hard to apply the same advice to? I have some thoughts.
Read 15 tweets

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