1. Quality > Quantity
When you work with data, the quality of your work is far more important than the quantity. Trust is difficult to build and easy to lose. Nobody cares how quickly you can be wrong.
Get in the habit of punching holes in your work before you share it with others. Just because you have the data, that doesn’t mean it’s the right data or that you’re presenting it the right way. Cognitive bias is a thing and it’s important you understand that.
Develop a reputation for being the type of person who knows what they know, knows what they don’t know, and most importantly, knows how to ask really good questions.
2. Questions are more valuable than answers
Data can be very valuable, but a lot of it is just measurement error & noise. Whatever data are ultimately worth to whoever it is that’s hiring you to work with it, the odds are pretty good that the questions you’re able to ask because of the data are going to be more valuable than the answers, especially over the long run.
This is particularly important for hiring managers to understand as they build their first data teams. Data, machine learning, & AI are not magical tools that you wave in front of questions and out pops useable answers.
The march of science is slow & grueling, and that remains true in AI & data fields as well. Generally speaking, you’re probably doing data right if it’s yielding more questions than answers, especially if you’re just getting started.
3. Communication is all that matters
When you work with data, in some sense your job is to manage the attention for other people (or to help them manage it better themselves). Doing data work is 100% about communication. What that means is….
4. You need to excel at conflict
Database conflicts. Personality conflicts. Team A is angry at Team B because Team B doesn’t have sufficient test coverage over the JSON garbage coming over the wire.
No matter what, every single day, you’re be resolving conflicts if you work with data. If you plan to work with data, you’ll want to develop a healthy relationship with conflict early on. Make sure you check out Why Are We Yelling by Buster Benson and Radical Candor by Kim Scott if you haven’t already.
5. Big data is B.S.
This really deserves a blog post all its own, so stay tuned! In the meantime, what other tips would you give to someone starting their data career? Get in touch and let me know your thoughts!