In my new job as Chief Data Scientist at DataCamp, I’ve been thinking about the role of data science within a business, and discussing this with other professionals in the field. On a panel earlier this year, I realized that data scientists have a rough equivalent to this concept: “scientific debt.”
Scientific debt is when a team takes shortcuts in data analysis, experimental practices, and monitoring that could have long-term negative consequences. When you hear a statement like:
- “We don’t have enough time to run a randomized test, let’s launch it”
- “To a first approximation this effect is probably linear”
- “This could be a confounding factor, but we’ll look into that later”
- “It’s directionally accurate at least”
you’re hearing a little scientific debt being “borrowed”.
Read the whole thing. I strongly agree with the premise.