Practical skills that practical data scientists need
Noah Lorang

Noah, rather than just being curmudgeonly in tidbits on Twitter, let me clarify my thinking here in long form.

What you are doing here is incredibly valuable and I think writing about this and sharing it with people demonstrates the values of your company and is part of what I love about 37 signals and why I have been a fan for a long time.

I raised the concern about the use of the term “data science” though because what you are doing here is just good old fashioned business intelligence. As you allude to at the end of your post, a lot of businesses don’t actually *need* to move beyond the things you are doing here and when they do they can grab those more advanced techniques.

The industry loves to grab onto new buzzwords and so right now all the cool kids are doing data science… and no one wants to be left behind. So you get people calling their Excel spreadsheet with VBScript “data science!” Is that really the level of company you want to join? The message you want to send to your readers about you and your company? That you are just a buzzword chaser?

Data science is a meaningful term and if people want to misuse it… well that’s their prerogative I suppose. There are lots of folks who know what “science” is though and they know the difference between simple information extraction from a data set and building statistical or machine learning models. Sure there’s lots of gray area but believe me when I say, the folks I hang out with in the applied stats lab at the university would have a bit of a chuckle at your use of the term “data science” here.

I really don’t want to be the curmudgeon and beat up on you and argue over semantics…but I would encourage you to seriously consider at minimum using Gartner’s new terminology “citizen data science” for what you are doing here.

This is just one guys opinion, I am sure there are others, so take it with a healthy spoonful of salt.