I think one of the most difficult and interesting data challenges is the ability to translate a question into a data question. I think that structuring questions to BE data questions is an art, and it sets the foundation for a reliable analyses (or a non-reliable analyses). For example, if you ask me a question: “Does featuring a topic affect the likelihood for someone to search and find and engage with a topic?” So this is gonna say “Given adding a Featured tag, will people who search for a specific topic and see it, are those people more likely to engage with it?” Notice how that question already has so many different dimensionalities, to break down and identify the behavior you’re impacting versus not impacting.
A bad translation of that would be “Are topics with more featured getting more views?” That’s a very bad translation of that data question. Because when you answer the second one, you have no idea what to do with that. There’s a million things that could drive that, and you have no idea what they are. So this doesn’t drive you to an actionable result of “Should you feature something or should you not?”
If you want to make an action out of the question that you asked, then the high level question, which is “Do posts that are featured get more views” doesn’t tell you if you should tag it or not. The first question helps you understand the impact of tagging something as featured. So that’s a big thing I would tell people to do, is to translate your questions into data questions accurately. This fascinates me.
The breakdown in actuality is ignoring everything the person says and trying to identify what action you’re trying to take. When you asked me a question of if featuring affects something, what you’re asking me is “Should I tag my posts as featured?” So that has to be broken down to “Given if I tag my posts, am I more likely to get them viewed, versus if they are not tagged.” It becomes a nuanced data question so I can tell you whether or not to tag your posts. Often, data analysts outsource that work to YOU, so that you, without seeing any information, guess whether it would work or not. And you end up trying things, guessing, and blindly working. And that’s not good! And this is going to be what we tackle next: at Narrator.ai, if we can make your analyses super-quick, then you can spend all your time talking to your customer to try to understand that question. And eventually, we’ll hopefully tackle this human-translation problem.