Keeping Up With Data — Week 26 Reading List
The image above comes from an article about increasing experimentation accuracy using CUPED — a method for estimating treatment effects in A/B tests that is supposed to be more accurate than simple difference in means. A/B tests are used in situations when you are looking for an answer to a very specific question (like does a red button have higher clickthrough rate than a blue one). But not all questions are that specific.
“Asking the right questions is as important as answering them.” — Benoit Mandelbrot
So, I guess you don’t have to A/B test for what is the main theme of this week’s reading list!
- A Simple Strategy For Asking Your Data The Right Questions: “Asking the right questions involves domain knowledge and expertise, coupled with a keen ability to see the problem, see the available data, and match up the two. It also requires empathy,” say Hilary Mason and DJ Patil. But, how can one practically do that? Brent Dykes suggests a ‘4D Audience Framework’. The framework has four components (dimensions) — problem, outcome, actions, and measures. It helps you structure your questions in the context of the starting point (problem), the desired state (outcome), the approach you can take (actions) and the progress tracking (measures). (Forbes)
- One question to make your data project 10x more valuable: Data analysts are often approached with data requests. They can either just do as they are told and see (and hope) if their outputs have helped the stakeholders or not. Or they can increase chances of a positive outcome by learning more about the problem. People often ask for data so that they can make decisions. So, what’s the magic question to help people make better decisions? Asking them what decisions they want to make. Simple, right? (Brittany Davis @ Narrator)
- Data-Driven Decisions Start with These 4 Questions: So far, we’ve learnt what questions are important for data professionals. Let’s now take a view from the other side — what questions should the decision makers be asking? Instead of taking the answers from the data at face value, they should ask: how was the data sourced and analysed? Data should be representative of the real world, but sometimes it’s not. That’s why we must always ask what the data doesn’t tell us. But in order to gain the most value out of data, we constantly need to be asking how can data help to redesign products and business models. (HBR)
I’ve been home alone this week for the first time in years. It was fun to grill some meat, drink beers and listen to grunge music. The first evening. After a week of these dinner rituals — I must say I really can’t wait for a proper family dinner!