One question to make your data project 10x more valuable

#crypto #bitcoin

Data folks are in the business of helping people make decisions. Whether it's a quick and dirty ad-hoc query or a super sophisticated statistical model, at the end of the day we're informing a decision.

How many people are viewing our website on mobile?

What discount should I offer to this customer?

Who is likely to churn?

Decisions are so core to our work, that we sometimes forget to acknowledge them before starting a data project. We assume it's baked into everything we do, so when the product team asks a question like: "Which products are commonly purchased together?" it's natural to jump straight into figuring out what data we need, which approach is best, etc, etc. It's a straightforward ask and the data work could be fun. How should we could visualize the results? What if the patterns have changed over time? We dive right into the data without taking the time to really (I mean really) understand the decision we're trying to inform.

The devil is in the details– and details are what matter here. Without an understanding of how the data will be used, we're not in a position to give the right answers.

"If I had one hour to save the world, I would spend fifty-five minutes defining the problem and only five minutes finding the solution."​
– Albert Einstein

The magic question

Luckily, we don't need to spend all of our time defining the problem. Here is the one simple question that will get to the heart of any data request within minutes:

"What decision are you trying to make?"

Subtext: What action will you take once you have the answers?

If there is no action, then there will be no impact. This question will cut through all of the clutter and get straight to the action.

And the answer can be VERY telling! That's why it's so powerful.

A good response is specific! Almost immediately, you should be able to picture what they'll do once they see the data.  If not, then that's a signal to pause and scope out the question before even thinking about the data.

A good response sounds like:

• "I need to know which customers are likely to churn because I want to send them $10 in loyalty credits to save them"
• "I need to know how to segment our customers because I want to build a look-alike audience based on our highest value customers"
• "I need to know the day of week with the highest open rates, because I want to know when we should be sending emails to get the most engagement"

These responses are specific, and have a clear action in mind. It's clear that the action will change based on what the data says. Unfortunately, not all responses start out as good responses.

Here are a few example red flags to look out for:

1. Responses that sound specific but aren't
   
   Beware of responses that sound specific, but aren't. "I need to segment our customers, so I can personalize their emails." What does that mean? How will they personalize the email? We might learn that the email team just wants to update the subject lines to improve open rates. That information can change the entire analysis.
   
2. "I'll know what to do once I see the data"
   
   This is usually a sign that there's no plan for action. These responses usually lead to the ever-so-common situation where an analysis is interesting but not actionable.
   If you hear a response like this one, I recommend playing out a few hypotheticals with your colleague: "What will you do if the data says X? What if it says Y?" I love this exercise because it allows you both to peek into the future where you have the answers, but still don't know what to do about it. Most people have a tendency to romanticize what the data can do for them, which is why it can be disappointing when you get the analysis but it doesn't solve any of your problems. The game of hypotheticals helps bring everyone back to reality and be practical about what can be accomplished with the data.
   
3. "Once I know the answer, I'll be able to ask {insert follow up question}"
   
   More questions are great, as long as they eventually lead to an action. Endless questions just lead to more learning, and learning is cool but it's not what runs a company-- decisions do. For responses like this, follow the question trail to see if you can eventually get to an action: "What do you plan to do once you answer the follow up question?"
   
4. "I don't know"
   A skilled data analyst can usually spot a data question that's not going to be actionable, but it can be difficult to help someone else see it the same way. Asking the stakeholder how the data will change their decision is a great way to help them see that their data question won't give them the results they're looking for. Ask the magic question often enough and you're bound to get a few "I don't know"s. When this happens, it's a great opportunity to reset. It's so much easier to start with a decision and figure out the data you'll need to inform it, than the other way around.

Next time someone comes to you with a data request, remember the magic question. And remember, a bad response doesn't mean game over. It should be a starting point to get you to the final, better data question. A skilled data analyst will always tease out the real problem and understand the real decision before starting any project. This makes the difference between a data project that is just "interesting" and a data project that drives an impact.

OhNoCrypto

via https://www.ohnocrypto.com

, @KhareemSudlow