I quit UX to get better at UX
I did Data Analytics for six months so that I could be a better UX Researcher.
I’m a former chemical engineer, software tester, programmer, and UX designer who is transitioning into quantitative UX research (next week!). I write here monthly. If you’re new to Quant UXR, check out my article How to get started with Quantitative UX Research. If you’re an expert, leave me a comment to let me know all the mistakes I’ve made in this post!
Summer 2020. I was waiting outside the doctor’s office while my wife and newborn daughter got a check-up. Due to COVID, I wasn’t allowed inside. So I killed the time by listening to a podcast. Marty Cagan was being interviewed on Design Better Podcast. And it blew. my. mind.
Cagan described the difference between what he called Feature teams vs. Empowered Product teams.
Feature teams are given a roadmap of features to build and they work through that roadmap. Empowered Product teams, on the other hand, are given a big problem to solve and are given time and space to figure out the best way to solve that problem. On Empowered teams, Product Managers work with Designers, Researchers, and Developers to make sure the product is valuable, usable, feasible, and viable. They do serious research up-front, they build prototypes, and learn as they go. And they do great work.
This is how the best product teams work, according to Cagan.
And this is how I wanted to work. To me, this was utopia. Work with a small team on a big, juicy problem, and figure it out over months or even years. Awesome.
I was buzzing as I drove my wife and daughter home from the doctor’s, then I realized: In order to reach this utopia, you need to focus on solving a problem as opposed to just implementing solutions. In order to know when you’ve solved a problem, you need to be able to measure outcomes.
And I didn’t know the first thing about measuring outcomes. Or measuring anything about a product, for that matter.
Just over a year later, I joined the Data Analytics team. No, I didn’t quite “quit” my job in UX like my clickbait title says. I just arranged a six-month Tour of Duty, inspired by the book The Alliance. My two amazing UX managers agreed to give me up for half a year, and my new amazing Data Analytics manager agreed to give me a chance on his team.
And now, as I publish this story, I am wrapping up that Tour of Duty and moving into a new role as a UX Researcher. I am more optimistic than ever that our team can measure outcomes, and therefore, take a step towards becoming an Empowered Product team.
This story is a collection of things that I learned and noticed as a Product Data Analyst and a former UX Designer.
Data gives you business context
In my UX Designer days, I asked lots of questions. Why are we doing this? Why not do this other thing? Over time, I built up a decent mental model of how our business worked.
Or so I thought.
As a Data Analyst, I was contributed to high-level summaries for executives. Understanding the numbers and speaking with those executives solidified in my mind what the business is doing and why. You can’t get that type of bird’s eye view of the product when you’re buried in user stories and epics.
There are a billion things I still don’t know about the business, but when it comes to product context, I really leveled up in these six months.
Data analysis is a superpower
In my six months, I built Dataiku flows to save accountants’ time (and their sanity), to understand engagement with a certain feature, and to summarize our overall product usage. These are things that I knew were possible, but I would have had no idea where to even start if I hadn’t worked in Data Analytics.
Going back into UX, I’ll be able to use these skills to quickly answer simple questions.
Meet the real superheroes
Don’t get me wrong, the things I did with data were cool. But they’re nothing compared to what the real data people do.
Just before my return to UX, I was asked to compile data bunch a bunch of APIs into a single source for easy analysis. I figured I might be able to do it, but I wasn’t confident. So I mentioned it to a data coworker. He said that another coworker, let’s call him Troy, had done a very similar project recently.
So I talked to Troy and he happily took over this scary API project from me.
My hero.
Going forward, knowing people like Troy and what he’s capable of will be very handy when I have data questions that aren’t simple enough for me to answer myself.
Diversity of thought pays off
On one rare day that I was in the office (like the real, physical building, where you see your coworkers in 3D instead of on Zoom), I asked some coworkers about a data problem they were having. There were two sets of data that they expected to match but didn’t. As a UXer, I wanted to know what the UI said about this data. So we signed in and checked things out and voila! The difference was clear. Problem solved. It was very satisfying being that outsider approached the problem differently and helped solve it!
Stats are for UX
If you would have asked me a year ago to define the difference between statistics, metrics, and data, I wouldn’t have had a great answer. I might have thought that they were all the same thing. But now I know that the book Quantifying the User Experience and the course UX Data Analysis are mostly about statistics: getting data from a sample then trying to extrapolate that to the whole population. Data Analytics, in my experience, rarely deals with this extrapolation because all the data is there (there’s no sampling). Metrics are just numbers that you track and they could be based on samples or not.
(Note: I’m still wrestling with these definitions and how to best describe them, so if you were cringing as you read that last paragraph, please let me know your thoughts in the comments!)
Quantitative UX Researchers deal with statistics, metrics, and data, but I believe that stats are most important for them.
Maturity in Data and UX
Turns out, Data and UX are similar fields. Both enable other teams to do their best work. Both are fields that have grown and matured significantly over the last decade or two. People in these fields come from ALL OVER. Hearing Data people talk about how they can’t wait to get into ML and AI but need to get the base infrastructure down first is just like hearing UX people talk about how they want their company to be more user-centered.
This shouldn’t have surprised me, but it did. In a good way. There’s a strange comfort in learning that there are other teams in the same company going through similar experiences.
So what?
I want to say that you, the reader, a UX person (or perhaps someone in another tech role), should follow in my footsteps and go do your own tour of duty, but I know that’s not usually realistic. The stars need to align for something like this to work, so if you can do it, do it! But if not, hackathons, datathons and meetups are a great way to meet people from diverse backgrounds and dabble in other fields.
If that’s too much to ask, just keep learning and reading. I post here monthly 🙂
