What a month of Peloton taught me about data literacy

Hello again datafam!

I’ve been finding myself really challenged to keep up with writing during this whole COVID period. Chalk it up to a mix of mental state, desire, and an overall lack of inspiration. Like many of you, I had my share of struggles during the past 18 or so months of COVID in the US, both physical and mental. One of the things I’m working on lately is spending less time with my phone and computer and investing time in myself and my family. Our oldest daughter just started her senior year of high school and I don’t want to miss or lessen my enjoyment of any of the precious remaining moments before she heads to college next year.

Part of the investment in myself is a refocused energy is on my own well-being. Eighteen months of working at home has gotten me out of my regular gym habit, and into some not-as-good-for-me snacking habits. I went for my annual checkup earlier this year and was heavier than I’d ever been. Not quite comfortable with immersing back into the full in-person gym experience yet, we instead invested in a Peloton Bike+ and subscription for our family. The bike arrived on August 10, so we’ve had it for just over a month at the time of this writing. After 30ish days and close to 60 workouts, I’m already feeling worlds better, both mentally and physically.

While getting immersed in the world of Peloton in my personal life, I’ve been spending the bulk of my professional time thinking about data literacy, most specifically about how to develop programs that measure and grow data literacy within companies. I’ve been doing a lot of reading from the likes of Jordan Morrow (Be Data Literate), Ben Jones (dataliteracy.com/blog), Sarah Nell-Rodriguez (bedatalit.com) and others, and pulling from my own experiences teaching and coaching people to be more comfortable with data over my career.

With so much time and mental energy going to these two activities, it only seemed natural that they would collide in my brain! I find myself drawing inspiration from the Peloton instructors, their approaches, and the data on the bike itself. Each providing important insight and clarity on my own experiences and thinking on this topic of data literacy. This post summarizes some of the most important lessons that I’ve learned.

Data Literacy (like fitness) is A BIT abstract

How many of us have ever said “I’m going to get in shape”, only to quickly find ourselves lost or overwhelmed on how exactly to do that? “In shape” is a vague goal that means something different to everyone. Instead, we have better success setting specific goals that contribute to our overall definition of “in shape.” Something like, “I’m going to get under 200 lbs by my next physical” or “I want biceps that are tight in my current T-shirts.”

I think data literacy suffers from a similar challenge. It contains elements of a lot of things, and we have to set more specific goals to keep us focused on the journey. Instead of “I’m going to work on my data literacy”, try something like “I’m going learn about two data sources my company uses this month” or “I’m going to spend 20 minutes every week exploring and interpreting a Viz of the Day.” These are more tangible targets that add up to getting into “data shape.”

The other thing this really reiterates for me is that the thing to measure is PROGRESS. The road is winding, perhaps even never-ending. What’s important is that we’re moving along it. What can we do now that we couldn’t do yesterday? Progress trumps perfection every time. Just putting in the time is a KPI. Not the only one, but an important one.

Data Literacy (Like Fitness) is a personal journey

One of the things that surprised me about Peloton was the breadth of content available. In addition to the bike classes, there are workouts and programs for strength, core, resistance bands, yoga, and even mindfulness and meditation. That made me realize that my fitness journey is going to be completely unique to me. I may wind up in the same place as someone else, but how we got there is sure to be unique.

Me, I’m a bit of a wanderer. In my first month I have taken a lot of bike classes (of course), but also explored many of the other offerings. That’s cool. Let people do that in their data literacy journeys too. There’s a lot of ways to get where you’re going, and doing what you need, or are most interested in today is a great way to keep people engaged in the process. Plus, I feel in control of my own journey.

And remember, since the journey is personal, so are the results. Don’t get caught up in trying to match someone else’s results. We all get their at our own pace. Just like fitness, data literacy is about becoming a better version of yourself, so the baseline for comparison are the previous versions of yourself. As one of the instructors said in a recent ride “Don’t compare your chapter 1 to someone else’s chapter 20.” Be proud of where you’ve been, where you are, and where you’re headed.

It’s habit, not training

If your goal is to get yourself in shape, which approach do you think would provide the best results?

  • Spending a little time, even just 10 minutes, every day working on something
  • Doing 2 hours of focused training once every month (or quarter)?

You might be able to get results with the second option, but you’re probably better off focusing on building the habit and making the time non-negotiable, especially at the beginning. No matter how busy I think my day is, I can always find 10-20 minutes to invest in myself. What if we thought about data literacy learning more like this. What can we get people to do new or differently for 10 minutes a day? Build data literacy habits, and make the time non-negotiable in your day. This requires organizational commitment, from your leaders, your learning organization and of course your learners. But it’s more reflective of the end-result we’re after, which is people using data every day, rather than just once in awhile.

Establishing Common Language makes People Feel Welcome

Every bike class I’ve taken, every single one, starts with a review of what metrics are on the screen, what they mean, and how they’ll be used in the ride. Then we cover the secondary information like where to find the leaderboard or playlist if you want them. This takes about a minute at the start of the ride, and there is an option to skip it if you want (say, after you’ve heard it a few dozen times!).

I love this practice because it sets a common baseline language for everyone, and basically assumes that every ride is someone’s first ride. How often do we take that one minute to do this in our data discussions? Just a simple, quick refresher on what’s in front of us and why it matters to what we’re about to do. Personally I’ve almost never done this, or even thought about doing it, in a work setting. But as a brand new user it sure helped me get up to speed.

After all, it’s called “data literacy” because it’s really about speaking the language of data. It’s about making us aware and confident enough to join the data conversation in our organization. When companies set out to improve their data literacy, we’re almost always focused on skills. And don’t get me wrong, skills are critical. But, what are we doing to make the conversation approachable and accessible, especially when it’s someone’s first ride?

everyone has get back to basics sometimes

As someone new to Peloton and coming off a pretty long period of not focusing on my own fitness much, I decided to start with a couple of “beginner” programs that I found on the platform. One on mastering the basics of the bike itself, and one beginner strength program.

The mastering the bike program is 6 weeks, and lays out the schedule of rides, workouts, and rest days for you to follow. The rides cover a variety of class styles, instructors and ride lengths to help you hone in on what you like. A sort of Peloton buffet. But there is a common thread to many of the rides I’ve taken so far. The instructors are giving form cues, reminding everyone about the fundamentals that underpin successful riding. Even in the most advanced hills and intervals classes, you’re reminded about basic form. And, there are several reminders that these courses are recorded, and that you can come back to them later if you need a “refresher” on something.

The beginner strength program follows a similar construct. We started with no weights, and focused heavily on the proper form for hinging, squats, lunges, pushing, and pulling. Most strength moves are derived from one of those five moves. They reinforced that often in the early classes, that these were foundational moves we’d build on later. When we did build on them, they went back to the basic move first. “Remember that hinge? Now we’re going to try it with our legs separated.” And again, the frequent reminder that you can (and should) come back to these courses periodically for a form refresher.

Even those of us with long histories working with data probably need to get back to basics sometimes. We all develop habits that are imperfect, and getting back to the core moves can provide a helpful reset. So, make your data literacy programs for everyone, including your seasoned practitioners. They can get a helpful refresher on “proper form”, and it also works to establish that common language for future conversations.

Be a Supportive community

Peloton has created an online community around fitness. When I log in, I’m told who in my friend groups are working out now, and I get notifications of milestones achieved by my friend group. “This person is on a 10 week streak” or “Someone just finished their 500th ride.” A little tap on the screen, and I can send a virtual high-five. A quick acknowledgement that I see you over there putting in the time, and I’m right there with you. A virtual celebration of a milestone or accomplishment.

This kind of gamification and social elements are not for everyone, and when I don’t feel compelled to participate, I simply don’t. Sometimes I’m really in the flow of grinding out a hard climb and don’t want to break focus. That’s fine. But some days, I really need those little pushes. The little virtual celebration from a friend just for clipping in that day. Because at the end of it all, clipping in for the 10, 20, 60, whatever minutes on that day is one of the KPIs. We showed up, and by showing up, something got better.

The datafam I’ve found online and through Tableau is this same kind of supportive group. Wherever you are on your journey and however you want to engage, they’re with you. Fostering that kind of community vibe inside your own company is a huge accelerator of success. Recognize accomplishments, even the ones that are “just showing up.” Reward the habits and behaviors that you’re trying to build. This takes time and commitment, but it’s a load that can be spread out. Find your group of champions, coaches, and enthusiasts. It might be small at first, but get them to start doing small things (virtual high-fives all around!) and let it grow.

Reflecting on my first month or so on Team Peloton has given me a clearer mental picture about tackling data literacy. It’s so much like a fitness journey. It means something a little different to everyone, it takes repetition and commitment, and we can get farther together with support and encouragement. Does your data literacy program have elements of all of these things? Should it?

I’d love you hear your thoughts on your own data literacy journey, or what your company is doing. And, if you want to get some virtual high-fives on Peloton, feel free to look me up. My leaderboard tag is “Jim_O_Tay” (my teenagers have taken to calling me Jimothy, and I’m just owning it!)

Until next time,

Jim (othy)

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