Making Sense of Data
Data doesn't mean anything until we make sense of it—and take action.
The women's high jump final at the Olympics on Sunday was a study in personal ritual. Each jumper had her own way of centering herself, psyching herself up, and beginning her jump.
One of the Australian jumpers, Eleanor Patterson, began each jump with a slow shoulder shimmy that looked more like she was finding her groove on the dance floor than preparing to launch her body 6 feet in the air. The gold medalist from Ukraine, Yaroslava Mahuchikh, saved the most visible part of her personal ritual for between jumps. After getting up off the pads, she climbed into a thick sleeping bag to keep her muscles warm.
Other jumpers slapped their thighs, closed their eyes, or let out a primal scream.
Australian Nicola Olyslagers, who was never seen without a huge smile on her face, looked to the crowd for support before yelling, "Let's go!" to begin her jump. Even when she missed, she sprang up off the pads, looking like she was having the time of her life. But it was what she did next that caught my (and many others) attention.
Olyslagers bounded over to the bench after each jump to record the attempt in her journal. She told ABC Sport (below) that keeping a training log is "athletics 101." But what she's found especially valuable is reflecting on "what did I learn, what went well, and what do I need to change." Recalling the 2021 Olympics in Tokyo, she added that those reflections turned into inspiration she could take onto the track with her for the event—like she'd written herself notes for the big day months, or even years, in the past.
Elite athletes have access to sophisticated equipment that can quantify every aspect of their performance. If you've watched diving or gymnastics during this year's Games, you've probably seen the replays that snapshot a dive or a tumbling pass split second by split second. The amount of data that even casual athletes like myself have access to via our wearables is mind-boggling.
But data is just data until we make sense of it—make it mean something.
That's what Olyslagers does when she records her jumps in her journal and reviews her past performances. She makes sense of what went into each good attempt and what went wrong in failed attempts. The data is there, but she has to make it meaningful.
Work today revolves around data. From retail to hospitality, technology to construction, consulting to marketing, and advocacy to politics, our work is saturated with quantification. I've written about the ways this information can lead us astray before—how we become seduced by the prospect of certainty and how complex values are reduced to metrics. But data isn't bad; we don't need to avoid data as long as we're conscientious about how we interpret it.
Nicola Olyslagers's journal practice has (at least) three lessons we can all learn from.
1. Interpretation takes time
Olyslagers has been keeping her training journal for years. Heck, it's been to the Olympics twice. No single jump or even a series of jumps in a single event can tell her much. However, reviewing and contextualizing her performances over time allows her to see meaningful patterns and offer herself useful recommendations.
Too often, we want to know what a particular dip in performance means or what to make of this specific standout success. However, these data points don't mean anything on their own. They only mean something relative to other data points—both quantitative and qualitative.
How long does it take to get meaningful data? Well, that depends on what kind of meaning we're looking to make and what kind of data we're dealing with. What might be "statistically significant" for Olyslagers won't be statistically significant for another elite high-jumper. What's statistically significant for me won't be statistically significant for you.
Trying to make sense of single data points will inevitably lead to reactionary choices. We’ll adjust things that don’t need to be adjusted or abandon tactics that don’t need to be abandoned.
Time is one of the great contextualizers. Data needs history to make it useful.
2. Making sense is a practice
Even once Olyslagers's silver medal was guaranteed, she kept recording her jumps—her commitment to the practice is essential to her performance.
While I don't want to suggest that making sense of data requires obsessive attention to detail, it does require commitment and habit formation. Whether or not that habit is externalized in the form of a journal or spreadsheet or dashboard, it's the process that allows meaning to emerge from data.
Again, collecting data is easy—so easy you don't even have to think about it—it's just there. However, processing the data and turning it into an idea or a trend takes effort. Luckily, we already have a baseline for that effort. We process data every time we have a conversation with a partner or friend about how our day was. We process data when we tell our doctor or therapist what's been troubling us. We process data when we listen to our kid relate a story from school and ask for some advice.
We just don't often extend that practice to the data we use at work. Whether it's page views, podcast downloads, newsletter subscribers, conversion rates, or sales, I've noticed that we try to let those numbers stand on their own.
I've been asked many times whether a conversion rate of 3% (or 1% or 12%) is good. Well, good compared to what? How is that conversion happening? Who is in your sample? While I rarely get questions about conversion rates anymore, I still get plenty of questions about podcast downloads. What's a good number of downloads to shoot for? Absolutely no idea. I work on podcasts that receive tens of thousands of downloads per episode and podcasts that receive tens of downloads per episode.
3. Act and observe again
We can't make sense of data if we don't act on what we think it indicates. Karl Wieck, the organizational psychologist who first described the sensemaking process, and his co-authors Kathleen Sutcliffe and David Obstfeld put it this way:
If the first question of sensemaking is “what’s going on here?,” the second, equally important question is “what do I do next?”
Making sense of data involves taking a stab at what's going on. For Olyslangers, that might mean guessing that her stride is a bit off or that her takeoff hasn't been as powerful as it needs to be. With that information in hand, she can go into the next jump and make a change based on that guess. After that jump, she observes again.
For a marketer, that might mean examining email list growth and guessing that sending fewer emails caused the dip they're observing. To test that presumption, they spend the next couple of months back on their old cadence—and observe again.
For an educator, this might mean observing that participants often get stuck at a certain point in a course and guessing that there's something that's not working in the curriculum design. To test that presumption, they adjust the curriculum to better guide participants through that common snag—and observe again.
For a journalist, this might mean observing a trend emerging on their beat and coming up with a theory for why that trend is happening. To test that presumption, they interview both experts and people participating in the trend to see if they're on the right track. From there, they might write up their article, or they might do more reporting to get even clearer on what's happening.
Developing ever-better frameworks
Many of us look at a pile of data and wish we had the exact layout, app, or algorithm to make sense of it. Data seem to represent facts that, when arranged properly, deliver objective answers to our questions. However, no algorithm could use all the data Olyslagers has captured over the years to discover the perfect formula for her next jump. There is, in fact, no perfect formula to discover.
Instead, her regular interaction with the data produces a mental framework she can use to adjust her form or try new things. She not only produces that mental framework but also becomes aware of it and improves it as she trains. She expands her idea of what's possible, likely, or effective based on how her framework shifts with new questions.
We use frameworks to navigate uncertainty and explore new domains of knowledge. "We might think that we are dealing with a familiar kind of problem, only to discover that we need to reconfigure our approach as we go along," writes philosopher Céline Henne. "Frameworks are crystallisations of our understanding of the world, and they remain transparent most of the time. We see through them instead of looking at them." But when we do practice engaging with those frameworks—when we pay attention to how we're thinking and not only what we're thinking about—we stumble on new meanings and ask new questions.
When we do, we don't only change how we interpret the data all around us. We often impact how others make sense of the world. I imagine there's a young high-jumper who watched a beaming Nicola Olyslangers scribbling in her journal after each jump and decided to do the same the next time they hit the track. By modeling a reflective and adaptive approach to her sport, Olyslangers inspires others to do the same.
As media makers, marketers, educators, managers, consultants, and colleagues, we can do the same for those who look to us for guidance or support. We can model an attention to the framework as much as an attention to the data. We can practice making sense at least as much as we practice finding the right answer. When we do, we encourage others to do the same.
Introducing Make Sense
I'm offering a new seminar on making media that makes sense starting in September, called Making Sense.
This is for you if you love questions like "What's really going on here?" and "What the heck does this mean?" It's for you if you want to create remarkable content in any form, and what a new skill set for doing it.
Think of Making Sense as content strategy meets psychology class—Marketing 101 meets Intro to Epistemology.
Over 8 weeks, I'll guide you through what I've learned about sensemaking and creating content over the years. We'll analyze great (and fun) pieces of sensemaking, observe our own audiences (or customers or clients), and develop our own theories.
You'll work week-by-week on a series of assignments to help you produce a sensemaking project you can use in your own work. By the end, you’ll have a draft of an article, script for a video, outline for a book, slide deck for a presentation, or any other type of media you’d like to create that’s designed to help your audience make sense of their world.