Our ability to affect change in organizations and systems is based heavily on our assumptions about similarities and differences between people. Unpacking and testing these assumptions require methods and tools that can identify and distinguish these patterns in a manner that allows us to take advantage of diversity and commonalities, not be limited by them.
The latest research-based insight for productivity making the rounds is a New York Times story that looks at wake times and healthy. The article recounts many tales from technology leaders, actors, and other celebrities who regularly begin their day by 4am. The piece by Adam Propescu cites research and researchers who have looked at the issue of sleep and health, productivity, and overall mental wellbeing and found some strong correlations between sleep and performance. Can you spot the problem?(and no, it’s not necessarily with the research).
The discussion of sleep and performance is tied largely to the waking hour — 4 am – and not to the actual sleeping time. As someone who’s naturally occurring wake time sits around 5 – 5:30 am (and has since I was a child) I’ve always been interested in these stories for the self-serving reason that I like to imagine myself to be ultra-productive, creative, successful, or whatever the ‘product’ of waking up early is supposed to be. But, as a psychologist, I know what it means to fool oneself.
The truth lies somewhere in the data.
Data and Assumptions
Assumptions are ways we create mental short-cuts based on data from past experience or values, beliefs, and attitudes we’ve formed or had imparted by others in the absence of data. In the NY Times piece, what’s being equated is a wake time and sleep deprivation without much attention to a key assumption that moderates that relationship: time to bed.
While some of the celebrities profiled in the article are ones that go to bed late and wake early, it is entirely possible that people who wake at 4 am also go to sleep at 8 or 9 pm. They might also sleep well when they do finally hit the pillow. The relationship between sleep amount and sleep quality is another that’s not explored. Sleep is further affected by things such as what is done prior to bedtime, what is done while in bed, the length of time, food or drink consumed (or not) and in what proximity to bed, and the amount of physical or mental activity performed prior to bed or anticipated the following day.
The mistake is going over all of these points to make a point about sleep and it’s role in performance and health. If we don’t have the data or make the assumptions without the data we might end up making inaccurate claims about something or leading us down the wrong path. This has much to do with the assumptions about the data we have (or don’t) and the types of data (and questions we ask) in the first place.
One of the best places to look for examples is in the field of design.
Diversifying data, testing assumptions
Seeing a local artist, citizen, athlete or sports team find glory provides lessons in transformation. The 2019 playoff run of the Toronto Raptors basketball team into the NBA Championship is a great example. Tens of thousands of fans from across Canada are gathering to watch the games outside, ordinary citizens are taking to wearing basketball jerseys, t-shirts, and caps to work, and entire neighbourhoods are gathering around TVs to take it in.
It’s something that is being replicated all over Toronto, Canada, and the world. While the initial effect of this championship run is naturally occurring, it is the amplification and transformation that comes from it that is happening largely by design and can be understood through design research.
Many of these fans didn’t care about basketball or sports until now. Yet, here is a movement springing forth from a game that has the power to unite many diverse people from across the nation together cheering for ‘their’ team. Everyday people who, prior to this playoff run, couldn’t imagine watching sports at all are now staying up past midnight on weekday to watch basketball, are reading the sports pages in the newspaper, and painting their face.
These events have the power to change (or reveal) culture and with it, test our assumptions about the data we have. What they require is the kind of data that allow for richness of perspective within and across people and organizations. By looking at how a sports team promotes connections, local municipalities have been creating Jurassic Park (the free viewing party zone the Raptors first developed for fans) spin-offs across Canada.
A costly (and related) example of this design treatment (in the wrong direction) is McDonald’s ill-informed marketing campaign to offer free fries to anyone in the province of Ontario anytime the Raptors score 12 ‘3-pointers’ in a game (which is now a regular occurrence). The campaign was designed based on models drawn from hockey promotions and further failed to account for many of the cultural aspects of professional basketball in 2019 and this current Raptors team. While it may yet yield benefit for the company, the campaign’s assumptions were deeply flawed and the golden arches are serving a lot more fries than they expected to a lot more people.
Three-points for culture
If one assumed that the culture of basketball or even sports fans was the audience, you would have missed the point. What this is all about is a cultural movement that is predicated on people connecting over something that is entertaining, reflective of certain values, or provides an opportunity to unite around something. Tapping into a diversity of data — by using ethnographic methods and tools as well as big data, forecast data, or the usual trend analyses — can help explain why the Raptors fan base is growing, diverse, and engaged as it is.
This is not a story of basketball, sleep, or Jurassic Park. It’s about taking a user-centred approach to data collection and seeking to understand the data we need for the cultures we’re working in and a part of. Taking this culturally-centred, design-driven approach to evaluation, research, and strategy is what will allow us to move beyond making assumptions into generating real insights and supporting intentional — and useful transformations in the organizations and systems we are a part of.