When we think of dataviz in the workplace, we’re usually thinking of Tableau and Looker dashboards, designed to make everyone smart and support big, strategic decisions. But there’s another overlooked benefit of visualizing an organization’s data: the power to align.
Every organization struggles to keep members aligned around a common purpose. Brilliant strategy and customer insights are useless if everyone is rowing in different directions. Goal setting and OKRs go a long way toward accomplishing this, but this is where the humble dashboard can really shine.
One of the more common uses for dashboards is visualizing a team’s recent performance. This might include a sales team’s pipeline vs. win / loss. It might show a field service team’s recent site-visits v.s. first-time-fix rates. Or for a product team, perhaps it’s user engagement for recent launches. …
Doing hard things is hard. Continuing to do hard things is even harder.
American culture glorifies persistence. When we hire, we hire for “grit.” We lionize the leaders who saw us through our longest, most difficult times (e.g. MLK, Washington, Churchill, FDR, Moses, Frodo). Resiliency inspiration porn saturates social media (e.g. Einstein: “It’s not that I’m so smart, it’s just that I stay with problems longer.”). Even Mitch McConnell found a way to contribute to society by extolling Elizabeth Warren’s tenacity:
“Nevertheless, she persisted!”
But what if persistence isn’t some innate superpower?
As we’ll see, our stick-to-it-iveness depends on our expectations. And our expectations depend on our available information. This makes expectation-setting a powerful use case for information design. …
As businesses becomes increasingly quantified, there’s a growing demand for new products and experiences to make sense of their data.
Sometimes this calls for specialized standalone products (e.g. Pendo, Github Insights, Yva), other times you might build on top of more general tools (e.g. Looker, Tableau). In either case, finding effective solutions to user problems means asking and answering a ton of questions.
Of course, not all questions are equally applicable (e.g. “What’s the distribution of eye-color for our target audience?” probably doesn’t matter unless you’re designing eyeglasses), but there are some common, inevitable questions that inform most products. …
A while back I was talking with my dad. He heard something wacky about Covid-19 testing accuracy: they were so inaccurate that “you might as well flip a coin.”
That didn’t sound right, but I realized my own understanding of testing accuracy was a bit fuzzy.
So, to clarify, let’s learn about Covid-19 tests, how they’re measured and what that means for detecting infections in a population.
I have trouble sleeping. Doctors often warned me that caffeine was the likely culprit. One time, in 2015, I even listened to them. I gave up coffee for about 3 weeks. Then I realized I’m an extremely unpleasant person when uncaffeinated. So I went back to the sauce.
Instead of giving up coffee entirely, I settled on a compromise rule:
“No caffeine after 3pm.”
Adopting the rule didn’t noticeably help my sleep, but at least it gave me an effective retort for the doctor. And even better: I didn’t need to change any behavior to adopt the rule.
Most days, later in the morning, I’d walk to the coffee shop and order the largest ice coffee I could carry. Then I’d walk back to my desk and wait for the liquid “focus” to kick in. Occasionally I’d also have an afternoon coffee, when demanded by meeting etiquette. …
By now we’re all familiar with OKRs. Objectives and Key Results were invented by Intel’s Andy Grove, passed onto Kleiner Perkins’ John Doerr, who then spread the gospel to his portfolio companies, most notably Google. Larry Page credits OKRs as the managerial secret-sauce behind their rapid growth.
As a testament to the power (and the pain) that comes in defining OKRs, Google’s CEO Sundar Pichai describes the process as “agonizing.”
Instead, I’ve analyzed 1,321 Tweets to answer a question many of us pandemic-bound remote-workers have wondered since Zoom became part of our daily lives: Do people like my room?!
Unlike Animal Crossing, there’s no authoritative raccoon we can rely on for objective feedback about our decoration skills.
Instead, here in the real world, the closest thing we’ve got is Room Rater (@ratemyskyperoom). As more and more (famous) people are revealing their homes via the laptop lens, Room Rater has stepped up to judge them, publicly and quantitatively.
Not all of our homes will be broadcast on national TV. At least not in the near future. But we can all agree, when that day comes, we want the world to see our rooms (and, by extension, our very beings) as worthy of a 10/10. …
Data visualizations are deceptively complex. Even simple visualizations feature multiple components that can interact in surprising ways. For this reason, testing visualizations with real people is crucial. In most cases, it’s probably sufficient to do a few (qualitative) user tests, fix what’s broken and then ship it.
But sometimes you need a bit more certainty. For example, if you’re designing visualizations for the general public, if you’re doing academic research or if the visualization will be part of a product or app, you may want to evaluate a few different variations of the design to understand which performs best.
Qualitative tests make comparing different versions of a design difficult because of issues like ordering effects. Traditional A/B tests don’t help either, because they won’t reveal much about whether users “get” the graphs. Fortunately, there’s Mechanical Turk. …
In 2017, some people were arguing on the internet. Notably, a few of them were thoughtful characters in the data visualization community, such as Stephen Few, Andy Cotgreave, Alberto Cairo, and Jeffrey Shaffer, to name a few.
The topic: What’s the value of a Lollipop chart? Is there something to the aesthetic (per Andy’s point)? Is the extra white space between symbols easier on our eyes (per Alberto)? …