data – Faster Than 20

Do the Work

It’s been one month since a white police officer in Minneapolis murdered George Floyd, a 46-year old Black father of five. I’ve found the subsequent response remarkable for its intensity, unprecedented diversity, and impact. While I’m moved by how many people and organizations seem genuinely compelled to act, I’m also vexed by some of the rhetoric around what “doing something” actually means.

Woke theatre aside, I get that it’s hard to know what to do or how. I can see how easy it is to be overwhelmed by the enormity of wanting to eradicate 400 years of structural and cultural racism or by the fear of doing or saying the “wrong” thing. Fortunately, there are a lot of resources out there, and folks have been circulating them with abandon. While many resonate with both my personal and professional experience, I’ve found several to be questionable or worse, and I can’t help feeling like most of this resource sharing misses the point. You can’t just work your way through a listicle and solve racism.

This work is hard, but maybe not in the way most of us think it is. The muscles required to create a more equitable society are the same ones needed to be skilled collaboration practitioners, and they can only be developed through practice and repetition. The key is to focus on the right things and to do them over and over again. The devil, of course, is in the details, and I want to riff on those here.

But first, I want to tell two stories. The first is about data, narratives, and human psychology.

According to the Mapping Police Violence database, 91 people have been killed by police in the 38 days since George Floyd’s murder. Nineteen of them (21 percent) were Black, a slight decrease from the overall percentage over the past eight years (25 percent). Thirty-two of the 91 killed were white.

I read all of the news items documenting each of these 51 killings (not counting the 40 victims of other or unreported race). The vast majority of the victims were armed. Many were violent criminals — rapists, murderers. Several of the deaths were the result of shootouts, and some cops died as a result. A few cases of both Black and white victims raised my eyebrows, but there was nothing that felt as clearly wrong and overtly racist as George Floyd’s murder.

Reading about these 51 deaths left me feeling depressed, but not outraged. As I dove more deeply into these incidents, I couldn’t help wondering how I would have felt about racialized police violence if I had not been exposed to countless stories like George Floyd’s over the years, if my only exposure to police violence were accounts like the 51 articles I read.

It was a troubling thought, because of all the numbers that I mentioned and stories that I shared, there’s only one that really matters: that 25 percent of people killed by police are Black. Why does that number matter? Because only 14 percent of Americans are Black, which means that Black people are disproportionately killed by police by a big margin. Even if George Floyd or Breonna Taylor or Philando Castile or any of the many Black women and men who were definitively unjustly killed by police over the years had never happened, that 25 percent number would still be a clear indication of a racial disparity that needs to be addressed.

Therein lies the essential challenge. No one has ever looked at a number and taken to the streets. There are lots of mental hoops required to make sense of that number, to trust its implications, and then to get outraged by it. We’re seeing this play out right now with the massive racial disparity of COVID-19 deaths, which is killing far more Black and Latinx people than police violence, yet hasn’t resulted in large-scale public outrage. In a perfect world, it shouldn’t take a shocking video of a Black man being callously suffocated to death by a smug white police officer for folks to recognize that the system is racist, but for most of us, that’s exactly what it took.

Except that’s not quite the whole story either. As visceral as George Floyd’s death was, it still wouldn’t have had the impact that it did without the massive amount of work and resources that the Movement for Black Lives has invested in organizing, mobilizing, and collectively aligning around a policy platform over the past eight years. Contrary to how it may appear on the surface, the Movement for Black Lives isn’t just a hashtag. It’s also not a single organization with a clear hierarchy of decision-making and leadership. It’s a network full of leaders, organizations, and activists, some more visible than others, but every one of them playing a critical role. That makes it harder to understand, talk about, or fund.

Human beings love simple, emotional narratives. We need to accept this about ourselves and leverage it to motivate change. But once we allow ourselves to be moved, we also have to be willing to let go of these simple, emotional narratives and dive more deeply into the messy and far less compelling nuts and bolts of the work. Real change takes lots of hard work, the kind that most people are completely uninterested in hearing about or doing.

The second story I want to tell is about basketball.

When your team has the ball and is trying to score, one of the easiest ways to help your teammates is to set a screen. This consists of positioning your body so that it serves as a kind of wall that prevents the defender from chasing your teammate. If the defender sees it coming, they can try to dance around the screen, but that split second of separation is often enough to give your teammate an advantage. If the defender doesn’t see it coming, then it results in a collision, which usually hurts them a lot more than it hurts you.

If you’re defending, and you see the other team set a screen, all you have to do is yell, “Left!” or “Right!” depending on where the screen is relative to your teammate. At best, your teammates can adjust and eliminate the offensive advantage. At worst, you save a teammate from a painful collision. It is a simple and effective intervention that doesn’t require any special athletic abilities. All it takes is attention and communication.

Still, it’s not intuitive. Many players — even experienced ones — have to be told to “call out the screens,” often by a frustrated teammate who has just been flattened by one.

I find this fascinating. Basketball is a hard sport to learn and play. I’ve played it my whole life, and I’m still mediocre at the shooting and dribbling part, which require physical acumen. But I’m great at calling out screens, which simply requires me to talk. Why is it so hard for others? Why isn’t this the first thing that people learn how to do?

It turns out that being an ally is a muscle, and that developing that muscle takes practice.

A few weeks ago, I was on a check-in call for a network of Black activists and allies. On the first part of the call, folks shared a number of inspiring stories about some of the amazing work happening on the ground in Minneapolis and other places around the U.S. Themes around being invisible and the importance of reclaiming one’s own agency and not replicating existing power dynamics came up over and over again.

Afterward, we broke out into small discussion groups. I was in a group with four other people, including a moderator. None of us knew each other, so the moderator called on people, one-by-one, to introduce themselves, and he inadvertently skipped me. I waited several moments for someone — anyone — to point this out, but nobody did, and the group started diving into the discussion. I finally found a point to jump in, saying with a smile, “I have a thought, and while I’m at it, I’ll also introduce myself.”

The moderator profusely apologized, not just in the moment, but throughout the rest of the discussion. I was touched by how badly he clearly felt. It was fine, I knew it wasn’t intentional, and I would have been okay regardless. And everyone in the group was lovely. What really stuck out for me, though, was how no one else in the group noticed or said anything, especially after all of the talk beforehand about the importance of seeing each other, of being seen, and of being good allies.

I’ll say it again: This work is hard, but maybe not in the way most of us think it is. The muscles required to create a more equitable society are the same ones needed to be skilled collaboration practitioners, and they can only be developed through practice and repetition. The key is to focus on the right things and to do them over and over again.

I’ve worked with all kinds of groups over the years, including many social justice groups, and I’m constantly struck by how bad most of us are at the fundamentals. It’s why I’ve moved away from larger systems change projects and have focused my energies on training and coaching. If you’re trying to create a more equitable world, but you can’t even run an equitable meeting, much less an equitable organization, you’re focused on the wrong problem. Everything is connected. If we just stepped back and started with smaller, simpler (but by no means simple) challenges, giving ourselves plenty of permission to make mistakes along the way, we would be far more likely to make headway with the bigger, harder societal problems that so many of us care so much about.

Which brings me to the thing I really want to say to collaboration practitioners and organizations who want to contribute to a more racially just world. Urgency is the enemy of equity. If you really want to make a difference, start by slowing down.

All of the racial equity training in the world won’t make a lick of difference if you don’t have the mechanisms and the right mindsets in place to get clear and aligned about success, to adjust based on what you’re learning, and to hold yourselves accountable to your stated values. In many ways, these are the easiest things to implement, and yet they’re the things groups are most likely to skip. I can’t tell you how many groups have approached me over the years wanting to change their culture somehow, someway, and yet weren’t willing to schedule regular time to assess how they were doing. Frankly, most practitioners I know skip these steps too, and our impact suffers as a result. We get away with it, because no one holds us accountable to long-term success, and the status quo continues merrily on its way.

Earlier this year, I wrote about my six-year journey to learn how to slow down. I know how hard it is to change these habits, and I don’t want to suggest that what I did will work for everyone. All I know is that it matters, that it’s an affliction that infects many of us, and that you’re more likely to propagate than address inequity if you don’t figure out how to fix this. It won’t be worthy of a press release, but it’s more likely to result in the impact you want to have in the long run. Moreover, if enough of us do this, the right things will start to happen in society at large.

Update: I clarified the number of victims since George Floyd’s death (91) above, explaining that I focused on the Black and white victims (19 and 32 respectively, for a total of 51). Thanks to Travis Kriplean for the suggestion.

Illustration from Black Illustrations: The Movement Pack.

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Predicting Ferguson: Data, Visualization, and Systems Thinking

Last night, a St. Louis grand jury decided not to indict Ferguson police officer, Darren Wilson, for the shooting death of Michael Brown. If there is a silver lining to this decision, it’s that the discourse around these events has been predictably emotional, but perhaps unpredictably thoughtful. I’ve seen and been a part of a number of conversations that have asked hard, critical, systemic questions about why this happened and what needs to change.

Why is it that black men are 21 times more likely to get shot dead by police than white men?

Why did the Ferguson verdict feel predictable, despite the mountain of evidence against Wilson?

What can we do to improve the system?

Code for America is an amazing group that organizes a powerful network of technologists, designers, data scientists, and concerned citizens to help create a government that is truly for and by its people. Shortly after the shootings, its staff started asking what it could have done to prevent another Ferguson. As Jen Pahlka, Code for America’s Executive Director, shared at its annual summit this past September, there were clear, data-driven indicators that something was majorly amiss in Ferguson.

Simply sharing data in this form doesn’t solve any problems. The trust-building and structural changes that Jen described are the hard nuts that need to be cracked. But having the data so clearly and powerfully expressed in real-time would, at minimum, have jolted us. It would have been clear, indefatigable evidence that this is something to which we need to pay attention.

The low-hanging fruit of systems change is to provide better, faster feedback mechanisms. Technology in today’s networked world offers one way to do that. But what ultimately matters when it comes to feedback isn’t information. It’s good, old-fashioned, person-to-person connectivity. It’s our need to talk to each other, to see each other as humans, to really understand what it is to walk in each other’s shoes.

We don’t need technology to have that conversation. We don’t even need new people (although that would help a lot). Start with the people around you. Talk to them about Michael Brown, about Darren Wilson, about tragedy and injustice, but also about the world we want to live in. Talk to them about what it’s like to be rich or poor, black or white or Asian or Latino in this country. Most importantly, talk about love — what it looks like, what it means to you and them, and what the world would be like if there were more of it.

My friend, Lauren Crew, a brilliant photographer who documented the protests in Oakland last night, shared these wonderful words from Richard Rorty:

My sense of the holy… is bound up with the hope that someday, any millennium now, my remote descendants will live in a global civilization in which love is pretty much the only law.

Getting Real About “Experiments” and Learning

Part one of a three-part essay on facilitating group learning.

Last year, I went to Cincinnati to visit my sister and her family. My older nephew, Elliott, who was eight at the time, asked if I could help him with his science experiment. He was supposed to pick a project, develop a hypothesis, and run some experiments to prove or disprove it.

Elliott explained to me that earlier that year, he had participated in a pinewood derby and had lost. He wanted to figure out how to make a car that would go faster. I asked him, “What do you think would make the car go faster?”

He responded, “Making it heavier.” That seemed like an eminently reasonable hypothesis, especially coming from an eight year old. I helped him define the parameters of an experiment, and he constructed a car out of Legos and a ramp using a hodgepodge of race track parts to run his tests.

In theory, mass has nothing to do with the speed of the car. The only thing that matters is the acceleration of gravity, which is constant. A heavier car should go down the ramp at the same speed as a lighter one.

Except that’s not true either. Wind resistance counteracts the effects of gravity, which might make a lighter car go slower as a result. Then again, the aerodynamics of the car might have a bigger effect on decreasing wind resistance than mass. Then there’s the issue of both the friction and alignment of the wheels. And so forth.

Wading through all of these variables would require a carefully calibrated measurement system. Suffice it to say, Elliott’s system was not very precise. When he ran his experiment initially, the lighter car went faster than the heavier car. He dutifully proceeded to conclude that weight had the opposite effect on speed than he had theorized. I suggested that he try the experiment again just to be sure. This time, the cars took the same amount of time.

He was thoroughly confused. So was I, but for different reasons. How was I supposed to explain to him all the possible reasons for his results without delving into the intricacies of physics and engineering?

It was a ridiculous thing to expect an eight year-old to figure out, but it would have been fair to have asked of a high schooler. Science is clean that way. You can set up experiments and controls, you can meticulously account for variables, and you can repeat and replicate your experiments to build confidence in your results.

This is not the case with people.

It has become en vogue in the business world to frame knowledge work around experiments and learning. This is the essence of the Lean Startup idea, but it’s not limited to lean. I’ve been as guilty of this as anyone, and I’ve been doing it for a long time now.

But what exactly does it mean to frame people-work this way? Unlike science, you do not have laboratory conditions where you can set up replicable experiments with controls. Sure, you can come up with hypotheses, but your conditions are constantly changing, and there’s usually no way to set up a control in real-life.

How can you fairly draw any conclusions from your results? What are you even measuring? The realm of trying to assess “impact” or “effectiveness” or (to get very meta about it) “learning” tends to devolve into a magical kingdom of hand-waving.

The reality is that experimentation without some level of discipline and intentionality is just throwing spaghetti against the wall. The worse reality is that — even with all the discipline in the world — you may not be able to draw any reasonable, useful conclusions from your experiments. If your ultimate goal is learning, you need more than discipline and intentionality. You need humility.

In The Signal and the Noise, data analysis wunderkind Nate Silver points out how bad humans tend to be at forecasting anything reasonably complex — be it political elections or the economy. There are way too many variables, and we have way too many cognitive biases. However, we are remarkably good at predicting certain things — the weather, for example. Why can we predict the weather with a high degree of certainty but not things like the economy?

There are lots of reasons, but one of the simplest is accountability. Simply put, meteorologists are held accountable for their predictions, economists are not. Meteorologists are incentivized to improve their forecasts, whereas economists generally are not.

What does this mean for groups that are working on anything complex and are trying to learn?

First, be intentional, but hold it lightly. Know what it is you’re trying to learn or understand, and be open to something else happening entirely. Measure something. Be thoughtful about what you measure and why.

Second, be accountable. Track your learning progress. Review and build on previous results. Be transparent about how you’re doing. Don’t use “experiments” as a proxy for doing whatever you want regardless of outcome.

Third, be humble. Despite your best efforts, you may not be able to conclude anything from your experiments. Or, you might draw “convincing” conclusions you might validate again and again, only to discover that you are totally, entirely wrong.