Standout Insights from Season 1 of Data Radicals

By Talo Thomson

Published on February 10, 2023

A zoomed in shot of a Rode podcast microphone

What do The Sopranos, Breaking Bad, and Data Radicals have in common? (No, they don’t all feature a charismatic criminal mastermind.) All three programs completed their first seasons with audience appetites whetted for more stories — and we’ve got plenty of stories left to tell!

That’s why I’m excited for the second season of Data Radicals, which launches on February 15. Our first episode features Tim Harford, the author, broadcaster, and columnist known as the “Undercover Economist.”

But as we count down the days until season two, I want to share with you some of our team’s favorite moments from season one.

How a sausage order reveals the power of data literacy

Moment:Jennifer Belissent, principal data strategist at Snowflake, demonstrated the value of data literacy by way of the humble (and delicious) breakfast sausage.

Quote:I was talking to the chief digital officer at Sodexo. (It’s a food service management company that runs cafeterias around the world.) She told me they first started looking at their data at one of their cafeterias and saw an increase in the sale of breakfast sausage.

And that was pretty curious because breakfast sausage isn’t among the culinary habits of the French. When they dug deeper, they actually found the increase in the sale of breakfast sausage started happening at about the time they had changed their traditional cash registers for point-of-sale machines with individual buttons for each item.

The cashiers — maybe in the interest of time, or because the breakfast sausage button was the easiest to press — kept pressing that button. If managers of their site used that data for ordering supplies for the next month, they wouldn’t have ordered anything but breakfast sausage. And clearly, that wouldn’t have resulted in a positive breakfast experience for their customers.

That really highlighted the fact that these cashiers probably didn’t know they were working with data. If they’d been asked, they would have said, “No, we don’t work with data within our roles.”

Reflection: If you ignore “how the sausage is made,” you risk leveraging invalid insights — and making costly mistakes.

Why specialists and generalists are both vital

Moment:David Esptein, bestselling author of Range and The Sports Gene, argued that Jacks and Jills of all trades and niche experts are equally valuable to innovation by way of Freeman Dyson’s writings on “birds and frogs.”

Quote: I think we need a mix. I mean, the way I think about it is the way Freeman Dyson (1923–2020) put it. He’s an intellectual hero of mine, an eminent physicist, mathematician, and a wonderful writer. And as he said, if we want to help the ecosystem, we need both birds and frogs: the frogs are down in the mud, looking at all these granular details. The birds are soaring up above — not seeing those details — but integrating the knowledge of the different frogs.He said we need both. The problem was that we’re increasingly telling everyone to be frogs and therefore we’re not going to have these integrators. And I agree with that: we definitely need both.

Reflection: The more you incorporate the full menagerie of your organization’s personas into your data culture, the more robust and complete it will be.

Data leaders are business leaders

Moment:Caroline Carruthers, co-author of The Chief Data Officers Playbook, argued that the best CDOs aren’t just birdlike generalists, but passionate and empathetic to the pains of their colleagues.

Quote:You don’t have to understand every single discipline within the data role to become a chief data officer. What you do need to have is a level of credibility so you can talk authentically about the subject you are passionate about… And you must spend time with your stakeholders, …Relax them, take them out for coffee and cake. Sit there and just get to know them as a person and ask them about what they’re trying to do, what hopes they’ve got for their departments for the future. What’s causing them to be awake at night?

Reflection: That’s because you can treat your data like numbers, but your people — those tasked with finding and leveraging that data — are individuals, not analytics.

Data storytelling is a superpower

Moment:Dr. Margaret Heffernan, bestselling author of Willful Blindness, summarized the challenges of data strategy when specialists and generalists must work together.

Quote:And so the data people didn’t understand context and strategy. And the strategy people didn’t know how to frame good data questions. So they would routinely have these experiences where the chief data officer would come with all sorts of insights drawn from the data. They wouldn’t have any natural allies and they wouldn’t necessarily tie the data to the strategy. And so nothing would happen.

Reflection: That’s why it’s important for data experts to bridge that gap by explaining to non-experts not just what the data says, but what it means.

Celebrate small wins (and failures!)

Moment:Cindi Howson, CDO of Thoughtspot, encouraged leaders to celebrate every data insight — even negative ones.

Quote:“So whenever a data-driven decision-making process is shared — or, “Hey, I found this great insight” — if that’s celebrated, even if the metric is negative, I think it should be rewarded that the data was shared…I also think the way to change the people is also to change the incentives and clearly communicate the why. When we keep throwing technology at people without explaining the why, that’s when you get the pushback.”

Reflection: In other words, “all” news is good news. The shortest distance between two points is a straight line, but some trips require detours informed by those “negative” metrics. Having as many insights as possible is the best way to execute data-driven decision-making.

Cultural norms can impact data culture

Moment: Former US Army General Stanley McChrystal connected a fear of failure in the US military to its data culture — and used that insight to make major changes.

Quote:“We had to go gather intelligence. We had to go create operations to change the dynamic on the battlefield, to create opportunities for us to go. And all of these things ran counter to an organization who at its heart and soul abhorred failure… It wasn’t lack of physical courage, it’s people didn’t want to do operations that failed. And so what they did was they tried to set up conditions so that everything was perfect before they pulled the trigger so that their probability of failure was very, very low. Well, of course, that is by definition self-limiting. If you’re not willing to do lower-probability things, you’re not willing to do very many things.

And so, as I used to tell them, your batting average may be high, but we’re not scoring many runs. We’re going to lose the games. To get very specific, when I took over, we’re doing about one raid a week and our mission was to go after the leadership: we call them HVTs, high value targets, the leadership of Al-Qaeda in Iraq, to capture or kill them, and therefore decapitate the organization…. And you do about one a week, four a month. We’re successful about 70 percent of the time. And at the end of the month, we can high-five and say, we took three enemy leaders out of the fight. Meanwhile, every month Al Qaeda in Iraq is growing significantly.

Our effect doesn’t seem to have any impact on them whatsoever. They’re getting more confident. They’re getting more momentum. They’re getting more lethal. And yet we say, well, look, we’re taking three leaders out, and it’s sort of like, so what?…So we start increasing what we’re doing just by pushing ourselves harder. And we actually get up to 18 raids a month, one every other night.

Reflection: Trusted data is why we can shift gears and avoid performing the same actions — while expecting different results.

Dat(a)ing with a wingman

Moment:Seth Stephens-Davidowitz, author of Don’t Trust Your Gut, used data to prove that in the dating world, so-called “attractiveness” comes down to more than just physical appearance. (Which means there’s hope for me yet!)

Quote:“Romantic happiness is pretty hard to predict, but the things that have some predictive power — psychological traits, like someone having a growth mindset or being conscientious, having a secure attachment style — all these psychology terms that I’ve ignored in my life have a lot of predictive power and things like a conventional attractiveness, or how tall they are, what occupation they’re in, all the things that basically everyone tries to date on, have no predictive power of how happy you are….

So I used to think … I was such a nerd, I’m like, I need to be less nerdy if I want to attract a woman because we’ve long been told that the nerd is an unattractive characteristic. after reading this study of Christian Rudder, I’m like, “Well, no. I just need one woman, basically, whom I’m attracted to.” And I really want a date to be really into me. And I’m going to probably have a higher chance of that if I just am who I am — which is more nerdy … I could be the nerds’ nerd.

And I think the best dating strategy, if you’re Brad Pitt or Natalie Portman, just get a normal haircut, dress very nicely, don’t rock the boat. Your situation is set, just thank the gods — the genetic gods — for giving you these gifts. But for those of us who are not in that category, I think the best strategy is really to appeal to some group for whom you are the Brad Pitt or Natalie Portman, and then ask a ton of people out, willing to get rejected until you get shocked by someone who it turns out is really into you.”

Reflection: If you’re on the hunt for your soulmate, you’re better off emphasizing your authentic (some may say unsavory!) traits to attract them.

Social justice demands diverse data

Moment:Tarak Shah, a data scientist at the Human Rights Data Analysis Group, showed how data can be a potent tool in the pursuit of human justice — even when it comes from an unlikely source.

Quote: “In particular, they were interviewing survivors, which is a very common way of doing human rights work, so people describe events that they witnessed or experienced, and annotators will read through those testimonies and extract incidents that are grave human rights violations, so things like killings, tortures, disappearances, and so forth. The things that I mentioned that were somewhat novel about that work, one in particular was that at that time, they were relying on a number of different data sources, rather than just using one as kind of the official data source. So in addition to these interviews that they were conducting, they were collecting similar data from other human rights organizations in the area. They were also reading through newspaper articles from throughout the conflict, and extracting narratives of different human rights violations and encoding those too.”

Reflection: That’s the power of finding data in disparate, unexpected sources. Insights become exponential.

Beware the complacency trap

Moment:Randy Bean, author of Fail Fast, Learn Faster, connected the success of data-driven organizations with a persistent (paradoxical) discomfort as they relentlessly search for a better way.

Quote:People ask me what are the characteristics that I see of data-driven organizations and I point to organizations like, for example, Capital One. In every conversation that I ever have with Capital One, they say, “What are others doing? What’s coming along? What should we be doing that we’re not.” In other words, their relentless drive to be data-driven means that they never rest. They’re never comfortable. They’re always looking for better ways. They’re always looking to see what others are doing because they know that unless they continue to do things to stay data-driven, that they won’t be the most data-driven going forward. And when I go into organizations and they sit back with our arms folded and they say, yeah, we have it all figured out, those are the organizations that I truly worry about.

Reflection: Restlessness is an asset! As powerful as data can be, it isn’t going to do the work for you — though it will keep stride if you constantly dig for more insights, apply them, and seek ways to improve.

How to build an exceptional culture

Moment: Business Objects founder and Silicon Valley icon Bernard Liautaud offered advice on building a truly great company culture.

Quote: need to think about it a lot. They need to decide what are the important elements they want as part of the company culture. And they need to practice it, and practice them all the time and communicate heavily about it. But probably the most important thing is the founder — CEO, leader of the company — needs to exemplify these values in this culture. I, personally, believe that companies’ cultures are modeled after the behavior of the CEO. The CEO, and therefore the executive team, behave in a particular way that basically creates the environments or the type of behavior that everybody feels that they should sort of mimic or adopt.

So, as a CEO, you can’t say, “Well, I’m doing this because I’m the CEO, but you cannot do it because you’re not the CEO.” If the CEO is sort of really rough with his executive team, then the executive will be rough with the level underneath… And the roughness will be part of the culture. If the CEO is very human and is a listener, and takes input from many different people, that will be part of the culture. So, to me, the number-one element of how you create culture is by setting the example. You have to set the example, you’re going to be modeled after unconsciously or consciously.

Reflection: Bernard’s definition of a true top-down culture means executing with accountability, not by fiat.

Join Us for Season 2 on February 15, 2023!

All this reflection has me truly excited about season two of Data Radicals. Join us with our CEO and host, Satyen Sangani, for more anecdotes, insights, and advice on the power of data when we kick off on February 15 with Financial Times columnist and bestselling author Tim Harford.

In the meantime, catch up on any episodes you may have missed from the first season.

  • How a sausage order reveals the power of data literacy
  • Why specialists and generalists are both vital
  • Data leaders are business leaders
  • Data storytelling is a superpower
  • Celebrate small wins (and failures!)
  • Cultural norms can impact data culture
  • Dat(a)ing with a wingman
  • Social justice demands diverse data
  • Beware the complacency trap
  • How to build an exceptional culture
  • Join Us for Season 2 on February 15, 2023!
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