The Curiosity Shop with Brené Brown and Adam Grant - Brené with Paul Leonardi and Tsedal Neeley on The Digital Mindset, Part 1 of 2

Episode Date: December 19, 2022

This two-part podcast with Paul Leonardi and Tsedal Neeley is an absolute game changer. Given how often I find myself working with leaders who are knee-high in uncertainty and vulnerability around digital transformation, I thought I had a pretty solid understanding of it. But The Digital Mindset book and this conversation turned everything upside down. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Hi everyone, I'm Bray Brown, and this is Dare to Lead. And I am just, God, I can't even tell you how grateful I am for you, for all of the people that we've interviewed, for everything that I've learned. It's just kind of mind-blowing. I'm so deeply grateful for this community. Of people who are thinkers or who are curious, who we're working hard to be daring leaders, wholehearted. I'm just really grateful for you.

So thank you. Thank you for walking with me. I'm grateful. Today, it's two-parter, first part and second part, both with Paul Leonardi, who is a professor of technology management at the University of California, Santa Barbara, and Soudaul Neely, a professor in the organization Behavior Unit at the Harvard Business School. I'm talking to them about their book. Wow, what a book, the digital mindset, what it really takes.

to thrive in an age of data algorithms and AI. And before you're like, oh, God, no, it is so amazing. This conversation is so good. Talking about incredible researchers and teachers and storytellers and metaphor users. And it's just, I think everyone needs to hear this message. And we actually just bought it for everyone in our organization as soon as we stopped recording the podcast. So I'm glad you're here. Thank you.

Before we get started, let me tell you a little bit about both of our guests. So Sadal Neely is the Naylor-Fitzhughes Professor of Business Administration, senior associate dean of faculty development and research strategy, and the faculty chair of the Christensen Center for Teaching and Learning at the Harvard Business School, recognized as one of the Forbes' future of work, 50, and one of the 100 people transforming business by Business Insider. She focuses on how leaders can scale their organizations, by developing and implementing global and digital strategies.

She regularly advises top leaders who are embarking on virtual work and large-scale change that involve global expansion, digital transformation, and becoming more agile. Prior to her academic career, Sidal spent 10 years working for companies like Lucent Technologies and the Forum Corporation. And she worked in a lot of different areas,

including strategies for global customer experience, which has really helped shape her work. She has such a huge reference set and brings together so much information. And they both received their PhDs together at Stanford, which is where they've met, and they've published a ton together, which is really, I love it. You can tell that they have a real connection

and real shared respect and understanding. It's just a shorthand that's really cool to listen to on the podcast. So Pauly Dardy is the Duke of Family Professor of Technology Management at the University of California, Santa Barbara. He's an expert on digital transformation and organizational change. He advises senior leaders on how to organize their workforces

to compete with data, analytics, and new technologies and consults with companies like Google, Microsoft, General Motors, and Discover, about using digital tools to enhance internal knowledge sharing, how to structure global product development operations

and how to manage the human aspects of new technology implementation. I know these sound like big, hard, academic titles, and they are big, academic, fancy things. These are very accomplished folks. I know the topic seems, Ooh, digital transformation, AI, algorithms. This is a heart-led, heartful conversation.

And let's get started. Sadal and Paul, welcome to Daredeley. Thank you all for being with us. Thanks for having us. It's great to be with you. So this started in the green room of the Texas Conference for Women. It sure did.

Yeah, when we met and... I had heard of your work. I came across your work through another podcast guest, who I'm assuming y'all know, Dr. Linda Hill. Oh, yes. Dear colleague. Yes, I had heard of your work. And then when I met you in person, the green room said all,

I was like, wait a minute, y'all need to be on the podcast. It was wonderful. It was actually wonderful to meet you there and to even see you in action, Bernay, and how you move and touch people. It was extraordinary, and I'm so glad we met and so glad we're here today. Well, I have to say that everyone in our organization is like, can we listen in in real time? Because we all have so many questions.

So I'm going to jump in. Y'all have written a book, The Digital Mindset, What It Really Takes to Thrive in the Age of Data, Algorithms, and AI, which makes y'all the most relevant, disliked, desperately needed, and feared people in my experience going into organizations. Does that resonate at all? I think a little bit. Paul, go ahead. Yeah, I was going to say many of those adjectives resonate.

Feared is probably the biggest one that I think we hear a lot. And that was a big impetus for why we wrote the book because we actually don't think it's that scary to develop the skills and the mindset you need to really be successful in this digital age. And hopefully that comes across in the things. things that we wrote and the way we tried to approach these topics. But I can see how it's scary at the outset for sure. At the same time, it's very clear to managers, executives and individual contributors alike that the world has changed and will continue to change in this digital area. And the

question is, how do we equip ourselves? How do we equip our organization? And how do we do it quickly? Okay. I'm going to start with some basic questions. I'm so excited. It's like free coaching. Okay, define digital transformation for us. I mean, it sounds really scary, I think, and I think everybody's in it and doesn't even know that they're in it. So what is it exactly? How would you define it? So a digital transformation is changing the organization. how it functions to use data, technology, in the form of algorithms, computing power, and reimagining your business models in order to serve your stakeholders very differently. So digital is about data, it's about technology, and it's about organization design. And it's a new way of approaching work and working as a result of it. It's a new way of approaching customers and service as a result of it. So it's a wholesale radical transformation of a company's DNA.

Yeah, and I just add to that that digital tools are technologies. And as tools, they allow us to do new things in new ways. They give us opportunities to act. And what a digital tool does primarily in our organizations is it provides us access with data. And so at the heart of any real digital transformation initiative is this idea that we need to learn how to cope with data, make sense of data, and use data for our strategic advantage and to help our employees and to touch our customers in more profound ways. So if we can think of how tools become an avenue for us to get access to make use of all these new forms of data, then we're on the right track to thinking about what digital transformation is. Okay, I want to stop for a second.

They always joke. They call this the pause cast. Because not only am I, I think, are in a pauser. I have no need to fill up a lot of space with bullshit. So let me think about this for a second. I have to tell you this is the first time I'm hearing some of what you're saying. And I spend 90% of my time in organizations freaking out going through digital transformation. So it's a definition that's really different.

So I want to think about it for a second. I don't know that people always think about digital transformation as data. The collection of data, the analysis of data, the implementation of the, implementation of new strategy and processes based on data. I'm not sure that that's how people in general think about digital transformation. Does that resonate with y'all when I say that's not been my experience? I think you're very right. But the reason why we're in this digital space today is actually, if you can imagine, three circles. One is access to data in ways that we've never had before. We're talking about million data points or what's called metadata, which is data about data. So the presence

of data in extraordinary ways, if you think about another circle is computing power. Computing power has allowed us to crunch data, to have so much data being processed in ways that was not possible 20 years ago. Can I raise my hand and ask a question real quick? Are these like Venn diagram, like Olympic rings? Are you making a target? Think of them as Olympic rings. Okay, got it. Okay.

Three of them. Okay. So data, computing power. Okay. Extraordinary. I mean, at this point, we're even talking about quantum computing at Harvard. And the third one is models, algorithms, statistics. So the presence of these three.

three forces is why in that center we're seeing digital transformation. And an example of this, an example that I think people can relate to is Netflix. So how does Netflix figure out the types of things that they should recommend to you? It's all algorithms, right? It's collecting historical data, being able to predict what are the things that an individual or a family or group consumes and doing some matching and being able to make recommendations, recommendation engines. That's all because of data, computing, and algorithms. That's what digital transformation is. Okay, I've drawn it. And if you're listening right now, you know, that I love the listeners. I'm going to make sure we give that to you on Braybrown.com

under the podcast that a graphic will be there so you can look at it. I'm clear on access to data and metadata. I'm clear on computing power. Paul, tell me about the model circle. Yeah, so models really are kind of strings of, how would I put this in kind of an easy to describe in an audio way? Just tell us the hard way. and then tell us the easy way.

Okay, well, you can think of sort of strings of equations that put together lots of inputs, permute that data in some way, and then there's another set of equations that will output that data in some format that you want to read. So what a model basically does, it says, okay, we've got all these data points. We need to do something with those data

in order to make sense out of them, and then we need to calculate those data in a way that provides, let's hope, actionable suggestions for people in our organization. organizations. And so models, I would say, are sort of commodities in many ways these days. Like, if you're trying to run some kind of machine learning algorithm, which we hear a lot about, Microsoft and Google and Amazon, AWS, all have these models that you can download and use and play around with. What's really important is understanding what are the kinds of data that

we need to have. What do those data represent? How are they going to speak? to particular business issues that we care about, and then have the wherewithal of what to do with some of the predictions that those models are making based on those data. And those two ends of that spectrum, knowing what data we need, and then knowing what to do with the predictions,

I think is really at the heart of what digital transformation is. Interesting. That's what we really try to get at in the book, is that all the stuff that's happening in the middle, you should know how that operates, and you need to get to a certain level of fluency about that to just be a good consumer. But the smarts, the real thinking that we have to do

are on those two ends. So under my model circle and my Venn diagram, is that where algorithms belong? Absolutely. Okay, great. So access to data, the computing power, and then the model or the algorithm we use,

we collected the information, we crunch it, and then what model do we pick to make sure what we're crunching and how we're crunching it leads to actionable things we can do to make our businesses better? Is that? Yes. A clunky but fair. No, you got it.

The model is really trying to turn data into some kind of insight. And one of the important things to remember is that the insight that that model generates is usually based on some kind of statistical model. And so really what it's doing is it's making a prediction. It's saying, given this that we know today, this is what we predict is going to happen tomorrow based on the data sources that you fed into me as the model. And this is an issue that we see a lot in many organizations is that people have a really difficult time understanding prediction and that prediction is not the future written out for us, right? But there are predictions about things that might happen if we made certain choices.

And so the real sort of management and leadership role is to figure out what are the choices we should make. And in order to do that, you need to understand a little bit about how those statistics operate for sure, so you can have confidence about whether they're pointing us in the right direction. And perhaps more importantly, we need to understand what those data look like because if we don't have the wrong data or we classified those data in ways that don't really make sense, then those models are ultimately useless for us. And dangerous, right? Yeah, potentially.

I mean, lots of stories about how bias creeps into these models. And we discuss a fair amount about that in the book as well. You do, yeah. It's really scary to me sometimes when I go into organizations. and I love to do focus groups with kind of the least powerful people and organizations first. And then I hear, oh, no, there can't be bias in hiring because we use an algorithm. And as a researcher, you're like, wait, you know, shit in, shit out.

Exactly. I don't understand what's happening here. Who developed the algorithm? Exactly. And what data are you putting in? Can I tell a fun, quick little story about that? You know, I love a story. It's even more basic than that.

So I was doing some work a number of years ago at this large research lab. And they had implemented this new computerized system to try to track all the work that technicians were doing across the lab and who was sort of the best at doing different technical tasks. And the manager who had recently got his MBA and was running this part of the group decided that the data that were in this tool would just be the perfect data to determine everybody's performance. evaluations for the year because they showed who was doing what jobs, how quickly were they completing them, how good quality were those jobs. And so he was using this to try to make sense out of who should get promoted and who got raises and so on and so forth. And there was one woman at the organization who'd worked there for about 25 years and she was just consistently getting really low evaluations. And she was so frustrated that she decided to quit. And that manager

was so happy because he's like, well, obviously she was the lowest performing person. And over the next two quarters, the customer service ratings, right, just went down and down and down. And what he didn't realize was that he was capturing one kind of data in that tool. And it was data about solving technical problems. But he wasn't capturing all other kinds of data that were really important for keeping the organization working. So this woman knew, for example, what solutions users were likely to like. She knew who maybe we should prioritize across the organization. to keep the business running the way that we wanted it to.

So it was all this sort of social and cultural knowledge that was not captured and stored as data in the tool. It was overlooked and it led to really poor decision making. See, I think such a cautionary tale because I do also think operationalizing those contributions is really hard. It is. And so I think when you start building models,

you're like, ah, this seems kind of fuzzy and soft around the edges. This is not binary. I'm going to leave this out. And then what an important tale. Yeah. And I think it points to one of the dangers. And Sadal and I see this happening in so many companies that when things are easily

quantifiable, they take on a permanence or they take on a seeming objectivity. And the things that aren't easily quantifiable, like knowing what jobs we should be doing first or knowing what jobs we should be doing second don't seem to take. take on as much authority as data that are presented in numbers. And I think that's where a lot of leaders really go wrong. It's really funny. I had, when I was in my PhD program, she was actually my MSW program is where I met her.

Karen Stout, she's since passed away, but she studied Femmicide, the killing of women by intimate partners. And there was a lot of pushback for me in my PhD program because I had the first qualitative dissertation in my program. And, oh, God, people were pissed. Really? Oh, yes. to the point where Barney Glazer, who developed Grounded Theory Methodology,

yeah, he was my methodologist on my dissertation committee. You're like, I need some legitimacy here. I need some legitimacy, but they were so hostile about the dissertation proposal that Barney legally removed himself from my dissertation committee until the proposal process was over, then added himself back because they required a lit review in it. And he's like, if you already know what literature to review in grounded theory, then this is a waste of time.

And so, as it turned out, I reviewed all of the, what you would expect and none of it was irrelevant. But where was I going with the story with Barney? The qualitative, the legitimacy of grounded, qualitative, inductive versus deductive. And it's so hard. And I've got a daughter now who's working toward her PhD, and she said that bias is really still there. I think it's really problematic. and the more and more I see people trying to quantify data that are best understood qualitatively, the more fearful I get.

What do y'all think? I think that's completely correct. And that's why when we think about the digital mindset and the way we've conceptualized it is that everyone needs to have some understanding of some of the technical dimensions in order to contribute to what are the important variables? How do we think about the factors? As opposed to just having our engineers or data scientists building these algorithms devoid of the biases

and the various means in which you not only garbage in garbage out, but one of the things about digital and artificial intelligence and machine learning is that you scale the biases. Yes. Scaling the biases and then propagating the types of injustices that are profound, whether it's around policing, something as serious as that, or your hiring practices. So that's why we truly believe that everyone needs to achieve a baseline literacy in this realm to ensure that all of the factors that we're thinking about, whether they're quantifiable or not, are taken into account in our models. Very, very important to do. Okay, can I ask a mashup question that's probably like a seven-hour answer? Go for it. What I want to understand, I want to understand two things. And I think they're related to each other, but you can detach them if they're not. What is a digital mindset? And how does understanding that help us know,

Sedal, to your point, who should be at the table when we're making these decisions? Because one of the things that I see happening is in an organization of, I'm just going to make this easy, of 100 people, 40 of them are engineers and data scientists, two or three of the non-those folks say, this is what we need. It gets handed over to the engineers, and the data scientists, no one else is at the table, no one else is expected to have a digital mindset, and it's a freaking disaster 100% of the time. So what is a digital mindset,

and then how does that tell us what the flow process should be in terms of who's at the table when we decide these things? Sadal, you want to start us? Yes, absolutely. A digital mindset, going back to our Venn diagram, is to use data, technology, and algorithms in order to see new possibilities in the work that we do.

And a digital mindset also says that we have a clear understanding of the intersection of these themes as we make these decisions. Who should be at the table? Okay, hold on. A digital mindset is understanding our VenniDiDioc. And as a result of understanding that Venn diagram, the decisions that we make, the way in which we implement, the way in which we think about the consumers of this, there's a concept that I call dual digital transformation because consumers have to understand too that the world has changed

and they're consuming from a different model. But if you think about that, the digital mindset says that we have a baseline literacy. So going back to your important question of who should be at the table, if we all have baseline literacy, all of us, no matter what work we do, we have the ability to collaborate, we have the ability to understand

what computation comes our way, and we have abilities to be part of not only designing organizations, but also having membership as teams using this mindset to do work. So let me concretize this just a little bit. If you think about speaking a different language or being a second language user of a particular language, if everyone doesn't understand at some level, this new language that people are using, they can't participate. So everyone at the table has to have a baseline understanding of this intersection in order to not only make decisions, but also implement them in all corners.

So it's learning a new language. It's learning and operating in this new world. It's so interesting because I love scholars and content experts who study hard shit and have had to learn how to make it easy for other people. That's my favorite because to me, genius is never just the first half. If you can't explain this to a fifth grader, then I don't know that you really know what you're talking about. So I really appreciate both of you and how you tell the stories and make this digestible for us. So I'll be interested, Paul, I want to stop for a second and say to my kindreds in the qualitative world that we do feel dismissed sometimes and the things that matter to us, empathy, vulnerability, trust, those kind of things that get shoved into zeros and ones in a weird way or get overlooked. I feel you, but I also often see us resisting, learning the new language and saying,

this is not for me, I'm not showing up for this, and I'm going to cross my arms, across my chest, and get really armored up, and then I don't understand the basics of Spanish when everyone's speaking that now. And that's also a choice. So Paul, what do you see when I talk about that? Yeah, well, a couple of things come to mind. The first is that at the heart of a digital mindset, I think, is really humility and curiosity, right? Because you have to have enough humility to recognize that I don't really know what everybody's talking about here.

And to be able to ask the questions and want to learn the skills and be able to speak the language so that you can be effective. You also, I think, on the flip side, have to have the humility to know, maybe I think I really know what this is all about, but I don't. And we see that a lot too with people who think that I got an undergrad in computer science. And so clearly I'm digitally fluent. When having a digital mindset, as I said all mentioned, is much more than just knowing how to code and knowing some technical skills. It's about how to think and orient. So that humility is really important.

but curiosity is equally as important. And I think for those, you mentioned Spanish. So I learned speaking Spanish in high school, in large part because I was curious. I had a bunch of friends who were first generation Mexican immigrants, and they would speak Spanish. And I was curious about what they were saying. And you can sort of analogize that to the digital mindset in developing a digital mindset because you're seeing all these things about AI and machine learning,

and how data are going to transform our organizations. And you have to have a curiosity to be interested and wonder what's going on here, even if you're not going to be the person again who's writing the code or doing the data science. And you know, Paul and I are, I think, bilingual when it comes to qualitative and quantitative. We've both published in both.

Oh, yeah. Oh, yeah. So Paul and I are academic siblings. We went to the same PhD program at Stanford. That's how we met. well over 20 years ago. We've written together many, many times because of our common interests with technology and organizations. And we both have had to become bilingual, do the hardcore quant, as well as hardcore qualitative. My first book was predominantly qualitative, in fact. And so it's, for me, as I think about this, and even the qualitative work today, there are tools like natural

language processing that doctoral students are now thinking about and using because you get a particular type of precision and accuracy with these tools provided that there's human in the loop. You always have to help create these templates and be very vigilant to make sure that all the right meaning is processed in the right ways. But these are tools that even current doctoral students are thinking about. So it's important to be bilingual. And that means we need to not only broaden our understanding, but also play around with some of these technical things. And then the other last thing I'll say on this topic is that we are raising digital natives. The world, if you think about those who are in high school, many excellent high schools are requiring for ninth graders to

take statistics because that's part of one of our circles. It's an important element to their training and education. Many undergraduate institutions, whether you're a theater major or not, are requiring for people to take coding, Python, to learn programming because it's a critical language to know and understand. And so there's this generation that's building where they are digital natives. And we need to be able to meet them as well because they're the ones who are now entering the workforce. It's really important. It's huge. I mean, I was listening to my 17-year-old and my 23-year-old had this conversation over Thanksgiving and my 19-year-old niece was in it. They're debating algos. And I was like, what? And they were debating whose music Algo was best.

I wish I'll see Sidal right now. She's doing like the Arsenio Hall. I'm trying not to contain my... She's trying to not to lose it. That is so cool. Oh my goodness. Oh, no, that Algo sucks because what they're doing in it, I can't figure out how much paid sponsorship

they're weaving into the data processing. If your music is coming in because you're paying for it, is that really an Algo or is that editorial? I was like, what is happening here? Right. But see, for N, those are exactly the kinds of questions and conversations that we're hoping. that a digital mindset catalyzes and encourages because you don't need to know all of the data

points that are going into these models. You don't need to really, really understand the programming that went into building them. But you do need to recognize that there is something called an algorithm. It's doing something to our data and processing it and how people are looking at that data matters. And having those conversations just makes us better aware and able to participate in meaningful ways as the world keeps changing. I just love this conversation. I remember what I was going to tell you about Karen, my mentor, who studied Femmicide. She defended my choice to do a qualitative dissertation and become a grounded theory methodologist.

And she was a quantitative person. And I said, why are you so passionately defending me? And she said, because I use mixed methods a lot. And I said, why? And she said, because when I'm talking to legislators or I'm talking to folks, grantors about domestic violence and women being killed by partners, it would be so great if the only thing that mattered were the numbers. But unfortunately, the stories also matter. And it would be so great if I could just focus on telling the stories, but I actually need the

numbers too. And I've always thought about that. My entire career, I've thought about Karen's doubt telling me the stories matter because my quote that I use all the time are stories are just data with a soul and so stories matter but stories without numbers don't matter because it could be a profoundly moving story in an end of one

and then I also think about Paul I'm really lucky because I teach some of this to leaders when we're working with them about the first day of my PhD program Paul Rafeel was teaching a class on heuristics or some thing

that you only take classes in when you're getting a PhD, which you can't really spell it, and you Google it before you go, because you're not sure what it means. And he drew this circle of science, and down one side was deductive reasoning, and up the other side was inductive reasoning. And I feel like, and this,

I don't know if this is true or not, because y'all are giving me little Legos and I'm making them into chunky Duplo blocks, because that's how I think. But I wonder if not everyone is at the table, table, you don't get the full circle of science behind digital transformation. If the inductive people and the deductive people, the technology people and the conceptual thinkers, if we don't have a paradoxical table to start with, if digital transformation is really going to be what we want it to be.

Thoughts, start with you, Siddal? I think of it a little bit differently in that you want the deductive and the inductive reasoning in the same person. So rather than having a paradoxical table where people are bringing their perspective and we're trying to meld the digital mindset and the 30% rule that we advance in this work, it was years in the making when we arrived at this idea of everyone needs the baseline.

So I show up understanding the quantitative and the qualitative, and boy, there's plenty of it. And everyone else is there at the table with that baseline. And this is how cross-functional collaboration can happen best. It's the common language that everyone needs. So this means that we each have to level up. Those of us who are grounded theory people, we level up. Those of us who are quant, we level up.

Everyone has to level up and continuously. learn. That's the new world. That's the new world. Holy crap. She has gone Walt Whitman. We have to contain multitudes. What? Okay. So digital transformation. Okay, you know what this is taking me to? This is taking me to Linda Hill's quote that digital transformation is much more about people than it is technology. Do you all agree with that? It is, but technology is part of it too. Right. Okay, yes. It's incredibly important to not shy away or hide from the technology. We need to understand the technology. And for some people, it means you need to learn a little bit of coding.

You need to learn a little bit of stats. For others, it means to understand the contextual factors, the design factors, what experimentation looks like for fast transformation, what it means to upskill an entire workforce. So it's a comprehensive approach. But the technology is something that we can't hide from. Because at the end of the day, digital also means technology, right? Yeah. I agree with Siddal. I said also 100% agreement to what Linda Hill said.

And the reason is because I think so often we have this idea that digital transformation means we are using new technologies or putting new technologies into place. And the transformation is about putting those technologies in the way. the workforce somewhere. The real transformation is in our people. It's really about how we get everybody to be able to sit at that table and have that conversation and then leave the table with a vocabulary that they can use out in the world. And the tools allow them to do something once they know how to think and act and reason in new ways. So I agree with Siddal. We can't ignore the technology. But I think the technology is the easy part in many ways. ways. It's how we transform our workforce, how we transform ourselves to be able to think about

the possibilities that these technologies enable for us, and then make sure that people feel the trust and the autonomy to go out and do things with their new knowledge. That's what digital transformation is really about. Okay, we're coming to the end of part one. I just got to say, whoa. Okay, I'm leaving with two big learnings. We're going to come back for part two. I'm coming out with so many learnings, but let me just tell you my, oh, holy shit moments were the Venn diagram of data, computation, and modeling or algorithms, and that piece in the middle is digital transformation.

I think my biggest aha, and I'm looking at you, take a note, we're going to have to train everybody. Our COO is in the room with me right now, because she's like, I'm getting the first listen on this, that it is not just about coming to the table with who I am with an openness. It's a leveling up to hold, not tension just at the table,

but tension within us that's based on an understanding I'm the mixed methods person. And so then there is some accountability, individual accountability and organizational accountability, I guess in digital transformation. As a CEO, a founder, I need to skill up people,

even people who think they're just watching this happen. I need to skill up people. And then people need to open their hearts and minds to being able to hold new information along with their expertise and help generate ideas about what we're going to do with this data, this computation power. and these models. Is that right?

Yeah, I think that's right. That's so right. Yeah. If you're just watching it, you're watching it pass you by. Okay. So you need to be involved. All right. We're going to come back for part two. Sadal and Paul. I thought I knew.

Way more than I knew, which is so exciting. Okay. We'll be back for part two, y'all. Great. Be at the edge of your seat, your walking path. Wherever you're listening, the train ride. All right. we'll be back. So good, right?

It's so good. I'm learning so much. Every podcast we do has an episode page on Braynea Brown.com. Go there to find a link to the book. You can find an image. We did the Venn diagram that we talked about in the podcast. You can find it there. We'll be back for part two next week.

Stay awkward, brave, and kind. It's a very stretchy mindset. Bye y'all. Deerleet is produced by Brne Brown, Education. and research group. Music is by the sufferers. Get new episodes as soon as they're published by following Dare to Lead on your favorite podcast app.

We are part of the Vox Media Podcast Network. Discover more award-winning shows at podcast.com.