← The Curiosity Shop with Brené Brown and Adam Grant
The Curiosity Shop with Brené Brown and Adam Grant
Brené with Paul Leonardi and Tsedal Neeley on The Digital Mindset, Part 1 of 2
00:43:57
Loading summary
Transcript103 lines
- [00:07]
A
Hi everyone. I'm Brene 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, who are curious, who were 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 Sudhal 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 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. This message is brought to you by Apple Card. Each Apple product, like the iPhone, is thoughtfully designed by skilled designers. The Titanium Apple Card is no different. It's laser etched, has no numbers, and it earns you daily cash on everything you buy, including 3% back on everything at Apple. Apply for Apple Card on your iPhone in minutes, subject to credit approval. Apple Card is issued by Goldman Sachs Bank USA, Salt Lake City branch terms and more at applecard.com.
- [02:12]
B
Avoiding your unfinished home projects because you're not sure where to start. Thumbtack knows homes so you don't have to don't know the difference between matte paint, finish and satin or what that clunking sound from your dryer is. With thumbtack, you don't have to be a home pro, you just have to hire one. You can hire top rated pros, see price estimates and read reviews all on the app download today.
- [02:37]
A
Before we get started, let me tell you a little bit about both of our guests. So Sidal Neely is the Naylor Fitzhughs professor of Business Administration Senior Associate Dean of Faculty Development and Research Strategy and the Faculty Chair of the Christensen center for 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, Sadal spent 10 years working for companies like Lucent Technologies and the Form 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. This is a shorthand that's really cool to listen to on the podcast. So Paul Leidarti is the Duca 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, huh? This is a heart led heart. Full conversation and let's get started. Sidal and Paul, welcome to Dare to Lead. Thank y' all for being with us.
- [04:55]
B
Thanks for having us.
- [04:56]
C
It's great to be with you.
- [04:58]
A
So this started in the green Room of the Texas Conference for Women.
- [05:04]
C
It sure did.
- [05:05]
A
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.
- [05:17]
C
Oh yes, dear colleague.
- [05:20]
A
Yeah. So I had heard of your work and then when I met you in person, the Green Rooms at all, I was like, wait a minute, y' all need to be on the podcast.
- [05:30]
C
It was wonderful. It was actually wonderful to meet you there and to even see you in action, Brene, and how you move and touch people. It was extraordinary. And I'm so glad we met and so glad we're here today.
- [05:44]
A
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?
- [06:14]
C
I think a little bit. Paul, go ahead.
- [06:17]
B
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 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.
- [06:43]
C
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. The question is, how do we equip ourselves, how do we equip our organization, and how do we do it quickly?
- [07:05]
A
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?
- [07:28]
C
So 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. So it's a wholesale, radical transformation of a company's DNA.
- [08:15]
B
Yeah. And I just add to that that, yeah, 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.
- [09:06]
A
Okay, I want to stop for a second. They always joke, they call this the pause cast because not only am I a thinker and 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. 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 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'?
- [10:13]
C
All?
- [10:13]
A
When I say that's not been my.
- [10:14]
C
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 pres. Presence of data in extraordinary ways, if you think about another circle, is computing power.
- [10:45]
A
Okay.
- [10:46]
C
Computing power has allowed us to crunch data, to have so much data being processed in ways that was not possible 20 years ago.
- [10:57]
A
Can I raise my hand and ask a question real quick? Sure. Are these like Venn diagram, like Olympic rings? Are you making a target?
- [11:04]
C
Think of them as Olympic rings.
- [11:07]
A
Okay, got it. Okay.
- [11:08]
C
Three of them.
- [11:09]
A
Okay.
- [11:09]
C
So data computing power, 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 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.
- [12:17]
A
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 brenebrown.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.
- [12:39]
B
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.
- [12:50]
A
Just tell us the hard way and then tell us the easy way.
- [12:54]
B
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 is 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 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 could 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. The 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.
- [14:14]
A
Interesting.
- [14:15]
B
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.
- [14:33]
A
So under my model circle and my Venn diagram, is that where algorithms belong?
- [14:38]
B
Absolutely.
- [14:39]
A
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?
- [15:07]
B
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 they're 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.
- [16:23]
A
Right?
- [16:24]
B
Yeah, potentially. Lots of stories about how bias creeps into these models, and we discuss a fair amount about that in the book as well.
- [16:31]
A
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 in 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.
- [16:53]
C
Exactly.
- [16:54]
A
I don't understand what's happening here. Who developed the algorithm Exactly. And what data are you putting in?
- [17:00]
B
Can I tell a fun, quick little story about that?
- [17:03]
A
You know, I love a story.
- [17:04]
B
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 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. Wow.
- [18:49]
A
I think such a cautionary tale. Because I do also think operationalizing those contributions is really hard.
- [19:01]
B
It is.
- [19:01]
A
And so I think when you start building models, you're like, 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.
- [19:13]
B
Yeah. And I think it points to one of the dangers. And Sidal 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 on as much authority as data that are presented in numbers. And I think that's where a lot of leaders really go wrong.
- [19:43]
A
It's really funny. I had, when I was in my PhD program, she's actually my MSW program is where I met her. Karen Stout. She's since passed away, but she studied femicide, the killing of women by intimate partners. And there was a lot of pushback for me in my PhD program. Cause I had the first qualitative dissertation in my program and oh God, people were pissed.
- [20:04]
B
Really?
- [20:05]
A
Oh, yes. To the point where Barney Glaser, who developed grounded theory methodology. Yeah, he was my methodologist on my dissertation committee.
- [20:12]
B
You're like, I need some legitimacy here.
- [20:14]
A
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?
- [20:46]
C
The qualitative, the legitimacy of ground legitimacy, qualitative, inductive versus deductive.
- [20:53]
A
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?
- [21:17]
C
I think that's completely correct. And that's why when we think about the digital mindset and the way we've conceptualized 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.
- [22:41]
B
Is gun reform a matter of law or culture?
- [22:44]
A
There are more people who are gun.
- [22:46]
B
Enthusiasts today who believe guns are a good thing for them and the safety of their homes, regardless of whether the public health data supports that or not. I'm Preet Bharara, and this week the president of Everytown for Gun Safety, John Feinblatt, and Second Amendment scholar Adam Winkler, joined me on my podcast Stay Tuned with Preet. We discuss the state of gun violence in this country, the biggest mistakes Democrats have made in seeking reform, and why the best prospect for meaningful change may take place outside the courts. The episode is out now. Search and follow Stay Tuned with Preet wherever you get your podcasts.
- [23:21]
A
Hello, Daisy speaking. Hello, Daisy. This is Phoebe Judge from the irs. Oh, bless, that does sound serious. I wouldn't want to end up in any sort of trouble this September. On Criminal We've been thinking a lot about scams over the next Couple of weeks, we're releasing episodes about a surprising way to stop scammers.
- [23:42]
C
The people you didn't know are on.
- [23:43]
A
The other end of the line.
- [23:45]
C
And we have a special bonus episode on Criminal plus with tips to protect yourself.
- [23:51]
A
Listen to Criminal wherever you get your.
- [23:53]
C
Podcasts and sign up for criminal plus@thisiscriminal.com.
- [24:00]
A
Okay, can I ask a mashup question that's probably like a seven hour answer.
- [24:06]
B
Go for it.
- [24:06]
A
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? Sidal, 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 fricking 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? Sodal, you want to start us?
- [25:18]
C
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?
- [25:49]
A
Okay, hold on. A digital mindset is understanding our Venn diagram.
- [25:54]
C
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 conc 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. 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.
- [27:45]
A
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. 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 gonna 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?
- [29:11]
B
Yeah, well, a couple 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 Sidal 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 on here, even if you're not going to be the person again who's writing the code or doing the data science.
- [30:50]
C
And you know, Paul and I are, I think, bilingual when it comes to qualitative and quantitative. We both published. 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 literacy 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 required 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.
- [33:13]
A
It's huge. I mean, I was listening to my 17 year old and my 23 year old have 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 could see Sidal right now. She's doing like the Arsenio Hall.
- [33:34]
C
I'm trying not to lose. I'm trying to contain you, not to lose it. That is so cool.
- [33:38]
A
Oh my God. They're like, oh no, that algo sucks. Because what they're doing in is I can't figure out how much paper 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?
- [33:53]
B
Right? But super Nate, 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 that'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.
- [34:32]
A
I just love this conversation. I remembered what I was going to tell you about Karen, my mentor who studied femicide. 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 Stout telling me the stories matter because my quote that I use all the time, our stories are just data with the soul. And so stories matter, but stories without numbers don't matter because it could be a profoundly moving story in an n of 1. 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 Rafel was teaching a class on heuristics or something 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. 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 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. You don't get the full circle of science behind digital transformation. If the inductive people and the deductive people, if 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, Sadhal.
- [36:49]
C
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 could 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.
- [37:56]
A
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 y' all agree with that?
- [38:16]
C
It is, but technology is part of it too, right?
- [38:20]
A
Okay, yes.
- [38:20]
C
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.
- [38:34]
A
Okay.
- [38:34]
C
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.
- [38:58]
A
Right?
- [38:59]
B
Yeah, I agree with Sidal. I said also 100% agreement to what Linda Hill said. And the reason is because is I think so often we have this idea that digital transformation means we are using new technologies or we're putting new technologies into place. And the transformation is about putting those technologies in 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 to 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 Siddharth, we can't ignore the technology. But I think the technology is the easy part. In many 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.
- [40:12]
A
Okay, we're coming to the end of part one. I just gotta say, whoa. Okay. I'm leaving with two big learnings. We're gonna 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 gonna have to train everybody. Our COO is in the room with me right now. Cause 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?
- [41:49]
B
Yeah, I think that's right.
- [41:50]
C
That's so right.
- [41:52]
B
Yeah. If you're just watching it, you're watching it pass you by.
- [41:55]
A
Okay.
- [41:56]
B
So you need to be involved.
- [41:58]
A
All right, we're going to come back for part two. Sidal 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.
- [42:12]
B
Okay.
- [42:13]
A
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 brenebrown.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 with a very stretchy mindset. Bye, y'. All. Dare to Lead is produced by Brene Brown Education and Research Group. Music is by the sufferers. Get new episodes as soon as they're published by following Dear Delete Lead on your favorite podcast app. We are part of the Vox Media Podcast Network. Discover more award winning shows@podcasts.voxmedia.com I just gotta get out most days, you see I like walking around it's good for.
- [43:25]
C
Me could you tell me where we.
- [43:27]
A
Could go eat Take me to the good times I just gotta get out most days, you see I love looking around Is good for me could you tell me where we could go eat, Take me to the good town.