The StoryBrand Framework: How Design Thinking Powers Data Science

Bill Schmarzo
Chief Innovation Officer, ex-Hitachi Vantara
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Podcast Topics

  • The StoryBrand Framework in the context of data science
  • Design thinking in data science
  • How to value data as a corporate asset (Current GAAP accounting standards do not permit data (intangible assets) to be capitalized on the balance sheet).
  • Chipotle use case - improving same store sales using data and analytics
  • We’ll talk about the MMMM of Digital transformation
  • Everything Bill learned about management he learned coaching little league baseball.


Joe Toste (00:00):

This episode is sponsored by Nagarro. Nagarro is an 8,000+ digital product engineering company that excels at solving complex business challenges through agility and innovation. We call it thinking breakthroughs. Thinking breakthroughs is how we've helped industry leaders to embrace digital and accelerate technology-led innovation. Our clients range from startups to Fortune 500 companies like Verizon, Honeywell, Siemens, Lufthansa, Google, Intel and many more. Our goal isn't just to be another vendor but a long-term strategic partner and what really separates us is how we see the changing and evolving world. The challenges that companies are facing are more unique and complex than ever before, especially with the technology disruption happening across the globe today and is this technology disruption that our clients look to us to find solutions in a forward-thinking, agile, caring, extension-of-your-team way that can help transform, adapt and build both the now and the future.

Joe Toste (00:57):

At Nagarro, we care. Caring is our superpower. It drives us to deliver excellence to our clients. It makes us responsible and it makes us better colleagues. It all begins with a conversation. You can email me at joe.toste@nagarro.com or message me on LinkedIn. For all information on Nagarro, check out nagarro.com. That's N-A-G-A-R-R-O dot-com. Let's talk about the possibilities. Now, after you, James.

James (01:27):

Thanks, Joe, and welcome everyone. You're listening to Season Two of TechTables, a Q&A podcast dedicated to interviewing industry leaders from across the globe ranging from startups to Fortune 500 companies, mixing it up each week with topics ranging from design and digital product engineering to AI and industry 4.0. Let's do this, Joe.

Joe Toste (01:49):

Thanks, James. I'm super excited today as we shift our focus to all things data and innovation. Huge thank you to Bill Schmarzo for taking time to come on the show and meet with me today. In today's episode, we'll cover the story brand framework and the context of data science, design thinking in data science, how to value data as a corporate asset. Current GAAP accounting standards do not permit data, intangible assets to be capitalized on the balance sheet. We're going to break that down.

Joe Toste (02:13):

We're going to talk about Chipotle use case for improving same store sales, using data and analytics and then we're going to walk through the MMMM of digital transformation and everything Bill learned about management, he learned coaching little league baseball, so we're going break that down, but that's quite enough for me. Without further ado, I'm thrilled to welcome Bill Schmarzo, chief innovation officer at Hitachi Vantara. Well, Bill, welcome back to TechTables. Super excited to have you on today.

Bill Schmarzo (02:41):

Hey, Joe, thanks for having me. This is great.

Joe Toste (02:43):

Awesome. I was on your Twitter feed and actually really love Donald Miller and story brand and I caught a tweet that you had. I never thought about using story brand's framework for the art of thinking like a data scientists. I know you have a visual that I'll add on to the show notes, but can you walk the audience through story brand's framework, starting with the hero in the context of thinking like a data scientist?

Bill Schmarzo (03:14):

What I found interesting about the story brand was how it's simplify telling a story, right? You got a hero. You got to challenge. You find a guru. He gives you guidance. You go together to battle or he guides you through battle and then either you are successful or you're not. The thinking like a data scientist process is similar in the sense that we find that data science is more effective when people can put themselves in the middle of the data science story. Data science as a one-off outlier doesn't get people excited, and if you can't get people excited, you're not going to get adoption.

Bill Schmarzo (04:03):

What I liked about the story brand format was that we could use the thinking like a data scientist process to put somebody right in the middle of solving a problem using data and analytics, using data science. That's why it seemed like just a natural to me because everybody wants to be a hero. We go to the movies. We want to be Han Solo, that's who we are or Indiana Jones or anything else that Harrison Ford wants to do. We all run into challenges in our life. Now there are interpersonal challenges, which you can talk about separately because there's a different way to have a conversation about those, but we all have business challenges. We're trying to reduce unplanned operational downtime. We're trying to increase customer retention or customer satisfaction or net promoter scores, whatever it might be and there are adversaries.

Bill Schmarzo (04:55):

What somebody needs is a guide, somebody sits on their shoulder and says, "Hey, I've experienced this. You might want to try this. You might want to do this." The story brand mapped very well to somebody who's trying to go through a thinking like a data scientist process, meaning you'd go through that process because you got a problem to solve. I like to think of myself as a very large Yoda sitting on their shoulder whispering, "Hey, you might want to try this. You think about this." You ask people these questions, "Have you bought in, brought in these diversity of perspectives?" Anyway, long-winded answer for your question, sorry.

Joe Toste (05:30):

No, this is great. I just want to recap this for the audience. We have the hero, if not familiar, so we have the hero. We'll call it the CIO or chief data officer who owns the company's data and then there's the challenge, senior management, leadership, etcetera demanding that the hero deliver on the promise of data monetization. Then we have the guide. The hero meets an experienced mentor who can help meet the data monetization mandate. The guide gives a hero plan. They put into action. They see the results and it really leads to their success or failure. I just love this and I'll have this visual up that I grabbed from Twitter that I thought was really, really great. I love how you broke that down. I think it really-

Bill Schmarzo (06:18):

Hey, Joe, can I say one thing too?

Joe Toste (06:19):


Bill Schmarzo (06:20):

The hero doesn't necessarily need to be just the CIO or the CDO. It can be anybody in the organization who feels empowered to go on this journey. That to me is what makes this really interesting. It isn't about empowering the senior executives. Hell, they already got too much power. It's about, "What are you doing down to the rank and file to frontlines of customer engagement and operation of the business? How do those people become heroes?" To me, that's where it becomes most interesting. It's not interesting at the ivory tower, at the top of the puzzle palace. It's more interesting what the frontline people can really do with this.

Joe Toste (07:06):

That's really a great insight. I really liked that word empower, especially with the people on the frontline. Definitely right, don't have to be the C-suite to leverage the hero. This actually leads really well into the next question I have for you. I know your huge proponent as far as design thinking goes. We talked about it on our pre-podcast call. Talk about empathy for your customer, how you think about customer journey maps and how you think about the decisions that have the highest value.

Bill Schmarzo (07:39):

Surprisingly, I think the data science process starts with empathy for your stakeholders. You could build the most powerful neural network in the world, but if you don't understand where and how it's going to be used in situation, why bother? Because at the end of the day, adoption is everything. There's two aspects to design thinking that I really like. One is I like the empathizing aspect, the customer journeys mapping, the stakeholder mapping, that persona development because it allows the data scientists to really walk in the shoes of the people who are going to use your analytic outcome, right?

Bill Schmarzo (08:18):

If you can't provide analytics in a way that's understandable and consumable by your target audience, you can't empathize with the problems that they have and how data science is going to help solve that and you've already lost a data science map. Start number one is all around that empathic conversation and really make sure you understand the problem you're going after, what decision they're trying to make, identifying, validating value prior to the decision, etcetera, etcetera, right? The second part of design thinking, I think, is equally powerful and that is for the ability to bring in diverse perspectives to move from an environment where you're compromising on the least worst option, but you're synergizing towards the best option and design thinking allows you to bring together and synergize all those different ideas, to move from, again, a mentality of compromise to mentality of synergize.

Bill Schmarzo (09:14):

When you do that, when you give everybody a voice, when everybody has a chance to be heard, when all ideas are worthy of consideration, which by the way doesn't mean all ideas are worth a damn, but they at least get a chance to be heard, what happens is you drive adoption. To me, that's so critical. This is where so many data science projects fail. They don't feel because the models aren't very good. They fail because they don't drive adoption. Many times adoption is caused by passive-aggressive behaviors because you haven't brought all the stakeholders in. They haven't had a chance to have their voices heard. You haven't had a chance to break apart, bend and blend of different perspectives into something, synergize into something more powerful.

Bill Schmarzo (09:54):

While I love design thinking from an empathy perspective to really understand what it is a user is trying to do and how data science can help them, equally powerful is the ability to design thinking to build those coalitions, to leverage diversity of perspectives to not compromise but instead to synergize.

Joe Toste (10:15):

That's really great. I think there was two things that stuck out. The first part of empathy I always think about is Jeff Bezos. I know he's got the classic story where he always keeps a chair empty in any meeting he has to represent the customer. No one sits in the chair. I think that's really great. That's a really, really great story and super important for everyday business people, data scientists to really put yourself in the shoes of the customer.

Bill Schmarzo (10:45):

Let's talk about the customer perspective, right? If there's any term that the companies pay lip service to, it's being customer centric. Everybody says they're customer centric, blah, blah, blah, "Oh, yeah, we talk to our customers." Well, you talk at them, but you don't listen. With a lot of times in product-centric companies, we'll ask question about, "How are you using the product?" Here's the one I love, "What do you need in the product?" as if product management has just advocated all the responsibilities to the customer. "Oh, the customer said they wanted this." That's a bunch of BS.

Bill Schmarzo (11:24):

Here's what you need to do. This is what empathy is all about in this design thinking. You got to understand what the customer is trying to accomplish or you need to think through what the customer is trying to accomplish, not what they're trying to do, "What is your endpoint mean?" They may be doing all kinds of crazy S stuff because they don't know better. If you just replicate what they tell, you just pave them a cow path, paving a cow path and not innovation. My favorite innovation story has to do with cereal in the morning. I'm a huge Captain Crunch fan. When I wake up in the morning, I want to get it, oh, my boy, Captain Crunch, big giant bowl of Captain Crunch, right? Nothing tweaks me off more than I go to the pantry, open the box, open the drawer up and there's either no box of the Captain Crunch or you shake it, there's just crumbs at the bottom of it.

Bill Schmarzo (12:13):

How do I today solve that Captain Crunch problem, right? How would you traditionally say, "Well, how do you solve that problem?" Well, I got to get dressed. I got to find the car keys. I got to drive the car down to the grocery store. I got a park it. I got to go at the back of store and find my Captain Crunch. I stand in line. I'm in an express lane. There's always somebody ahead of me who's got more than 10 items. I stand there longer than I need to. Then I have to pay and drive home, blah, blah, blah, before I have my Captain Crunch. If we're going to take that process and replicate it and pave the cow path, maybe we could get a faster car or we move the cereal easier to find or such. Maybe you walk in and you scan your Captain Crunch and you walk out. That's all great stuff. That's, digitalizing the journey.

Bill Schmarzo (12:58):

I love this instead, right? What if the model, I opened this thing, I shake this thing, it's half empty and I go, "Hey, Alexa, two boxes of Captain Crunch ASAP, please," he next thing I know, within 30 minutes, there's a drone dropping off two boxes of Captain Crunch on my door or maybe even better yet somehow they figured out how when the box gets down to a certain point, there's sensors in the box and it sends me automatically? To me, understanding the customer journey is really critical, not just the customer themselves.

Bill Schmarzo (13:31):

The reason why it's important, as you go through, if you map out the customer journey, you'll find points of high value in that customer journey, things that customers really are on the journey for, but you're going to find all these inhibitors of value. The surprising thing is you can monetize inhibitors. If those inhibitors that you can monetize by getting rid of, using technology and customer experience and analytics, whatever you got to really eliminate those inhibitors. When an organization says they're customer centric, first off, it's typically Bs, but more importantly, they don't take the time to understand what the customer is trying to accomplish and the journey they have to go on today and then decomposing that journey to figure out, "What are the parts that I can get rid of entirely?" Don't pave the cow path, reinvent it.

Joe Toste (14:18):

That's really, really great. The third question I have for you, there's a very interesting article titled What is Data Value and Should it be Viewed as a Corporate Asset? In the article, the author writes, "The research firm Gartner predicts that by 2022 companies will be valued on their information portfolios, current GAAP, generally accepted accounting principles, accounting standards do not permit data which is an intangible asset to be capitalized on the balance sheet which currently can lead to considerable differences between book value and market value of a company. If a tech company wants to IPO, it can lead to valuation pricing issues." The author listed did four frameworks, one of which was yours, the prudent value approach?

Joe Toste (15:04):

I dropped this question in because I like accounting and investments and things like that and then seeing your approach tied in really nice. You said the prudent value approach is basically a way to measure or value data based on the extent it is used or leveraged to make decisions on advancing key business initiatives that align with the company's overall strategy. Talk about the benefits of the prudent value approach to valuing one's data.

Bill Schmarzo (15:32):

When you do this, you have to choose obtaining two frames. The traditional frame that we get thrown at, Joe, was one that you started off with. "I want to put data on my balance sheet. I want to figure out how I create GAAP rules that allows me to capitalize data." Let's be real honest, having data is no value. In fact, just having data is not only a cost, but it's a potential liability. The possession of data is not like the possession of gold or the possession of boxes of Captain Crunch, right? It is a cost, you got to store it and a potential liability if somebody hacks it. The accounting perspective is what gets people in trouble.

Bill Schmarzo (16:17):

Accounting, by the way, is a valuation method. It's based on value in exchange. It is the value of an asset, the value of this chai tea latte from Starbucks. It's $4.45. Why? Because that's what I paid for it, right? There's a value in exchange. Accounting is a value ... By the way, it's going to show up on their balance sheet or their income statement and revenues, blah, blah, blah. Accounting is a value in exchange methodology. The proven methodology that I recommend doesn't look at it from an accounting perspective, but looks at it from an economics perspective. You're going to say, "What's the difference, right?"

Bill Schmarzo (16:53):

Well, economics is a value in use approach. From an economics perspective, having data has no value but using data to create value is where the value is. "Using my data to improve customer acquisition by 2%. Using my data to improve cross sell by 3.5%. Using my data to reduce unplanned operational downtime by 6%," that's where value occurs. It's the value in use conversation. The first thing that I have to do with organizations is transition them from an accounting perspective to an economics perspective, which most organizations are really geared more around accounting than around economics.

Bill Schmarzo (17:38):

The other one though is you got to move organizations from as data centric, "We want to be a data-centric organization." Really, no, you don't. You don't want to be data centric. You want to be a value-centric organization. You want to use that data to create value. Now this is where it gets interesting, this is where design thinking kicks in, because if you look at the data, how do you separate signal from noise in the data? Well, it's all dependent upon the use case. In some use cases, "This particular signal data is very valuable and I can monetize it and everything else is noise," but other use cases, "This is valuable and this is noise."

Bill Schmarzo (18:15):

You are forcing the organization. You take this prudent approach to look at a use-case-by-use-case basis. I would argue if we had a lot more time and a lot more whiteboards here that use cases are basically aggregations of decisions around a common subject area, their decisions. If I focus on a use-case-by-use-case basis, now I can bet on my use cases. I know what data I need, what signal of data I need, what analytics I need in order to drive value around that use case. Not an accounting conversation where possession is sufficient. It's an economics conversation where you have to use the asset to drive value.

Joe Toste (18:56):

That's really great and I'm curious, what are some of your favorite use cases that are publicly out there today?

Bill Schmarzo (19:02):

Wow, where does one stop?

Joe Toste (19:05):

Let me help you lead. It was a little bit of a leading question. I saw Hitachi working with Disney is one example. I love Disney, so I had to bring it up.

Bill Schmarzo (19:15):

Think about all the things that Disney is trying to do. I can't talk about what we're doing at Disney, but I can talk generally about all kinds of different companies. Now think about there are companies who are trying to value their customers. Looking at customer acquisition, customer retention, customer cross sell, customer net promoter scores. There's a lot of stuff you can do around customers. There's obviously a lot of things you can do around product and product performance, predictive maintenance. There's a lot of things to do at a system level, unplanned operational downtime, reducing inventory costs, reducing mean time to repair, first time fix problems.

Bill Schmarzo (19:58):

From a university perspective, you're looking at things, for example, from student acquisition. Freshmen retention is a huge problem for universities by the way and it's really been exposed even more during this pandemic. University don't make any money your freshman year, they don't make money off students until their sophomore junior year, right? Retaining students is really important. Look at healthcare, think about healthcare, think about how we use data and analytics. We could create a score on every person in America and their likelihood of catch COVID-19. We could create a score in every person's likelihood if they catch it to die from it. We could also probably create a score that measures their likelihood to spread it.

Bill Schmarzo (20:41):

There's almost an unlimited number. In fact, there is an unlimited number of opportunities where and how you can use data and analytics. The only thing that boxes you in is the problems of creativity and the problems you want to go after. You can darn yourself almost anything with data and analytics, but you've got to take a holistic approach. It isn't for the most part about the data and analytics that we talked about early on, Joe. It's about this empathy. Understanding your customers, understanding their journey, not just their persona, their journey, and then figuring out how does data and analytics help them achieve what they're trying to achieve more successfully.

Joe Toste (21:16):

That's really great. I'm curious. You talked about Captain Crunch earlier. We talked about pre-podcast call. I know you love Chipotle. We talked about that. Same thing, I'm curious if there's some other use cases out there when we think about the full journey that you can maybe tell, if there's a specific case, not Hitachi, but a specific case that you really like that maybe you've read about on a blog or listened to on a podcast. I'm curious if there's a specific case you really, really like.

Bill Schmarzo (21:48):

Oh, wow, good question. I use the Chipotle one in my class because it's all publicly available data, they're a public company. We take a look at in the class at how we use data and analytics to improve same store sales and all the different use cases support that and the data and analytics and the research paper of University of San Francisco is all built around that. There's all kinds of creative ways that you can solve these problems. Joe, I'm not going to answer your question in a way that's satisfactory to you, but I will answer it this way. I believe that any question that you can ask about what happened, you can likely also predict what's likely to happen.

Bill Schmarzo (22:35):

What I mean by that is sometimes our customer engagements, start by trying to understand, "What are your most important questions?" "No, I need to figure out now what were revenues and profits last quarter. Well, what if we could predict revenues and profits next quarter? I need to know how many customers have tried it last month, but what if we could predict?" Again, the opportunities are almost boundless. That's part of the problem with this data and analytics isn't you failed because of lack of use cases, you failed because you have too many and you try to attack them all. You've got to have a process that allows you to identify, validate, value and prioritize to create a roadmap for how all of these different use cases to build on each other.

Bill Schmarzo (23:20):

Maybe there is one example of analytics that impresses me more than anything else and I think it's what's going on with Tesla. I think what Tesla is doing with autonomous vehicle is game changing. What I mean by that is what they're doing and Elon Musk had this quote that he believes that when you buy a Tesla, you're buying an asset that appreciates, not depreciate through usage. Throw that through your accounting GAAP methods, right? We buy assets and they depreciate. We have depreciation schedule and depletion schedules. It shows up on our balance sheet, blah, blah, blah. Then Elon Musk now here says, "Bullshit. No, this is not a traditional asset. It doesn't depreciate. It appreciates. It gets more valuable the more you use it."

Bill Schmarzo (24:04):

Now think about that for a second. Think about how game changing that is, that by virtue of the 600,000 Tesla's rolling around the world here, they're learning through their embedded AI capabilities, their experience and all these different situations. Water puddles and passing cars on the road and pedestrian safety and they're learning and they're passing that learning back to the big giant Tesla cloud in the sky. They're learning and then propagating all of learnings back down to those cars every day. Every day those cars are getting more reliable, more effective, more efficient, more predictive and safer, every day. I think that use case is so powerful and I guess it just blows my mind. That's how few people have really caught on to the game and he's got there.

Bill Schmarzo (24:56):

He's building an economic moat like no one's ever seen because he has all this data about what makes these cars run more efficient and he's got a model around. To me, if there's anybody out there who I think is showing the path for the power of data and analytics is what Elon Musk is doing with Tesla to create these autonomous, these composable, reusable, continuously learning and adapting analytic assets.

Joe Toste (25:24):

I think the Tesla example is really great and super fascinating too. I think it's funny how these two polarizing sides, you have this old-school mentality of analysts think Tesla is a car manufacturer and you have this new age of investors and other folks who are saying how much value there is in the data. Well, clearly the stock is going to the moon right now.

Bill Schmarzo (25:46):

That's a good example. It's not just the data that he has. It's the data and how he uses it to make new cars, right?

Joe Toste (25:55):

That's right.

Bill Schmarzo (25:56):

He's collecting all kinds of data. I believe that he uses video not LIDAR for specific reason. Those cars driving around probably know more about the US economy on a day-to-day basis than any other organization in the world. They drive by malls. They see how busy malls are. They drive by schools. They say who's ... They're seeing everything as they drive around. He's collecting this wealth of data and the guy's a fricking genius. He's going to do all kinds of great things to monetize that data. He's only limited by his own creativity and we know his creativity is pretty boundless.

Joe Toste (26:31):

You brought up a really, really great point. You're right. It's not just about the data. The example I think we talked about in the pre podcast call that was really great was, if you're driving a Tesla and you have a car accident, typically in an old-fashioned car, normal car, only you potentially would learn from that, but with a Tesla, everyone learns from that as the data goes back to headquarters, and then you get the update software update next time around.

Bill Schmarzo (26:59):

Continuously learning and then continuously adapting with minimal human intervention. That's the power of AI. That's the power of what we could do is, again, whether it be cars, whether it be products, whether it be policies or processes, this theme, autonomous concept, would work for policy decision. Most governments make horrible policy decisions, right? They're overly generalized and they're fixed in stone. It's like etched in a series of if then statements, right? They don't adapt at all. What if you had an AI policy process that was continuously learning about what's going on with COVID and economic ramifications and it was constantly tweaking, so that instead of having these generalized policy decisions, we had very individual policy decisions, individualize welfare programs, individualized health care programs? That's not a pipe dream. That's a fricking reality if we only would do it.

Joe Toste (27:57):

That's really great. One of the last questions I have for you, we're talking about digital transformation. I know it's definitely a buzzword phrase, but one of the meanings behind is obviously it's like, "Make me more money," right? I'm curious how has the "Make me more money" digital transformation evolved in the last four to five years through your lens and then even this micro window through COVID where everyone thought they had a digital transformation plan and then COVID smacked them in the face and blew up their plan? Just curious behind that.

Bill Schmarzo (28:36):

That's an interesting point, Joe. I think the COVID situation may have done more to make people realize what digital transformation is really about. A lot of people think digitalization is digital transformation, right? Taking a current human-centric process and digitalizing it with a phone or a tablet or a sensor, they think that's digital transformation when it's not. I would argue that there's a certain percentage of you talk to CIOs, I think like 37% say their digital transformation is done. I'm like, "You don't know what you're doing if you think it's done." Digital transformation never ends, right? We're in an economy, we're in an environmental situation, we're in a society situation, we're in a technology situation where change is the only constant. Things are continuously changing.

Bill Schmarzo (29:31):

When I think about digital transformation, especially I think about my role as chief innovation officer, I think about it from two perspectives. How do I leverage analytics, AI, machine learning, deep learning, etcetera to build assets that are continuously learning and adapting with minimal human intervention? Assets, policies, products, processes, right? How do I leverage that from the technology side? Then how do I marry that with empowered humans on the frontline who are empowered to make change who have maybe this little AI Yoda on their shoulder, whispering in their ear things that they should be trying to do that, "Hey, we might want to look at this"?

Bill Schmarzo (30:15):

When you combine this continuously learning aspects of AI with a continuous learning and adapting empowerment of humans, that's when innovation happens. Innovation happens when there's ambiguity. Innovation happens when there's diversity, when there's tension, when there's friction. What's going on in our country right now shouldn't be looked upon as being evil. It's what's going to get us to the next level, right? How do we embrace healthcare, social justice, the economy, right? There's not an or. We're not going to do health care or social justice or economy. We got to do all three. It's that synergizing of all the three that's going to happen when you bring AI, machine learning, continuous learning and adapting that can help to humans with empowered frontline people who have been empowered to learn and adapt as quickly. That is where innovation happens.

Joe Toste (31:16):

I love that. I think I said that was going to be one of my last questions, but I failed.

Bill Schmarzo (31:21):

Ask away. I got more coffee here.

Joe Toste (31:25):

I love it. I love it. This this really is one of my last questions. I love this on our pre-podcast call. There's so many things we have in common Chicago Cubs, Chipotle, Captain Crunch, basketball, you played basketball in college, Golden State Warriors. The list goes on. I love it. I love it. One of the things you said that took note of was that everything you learned about management you learned on a basketball court. Can you unpack that?

Bill Schmarzo (31:53):

Yeah. Everything I've learned in management, I actually learned coaching little league, I said or coaching basketball-

Joe Toste (32:02):

That's right.

Bill Schmarzo (32:03):

Right? What happens, I found to be a good coach. I had to get down to the individual level and understand everybody what they could do, what their skill set was, what they're really strong at, what they really weren't not strong at and put them, not only help to develop their strengths and cover their weaknesses, but to always put them into situations they could be successful, but I also had to train them, coach them is a better term than train them, to not be afraid of failure, to go ahead and push. "Now we're going steal second base, right? Here comes the sign. We're going to steal second base."

Bill Schmarzo (32:42):

You can look a tear in their eyes. "I can't steal the second base." "You know what? Let's give it a try, right? Let's give it a try. Let's go steal second base, right?" Gets thrown out. "Okay, what'd you learn?" "Well, I didn't get a very good jump. I wasn't watching." "Good, good, good, good. That's what learning is about, right? Next time we do this, you get a good jump and you get that extra two steps and you steal second base." Everything I've learned about management and about winning starts not with the winning, it starts with the individuals you have at individual level understanding their skillsets, understanding their diversity and embracing that diversity to create something more powerful.

Bill Schmarzo (33:25):

Whenever we picked our All-Star teams in basketball in particular, remember before, you had the tryouts. I'd always go to the parents and say, "I got to tell you something right now. I'm not going to pick the 10 best players. I'm going to pick the 10 players who play best together. I need somebody who can play defense. Maybe they can't shoot for lick, but I might need somebody who I can stick on the other team's leading scorer. I need people who can rebound. I need people who can bring the ball up the court and pass and set up. I need shooters, but I don't need 10 shooters, right?" I'm always thinking about the Chicago Bulls and Michael Jordan's year, right?

Joe Toste (33:59):

I was just thinking about that.

Bill Schmarzo (34:00):

Michael Jordan is phenomenal, but he had around him all these like Dennis Rodman. Dennis Rodman was just a beast on the board. Everybody needs a beast on the board who's going to go after every rebound. Even if he's knocking his own players out of the way, he's going to get that rebound. I guess, Joe, everything I've learned about management I learned in coaching youths because also the final aspect of learning from this is that you will quickly learn, you better be humble because you're going to learn along the way that you don't know shit and that you're going to have to be as adaptive to those students, to those players, to those people you're working with, you're going to be as adaptive as they are to you.

Bill Schmarzo (34:39):

It's a great learning experience. I love coaching I think it's prepared me for my current role and I hope the team I've got at Hitachi Vantara, I hope I'm a good coach to them as well.

Joe Toste (34:50):

I love that. That's right, little league. I was thinking little league, but since I coach basketball, I put that down. I love that example of Dennis Rodman. Last year on the JV basketball team, where I'm an assistant coach, we had a guy Quran and he had that Dennis Rodman like couldn't shoot, but he was so strong and he played on the football team. He would just bulldoze his way in and he would bring that that Draymond Green, Dennis Rodman fire. I loved it. I loved it. It was so great. You're right. You don't need 10 shooters and the whole thing. As we wrap up, I don't know if you know, you're pretty special, this is the first recording for Season Two.

Joe Toste (35:39):

For everyone listening, I know I sound a little bit rusty today, I'm getting back into the groove of doing the podcast. I was thinking about, "Hey, how do I wrap this up?" Last season for the first season, I had a different ending, but this one I'm going to do a little bit different, a little bit unique, unique for this season. I took it, I guess it's maybe not that unique, but I took it from a podcast called Invest Like The Best by Patrick O'Shaughnessy. Do you listen to that podcast, by chance?

Bill Schmarzo (36:08):

I've heard of him, yes.

Joe Toste (36:10):

It's really, really good. That guy is really smart. He always ends with, "What's the nicest thing someone has done for you?" I've decided for Season Two to end with, what's the nicest thing someone has done for you, Bill?

Bill Schmarzo (36:26):

I only can pick one. I'm a blessed person. I've had a lot of people throughout my life when I was young and all the way through who have always done great things for me. What's the nicest thing someone's ever done for me? Well, my wife married me. I'd asked her three times, but she finally married me. We've been married 40 years now. I guess that was the right choice for somebody. I think my friend, Mouwafac Sidaoui who got me into teaching at University of San Francisco, that sticks out as one. I've had high school teachers who have supported me in endeavors that I didn't probably deserve, but they pushed me forward on it.

Bill Schmarzo (37:08):

I would be remiss if I didn't say my mom. I know that's not the question you asked, but if I think of somebody who has formed my life, who's constantly been a positive form of my life, even though she wasn't afraid to put me in my place when I needed to. Joe, probably a really crappy answer to start your season off, but I've been very fortunate, even today. I got so many people who send me such kind words of support and positive things in a world where it seems like everything's falling apart. There's a lot of great people out there. Sorry, I didn't give you a better cooler answer, but that's the best I can do.

Joe Toste (37:47):

No, that's a great. That's great. I love it. Where can people find you, Bill? Where do you hang out, LinkedIn, Twitter? What's your jam?

Bill Schmarzo (37:56):

Well, good question. LinkedIn is where I post a lot of professional stuff. I do in Twitter as well. Twitter and LinkedIn are probably two places I go. I don't venture off the safe path on Twitter. There's all kinds of ... You can get in all kinds of evil conversations on Twitter. If you're trying to pull me into a political conversation on either side, I will drop out or block you because I don't want to talk politics. LinkedIn and Twitter, the places I like to go.

Bill Schmarzo (38:23):

I have different conversations on both of them. The LinkedIn conversations tend to be deeper. When I post something, I don't post because I just want, "Blah, blah, blah, here I am." I want conversation. I want to throw things out that are provocative. I love the fact when people are bouncing ideas back off because that's where I learn. I probably have deeper conversations on LinkedIn. It's probably the best place to go if you want to find conversations about me or have a conversation with me.

Joe Toste (38:50):

That's awesome. I want to give you a short plug. I didn't talk about it too actually at all really, but talk about a couple of the books that you have out there and I know I totally cheated. I said I don't have any more questions, but-

Bill Schmarzo (39:02):

That's okay. You're talking about my book. That's not a cheat.

Joe Toste (39:06):

Some of the books and I want to hear about the Dean of Big Data. Where did that come from too?

Bill Schmarzo (39:13):

All right, I've got three books out there. One is called Big Data. The second one is called Big Data MBA and the third one is called The Art of Thinking like a Data Scientist. The third book is a workbook. I've got a price very inexpensively because I want people have access to it. The book I'm probably so far most proud of is the second Big Data MBA. That's the book that's used by a lot of different universities, not only in engineering programs but in MBA programs. It really helped to drive that bridge. I've got a lot of universities who are using the Big Data MBA and I just finished my fourth book, that's called, get ready to snore, The Economics of Data Analytics and Digital Transformation. It's really this whole economics conversation but apply to really how do you drive value from data analytics to drive your digital transformation.

Bill Schmarzo (40:04):

The title is probably horrible, but it's really geared for universities who are trying to find a vehicle that they can use to help data scientists build products to have value and business people understand. I want to be that bridge that brings them together. That fourth book, the chapter that I think is the most powerful chapter was almost a chapter by accident. It's my last chapter, Chapter Nine. It talks about empowering teams. I wrote that chapter. My editor read that chapter and she goes, "Wow. Where has that been all these years?" That's the fourth book is this, The Economics of Data Analytics and Digital Transformation. It's guaranteed to be a snoozer. I've not figured out who's going to publish it yet, but the book is done. It's one of those weird situations where the book is done and ready to get it out there, but I haven't quite figured out who's going to publish it yet. That's that.

Bill Schmarzo (40:56):

The Dean of Big Data, there's a story here. At one of the very first Strata conferences here in San Jose, the organizers reached out to me. They had seen me at an event where I did a talk about the business aspects of data science. They said, "Hey, we're having this Strata Conference." I think it's one of the first ones they had. "It's all a bunch of tech weenies. It's going to be really in the weeds. Can you come in and talk about the business aspects of data science?" I said, "Sure." I had a session called The Big Data MBA. I talked about a lot of MBA concepts and how they apply to data science, the value chain stuff and a lot of Michael Porter and Peter Drucker sort of stuff. It was a traditional MBA of a presentation, but it was all geared around data science.

Bill Schmarzo (41:51):

After this, I've tried to get them in my class, the session. I get interviewed by the CUBE guys, Jeff Frick. Now I can't think of ... Anyway, that group, Jeff just talked to me recently. I'm talking to them. They go, "Tell about this class" so I explained this session. They go, "Wow, you're like the dean of big data, right? You're like the dean of big data. Because of the cube and the SiliconANGLE folks, that name just stood, so that's how I got it.

Joe Toste (42:24):

Love it. You've been on the CUBE a couple of times. I remember-

Bill Schmarzo (42:28):

They do bring me on. It's great conversation. I love these kind of conversations. By the way, I love being asked questions I haven't planned for. I like the spontaneity because I want to be pushed out of my comfort zone.

Joe Toste (42:41):

I love that. Well, we're going to be wrapping it up today. I'm super excited. Maybe I will actually read your book when I go through MBA programs in the near future. I'll be seeing, "Where's Bill? Where's Bill at?" going through the different-

Bill Schmarzo (43:01):

By the way, the CUBE, the other guy, John Furrier. I'd be remiss if I didn't remember John Furrier, of course, John and that group. Anyway, sorry, John, I forgot. I blanked, old age. If you read the book, give me feedback on. I hope it's still relevant. It's MBA class, so it's all business bore- bore, but hopefully you get a chance to read it. You will actually get through it without falling asleep.

Joe Toste (43:23):

I'm sure I won't fall asleep. You got a lot of energy. I'm sure the book will exude that too. Awesome. Well, thanks for coming on, Bill. Appreciate it.

Bill Schmarzo (43:31):

Thanks, Joe.

James (43:33):

If you're interested in seeing what Nagarro, a digital product engineering company that excels at solving complex business challenges through agility and innovation, can do for your company, you can email Joe at joe.toste, That's T-O-S-T-E, @nagarro.com or message Joe on LinkedIn. For all information on the Nagarro, check out nagarro.com. That's N-A-G-A-R-R-O dot-com. You've been listening to the TechTables podcast.

James (44:02):

To make sure you never miss an episode, subscribe to the show in your favorite podcast player. If you have an iPhone, we'd love for you to open the Apple Podcast app and leave a quick rating for the show. Just tap the number of stars you think the podcast deserves. To catch more TechTables episodes, you can go to techtablespodcast.com. To learn more about our sponsor, please visit in nagarro.com. That's N-A-G-A-R-R-O dot-com. Of course, you can find Joe Toste, your podcast host on LinkedIn, Twitter, and Instagram. Joe's last name is T-O-S-T-E. Thanks for listening.

Joe Toste
Joe Toste
Host of TechTables Podcast

Host of TechTables 🎙- Conversations with Top Technology Leaders