In this week's Industrial Talk Podcast we're talking to the Titans of Data, Bill Schmarzo, Chief Innovation Officer with Hitachi Vantara and Kirk Borne, Data Science Fellow and Executive Advisor at Booz Allen Hamilton about “The Power of Data and Impact to Business and Culture“. Get your answers to the real power behind data analytics along with Bill's and Kirk's unique insight on the “How” on this Industrial Talk interview!
You can find out more about Bill and Kirk and the wonderful team at Hitachi Vantara and Booz Allen Hamilton at the links below. Finally, get your exclusive free access to the Industrial Academy and a series on “Why You Need To Podcast” for Greater Success in 2020 and beyond. All links designed for keeping you current in this rapidly changing Industrial Market. Survive! Rebuild! Prosper!
BILL SCHMARZO'S CONTACT INFORMATION:
Personal LinkedIn: https://www.linkedin.com/in/schmarzo/
Company LinkedIn: https://www.linkedin.com/company/hitachi-vantara/
Company Facebook: https://www.facebook.com/HitachiVantara
Company Website: https://www.hitachivantara.com/en-us/home.html
Company Twitter: https://twitter.com/HitachiVantara
Personal Twitter: https://twitter.com/schmarzo
KIRK BORNE'S CONTACT INFORMATION:
Personal LinkedIn: https://www.linkedin.com/in/kirkdborne/
Company LinkedIn: https://www.linkedin.com/company/booz-allen-hamilton/
Company Facebook: https://www.facebook.com/boozallen
Company Website: https://www.boozallen.com/
Company Twitter: https://twitter.com/BoozAllen
Personal Twitter: https://twitter.com/KirkDBorne
EVENTS TO PUT ON YOUR CALENDAR:
Deliver on bottom line, Realize the economic value of data: Register Here!
Speed Discovery, Comprehension and Trust in Data at Scale: Register Here!
Adopting to Corona Virus Supply Chain Disruptions: Register Here!
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Welcome to the industrial talk podcast with Scott MacKenzie. Scott is a passionate industry professional dedicated to transferring cutting edge industry focused innovations and trends while highlighting the men and women who keep the world moving. So put on your hard hat, grab your work boots. And let's go.
And again, thank you very much for joining the industrial talk podcast about number one industrial and manufacturing related podcasts in the universe. That is my story. That is what I'm sticking to. And it is solely dedicated to celebrating you the industrial and manufacturing professionals, the companies that get it done, you are bold and you are brave, you dare greatly you innovate pointy, you innovate. And you're changing the world and you're changing lives. You're changing my life. And that's what's important. And thank you very much for what you do. Is it getting old? Or is it just me that we just hear on the industrial talk podcast, have great interviews. This one is lengthy. However, it highlights two titans of data. One by the name of Bill marzo and the other woman by the name of perky born. She marzo, of course, is the Chief Innovation Officer at a touch event, Tara and correct. He's got to like the title, I'm looking at on a stat card on LinkedIn, it's it's the principal data scientist at data scientists fellow and executive advisor at Booz Allen Hamilton. Now you go out there, you're gonna look at a stack guard, you're gonna say, Holy schmoly 30,000 limit, he's already got 30,000 followers, guys, machine, let's get going with this interview on the types of data. Okay, so I'm living, I'm living the dream. This was a spectacular interview, and you're gonna say to yourself, Scott, it's pretty long, it is, I will cut it up. But for now, just just you just go out to the industrial talk.com, right, it does real talk.com, find the player, click on the little speed button, and you can speed it up. And although you're saying is up, it's pretty doggone long, you have no idea, the amount of just incredible, insightful content that exists. In this particular podcast, I've always been one that just says, Hey, you got to innovate, you've got to figure this stuff out. And now is the time to truly, truly learn to dedicate your time, your free time to learning more about what is happening in the industrial and manufacturing world today. These two jets are at the cutting edge, they are absolutely spectacular. And so we took a little bit of a different approach. And, you know, some people are saying I'm not sure about data, you know, there might be a little squishy, or wishy washy on the power of data. But what we're going to do in this particular podcast, what we did was, we talked a little bit about the positives of power behind data analytics, as well as the impact on business and culture, right. And I took the position that I'm going to challenge them, and then they take the position of defending their position, right. And so it's just steeped in Doc, God values value, I've just died and I you know, what I tried to do as a, as I edited this particular podcast, try to cut it up a little bit, I'm gonna have to do that after the fact just because it's, it's so it flows. And it just keep on talking. And, and who says data analytics is not fun. Those two jets passionate, big time, passionate, big time. Now, we're gonna do a couple of things on business here. Let me just sort of just pull something up here because I want to make sure you get this on your calendar. Now there, there are a couple of events that I want you to consider and put on your calendar and it is sponsored by Hitachi Ventura. The first one is called manage, delivering on bottom line realize the economic value of data now that's brought to you by Bill shamar. So he's going to be the speaker. And it's you're going to have the links out there. So you don't have to, if you want to write it down, you could write it down but it is going to be out on industrial talk on there the particular podcast September 23 at 1pm and it looks like the time zones Denver, so Mountain Time, make a note of it. I'm gonna put that registration link out there and the other one is manage speed the cup discovery, comprehension and trust in data at scale. long title, I am sure that is going to have tremendous value, right. And that is September 15. And that's 4:30pm. This is London time now, just go out there, you're going to have the registration, but you've got to be, and big time committed to
committed to the fact that you've got to learn and you got to keep on learning, because it is so important. Now the next event I want to bring your attention to so you got those two that are being provided by Hitachi Ventura, important, get engaged, reach out, the next event that I want to bring your attention to this was brought to you by Colorado University and cap logistics. And one of the things fortunately for me, when I had the opportunity to go up to northeast Ohio manufacturing, are interviewing a lot of manufacturers, some of the things that they talked about was, of course, the their supply chain, and how COVID had interrupted or modified their supply chain. Here's a great webinar called adapting to COVID virus supply chain disruptions brought to you by those Colorado university as well as capital logistics, and they're going to be just nailing it. And you're going to find out some solutions on supply chain, great group of people that are going to be into that. So you got a couple of things that you need to put down on your calendar and make it happen. Oh, by the way, this particular webinar is going to be out there, the link, so don't worry about it, September 16 12pm. That's mountain time. So they're all over the place, right all in all this time, but you'll get it. Once you, you know, register for the event, it's all good. So we got to be about learning. So don't, don't push that off, more so than ever. Okay. All right, I want to make sure that you understand. This is a lanky podcast, it's chock full of great stuff, you can fast forward, you have the power to do that, you can compress it, you can just go out to industrial talk or go out to all the podcast platforms that the industrial talk is on. And just fast forward through it. But you've got to listen to bill and Kirk, talk about data, the positive impact of that data, not just from, from a business perspective, but from from a cultural perspective, and where it's going. I mean, it's, you could be nervous, or you can get engaged, you can you can embrace it, or you can run away, I honestly and just recommend that you begin embracing it and just enjoy what this is all about. Alright, let's get on with it enough medium and so here we are. This is Bill Shimazu and Kirk born, enjoy this data conversation. All right, Kirk, and Bill, welcome to the industrial talk podcast you listeners out there, what we've got are titans of data. I'm not sure if that's a superhero or whatever. But titans of data. I like it. And you guys deserve it. All right. For the listeners out there, Kirk. Give us a little 411 a little background on who you are, where you come from outside of the fact that we had a heck of a conversation, pre video, and you're much better than bill.
Well, whatever happens today, Remember the Titans. That's all I can say.
keep that in the recording. But so my name is Greg born. I'm apparently the principal data scientist and an executive advisor at Booz Allen Hamilton, which is a global technology consulting firm. And I am the principal data scientist. But I'm not the only data scientist there. We have over 2000 data scientists doing work all across the federal government sector in cybersecurity in the commercial sector. And so, how do I get there? Well, my background is astrophysics. Obviously, I belong there, right? Well, no, my background is astrophysics. But it's been 20 years at NASA, as an astrophysicist. But my day job which paid the bills was running data systems for astronomy, space missions for NASA. So I was always working with data systems. So I would say I was doing data at night and data at day as an astronomer, like it's Yeah, and so I just, that was 20 years of handling data, doing discovery from data, working with data, analyzing data, making data available to other people. And about 22 years ago, there abouts. The data sets that we were working with just grew astronomically in size. That's supposed to be a joke, are astronomical data sets grew astronomic. But reality was they really did. We went from 15. We had we were archiving data from NASA space missions. We had 15,000 experiments of data that totals all together 15,000 less than one terabyte. And then in one day, we were offered a new single experiment, which had over two terabytes of data. So we went from 15,000 to 15,001 experiments and we and we had to triple the capacity. Have the data center store.
What year was that? Just?
I was in 1997. She 23 years ago. Yeah. Anyway, so at that point, I realized something was dramatically different with what was going on in the world. And the volume of data astronomy was growing enormously. And then as I started looking around, I started seeing the same thing was happening and other sciences. Because I was in science, I focused on the sciences, it was happening, the other sciences. And then before long, we realized it was happening in the commercial sector, on the government sector and everywhere. And so I asked myself, What can one do with all this, and I discovered machine learning and data science. Yeah. But in those days, we called it data mining. So I basically discovered the field of data mining. And the fancy term for data mining in those days was knowledge discovery in databases add, but I like because my initials are k dB. So it's a sort of. So I started digging into that and learning about it. And being a scientist and a mathematician at heart. I just fell in love with it, because there's really cool things to do with math and data that I never saw before. And I've been doing that ever since for the last two decades, with a 12 year stint in between NASA and Booz Allen as a university professor. I was a professor of astrophysics at George Mason University for 12 years, but I never actually taught astrophysics courses. I taught data science course.
He got a mad data street cred out there. That's that's a, that's a summation of that. And by the way, listeners, he is an LSU Tiger fan, Go Tigers,
Go Tigers, right. I'm an alumnus.
That's even better. Now it all my whole family. Now it stinks to be you, Bill have to follow that. But bill, follow that give us a little 411 on your background too.
I'm sure. Well, I'm the Chief Innovation Officer at Hitachi vantara. And it taught you vantara is the digital arm of Hitachi, and background wise. I also teach at the University of San Francisco and at the National University of Ireland in Galway, which is really the best part of what I do I I find teaching much like coaching to be exhilarating. I spent I spend quite a bit of my time doing coaching and sports so i i like that and my my interest in data and analytics probably started when I was, gosh, 10 or 11 years old and I discovered the game. Try to imagine baseball's trichromatic basketball.
Are you kidding me? You just dropped the strata Matic.
I've got I've got two boxes in the garage.
I I've never met anybody outside of my buddies. We used to play baseball strata, medic baseball, tried football, like the baseball better.
baseball, basketball became my real favorite one. Because Because I cheated. I would take the cards and because I had kind of a head artistics I could look at a card and figure out which players were actually the best. And so I gravitated towards the Lakers that had Wilt Chamberlain on it. Because he didn't double team Wilt Chamberlain he would score every time was unstoppable. Which of course you double teamed him at left happy Harrison, Gail Goodrich and Jerry West wide open for jumpers which they made if I'm guarded all the time. All the neighbors could never figure out how can we never can beat schmersal righties, because I had the day that this team and the best combinations of players on the court at all time. So So yeah, I became kind of this, this data analytics. And I realized that if you really understood data and analytics, it gave you a huge advantage, which really, for me came to life in in the book Moneyball, which is, you know, really brought all the I mean, all the cyber metrics and everything sort of comes together around the concept of Moneyball. So I like I like analytics. I'm not a fan of data. I'm not I'm a fan of analytics, and I endure data, because it's the fuel for me to do analytics, but and I've just been, you know, doing data and mostly analytics my
whole life. Okay, for the listeners out there, Bill, you just sort of popped in with a difference between data analytics, give us a little sort of background on what's the difference.
So um, I have many, many times in my life, I have Forrest Gump moments. Right? Right Place right time, not because I'm tall or good looking from Iowa. Sometimes you just get lucky in life. In the in the late 1980s. I had one of those moments where I got lucky in life. I worked for a company called metaphor computers. And our biggest customer who ended up being our biggest shareholder was Procter and Gamble. And so Procter and Gamble back in those days, I live in Cincinnati, and I worked at Procter and Gamble and Procter and Gamble was moving towards what they call data driven decisions. And after talk to them, I quickly realized that no, it's not data driven decisions. It's it's analytics driven decisions, right? It's using the data to make better decisions and so a lot of my difference between Having data and monetizing data was influenced heavily by those early days at Procter and Gamble, because we were using data for almost every aspect of what was going on inside of Procter and Gamble from campaigns where we put buildings to new product introductions. I mean, it was it was everywhere. And I, I'm proud to say that I was in the 1987 88 timeframe, I actually created one one of the very first large data warehouses that Procter and Gamble. Now large back then was 10 gigabytes. And it was stuck in a closet with a bunch of servers, and storage, and we had wires hanging in everyone's closet. And only two people were allowed to go into that closet and do anything with it. I was one of the two, right, because that was 10 gigabytes. And we had gotten all this like funny point of sale data from Walmart. And we were like, this is great shit, because we can see what's going on in the markets. We know what pricing impacts, we can do this. And so long winded answer was that the my experience of Procter and Gamble really helped me to understand the difference between having data, and monetizing or making decisions on data.
You had 10 gigabytes, you also have feet. I think back to my early days that the Hubble telescope, I was working on colliding galaxies, I needed to buy a disk drive to store the output from my computer simulate. So I had to write a proposal for a one gigabyte hard drive, which was flat, which costs in 1919 $38,000. I had to write a proposal by what she could get 32 or 64 gigs and thumb drives as giveaways at conferences.
Throw them away. Now I wish
I had a time machine on my thumb drives and go back to
my iPhone has half a terabyte of space on it right half a terabyte
part of the terabyte data warehouse was a big deal.
Well, you know, my funny is doing about this, about these smartphone things is we we have more power in the smartphone that NASA used to send the astronauts to the moon. And so they use that they use that compute power to send people to the moon and back and we use it to throw birds at pigs.
The latter is really a lot of fun. Of course.
I got addicted to birds and pigs once
I played every single level on my smartphone when I bought that first pack and played it all again and improved to I got the maximum possible score and all 256 levels. And then I never played it again ever since that was eight years ago.
or six years. But did
they make a movie out of that? I thought they made a movie to that like that.
Yes, they did. Amazing. Brilliant.
All right. So
what exactly the Academy Award material that okay.
So this is what we're gonna do you to one of the challenges that fortunately for me, I've interviewed a lot of leaders, leaders like you, and some of the conversations we have have. And if I had a nickel, like you guys have a nickel, if I had a nickel, if somebody says industry for Dotto, Oh, that's great. What is that? Well, that's digitisation, that's your journey. This is a four, this is AI, this is ml, this is the edge. This is the cloud This is and it's all just data scientists data analytics data, this and there's, I'm trying me my journey in this conversation is to try to navigate those waters to make it somewhat simple for a butter knife like me, I'm not the sharpest tool in the drawer. But somebody like me, that can say, I get it, I understand why am I concerned here, but I'm not concerned here who's gonna, you know, who's driving this bus, that type of thing. And that's, that's why you have said yes to get on this particular podcast to have that conversation. And I think a bill for that. So if it gets out of hand and everything, it's Bill's fault. If it if it works real well, it's my my ideal. That's how it is gonna
rub it over me again.
Alright, David just said that. So with that said, and with that all that out, you know, information is just flying around out there, and especially with this whole COVID to finger death punch world we live in. I want to know why. And I'll throw this out to you, Kirk. Why is data Why is it all of a sudden data's important?
Because it's a four letter word and we love them. But anyways,
well for me, ah, I want to just reflect something Bill had said a few minutes ago, which is why I think it's so important and that is the analytics. It took me a little while to get to where bill got 1980s I worked with data for years. And I, you know, I really love data, making it available to people. And I mean, my job at NASA was doing that. But then it just sort of dawned on me not too many years ago that it's not about the data. It's about the outputs. It's about the analytics, it's about what you can do with that input. So the real difference between data and analytics, and data are the input, and everyone has lots of input, that's not a differentiator of any kind of every every business, every organization has data, you can't just say I have a lot of data and expect that to be of any value to anybody. But what's valuable are your unique outputs. And so a data is good for is improving decisions, improving outputs, creating new data products, creating new experiences, creating new services, that the data fuels. So it's, I have to tell people, it's like getting a fancy car, you don't brag about the fuel, you're putting in it, you brag about the cool car, you don't just have the cool car just for its own sake, but to get somewhere, right. So the journey, that's the journey and the destination that matter, and that you care about, it's not the field. But if you don't have the field, none of that is possible. So data is absolutely critical to feel thing, doesn't mean we can can't go wrong with what we do in the same way, you know, you can make a wrong turn and your car and get lost, right? Not the fuels fault. It's the humans fault for not using it correctly.
Now, Bill, I'm gonna, I'm going to show to throw this out, it just seems like, I don't know when but it just seemed like, last year, we could do this or do whatever it is, but this year, we can. And and for me, what what fundamentally changed, so that all of a sudden, companies had Data, Data, Data, Data, Data, Data, Data, Data, Data, but then all of a sudden, we can do the analytics, we can all of a sudden create outputs, what happened.
I think we change the frame of the conversation. And what I mean by that is that the the smart organizations realize that the data conversation was circular, and spinning wheels that when you when you change the conversation and framed around, as Kurt mentioned, the output you're trying to drive, everything became self evident, when you understood what it is you're trying to accomplish, what it is the you're getting these outputs trying to drive, then you realize that, well, it's the analytics that I need to drive those improvements and outcomes. And then there's data that fuels that, and it changed the conversation from from people trying to figure out what's in the data, that's a value, which is a fruitless conversation, because if you don't know the use cases, you can't differentiate signal from noise in the data. And so the smart organizations, which, by the way, not everybody smart yet, but there's, if you start with the use cases, the outcomes, the you're trying to drive, that, that frames everything. And that's one of the things that we this University of San Francisco research project we did on the economic value of data we went through, and I think I talked about this, you know, this guy on the on the previous call was that one of the advantages of being a professor is I have access to a lot of really smart, really eager, hard charging free resources, and turn them loose on this problem of how do you figure out the value of data. And they kept coming back around to every time they started down the data path that ended up being a dead end, they couldn't drive through data and get to where they wanted to go. But when they realized that, if you started with the use cases, what problems are we trying to solve? What decision are we trying to make it, everything else fell out behind it, including the ability to now have a methodology for actually calculating and quantifying the economic value of data?
Yeah, and I see where you're going with that. And I understand exactly what you're saying, I like that. And, and that, given my past experience with data, it's, it's, it's a lot. I mean, it's, uh, if you start saying, hey, I want to start collecting data, and I've got a business and I want to start pulling in the data, you quickly realize that you better have your use cases to get you better figure it out, or you're not going to get anywhere. You're just filling up that car with gas. And it's, that's it, you're not going anywhere. Who do I trust? Kirk? Who do I trust to come up with the right use cases? Because I can only imagine. Just FYI. I can only imagine say, Oh, yeah, that number is real important. And that number, if I divide it by that number, I get this result. And that's this action. And then all of a sudden, I do that action, and it doesn't work. Now, it's not my fault. It's your fault, Kirk, period. And I'll make sure they already do that. Yeah. My denominator doesn't work right. And now you grabbed the wrong data. What How do I how do I begin to even trust you in determining the data? Because my business is very special. It's a unique business.
I think the important thing is, again to focus on the business, right? So I don't know if there's one necessarily one person or one department or level of person in a business that figures out those use cases, it's got to be a conversation. Of course, you know, it starts from the leadership. But it really where it starts is from sort of the mission statement of the organization. I mean, it's like, what are we here for? What are we trying to accomplish? Right? We have all kinds of interesting data on our customers and say, Well, I can use this data to sell more aspirin to my customers and say, Well, are you in the business of improving people's health? Or are you really What? What are you in the business for? So don't go chasing use cases, because you have data that might satisfy a use case? Figure out what your Northstar is, what is your mission? I started recently talking with a guy who's a fairly early on in yours. And he's suffering from cancer right now. So I've had some conversations with him, his family encouraged me to reach out to them and I just enjoy talking with him. He's a former NASA engineer, he worked on the Apollo mission. And he introduced me to this concept of mission engineering. Right, I used to, when I was at NASA, I learned about system engineering, which is something I never learned in graduate school astronomy. I used to learn the system engineering in astronomy at school, I learned about system engineering, which is defining user requirements, system requirements, functional requirements, before you build something, what is what is it we're building towards? All right, you start with the end goal in mind. And then you design to those end goals, right? Then you decide what you need to build, what data you need to collect, what you know, what is it going to be towards what end? And so I was so where he and I were talking about systems engineering, he said no. And his years at NASA, especially during the Apollo program, they focused on something which they never put a name too. But he this guy now that I'm talking with, he and I are going to try to write a book on this new concept called mission engineer. And he said, what the mission engineering concept is exemplified in the Apollo program, because all those different components of the Apollo mission had systems engineering behind it, right. So the landing vehicle, the launch vehicle, the road, and I didn't have rovers, but I mean that the ascent vehicle, Command Module, they all had systems engineering behind them, right. They had to perform a certain way, etc. They said what the mission was to send those people to the moon, gather, whatever scientific result, returned them home, and then safely to their family. The mission was not a success until those astronauts were back home, in Houston with their families. So it wasn't about the systems, it was about the mission. And so this whole idea of what is your mission, let that now dictate what systems you need? You know, what profit goals and objectives and therefore use cases to meet those goals, objectives? And then consequently, what data do I need to use to get there? And so it's a conversation among multiple people, again, leadership leads, but there are people who are who have their boots on the ground in any organization in the business, they sort of have a sense of what's happening. Don't don't exclude those people include those people. Again, the data is the data analytics people are the ones who know the data, get those people in the conversation, because then they can tell you yea or nay we can do this.
Alright, so we just got done talking about the mission, we've got this mission. And I like the way Kirk sort of laid it out, I get it, because I'm a big Apollo, anything Apollo fan, and I get it. Now,
at that point, you're gonna have to pull in, you're gonna have to pull in those data analytics, because you don't know what the data is. Those data individuals know what the data is, is that when you start pulling them in? I love Kurt's explanation of mission engineering, and focus on the mission. I, I think that's, that's the starting point. And what we do is we similar we focus in on what is an organization's strategic business initiatives, like What is he trying to accomplish? I love this idea that the mission for the Apollo program was getting people safely home to their families. It wasn't launching, it wasn't even returning it was all the way through. organizations do have missions. And these missions, by the way, change year after year, some are looking to acquire new customers, some are looking to open new branches, some are looking to reduce operational costs or reduce unplanned operational downtime. So what we do is we also believe you need to start with that kind of a mission. And then what we focus in on are the decisions that the key stakeholders need to make in support of that mission. We we find that if we focus on the decisions, we're able to not only focus the data and analytics aspects, once we understand the decisions right, then we know the decisions, the challenging becomes put in place, a process To identify, validate value and prioritizes decisions, not all decisions are of equal value, some have to happen for others decisions happen. And so that understanding decisions tells us what data we're going to need, what analytics we're going to need. But it also has a very surprising side benefit. Most data science projects in my experience failed not because of technology, but because of organizational problems. Particular passive aggressive behavior, people, passive aggressive behavior kills lots of projects, because you haven't brought all the key stakeholders into the conversation, and they haven't had a chance to have their voices be heard. So by focusing on the cross functional group of stakeholders, those people who either are impacted or who either impact are impacted by that strategic business initiative, or by that mission, by focusing on them, you bring them in to have a chance for their voices to be heard. you embrace the ambiguity of diversity of perspectives, and you basically build and synergize upon that. And so, and here's a really key point, organizationally, when you do that, when you embrace the diversity of perspectives, what happens, you transform the conversation to one of compromise, by the way, I'm not a fan of compromise. Compromise means that people are giving up things of value in order to move forward, strike the word compromise, Instead, focus on the word synergize. What I want to do is I want to leverage these different perspectives, I want to bend them and blend them together and come up with something best, I don't want the least worst option through compromise, I want to synergize to find the best best option.
Okay, so I'm gonna, I'm gonna take my, my fantastic radio hosts hat off, I can't, but I would, and then I'm going to put a business hat on, and what I'm hearing what you're saying, I'm the CEO. And I know that there's value, whatever that value might be in my data, got this business data is out there. There's got to be value in there. I just in trinsic Lee know. Now, here's where I struggle. So, Kurt, I contact you, Kurt, I need for you to come on in here. Let's get this. Let's get this mission all you know, dialed in, and all that good stuff. I'm concerned that because I've got data everywhere. I mean, it's a doggone data soup. I'm going to be living this for the rest of my career. Tell me why I'm not going to live this data soup. And I'll use yours black box life going forward? How do I sort of make it a little bit more consumable for me, Joe, sixpack, owner of this company?
Oh, oh, order, right. Wow, I need to just keep stealing from Bill's ideas, but he's got so many. But But I definitely love the synergy synergize idea. Because if you think about an inner join, and databases, right, you're just basically removing everything that doesn't conform to one particular sort of a question or a query, right. And so an outer join where you bring in other things besides the thing that matches you bring the things that maybe disagree that there's divergent data points. And so if you call me up in that conversation, I want to be sure there's other people in the room who, again, not just gonna rally around my opinion. I mean, I don't want to I don't want there to be like an echo chamber and a confirmation bias thing going on there.
But, you know, that mean, that sort of sounds like,
I don't know what that sounds like. To me. To me, I hear myself say it. I don't like myself saying that, because it sounds like Oh, look at him how cool he is he's bringing in people with divergent views. You know, it's not about that. It's about the reality that if we don't get these multiple perspectives, and these external things, we won't know what the signal and the noise. I mean, I think back in my early days of astronomy, we did a lot of love. It was just data analysis, it wasn't anything, you would call data science with just analysis of data, right? And so there's all this kind of separation of signal from the noise that is anything that was greater than three sigma from the mean, we deleted it. I think back of all those days, when I used the standard data analysis software packages that astronomers used, actually had a three sigma clipping algorithm, you would just basically remove every data point from the data set that was more than three standard deviations from the mean. And I'm thinking oh my gosh, how many discoveries that I throw away like clicking on that?
She put the whole whole I'm gonna I'm gonna interrupt you, Kirk. That's the problem I'm having is because you data scientist, Bill, you data scientist, you love this stuff. And you see value in just moving this stuff around. I, I just want, I just want my business to run a little bit better. I just want to have better bottom line value. You can continue to look at data and say Oh, oh, and then there's this, you know, there's this big incremental value, and then all of a sudden, it's just starts getting smaller and smaller. Am I looking at that, or I'm
guilty as charged.
I loved it, I can't help myself, sorry.
But I've been in that room that you've talking about, I have been in that spot, where I'll get all excited and energized by the things I love. And then the client, or the boss, or whoever's in the room, this is looking at me sort of like, I'll never, I'll never forget when I worked for this. When I was at NASA, I worked for a small, small company that was then bought by a larger company. So I was a contract manager, not nasa. gov boy. Anyway, so when I caught this data science bug I went, was long before we had this word data science, it would be called data mining. And I wanted to explain to these people, this is what we need to do with our data. And so I didn't I didn't have I didn't have the words analytics and data science. This was 20 years ago, I didn't have those words in my Congress, my terminology, and my belt. So I didn't know what to call it. Data Mining, didn't mean making it mean anything to do machine learning didn't mean anything to them. So I just basically called it Information Science. So as someone Information Science, how we collect data, to find patterns and data, make better decisions, build predictive models without a doubt about and at the end of this sort of short presentation to the company, VP and his executive staff, some of one of the staff members racist. And when I was doing, he says, Why do we need more printers? I just looked at him. And then he said, Why do we need more system administrators? And I'm scratching my head, also. And I realized what I was talking about information science, his brain translated that into information technology. Yes. So I was talking about we needed more information science in the business. He heard what he what he heard me saying is we need more printers and system. Yeah. And I mean, I was just like, mind boggled. I mean, it was like, I did what I say get translated into that. But the good news is, after we had this sort of discussion, and it sort of seemed like it was a complete dead end, the, the VP of the organization of the business dismissed as executive staff. And I was actually making a pitch for some funding to start up an Institute, which was my big idea is we could have this sort of internal Institute around Information Science within this business. So anyway, then VP dismissed the other people. And he before I left the room, he reached out to me and he said, Kirk, you have your money. He said, they don't get it. But I get it. So so you need multiple people in the room. So that somewhat, it's not just me having a conversation with the person who doesn't get it, it's other people in the room who can help the dialog and set me straight if I'm, if I'm getting too hyperactive and hyper excited about my data, you know, rein me in a man. Back up, Kurt, we got a business to run here. Okay, I get it. Okay.
Okay, so we've had this conversation now built as an owner of the company, and you need a team. And they're all in this, this room together. I believe that this initiative of trying to really dive into the data, trying to be more efficient, and pull out that analytics to be able to make better decisions. You're taking my job away, and I'm not going to cooperate.
gonna I'm gonna, I'm gonna deviate, deviate slightly. Kurt said something that was very powerful. He was talking about auto clipping things outside three standard deviations.
Yeah, I heard it clearly.
Which, which is how many organizations operate, they operate around averages and standards. And if you if you run a business targeting averages, at best, you're going to get average results. And what happens is in order, there has to be a mental frame shift,
that outliers are bad. outliers might be new discoveries might be ways to monetize new things. And I get I'm sorry, I'm taking this conversation is strange, but I was I was very intrigued by what you said. And then you talk about, you know, Information Management Science. And it made me think about the challenge organizations have. And I see this in spades. When I talk talk to a chief data officer. I gotta tell you right now, I hate that term. I hate the term chief data officer, because if there's somebody out there, whose job is to kind of sweep up all the data, your eyes knows Rocky and Bullwinkle movies, the guy walking behind the
elephant sweeping up all the elephants, right.
That's your job to sweep up all the data poop, right? No, the CIO, the chief data officer has the wrong title. It needs to be a Chief Data monetization officer and what we need to have is we need data
on long names
Yeah, roll off
the analytics officer all the way.
I mean, think about it. It's it's the science of data monetization. You want to figure out how do we get people to adopt this? How do we get people to embrace analytics? It has got to be around data monetization, because I think the best data scientist leader or the most powerful data scientist image out there is Mr. Krabs, from SpongeBob. And what does Mr. Krabs care about? Money, money, money, money, money, money, money, money, money, right? That's what people want. They've got this data, and they want to get money out of it. I used to, I got it, you know, I always get frustrated, people talk about the three V's of big data, volume, velocity, and variety, right, blah, blah, blah. I've not met a CEO, anywhere who gives a shit about the three V's of big data, what they care about are the four M's of big data, make me more money, money, money, money, money, money, money, there's a crowd, okay. It's true. So I think what happens to your Scott is, is you take, and you shift the frame. And you're thinking about the mission that Kurt talked about earlier. And we talked about understanding how we optimize decisions, how we empower people across the organization through data and analytics to make better decisions, so that they can all make more money, and they can all get, you know, raises, and they all can make their numbers and what's in it for me, blah, blah, blah. But it has to shift. It can't be about data. And it can't be about analytics, if it's not about money first. But
here's the funny thing there. Bill, you're absolutely spot on. You're absolutely spot me business hat on right now. Yes, I want revenue, I want money to come on in, I want to be able to figure that out, day in, day out. And that is the right message. It's a messaging challenge. That's one, but two, I go out on Google, and I can type in data, whatever. And there is noise out there. And all I want to do me business owner is make money. How the hell do I shift and sift through that kind of crap? that pisses me off? What do I do? What do I do? I don't know where to go. I don't, because that message of money, money, money, money, it's not resonating. And then that message needs to resonate, because then all of a sudden, me, employee is not fearful of losing my job. Because you're coming in and saying, day to day to data. It's a difficult,
let me do something Kirk said earlier, too. He said, Every company's problems are different. If you think you're going to go to Google and get your answer, you've already lost the battle. Right? The Game Over, you know,
for out of business, I put it on the door right now, the challenge is organizations need to understand what their mission is, what it is they're trying to accomplish. What are their key business initiatives? They know that if you go down into the decisions, the thing that I've learned in my 40, some years in this industry is every business person I've talked to knows exactly what decision they're trying to make, even trying to make them for years, decades, generations. Right, that the decisions haven't changed. What's happened is that by virtue of the data and analytics, is we've changed the answers. We're no longer clipping data at the third standard deviation. Because it's those things out there that I you know, I don't ignore my outliers. Now, I monetize outliers, right? And so there's this,
you've got to figure out how to monetize those outliers. You know, you got out I know,
I focus on the decisions. I know that decisions will tell me which customers are most important. Well, they may be stuck on the outliers, right? They need a third group of customers that are three standard deviations outside the norm. So I'm gonna ignore them. Well, maybe not. Maybe those are your most important customers. So I think when you focus on the decision you're trying to make what is who my most important customers, which, by the way, is an incredibly hard question. incredibly hard question can't be answered with a single query probably can be answered with a single meaning, right? It's, there's so many variables to go in there. But you if you look at the averages, right, businesses get lazy. We do. We don't look at the granularity of the data we get hung up on on these averages. And when you focus on average, less than before, at best, you're going to get average results.
Yeah, let me jump in there. Yeah. I love outliers.
Yeah, that's a bumper sticker. Now. I do.
Hope so. The majority of my doctoral students did their dissertation on surprise discovery, which is my phrase for outlier detection, novelty discovery, anomaly detection, and I always say Oh, those are such negative terms outlier detection anomaly. Really what you're talking about is this is the surprising, unexpected thing in your data. And so if you look at my statistics books, you're not going to discover you're not going to see the phrase to surprise this guy. You'll see outlier detection. Yep. And now all these algorithms, I'll finding data points that are far from the mean, what about the data point is right, right in the middle of the distribution, where there's not supposed to be any data points at the point of this in the middle of the distribution, that is a surprising discovery. Anyway, so. So for me. It's, it's like the old joke, or the old phrase, one man's garbage, another man's treasure, right? So one person's outliers and other signals another person's noise, right? The noise might be where that monetization happened. I love what Bill is saying about that, you know, you don't want to be average, you want to be special, right? You want to you want to have a unique district, unique business. And I also want to tap into something else he said, about that. You don't go to Google to figure out how to do something, because then you're gonna get at the
No, go ahead. Go ahead.
So so so so far, you know, if 15, or whatever, years ago, when Jeff Bezos started selling books out of his garage, if he did a web search on how do you run a big book business, we would never have Amazon today rang true. And, and I want to tie that back to Google, if I may. When I mentioned mission, engineering and mission, business mission before, whenever I talk about this with students, I always have them try to guess what Google's mission statement is. and frequently, they talk about Google being a search engine, and it's all about search and whatever, or maybe, you know, making you know, money off of ads and stuff, what I said, but what is their mission, and usually gets a blank stare. So I'm gonna literally read it to you. I'm not doing this by memory, I'm actually email@example.com. Our mission statement is to organize the world's information, and make it universally accessible and useful. Period. That has been their mission statement since the beginning. They are a trillion dollar business. Personally, I never spent a dime on anything that I ever used from Google, Gmail, Google Maps, you know, Google Cloud. Sure. I mean, I have never paid them anything yet. They're a trillion dollar business, they don't sell anything except your data. Basically, they figured out that what they do if they if they take this data, and they can organize it and make it accessible and useful, they can change the world. And they can become a trillion dollar business. Well, of course, not everybody's gonna be a trillion dollar business, obviously, like Amazon and Google, but it's how do you use your data? And that's what that's what Jeff Bezos did with Amazon, right? Excuse me. I like once he spent several hours each morning just looking at the data, looking at the data. And the most he he gets it.
Okay. So again, I have my head on there, Kirk, and I come to you, because apparently I am I, I can't go, I gotta do something, I gotta find a solution with this data. So Google's out, I gotta figure it out. So I find you, I go to LinkedIn, find you. There we go. Now, I'm the head cheese at this particular organization. I say I like what you're saying, Let's start down and let's create the mission. Let's do this stuff. And how is that sustainable going forward? How? I mean, you're, you're telling me that there's gems with surprise detections within the data. And it sounds to me like it's just an ongoing relationship, or engagement with you, the data scientist or your team? And it just never stops? Is there a point where I can say, I made it, and it's sustainable? And it's gonna, you know, keep reaping benefits along into the future after I leave? Is there a point? Or is it
forever? Well, I think I think you just gave me an idea for a new business that Bill and I are gonna start. We're gonna call it we're gonna call it analytics therapy. Because what it is, is you need to have the conversation. Why did you get into this in the first place? What is it you really care about?
What is your passion?
Right? If you want to start businesses, yeah, I want to make money. But is that why you become a doctor? Why you become a fire person why you become a school teacher? Well, not a school teacher, you're not gonna make too much money. But I mean, you do it because you have a passion for right. I spent 20 years at NASA. And I caught the data science bug and I decided I want to teach the world about data science. So I took a 50% pay cut to go to a university to teach data science. People thought I was crazy. My wife gave me the green light. She said Go for it, because this is what you feel passionate about. So I have to have that conversation and it's ongoing. Okay, so so I want to sort of disagree a little bit with a word I heard bill say earlier, he they said mission, you know, is to make more revenue. The mission is to sell more widgets and improve customer satisfaction and revenue. Now, those are objectives. Those are objectives. That's not your mission. Statement just like Google, right? Google sells ads and they make a lot of money and that, but their mission is to organize the world's information and make it universally accessible and useful. So, so have that conversation. That's the analytics there. We talked about what why are you? Why are you in this? And once we do that, then we can start moving that conversation to what are the analytics? Are you trying to optimize something, predict something, detect something, diagnose something, have that conversation before we talk about neural networks, and all kinds of other boring? Talk about what is your objective, you want to optimize something, you want to detect something that let's say you're trying to, you know, bring brand cure to COVID or something and you want to you want to be able to detect early warning signs of the next pandemic? or something? I mean, you have to think about what is it that I care about what you're doing, and have that real conversation. And ultimately, it comes back to the analytics, and then the data that needs to drive those out.
But But then again, there's no end to it. Kirk, there's, it's like data's data. I mean, you guys are just into it, baby. That's
what I call it. That's why I call it therapy, right? We need to have the conversation frequently to bring you back to reality. And this is, this is really what you care about.
I love the fact that you talk about messaging, again, to build to your point, there is a real clear message that has to get out there. That makes sense, right? That makes sense. And that that the the the data pill is easier to swallow. It's not a threat, from what I hear, it's not a threat to my business, meaning my job my livelihood, it could be if I choose the wrong data set, and said, Hey, Bill, I like that, let's run with it and make decisions off of it. Let's write, and then, you know, go south, and it doesn't work with a lick. But the reality is, is that data has a return on that invest, if I can find the data, it can reap, returns, and then recognize that my business is changing, it changes all the time, my equipment out there might change. So the conversation of data and collecting the right data has to always evolve and grow and expand because things change. It's not a static environment. So that's, that's just me going off on a tangent.
Alright, so the world of data, data analytics, data science, machine learning, AI has changed dramatically. And 25 years since the birth of Google, but Google has not deviated from its mission statement on 25 years, or 20.
That's that's key. That's
what I mean is that it's the thing, that's the anchor, right? If your anchor is to make a lot of money, well, I mean, there's going to be years, that's not going to happen. So you do so you throw in the towel, no, if you if you really are passionate about what you do and what you believe, then you don't give it up when the first storm comes.
See, and that's a real important component that's just fundamental with anything, right? You got to stick it out if you truly believe if I'm a business owner, and I truly believe that there's, there's gold in that data. And I've got to go mine that data. And I've got to get the right people in place to be able to do that. I've got to define my mission, to be able to say, Okay, I got it I this is where I'm going right over here. You got to stick that out. because like you said, Kirk, it might take some time before you truly receive and reap the benefits. Let me ask you this question. Go ahead. Go ahead, though.
So the your question was,
how do I get started?
And I'm gonna Kirk, and you know,
what, I have conversations with executives. I know I'm a very simple boy from Iowa. And I always say, Well, how do you make money? How does your organization make money? If you want to figure out where to start? Tell me how do you make money? Now, profits and nonprofits alike? Need to make money, you can be a nonprofit, but if you don't make money, you're not going to exist. So no, how do you make money? And then I like what Kirk said. And the way that you help organizations sort of start transitioning this idea of how to leverage data is is I know what decision you're trying to make. But what would you like to predict? I find that if I talk to organizations and look at their reports, you know, if you can take those reports, the questions they're asking today and turn them into predictions, now you have something that they can hang on to, because the problem isn't the data and analytics. The problem is getting their mindset around. Why should I do this? So for example, instead of you know how many customers new customers that I have last month, wouldn't you like to know how many new customers you're going to likely get next month. You instead of asking, you know, what were my supply chain or inventory costs last month? Would it be great if you could predict what they might be next month. And that's not a hard transition, it's an actually very simple transition in order to get sort of their head around what they want to do. So if you want to figure out how to get started, you work in profit or nonprofit nonprofit, you companies need to make money to survive to to chase the mission to chase that passion. Right. And they've got initiative they do they go after, and, but we can make sure this conversation is very straightforward by not overwhelming them with technology. But instead, let's focus in that conversation they want to have about how do we help them, you know, make more money?
See, I like that. And because I'm a simple guy, I need simple solutions. And that messaging is always very important to you, Bill. When you have that conversation with executives, when you have that conversation with, you know, big thinkers. What is the biggest pushback that you sort of see a trend happening in a data guy, you see those trends? What's happening? The biggest pushback,
this will get me in trouble, but I'll say it anyway, I think CIOs are my biggest challenge. they've they've made, they've gotten to where they have gotten because they've made decisions that have not been bad. I run into a lot of CEOs who have gotten risen to fame because of their eirp decisions, right? And now they have that eirp mindset and they're used to projects that take five years and cost $30 million, and thousands of people die in the process. But you know, it's, it's, it's the CIOs are our challenge to to be able to have a conversation about the business, because they've never had those conversations before. And a lot of the cxos is looking at the CEO saying, well, help me figure out how to do this. CIOs have never been trained to do that. Yeah, you're gonna run into some Yeah, who really are forward thinkers who, who really know they need to make this shift. But overall, overall, what dooms most organizations is their inability to unlearn what gotten to where they are, in order to learn how they go forward, if you're climbing a ladder, at some point in time, you've got to let go that rung below you to reach up and getting executives to unlearn so they can learn something new is a huge challenge in my perspective. See, this is interesting, because what I hear, I hear there's, there's a theme that's happening here, and it's good, fine. Data is important.
There's, there's golden, that num hills, it needs to be properly evaluated and create the mission and all that good stuff. And it's all there. It's it's, and I think it's on disputable got it. It gets down to people, it gets down to that messaging, it gets down to people just saying I got it. And and and as a while it just keeps on ticking and ticking and ticking. Meaning Time goes by and people are missing opportunities. Do you see that? Kirk?
I think so I think one of the biggest impediments I see is more cultural about getting this stuff forward. I mean, I sort of agree with what Bill saying about sort of the organizational roadblocks. But cultural, to me means as a scientist, that the culture of experimentation is just not acceptable to some organization. They don't accept the idea that you got to have some failures in order to learn what to do, right. And I mean, everything we have in our world is based upon experimentation, even though our forms of government and the agencies within government, we don't think about it that way. But even there's historical precedents of things that have worked did not work. Okay, so maybe your organization hasn't gone through that experimentation phase, well, then maybe you should. Because it's until you try new things, you're never going to know what's going to work. And so culture of experimentation is basically a learn, basically fail fast to learn fast. It's a learning organization. When I say learning organization, a lot of times people just immediately think, oh, we have a training program. No, I'm not talking about training programs. I'm not talking about skill, I'm talking about the organization itself is a learning right. And just like children learn not to do things like they don't touch the stove again, after they burn their finger, the first time that you learn from experience. There's something else in the world that learns from experience. It's called machine learning. The first definition I ever saw machine learning 23 years ago, and Tom Mitchell's fabulous book on machine learning. He said machine learning is the set of mathematical algorithms that learn from experience. And that was that I said, What say that again? learn from experience. I said, No, there's got to be something more than this. I mean, I mean, you're 13 years of calculus and linear algebra and abstract geometry is this like it's gotta be boring. In that one sentence. But then it occurred to me that it's about learning the patterns in the data and you learn from experience, and what is learn from experience if you learn from your mistakes. So I always tell people think like a teenager. Right? good judgment comes from experience and experience comes from bad judgment. Let me say it again, experience. good judgment comes from experience and experience comes from bad judgment. Okay, that applies to raising teenagers. And it explains to machine learning and it applies to business organizations. So I'm actually literally talked about the fail fast learn fast mentality. clients, and I get this strong pushback, oh, no, we, we don't fail here. We're not going to fail. And I say you're talking about strategic failure. I'm not saying you are strategically planning to fail. You're Sir, you are strategically planning to win. In order to win the war, sometimes you got to lose the battle. And to win the battle, sometimes you got to lose the hill. I mean, you got to like, choose your battles, and realize that this the small failures that lead to the big understandings and experience insights that lead to the big victories later, okay.
Kurt has nailed it. Right? It's about learning. In the in the digital economy. In digital industries, economies of learning are more powerful than economies of scale, the ability to learn faster, than your competition is going to differentiate those companies that survive from those that perish. It's about learning and you're spot on, and machine learning and deep learning. These are all technologies to help us learn. But you can't learn if you're not willing to try things. This is why I'm a huge fan of design thinking. Design Thinking is designed to push you out of your comfort zone, to to embrace things that may not be right, in order to figure out what is if you don't have experimentation, you're not going to learn. And if you're not going to learn, you're not going to survive, and that his interest in this whole this whole economies of learning is manifesting itself in the technology. We talked about machine learning, deep learning and such these are all learning technologies. But equally important, if not more, so is in the humans. How are we empowering humans, to be able to try things, be comfortable with failure, and learn from that if you're not failing? You're not trying? Yeah,
it's interesting, because, you know, this whole COVID pandemic, I think, pre pandemic, we were comfortable doing whatever, you know, live in life, right? You hit get hit by the pandemic, and then all of a sudden, you're forced to think differently, you're forced to figure out solutions, you're forced to actually have to learn new things and do certain things. And so, from my perspective, and what I hear you talk about Bill is this sort of positive element that's associated with getting people to learn and being comfortable with failing, that's a big deal, being comfortable to fail. But I do it all the time. You know,
my, my company, Booz Allen did a survey of our data scientists. This was before I joined five years ago. And at that point, we had had only 600 data scientists now we have over 2000. But they did they they basically did a what's it called a Hogan personality test, which a lot of employers do to figure out what people's strengths are, and its strengths and weaknesses, and to help them place them in the right jobs in the company. I mean, it's not a scary thing. It's just it's a common sort of thing to make sure people, you build the right teams, etc. And out of that study of our data, scientists figured out sort of what makes a good data scientist, one statistic, one particular characterization of successful data science, it really stuck out and that one was something that the Hogan test calls tolerance for ambiguity. No, you cannot be a good scientist. If you think everything has a yes or no answer and nothing else. And data science, most things are probabilistic answers. There's some likelihood This is the answer. But there is no. And that's really hard. I would imagine, for a CEO to accept the fact that you're only telling me that this has a 60% chance of success. I want something that has 100% chance of success. Well, I guess if you ignore the data, you're 100% chance of failure. You do have 100% chance of failure if you don't follow, you know, the evidence if you don't follow the evidence that's pointing you to a certain decision.
This is this is just fascinating. I mean, you're this conversation outside of the day. Outside of the analytics outside of whatever we talked about, it goes to the heart, the core of, you know, companies, people, leaders, and changing that culture to be able to be a learning organization, and, and feel comfortable with a research that could possibly fail. And that CEO is saying, Yeah, I'm okay with failure, you know, be able to say with a smile on your face, that's huge. What you're talking about is huge. But the future, whenever that looks like the next normal, the future demands, that you'd be innovative in that sense. You agree with that bill.
So Scott, the the idea of failure, and learning through failure is important. And I love the fact that a tolerance for ambiguity is number one data science characteristic, we all have our data science projects, have a design thinking person on the project, we group them together, because design thinking gives you tools for how you manage and exploit ambiguity. I can't walk into a CEO and saying you got to embrace an organizational mentality of failure or accepting failure. But what I can do is I can I can talk to them at the level of the decisions, and how to embrace ambiguity or do or diversity of perspectives to help come up with better decisions, right? Kirk hit it right on the head, right. And Yogi Berra said it very well, making predictions are really hard, especially predictions about the future, right? They're not 100%. So how do I go from that? 80% to 84, to 86 to 87.5%. I do it by bringing in a whole bunch of other different perspectives and trying lots of different combinations in data science, is what is data science, data science, is all about finding those variables and metrics that might be better predictors of performance, period, it's all it is, right? And so I want to bring in a wide diverse, set a perspective and see if we can find those variables and metrics that might be better predictors. So I can go from again, 8282 to 86, etc, dot dot dot. It's the the ambiguity isn't to be avoided is to be embraced. And by the way, I'm going to go off on a real big tangent here. The problem with our society today is we don't have a tolerance for ambiguity. We don't we see the conversations happening across the news channels and social media and all the political side. It's about the ambiguity is bad, right? We got to be we got to all be the same bullshit, right? ambiguity is what makes ambiguity is how the human race has evolved and survived. If we were all clones of each other, we've been wiped out decades or centuries ago. So this idea that ambiguity and, and, and, and this diversity of perspectives is bad, is exactly that data science will fail. If we don't embrace ambiguity, and design thinking provides you these kinds of tools. It says, Don't avoid ambiguity, friggin embrace it, grab it by the
year solutions. I could see, I'm sitting in my corner office, I'm having a grand old time, I'm the CEO and I'm a big thinker. And I'm just sitting there and here comes a knock on the door. It's Kirk, and I'm telling you, Kirk was wonderful last week, but Kurt came back in and, and he now he's starting to chew my ear on this little incremental improvement. And I guarantee I'm gonna get to Kirk and I'm gonna say, just just deal with it. Just done. I want I want just make it happen. That's, that's how I see it. And that's you guys live in the world. I live in the world of just like, Is it gonna bring money in you know, is it a right or wrong? And a lot of people live in that world too, as well. This is a whole messaging issue. It's a whole mission. II Crikey, I got it. I got a question asking. This is all fascinating. This is all pretty cool stuff. I don't have a clue. It's, uh, you know, I see Picasso out there. I don't know when Picasso decided to pull away from that painting. And he said it was done. Where do you see it going? Where do you see? I mean, put that future cap on. And where do you see this? I mean, what can businesses expect?
Oh my gosh.
I just don't know. It's like, it's just wild west out there of data. Now having a grand old time.
The best way to predict the future is to create it. So let's see what we can do here. Yeah, I think I don't want to I don't if I'm going to answer your question, but I've had some thoughts towards that end, one of which I was thinking a little while. ago, you know that over 100 years, 120 years ago, one of the primary one of the major executives in the corporate executive suite of corporations was the CEO, the chief electricity officer. Because electricity was this new thing, they had to manage the fear and manage the implementation of this new thing. And this, we never done it this way, before. I don't know about this stuff, I might lose my job, because my job is to turn on the gas lamps every night in the business and turn them off. And people leave. I mean, so they had this person whose job was to manage that transition, manage the digital asset, that case electrical transformation, if we had Twitter, in those days, electrical transformation would be a trending hashtag. So 100 years from now, people will have the same humor and laughter we're giving, we're having right now looking at us saying, Oh, those people talk about having Chief Data officers. I mean, the whole everything is digital, and data, what were they thinking? I mean, why do they need to have data? And so so I think this stuff will eventually become just part of the fabric of just doing business. I mean, I mean, we talked about digital transformation. I mean, if you're not, if you haven't done that already, you might as well just fold up your 10. I mean, the whole The world is digital, like the world is digital. And so it's not, it's not really a unique conversation, just like I said earlier, brag that you have a lot of data as an organization is a non starter, everyone's got lots of data. But what's the point? What's the point of talking about that? I would tell I would work with my students, and we had a spreadsheet of galaxy data, they could probably copy all the numbers from the spreadsheet, in a couple hours on a piece of paper, is that big data? Absolutely not. But it had 26 columns of data for these few thousand galaxies. And if we were to explore every combination of those 26 features, taking one at a time two at a time, three at a time for them, and doing all our different types of analyses, outlier detection, principal component analysis, clustering, analysis, all this stuff, it would take them 100 billion lifetimes of the universe to analyze all possible combinations. That's big data, I can write it down on a piece of paper and a couple hours. But that's discovery potential is enormous, from a small data set. So let's get over that that is not so what we need to be talking about what differentiate you is the things you're doing with all that digital data stuff, which is part of every fabric of everything, right? If anyway, so I think we, I think what we're doing here is that we're going to start to get over some of the fear factor, and the chief data officer factor that we need that person to manage the fear and transition to being a data organization now that that's, that is what you want to be anyway. And I understand you need to have such a person under certain circumstances, I'm not negating that. I understand that is a necessary position in a lot of organizations. But I think I think the movement is towards focusing, again, like a broken record today, focusing on outcomes, focusing on the analytics, which are the outputs and the outcomes of all these things we're doing.
But I agree with you querque. I mean, I like the fact that you keep on going focus on the outcomes that that helps sort of take this, you know, hairy monster that I look at, not from you guys, because you guys live in the world of hairy monsters. I look at it, and it's just like, Okay, I get it, by the way. Yeah, you guys are just throwing your hair around. I don't have any hair. Anyway, I like it. And I yeah, don't, don't even don't do that.
points up my balls.
Yeah, right. What do you think about that? You had you had a comment about that?
I'm sorry, me. Yeah, yeah. What I was gonna say is, I think I love Kirk, your example of a chief electricity officer, and how electricity just worked its way into the DNA of organizations, you don't think about it, you turn the switch on internet switch off, we're gonna see that with data and analytics, one of the things that I most enjoy about teaching, is I get a chance to heavily unduly influenced tomorrow's leaders, the teaching, I'm doing to, to not think that data analytics and anything special, it's the new normal. And we're gonna find I think here, Scott, is that in, in the future, you know, 10 2070 years from now is it No one's going to talk about you know, being a chief data officer or date or chief analytics officer. It's just the way that you run a business it will be it'll be integrated into the DNA of organizations will have everybody will have AI assistance, helping them to make better decisions, help them churn through the data, that the AI system will be like a Yoda sitting on your shoulder offering you advice. You'd be having a conversation you can you'll be learning together and they won't think anything of it. It'll be the way that things are. It's unusual for us because we are as any change takes place. The people who have hunker down we don't want to unlearn because we got to where we are because We knew something. And now what we know we're being told isn't valuable anymore. Doesn't mean we're not valuable. It means that we better start learning what's new, this new valuable stuff? We're humans, right? I'm not a robot is programmed to run certainly I can actually learn and change things. So I, I think what's going to happen is that there's going to be people in the business today who we're going to start making that transition into that data and analytics. It's just part of the DNA. If you don't have an aptitude for it, dammit, you better have it, because it's going to be like breathing air, right?
I love this idea you're talking about in the teaching attaining the next young minds, because I'm passionate about that, and doing that myself and I, one of the characteristics of the current Fourth Industrial Revolution, okay, that I threw it out there.
Dude, you dropped it,
is that the previous industrial revolutions the first, it didn't transition into the second until over 120 or so years later from the steam engine to electrical power, over 100 years, and then from electrical power to the computer age was like a 60, or 70 year transition. And from the dawn of the computer age to now there is this age of hyper connectivity, where data flows basically facilitate and negotiate everything. It's taken basically, like 30 or 40 years, from the birth of the computer age to the third, and does it now have the fourth. And so what we're seeing is that the the time period of these revolutions is rapidly decreasing. And so what that says to me is that the lifespan of a person's career is going to extend over more than one revolution, industrial revolution, the way business is done, etc. And so anyone who's getting education and training 40 years ago, they can probably expect that once they were through with school, whatever level of schooling it was, they could carry that through for an entire career. 45 years later, retire, and so forth. Now, somewhere in that career journey, you're going to have to relearn and unlearn a lot of stuff. And so if we're not learning oriented, as individuals and organizations, we're doomed to go extinct in the next over the next revolution, because they're the one that's happening now, there's going to be something else that I you know, I don't know what it's gonna be. No, maybe it's going to be more related to this work from home and talent that we're going to see an entire revolution where the world maybe you don't need to travel, we're gonna send our avatars, and we're gonna have augmented reality or virtual reality, everything. All right, I probably don't even need to go to a store. It's like that song. 2525 I hate to think that that sounds 2025. Were some machines is doing that for you. You don't need your arms and legs. Yeah, go ahead. But maybe that's, but maybe we're gonna see more of that sort of virtual business. And I mean, that that's a trivialization of industrial revolution. I don't know what it is. I'm not a future sound exactly what that next thing is going to be but but if we don't realize that the span of a career now, especially young people coming out today, there's going to be a another Industrial Revolution, probably in their career lifespan, and so they better be in a learning and unlearning mode in order to succeed.
That's a that is a great point. And I agree with you, 100%. And, and I'm gonna digress real quick. My son just bought a VR headset. for PlayStation. Have you ever put one of those on? It's stupid. It's Yeah, it's amazing. So anyway, to your point VR,
I literally participated in some VR based training that our company is giving to the Air Force personnel who are who are loading these big jumbo aircraft that are that are transport heavy equipment. And so I was actually part of the learning experience. I was just I was just in a little room with him goggles on. But I felt like I was inside of an airplane
right there and you're scared
but the scary thing was they opened the door and I walked to the edge look out to see the engine see the wings say that see the tastebuds. And I my body felt like, I feel like when I have my climb something I have fear of heights. I literally felt sick, like I was going to fall out of the plane. Wait a minute, I'm just standing in a room with goggles in my body. My brain didn't process it that way. My brain brain process the sensory input. And so yeah, so so. So my whole new fields of psychology and sociology. branches of these known known disciplines are going to evolve as we start having these kind of social, virtual social, virtual business virtual life. I don't really agree. Maybe I'm losing my mind here, so I'll just shut up right
got it. Kirk, have you been on the Star Wars ride at Disney World
yet? I haven't know.
You would like that. That's a lot. Um,
I get sick on rides. So these aren't the rides I'm looking for.
Well about these virtual rides, if you ever get sick, you just close your eyes. And then your your ride is no longer
exist. I don't know if that helps. Because when I read and someone else is driving in the car, I don't normally get carsick. But if I'm reading and not paying attention to where the horizon is my body, Ah, good point, that's what it is that you need that you need to have that level set. And that's a good life lesson there too. You need to have that horizon in your frame of view, which is why mission orientation is important.
You're amazing care kids. Brilliant, like they were down this route. And all of a sudden, oh, yeah, right mission. So this is, this brings up a segue into another quick topic that I want to just at least address, okay? This is gonna be for you, Bill. what you're talking about, just, once again, me being a person that is not steeped in what you guys are doing. There's a fear on my side, there's a fear of social and, you know, personal, you know, inside my head type stuff, where you're pulling data of my behavior, my activity who I am, right, and you're able to, in essence, exploited. And right now, I don't see a governing body out there that says, well, oh, you're a nefarious type of guy, and you're doing nefarious type of things. And I see that you're smart, and you're taking data and you're in, you're doing nefarious things. That to me is that's a scary proposition. And it's getting more and more so that way, tell me why I shouldn't be scared.
You shouldn't be scared, you should, you should be very scared. Um, they, if you expect legislation is going to save you here, you're doomed. The only way that you're going to be able to manage and live in a world where people are eyeballing and capturing all of your tendencies and behaviors. And exploiting your biases is that if you embrace the the art of critical thinking, you you have to think for yourself, the we were learning in a very harsh way, how easily manipulated we are through social media, and how organizations can very quickly pick up our biases, everybody has biases, if we didn't have biases, we'd all be the same, right? We all have biases that have come because of experience or upbringing, there might be so and these organizations are leveraging these biases to to heavily influence you. So it's on you as an individual to be accountable for your own thinking, you can't blame your thinking on somebody else, you can't point to the Russians or the Chinese are or any other wack left or wack right organizations for how you think and how you act. It's, it's on you. And I think that's what frustrates me a lot about the conversation we're having today. And in particular, and in the education I have, when I teach, I have a whole section on critical thinking, how it's important trusting it you have master I, I spend a lot of time with my kids talking about the importance of critical thinking, My daughter will send me something that's, you know, it's like, really, you think this is true research and do some drill down, you think that, you know, the pope didn't vote in the election three years ago, I can guarantee that right. So it's, we can't blame others, we we have a blame society, we were very quick to blame other people, instead, you got to be accountable. So to me, if you're expecting the government to bail you out, baby, it ain't happen. You have to be prepared yourself to have that to protect yourself from the onslaught of how people are going to exploit the data, you have to use it against you.
Gosh, no more now. Sorry. I got character. sledge, please. Now,
our page, another chapter from Bill's gospel here. And that's design thinking. So I'm all on board with the critical thinking message he just gave us, Bill, thank you. But also, the things you've said about design thinking over the years, I mean, I've read a lot of your things that you've the articles you post, and, and I was first introduced to design thinking before that, but then I saw I saw how you sort of blanket a lot of your messages around design thinking and I really decided to just embrace that as much as I possibly can. And what that means for organizations is that yeah, if I have access to your data, Scott, right, if I have access to your data in my business, I need to have other perspectives in the room. Okay, other you know, you know, ethics officers and whoever officers and, and ombudsmen for customers or whomever you want to whatever you want to call us. People think, looking at what's going on and said, should you be using Scott's data this way? I mean, should you I mean, there's famous examples of companies who, who were found out using data inappropriately and it was a, it was a branding nightmare. It's better to discover before the nightmare that you're doing something wrong. And I think the, the way, the best way you do that is you have more eyes looking at it. And so so so more eyes looking at as a part of design thinking. I mean, it's getting the multiple perspectives on thinking about the design of this thing. What is the purpose of serving? I always like to harken back at this, this may seem off topic, but that to me on human subjects research and I say oh, well, that's off topic. So human subjects research has sort of three principles, and that is to do no harm. And basically to be a you know, basically having an informed consent. Alright, so so a sort of informed awareness of what's going on, do no harm anyone and and then shared benefits and shared risk, okay, so no single sub segment of your population benefits, while some other segment segment endures the risk, it's got to be equitably shared benefit and risk. So you can do things that are risky, as long as it's distributed equity economics, acronym, honestly, we have that's the right word. Equally, let's just safely got it. But anyway, not to get into the whole long topic of, of those principles of human centered research. But I'd like to say AI is like an experiment. It's a grand experiment on humanity. And therefore it is human subjects research. So we need to take these basic principles that have been applied to medical research for decades, and apply them back to ourselves when we design things that think about those benefits those risks. Do we need to inform people? Or are we are we doing no harm? I mean, all those kinds of questions need to be addressed
if they do and now, just one second. here's here's the interesting part. We talked about this industry for Dotto, whatever the fourth industrial revolution, what always fascinates me, Kirk, and Bill is the speed. Right? It's It's fast, just like I I went to a conference, I broadcast from a conference, we're talking about AI, right? Yeah, it's all great. It's all wonderful, still need to beat whatever. I come back the next year. And it seems like there's been just drastic improvements or drastic progress in AI, and it's the speed me human being, I haven't hard time consuming it. And so when we start talking about these bodies, it has to happen. Now we've got to start, how do we get the community recognizing that this exists? Bill?
The question of ethics is, is a challenging one?
And AI is going to really force us to have to deal with it. You know, the Terminator didn't do anything outside of what their AI utility model told to do. It terminators bad behavior, is it because the determiners their AI utility model was poorly programmed,
optimizing its optimization function,
it was optimize this utility function and and it was just wailing away at people, right. And so when I think about this, the the what is going to happen in Kirk talking about a chief ethics officer, I think that's, that'd be a very good career choice for a lot of people, because we're gonna wrestle with ethics, but ethics is hard. Let me let me give you an example. Do no harm versus do good. There's a big difference between the two. And if you don't, I'm gonna, I'm gonna refer you back to the parable of the Good Samaritan. And so what we've got to do is as, as AI becomes more and more prevalent in our organizations, we need to think carefully around the ethics of the AI utility model, how vital utility models are optimized around financial and operational executive and around, you know, customer experiences. But there's other variables too, right? There's, there's ecology. There's, you know, environmental, there is spiritual, that need to be factored into this thing. And I'll tell you right now, there is no right answer. When you start bringing these different perspectives together, you're gonna have ambiguity getting back to what Kirk had talked about earlier. Regarding this, this embracing a culture of ambiguity. AI is going to force that on us. We're gonna, I believe that AI ultimately is going to make humans more human. Because we're going to have to focus in on what it is that makes us human interest in AI is going to take care of all the mundane bullcrap, right? They're going to take care of that. And so the this ethics conversation becomes, becomes almost everything for us, because if we get it wrong, if we get it wrong, the terminators will be walking up and down the streets having a great time. Right. So it's, it's again, we have to think very carefully about this ethics conversation. And it's not a data scientist. Comment. data scientists aren't the ones who are setting the defining a utility function that takes this curve talking about this design thinking constantly bringing in lots of diverse perspectives, you know, bringing in lots of different ambiguity into the conversation in order to sort of figure out and wrestle with this.
And that's that, that brings a good point. This is not just a regional or a company, this is a global conversation. It's It's huge.
And it's a civilization, compensate civilization.
And the reality is, it's gonna have to happen. It's already there, because you got smart people like you, too, that are sort of in that world. And you look at it that way. You're, it's just a, it's not a big deal. They look good. Kirk, what do you what do you think? Talk to me about that, that ethics stuff. I like it. It has to happen. You're a smart guy.
I'll tell you one thing, I'm proud of my humility.
I like my humility, too.
Okay, so that was intentionally humorous, but I mean, but more intensely I what I mean is that I don't want to assume, because I know something about AI. And I know something about data analytics, that I you know, that I'm going to be any kind of an expert on ethics, it was because that's a human broad civilization conversation. And I think we, as scientists in this discipline, and practitioners, whether or not we're scientists, or not data engineers, data scientists, you know, data decision makers, whatever, you know, we're not the end all be all of the world, right? So we have a part to play on that. And we need to talk because we're, we have, uh, you know, we're sort of driving that technology, a speedboat right now, and maybe we need to dial it back a bit. And we need to have other people help us to know where and when we need to dial it back.
See, it's interesting, when you start talking about dialing it back, I had this conversation with another gentleman. I think it was AI, right or something to that effect. And I said, I'm still looking at your version one, and you're already on version 10. And it was only been a year, and I only don't know what what has changed. I haven't even went to two. So it's
1000 years have elapsed in the last six months.
Yeah. And gallons of alcohol. Big time.
And blue. Powerade Powerade, my wife hates it.
But it matches my shirt.
color coordinated. Always. I love it.
Yeah. Anyway, I just, those are some of the questions. Okay, I have to wrap this up. This has been going on, I think we're gonna have to cut this in a couple of slices. And I appreciate your willingness for the listeners of industrial talk to talk about this very important and dynamic. You know, subject that the the last question I have, and this is to you, Kirk, and to you, Bill, but to you, Kirk and Bill, you get a chance to think about it. I'm just, I'm just Joe sixpack out there. I'm just trying to live my life. I'm trying to run a business I'm trying to do you know, just live live a life. Just one parting shot that gives me warm and fuzzy and why why data is important for me.
Well, data for me is in a sense, the objective evidence about something that's going on. And if we're going to get sort of beyond the subjectivity of a lot of things that we may feel in the world today sort of paid more attention to the things that are observable facts. I'm not I don't mean facts that people state the things that you can actually measure. And from that, you know, try to be more objective. And so I think what's happening for us is, is that we're, it's almost like a bifurcation, there's a lot of people who are being very evidence and data driven. And some that, you know, maybe feel more like, that's, I don't want to go that way. Because I've always my gut feeling is always been right, or something like that. Right. And and so I think we need to find sort of that common ground where you can have both, what is the evidence telling us? Now again, you can make decisions, regardless, either way, but but I think we're getting to more sort of evidence based objective measurement of everything that's happening in the world. I think that's what digital transformation means is that we sort of had digital signals emanating from just about every process, person, product and thing in the world. And as much as we can get objective evidence out of those things as what is the next best thing, excuse me, the next best thing to do or to think about or to decide, and I think we'll be on a better path, I think.
I agree with you, Bill. But can you add to that give us a little parting shot?
And I'm not I'm not gonna answer your question at all Scott. I'm gonna I'm gonna go for the path which is typically I think this conversation, if there's anything I want somebody to take away from the conversation we just had today, it's the power of learning. It's the power of embracing ambiguity. It's the power of embracing diversity of perspectives, the data is going to help us see things more clearly. But we're never going to be able to evolve as an organization or as a society. If we're not looking at our ability, as a, as a, as a group,
look at that data from a whole bunch of different perspectives, there's, there's a lot of noise in the data. And the signal in the data is very heavily defined by the use case you're going after. But what's the value in that data comes from different perspectives? Who look at this and say, well, that is, that's a useful piece of information. Well, that's a useful piece, when you have these people bringing this together, then you do come up something that's better. So to me, it's all around this ambiguity, difference of perspectives. And the ability for us to continuously learn to learn from the data with telling us to get more data and learn more it's going to be, you know, what this is, is the ultimate in job security, because learning never stops.
So yeah, I'm now you know, I pushed back on some of the points in here, they're throughout this conversation, right. But for me, personally, when I hear what you guys are talking about, it's a, it's exciting. As a former owner of a big company, I, I am excited by it. I didn't have this ability back then. Right? I didn't, it's just, but this is exciting stuff. And I and for me, I'm very optimistic, very bullish on the future, and the future of businesses in general, because of what is available through ship, big thinkers and great thinkers like yourself, that are providing real valuable long term resilient type of solutions that help make me a better business person. You know, long term success, whatever it might be. And to me, it's a it's a comforting conversation. Yeah, there's challenges. But there's challenges with everything. I mean, I don't wake up without having challenges. But it's, it's comforting to know that that there's great and real innovative solutions out there that are just changing lives. I really appreciate it. Now, Kirk, I just started, go swing over to your stat card, on LinkedIn. I'm swinging over right now, if you're out on video, I'm swinging over. And I noticed that I've reached out to you, I've reached out to you. And it's still in pending mode. Now, I'm not sure if you're trying to block me, but I'm just looking at it. It's still in pending mode. Oh, that's just data. I'm just don't kill the messenger. It's just data. I'm just wondering if you're evaluating me. So that's one. But anyway,
that's, that's a prime example of information overload. I get I literally have thousands of requests in my LinkedIn to take six to connect with people. I'll send you the amount LinkedIn has a limit of 30,000 connections. So I've reached my limit, and there's nothing I can do. But I will find I will find a way I will find a way Scott. I'll let you into the inner circle.
Make it a big deal. You'll know what happened to me to your readers,
though. You're gonna learn just how boring my posts can be.
But you're out there and I want to make sure you listeners out there you need to go out to Kirk born that's KRKBOR and he right go out there. And I think you're gonna probably have to put a little comment in there put Booz Allen you'll find him good looking guy flown head hair, you know, unmistakable right there. All the way reach out and he's got great. I mean, you're always and you're out on on Twitter. Man, you're you're popping to Twitter.
Yeah, I'm on Twitter. But I'm only three 1,000th as famous as Taylor Swift. So I need more followers.
I'm following you.
I'm following you. Okay, there you go.
I'm doing my part. And Bill I know that you're a very active out there on LinkedIn. And I really appreciate So reach out and this is our friend go out to I think it's no, it's bill, not William. I see William on your bill shamar as well. That's s ch Mar z. Oh, and just now you're probably the only marzo out there. I think you are. So you know that Mars was out there, but we're all the same family. So Okay, nevermind. So he's, he just left that. And if you're having a hard time finding that good looking jet, and and you're active out there too, as well. So if they want to reach out, find out more, I'm telling you, it's learning. I love your point there bill. It's about learning. And I mean, there's no excuse No, no excuse to not learn it. It's all out there. It's fun. Like you said,
it's fun. It's great to learn stuff and to be exposed to new things you just every day is awesome. To learn something new, it's great.
To catch point it's happening fast, right? And so you can't you can't let up on the gas. You got to keep on going. And like for me, I'm, I'm just soaking in the knowledge man, I just sit here and just soak in the knowledge I I could be the dumbest, right? knife, the drawers, the dollar, snipe the butter knife, but man, I'm just getting the best education because I get to interview great people like you. That's what we have we have you fooled and don't wait. Hey, we did this. Kurt, right there. Thank you very much for joining the industrial talk podcast. And you shamar. So, again, thank you very much for joining the industrial talk podcast, you listeners out there, do not go away. Because we are going to be right back and you're gonna wrap it up, so you're not gonna want to go away. So stay tuned, we're gonna be back in just a jiffy.
You're listening to the industrial talk, Podcast Network.
Alright, thank you again for joining the industrial talk podcast, a platform, a platform that is solely dedicated to you. The industrial and manufacturing professional says get it done. We celebrate you each. God, we can podcast every day. That's what we celebrate you all the time. Because you deserve it because you're changing my life and you're changing the lives around the world. Ooh, now that listeners was a great conversation that was absolutely insightful need to reach out to Bill bill Shabazz, oh, you won't have all his contact information out on the industrial talk, podcast, landing page, website, wherever you want to go. That's industrial talk comm as well
as Kirk born.
They see the world and the world is bright. I'm just telling you, they're very excited about what's what can be done by leveraging data, the data analytics, and making companies manufacturing far more successful because they are out there. Just doing great work with data and thinking ways of making huge success. That's what they're all about. Alright, a couple things. Once again, you're gonna go out to industrial talk.com. Yes, check. You're also going to go and you're going to sign up for the adapting to the Coronavirus supply chain disruption, the webinar, find out more you got to come. You gotta keep learning, right? Yeah, just gotta keep learning. And the other one that we just not just gonna have to make sure that you make it a point, Bill's gonna be hosting it. And that is delivering on bottom line realize the economic value data. He talked about it here in this particular podcast, and that is going to be September 23. And then the other one is speed, discovery, comprehension and trust in data at scale. Keep learning, because once again, you need to collaborate. You need to innovate. You need to educate and you need to do it with a sense of speed of purpose. Do not delegate. We can't have you lollygag Alright, we're gonna have another great interview right around the corner. People will be brave dare greatly change the world. hang out with people who are pulling frame and dare greatly. your view on life will be completely different. That's what we do here on the industrial talk podcast. Adios. Thank you very much for joining and we will be back with another great interview.
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