>> Commentator: From theCUBE studios in Palo Alto, in Boston, connecting with thoughtleaders throughout the world. This is a CUBE conversation. >> Hey, welcome back, you’re ready. Jeff Frick now with theCUBE. We are still gettingthrough the year of 2020. It’s still the year of COVID and there’s no end insight I recall until we get to a inoculation. That said, we’re really excited to have one of our favorite clients. We haven’t had him on for a while. I haven’t talked to him for a very long time. He used to I contemplate have the record for the most CUBE appearancesof probably any CUBE alumni. We’re excited to have him participating us from his house in Palo Alto. Bill Schmarzo, you know himas the Dean of Big Data, he’s got more entitles. He’s the bos innovationofficer at Hitachi Vantara. He’s also, we used to callhim the Dean of Big Data, kind of for entertaining. Well, Bill moves out andwrites a bunch of volumes. And now he schools at theUniversity of San Francisco, School of Managementas an administration fellow.He’s an honorary professor at NUI Galway. I think he’s just, he likesto go that area of the pond and a many time authornow, be checked him out. His author profile onAmazon, the “Big Data MBA, ” “The Art of ThinkingLike A Data Scientist” and another Big Data, kind of a workbook. Bill, immense wants to talk to you. >> Thanks, Jeff, you know, I miss my hour on theCUBE. These conversationshave always been great. We’ve always kind of pokedaround the edges of things. A quantity of our conversationshave always been I saw, awfully forward edge and the titleDean of Big Data is courtesy of theCUBE. You guys were the first onesto give me that word out of one of the very first Strata Conferences whatever it is you dubbed me the Dean of Big Data, because I coached a classthere called the Big Data MBA and look what’s happened since then .>> I cherish it. >> It’s all on you guys. >> I love it, and we’ve outlasted Strata, Strata doesn’t exist asa conference anymore. So, you know, one of the purposes of that Ithink is because Big Data is now everywhere, right? It’s not the standalone thing. But there’s a topic, and I’m maintain in my handsa paper that you worked on with a colleague, Dr. Sidaoui, talking about what is the value of data? What is the economic value of data? And this is a topic that’sbeen thrown around quite a bit. I think you roll a totalof 28 citation generators in this document. So it’s a well researchedpiece of textile, but it’s a really challenging problem.So before we kind of getinto the details, you are well aware, from its own position, havingdone this for a long time, and I don’t know what you’re doing today, you used to travel everysingle week to go out and inspect customers andactually do implementations and really help peoplethink these through. When you think about thevalue, the economic value, how did you start to kindof chassis that to make sense and make it kind of amanageable trouble to affect? >> So, Jeff, the researchproject was eyeopening for me. And one of the advantagesof being a professor is, you have access to all thesevery smart, exceedingly caused, unusually free investigate generators. And one of the problemsthat I’ve wrestled with as long as I’ve been in this industry is, how do you figure out what is data value? And so what I did is I tookthese study both students and I attach them on this problem. I said, “I want you to do some study. Let me understand whatis the value of data? ” I’ve seen all thesedifferent papers and specialists and consulting conglomerates talk about it, but nobody’s reallygot this thing clicked.And this is why we launched this research project at USF, prof MouwafacSidaoui and I together, and we were bumpingalong the same old itinerary that everyone else came, which was inched on, how do we get data on our sector balance sheets? That was always the motivation, because as a companywe’re usefulnes much better because our data is so valuable, and how do I get it on the balance sheet? So we’re headed down thatpath and trying to figure out how do you get it on the balance sheet? And then one of my experiment students, she comes up to me and shesays, “Professor Schmarzo, ” she goes, “Data is kindof an extraordinary asset.” I said, “Well, what do you symbolize? ” She goes, “Well, you thinkabout data as an asset.It never saps, it never wears out. And the same dataset can beused across an unlimited number of use cases at a marginalcost equal to zero.” And when she said that, it’s like, “Holy crap.” The light bulb went off. It’s like, “Wait a second. I’ve been thinking aboutthis entirely wrong for the last 30 some yearsof my life in this space. I’ve had the wrong enclose. I deter thinking about this as an accomplishment, as an accounting conference. An statement ascertains valuation based on what somebody is willing to pay for.” So if you go back to Adam Smith, 1776, “Wealth of Nation, ” he talks about valuation techniques.And one of the valuationtechniques he talks about is valuation and exchange. That is the value of an assetis what someone’s willing to pay you for it. So the value of this bottle of water is what someone’s willing to pay you for it. So everybody fixates on this asset, valuation in exchange methodology. That’s how you kept it on balance sheet. That’s how you run depreciation schedules, that dictates everything. But Adam Smith alsotalked about in that book, another valuation technique, which is valuation in use, which is an economics communication , not an accounting exchange. And when I realizedthat my chassis was wrong, yeah, I had the right book.I had Adam Smith, I had”Wealth of Nations.” I had all that good stuff, butI hadn’t speak the whole book. I had missed this whole conceptabout the economic value, where quality is determined bynot how much someone’s willing to pay you for it, but thevalue you can drive by applying it. So, Jeff, when thatperson stimulated that mention, the entire investigate assignment, and I got to tell you, my entire animation did a total 180, right? Just total of 180 degreechange of how I was thinking about data as an asset. >> Right, well, Bill, it’s funny though, that’s kind of captivated, I always think of kind offinance versus accounting, right? And then you’re right on accounting. And we learn a great deal ofthings in accounting. Mostly we learn morethat we don’t know, but it’s really hard to putit in an record framework, because as you told me, it’snot like a regular asset.You can use it a lot of ages, you can use it across lots of use events, it doesn’t degradate over duration. In fact, it used to be a liability.’ lawsuit you had to buy all this hardware and software to maintain it. But if you look at the finance side, if you look at the pure playinternet firms like Google, like Facebook, like Amazon, and you look at their valuation, right? We used to have this thing, we still have this thing announced Goodwill, which was kind of this captivate between what the market establishedthe value of the company to be. But wasn’t manifested whenyou summing-up up all the assets on the balance sheet andyou had this leftover thing, you could just plug in goodwill. And I would hypothesize that for these large-hearted monstrous tech companies, world markets has cooked in the value of the data, has kind of putin that present value on that for a long period of timeover multiple projects.And we see it capturedprobably in goodwill, versus being kind of called out as an individual balance sheet item. >> So I don’t think it’s, I don’t know accounting. I’m not an accountant, thank God, right? And I know that goodwillis one of those things if I remember from myMBA program is something that when you buy a company and you look at the quality you paidversus what it was worth, it stuck into thiscategory called goodwill, because no one knew how to figure it out.So the company at bookvalue was a billion dollars, but you paid five billion for it. Well, you’re not an idiot, so that four billion extrayou paid must be in goodwill and they’d stick it in goodwill. And I think there’s actually a method that goodwill goes devalued as well. So it could be that, but I’m totally away from the accounting framework. I think that’s confusing, trying to work within the gaprules is more of an inhibitor. And we talk about the Googlesof the world and the Facebooks of the world and the Netflixof the world and the Amazons and companies that aregreat at monetizing data. Well, they’re great at monetizing it because they’re not sellingit, they’re using it. Google is using their datato dominate search, right? Netflix is using it to be theleader in on-demand videos. And it’s how they use all the data, how they use the insightsabout their purchasers, their produces, and the continuing operation to truly drive brand-new sources of value.So to me, it’s this, whenyou start thinking about from an economicsperspective, for example, why is the same car that Ibuy and an Uber driver buys, why is that car morevaluable to an Uber driver than it is to me? Well, the bottom line is, Uber drivers are going to use that car to generate value, right? That $40,000, that vehicle theybought is worth a lot more, because they’re going touse that to generate value. For me it sits in the drivewayand the chicks poop on it. So, right, so it’s thisvalue in use concept. And when agencies can establish that, by the way, most organizationsreally struggle with this. They struggle with this price in use concept. They just wanted to, when you talk tothem about data monetization and say, “Well, I’m thinkingabout the main data officer, try not to trying to selldata, slapping on doorways, shaking their tin beaker, saying,’ Buy my data.'” No , no one wants your data. Your data is more valuablefor how you use it to drive your operations then it’s a sell to somebody else .>> Right, right. Well, on of the other thingsthat’s really important from an financials conceptis dearth, right? And a whole lot of economicsis driven around scarcity. And how do you price for dearth so that the market evens out and the price competitions up to the supply? What’s interesting aboutthe data concept is, there is no scarcity anymore. And you are well aware, you’ve sketched and everyone has giant numbersgoing up into the right, in terms of the quantity of the data and how much data thereis and is going to be. But what you point out extremely eloquently in this paper is the scarcityis around the resources to actually do the work on the data to get the value out of the data. And I think there’s just thisinteresting step office between simply raw data, which has really no valuein and of itself, right? Until you start to applysome theories to it, you start to analyze it. And most importantly, that you have some situation by which you’re doing all this analysis to then drive that value.And I thought it wasreally an interesting part of this paper, which isget beyond the arguing that we’re kind of discussing here and get into some specificswhere you can measure value around a specific business objective. And not only that, butthen now the asset of the resources on top of the data to be able to extract the significance to then drive yourbusiness process for it. So it’s a really differentway to think about dearth , not on the data per se, but on the ability todo something with it. >> You’re recognise on, Jeff, because companies don’t miscarry because of a lack of use instances. They neglect because they have too many. So how do you prioritize? Now that scarcity is notan issue on the data side, but it is this issue onthe people riches side, you don’t have unlimiteddata scientists, right? So how do you prioritize andfocus on those opportunities that are most important? I’ll tell you, that’s not adata science conference, that’s a business conversation, right? And figuring out how you alignorganizations to identify and focus on those application casesthat are most important.Like in the paper we go throughseveral different use bags abusing Chipotle as two examples. The is why I pickedChipotle is because, well, I like Chipotle. So I could go there and Icould write it off as investigate. But there’s a, think aboutthe number of use cases where a company like Chipotle or any other companycan leverage your data to drive their key business initiatives and their key operational exert occurrences. It’s almost unbounded, which by the way, is a huge challenge. In fact, I remember partof the problem we read with a lot of organizations is because they do such a good job of prioritizing and focusing, they try to solve the entire problem with one big die swoop, right? It’s slightly the oldERP big-hearted blow projects.Well, I’m just going to spend $20 million to buy this analyticcapability from company X and I’m going to install it andthen magic happens next. And then spell is going to happen, right? And then wizard happens next, right? And magic never happens. We get crickets instead, because the biggest challenge isn’t around how do I leverage the data, it’s about where do I start? What difficulties do I go after? And how do I make sure theorganization is bought in to basically use case by employ bag, build out your data andanalytics architecture and capabilities. >> Yeah, and you start backwards from certainly specific business objectives in the use cases thatyou summarize here, right? I want to increase my median ticket by X. I want to increase myfrequency of visits by X. I want to increase theamount of components per lineup from X to 1.2 X, or 1.3 X. So from there you get anice kind of large-scale income thump that you are eligible to propose aroundand then work downwards into the amount of try that it takes and then you can come up, “Is this a good investment or not? ” So it’s a really different road to get back to the value of the data.And more importantly, theanalytics and the work to actually call out the information. >> The technologies, the dataand analytic technologies available to us. The very composablenature of these are adequate to take this use caseby use case approach. I can construct out my datalake one use case at a time. I don’t need to stuff 25 datasources into my data lake and hope there’s someone more valuable. I can use the first use action to say, “Oh, I need these three datasources to solve that use case. I’m going to employ those threedata sources in the data lake. I’m going to go throughthe entire curation process of moving sure the data hasbeen converted and cleansed and aligned and improved and complied with of, all the other governance, all that kind of stuff this goes on. But I’m going to do thatuse action by squander client,’ compel a abuse suit can tellme which data sources are most important for that given situation.And I can build up my data lake and I can build up my analyticsthen one use case at a time. And there is a hugeimpact then, gargantuan impact when I construct out application dispute by application speciman. That does not happen. Let me shed somethingthat’s not really covered in the paper, but it is very much covered in my brand-new volume that I’m working on, which is, in knowledge-based industries, the economies of learningare more powerful than the economies of scale. Now think about that for a second. >> Say that again, say that again. >> Yeah, the economies oflearning are more powerful than the economies of scale.And what that implies is what Ilearned on the first consume occasion that I improve out, Ican apply that learning to the second use case, to the third use case, to the fourth operation client. So when I employed my data into my data lake for my first utilize occurrence, and the working paper handles this, well, once it’s in my data lake, the cost of reusing that data in a second, third and fourth call bags is basically, you know negligible expenditure is zero. So I get this ability tolearn about what information and data are most important and to reapplythat across the organization. So this learning concept, Ilearn use case by implementation event, I don’t have to do a bigeconomies of scale approach and start with 25 datasetsof which only 3 or four might be useful.But I’m incurring the overhead for all those othernon-important data sets because I didn’t takethe time to go through and figure out what are mymost important use cases and what data do I need to support those use bags. >> I make, should peopleeven must be considered the data per se or should they reallyreadjust their imagine around the application of the data? Because the data in and ofitself means nothing, right? 55, is that fast or gradual? Is that old-time or young? Well, it depends on a whole lot of things. Am I strolling or am I ina brand new Corvette? So it simply, it’s funny to methat the data in and of itself certainly doesn’t have any valueand doesn’t really equip any counseling into adecision or a higher order, predictive analytics until youstart to manipulate the data. So is it even the wrong discussion? Is data the liberty discussion? Or should we really betalking about the capabilities to do nonsense within and reallyget beings focused on that? >> So Jeff, there’s so manypoints to hit on there.So the application ofdata is what’s the importance, and the queue of youguys used to be prominent for saying, “Separatingnoise from the signal.” >> Signal from the racket. Signal from noise levels, right. Well, how do you know inyour dataset what’s signal and what’s noise? Well, the use case will tell you.If you don’t know the usecase and you have no way of figuring out what’s important. One of the things I use, I still rail against, and it happens still. Somebody will stroll up mydata science team and say, “Here’s some data, tell mewhat’s interesting in it.” Well, how do you separatesignal from sound if I don’t know the use case? So I think you’re place on, Jeff. The direction to be considered thisis, don’t become data-driven, become value-driven and valueis driven from the use case or the application or theuse of the data to solve that particular application case.So the organisations that getfixated on being data-driven, I detest the expression data-driven. It’s like as if there’ssome sort of frigging trickery from having data. No, data has no value. It’s how you use it toderive patron concoction and operational insightsthat drive ethic,. >> Right, so there’s aninteresting pace serve, and we talk about it all the time. You’re out in the grass, working with Chipotle lately, and increase theiraverage ticket by 1.2 X. We talk more now, various kinds of conceptually. And one of the large kindof conceptual holy grails within a data-driven economy is kind of working up this gradation function.And you’ve talked about it now. It’s from descriptive, todiagnostic, to predictive. And then the Holy grail prescriptive, we’re way ahead of the curve. This will enter into tons of material around unscheduled upkeep. And you know, there’s a lotof specific lotions, but do you think we spend toomuch time kind of shooting for the fourth guild of greatness impact, instead of kind offocusing on the small acquires? >> Well, you certainly haveto build your course there. I don’t think you can get to prescriptive without doing predictive, and you can’t do predictive without doing illustrative and such.But let me throw areally one at you, Jeff, I think there’s evenone beyond prescriptive. One we’re talking more and more about, autonomous, a ton of analytics, right? And one of the thingsthat paper talked about that didn’t click with meat the time was this idea of orphaned analytics. You and I kind of talked aboutthis before the ask here. And one thing we noticed inthe research was that a lot of these very matureorganizations who had advanced from the retrospective analyticsof BI to the descriptive, to the predicted, to the prescriptive, they were building one offanalytics to solve a problem and get ethic from it, but never reusing thisanalytics over and over again. They were done one off andthen they were thrown away and these organizationswere so good at data science and analytics, that it was easier for them to precisely construct from scratchthan to try to dig around and try to find something thatwas never actually ever built to be reused. And so I have this whole ideaof orphaned analytics, right? It didn’t really occur to me.It didn’t make any sense intome until I read this paraphrase from Elon Musk, and ElonMusk made this statement. He says, ” I believe thatwhen you buy a Tesla, you’re buying an assetthat admires in significance , not depreciates through usage.” I was thinking, “Wait asecond, what does that planned? ” He didn’t actually sayit, “Through usage.” He said, “He believesyou’re buying an asset that regards notdepreciates in value.” And of course the firstresponse I had was, “Oh, it’s like a 1964 and a half Mustang. It’s rare, so everybody isgoing to want these things. So buy one, put it in your garage. And 20 years later, you’re bringing it out and it’s worth more money.” No , no, there’s 600,000 of these things ranging around the streets, they’re not uncommon. What he signified is that he isbuilding an autonomous asset. That the more that it’s used, the more valuable it’sgetting, the most reliable, the more efficient, the more predictive, the more safe this asset’s getting.So there is this level beyond prescriptive where we can think about, “How do we leverageartificial intelligence, reinforcement, learning, deep memorize, to build these resources that the more that they are used, the smarter they get.” That’s beyond prescriptive. That’s an environment wherethese things are learning. In many cases, they’relearning with minimal or no human intervention. That’s the real aha moment. That’s what I miss with orphaned analytics and why it’s important to build analytics that can be reused over and over again.Because every time you use these analytics in a different use case, they get smarter, they get most valuable, they get more predictive. To me that’s the ahamoment that blew my recollection. I realise I had missedthat in the paper perfectly. And it took me basicallytwo years later to realize, dough, I missed the mostimportant part of the paper. >> Right, well, it’s aninteresting take really on why the valuation I wouldargue is reflected in Tesla, which is a function of the data. And there’s a phenomenalvideo if you’ve never seen it, where they have autonomous vehicle epoch, it might be a year or so old.And he’s got his number one engineer from, I conclude the MicroprocessorGroup, The Computer Vision Group, as well as the autonomous driving radical. And there’s a couple of reallygreat theories I want to follow up on what you said. One is that they have thisthing announced The Fleet. To your point, there’shundreds of thousands of these things, if theyhaven’t ten-strike a million, that are calling homereporting home every day as to exactly how everyonetook the Northbound 101 on-ramp off of University Avenue. How fast make they start? What boundary did they take? What G-forces did they make? And each and every one of thosecars feeds into the system, so that when they dothe autonomous inform , is not simply are they usingall their regular things that they are able to use to mapout that 101 Northbound entry, but they’ve got all thedata from all the cars that have been doing it. And you are well aware, when that other car, the autonomous auto coupleyears ago thumped the pedestrian, I think in Phoenix, which isnot good, sad, killed a person, dark tough situation.But you know, we are doingan autonomous vehicle testify and the chap who made a reallyinteresting point, right? That when something like that happens, often if I was in a carwreck or you’re in a gondola shipwreck, hopefully not, I learned theperson that we made learns and maybe a couple of observers learn, maybe labour inspectors. >> But nobody else learns. >> But nobody else learns. But now with the autonomy, every single person can learn from every single experience with every vehicle contributingdata within that fleet. To your point, it’s justan order of magnitude, different acces to think about things .>> Think about a 1% improvementcompounded 365 goes, equals I envisage 38 X better. The supremacy of 1% improvementsover these 600,000 plus autoes that are learning. By the direction, even when the autonomous FSD, the full self-driving modemodule isn’t turned on, even when it’s not turnedon, it runs in darknes state. So it’s learning from the human drivers, the human overlords, it’s constantly learning. And by the way , not only they’recollecting all this data, I did a little research, I pulled out some of their job search ads and they’ve built agiant simulator, right? And they’re there basically each night, simulating billions andbillions of more driven miles because of the simulator. They are building, he’sgoing to have a simulator , not only for driving, but think about allthe data he’s capturing as these autoes are riding down the road.By the acces, they don’t useLidar, they use video, right? So he’s driving by plazas. He knows how many automobiles are in the mall. He’s driving down streets, heknows how age-old the cars are and which ones should be replaced. I necessitate, he has this, he’s sitting on thisincredible opulence of data. If anybody could simulatewhat’s going on in the world and figure out how to getout of this COVID problem, it’s probably Elon Muskand the data he’s captured, be courtesy of all those cars. >> Yeah, yeah, it’s really interesting, and we’re assure it now. There’s a new autonomousdrone out, the Skydio, and they are only announcedtheir commercial commodity. And again, it completelychanges the way you think about how “youre using” that tool, because you’ve just eliminatedthe complexity of driving. I don’t want to drive that, I want to tell it what to do. And so you’re saying, this whole application of air force and companies around things like quantifying collections of coaland weighing these enormous assets that are volume metric quantified, that these things can go and map out and farming, et cetera, et cetera.So the sovereignty case, that’s really insightful. I want to shift gears a littlebit, Bill, and talk about, “youve had” some thoughts inhere about thinking of data as an resource, data as acurrency, data as monetization. I mean, how should people think of it?’ Cause I don’t thinkcurrency was good. It’s really not kindof an exchange of value that we’re doing thiskind of classic asset. I guess the data asoil is horrible, right? To your point, it doesn’tget burned up once and can’t be used again. It can be used over and over and over. It’s basically like feedstockfor all kinds of stuff, but the feedstock never goes away. So again, or is it that eventhe right way to think about, do we really need toshift our discussion and get past the idea ofdata and get much more into the idea of informationand actionable information and useful informationthat, oh, by the way, happens to be powered bydata for the purposes of the reports? >> Yeah, good question, Jeff.Data is an asset in the sameway that a human is an asset. But only having humans in yourcompany doesn’t drive value, it’s how you use those humans. And so it’s really again the application of the data around the use cases. So I still recollect data is anasset, but I don’t want to, I’m not fixated on, putit on my sector balance sheets. That delightful talk about putit on a balance sheet, I immediately positioned the blinders on. It inhibits what I can do. I want to think about thisas an resource that I can use to drive value, price to my clients. So I’m trying to learn moreabout my customer’s propensities and inclinations andinterests and feelings, and try to learn the samething about my car’s behaviors and tendencies and myoperations have partialities. And so I do mull data is an asset, but it’s a latent asset in the sense that it has possible ethic, but it actually has no value per se, inputting it into a balance sheet.So I think it’s an resource. I worry about the accountingconcept medially hijacking what we can do with it. To me the value of databecomes and how it interacts with, maybe with other assets. So maybe data itselfis not so much an resource as it’s gasoline for drivingthe value of resources. So, you are well aware, it fuels my give cases. It fuels my ability to retain and get more out of my customers. It fuels ability to predict whatmy commodities are going to break down and even haveproducts who self-monitor, self-diagnosis and self-heal. So, data is an asset, butit’s only a latent asset in the sense that it sits there and it doesn’t have any appreciate until you actually applied something to it and stupor it into action. >> So let’s shift gears alittle bit and start talking about the data and talkabout the human factors.’Cause you told me, one of thechallenges is parties trying to bite off more than they can chew. And we have the role ofchief data officer now. And to your point, maybethat goos things up more than it helps. But in all the customercases that you’ve worked on, is there a consistent kindof motif of behavior, temperament, types of projectsthat enables some individuals to grab those resourcesto apply to their data to have successful programmes, because to your point there’s too much data andthere’s too many jobs and you talk a lot about prioritization. But there’s a great deal of assumptionsin the prioritization pose that you are eligible to, that youknow a whole lot of things, especially if you’re comparingproject A over in group A with programme B, with group B and the two may not reallyknow the economics across that.But from private individuals personwho understands the potential, what opinion do you give them? What kind of characteristics do you accompany, either in the type of theproject, the characteristics of the boss, the type of the individualthat really gives itself to a higher probabilityof a successful outcome? >> So first off you need to find somebody who has a vision for howthey want to use the data, and not just collect it. But how they’re going totry to change the fates of the organisation. So it always takes avisionary, was not possible to the CEO, might be somebody who’sa head of marketing or heads of state of logistics, or it could be a CIO, it could be a chief data officer as well. But you’ve got to find is someone who says, “We have this latent assetwe could be doing more with, and we have a series oforganizational difficulty challenges against which I could refer this asset. And I need to be the matchmakerthat delivers these together.” Now the tool that I thinkis the most powerful tool in marrying the latentcapabilities of data with all the revenuegenerating opportunities in the application side, becausethere’s a countless figure, the most important tool that I detected doing that is design thinking.Now, the reason why I thinkdesign reputing is just as important, because one of the thingsthat design conjecture does a great job is it devotes everybodya articulation in the process of defining, supporting, appreciating, and prioritizing operation casesyou’re going to go after. Let me say that again. The challenge organizationshave is identifying, supporting, appraising, and prioritizing theuse instances they want to go after. Design thinking is a stupendous tool for driving organizationalalignment around where we’re going to startand what’s going to be next and why we’re going to start there and how we’re going tobring everybody together.Big data and data scienceprojects don’t die because of technology failure. Most of them die because ofpassive aggressive actions in the organization thatyou didn’t produce everybody into the process. Everybody’s utter didn’tget a chance to be heard. And that one person who’svoice didn’t get a chance to do listen, they’re going to get you. They may own a certain piece of data. They may own something, butthey’re just waiting and arrange, they’re just laying therewaiting for their chance to come up and snag it. So what you got to do is you got to proactively imparting these beings together. We announce this, this is part ofour value engineering process. We have a value engineeringprocess around foreseeing where we bring all these people together.We help them to understand how data in itself is a latent asset, but how it can be used froman fiscals perspective, drive all those value. We get them all fired up onhow these can solve any one of these use events. But you got to start with one, and you’ve got to embrace this idea that I can build out my dataand analytic capabilities, one use case at a time.And the first operation caseI go after and solve, starts my second one easier, draws my third one easier, right? It has this ability thatwhen you start exiting usage case by squander dispute two reallymagical things happen. Number one, your insignificant expenditure flatten. That is because you’re building out your data lagoon one use case at a time, and you’re bringing allthe important data lake, that data lagoon one use case at a time. At some point in time, you’ve got most of theimportant data you need, and the ability that you don’t need to add another data source. You got what you need, so your insignificant costs start to flatten.And by the way, if youbuild your analytics as composable, reusable, continuous hear analytic resources , not as orphaned analytics, pretty soon you have all theanalytics you need as well. So your insignificant penalty flatten, but impact number two is that you’ve, because you’ve have thedata and the analytics, I can intensify time to value, and I can de-risked projectsas I move use action by consume suit. And so then the biggestchallenge becomes not in the data and the analytics, it’s getting the all the businessstakeholders to agree on, here’s a roadmap we’re going to go after.This one’s first, andthis one is going first because it helps to drive the value of the second and third one. And then this one drives this, and you create a wholeroadmap of ruffling through of how the data and analyticsare driving this value to across all these use examples at a insignificant expense approaching zero. >> So should we have chiefdesign thinking officers instead of chief data officers that are actually actually movethe data process along? I make, I firstly heard aboutdesign thinking years ago, actually interviewing DanGordon from Gordon Biersch, and they only, he had just hireda couple of Stanford grads, I think is where they pioneered it, and they were doing somework about feeing, I think it was a a newapple-based alcoholic beverage, apple cider, and theytalked a great deal about it.And it’s pretty interesting, but I represent, are you seeing design theory proliferate into the organizations that you work with? Either formally as designthinking or as some source of it that attracts some of those properties that you highlighted thatare so key to success? >> So I think we’re seeingthe birth of this new role that’s marrying capabilitiesof motif conceiving with the capabilitiesof data and analytics. And they’re holler this buster or dudette the premier invention officer. Surprise. >> Title for someone we know. >> And I got to tell a little story. So I have a very experienceddesign thinker on my squad. All of our data science projects have a design thinker on them. Every one of our data scienceprojects has a design thinker, because the nature of how you build and successfully executea data science assignment, sits almost exactly howdesign concluding works.I’ve written several papers onit, and it’s a stupendous space. Design thinking and datascience are different line-ups of the same coin. But my respect for data science or for pattern concluding tooka major shot in the arm, major elevate when my designthinking party on my crew, whose name is John Morley pioneered me to a elderly data scientist at Google. And I was foot coffee.I said, “No, ” this is back in, before I even joinedHitachi Vantara, and I said, “So tell me the secret toGoogle’s data discipline success? You guys are stupendous, you’re doing things that no one else was even contemplating, and what’s your key to success? ” And he giggles and shrieks andhe goes, “Design thinking.” I disappear, “What the hell is that? Design thinking, I’ve never even heard of the stupid thing before.” He runs, “I’d make a deal with you, Friday afternoon let’s daddy over to Stanford’s B clas and I’ll teach you about designing thinking.” So I went with him on aFriday to the d.school, Design School over atStanford and I was blown away , not only in how designthinking was used to ideate and introducing and to explore. But I was blown away about how potent that theory is when youmarry it with data science. What is data sciencein its simplest appreciation? Data science is about identifyingthe variables and metrics that might be betterpredictors of performance.It’s that might phrasethat’s the real key. And who are the peoplewho have the best penetrations into what importances or metrics orKPIs you might want to test? It ain’t the data scientists, it’s the subject matterexperts on the business side. And when you use design contemplation to bring this subject matter professionals with the data scientists together, all kinds of magic stuff happens. It’s unbelievable how well it labor. And all of our projectsleverage blueprint contemplating. Our whole value engineeringprocess is built around marrying designthinking with data science, around this prioritization, around these concepts of, all intuitions are worthy of consideration and all tones need to be heard. And the relevant recommendations how you hug ambiguity and diversification of perspectivesto drive innovation, it’s marvelous. But I feel like I’m a lonevoice out in the wilderness, crying out, “Yeah, Teslagets it, Google gets it, Apple gets it, Facebook gets it.” But you are well aware, most otherorganizations in the world, they don’t think like that.They review scheme thinkingis this Wufoo thing. Oh yeah, you’re goingto bring people together and sing Kumbaya. It’s like, “No, I’m not singing Kumbaya. I’m picking their abilities becausethey’re going to help make their data science teammuch more effective and knowing what problemswe’re going to go after and how I’m going to measuresuccess and progress. >> Maybe that’s the nextDean for the next 10 times, the Dean of design thinkinginstead of data science, and who knew they’re one and the same? Well, Bill, that’s a superinsightful, I want, it’s so, is validated and supported by the trends that we find everybody, really in terms of democratization, right? Democratization of appropriate tools, more beings having accessto data, more rulings, more perspective, morepeople that have the ability to manipulate the dataand mostly experiment, does drive better business outcomes.And it’s so consistent. >> If I could add one thing, Jeff, I are of the view that what’s reallypowerful about blueprint conceiving is when I think about what’s happening with neural networks or AI, there’s all these conferences about, “Oh, AI is going towipe out all these positions. Is going to take all these chores away.” And what we’re actuallyfinding is that if we think about machine learning, drivenby AI and human empowerment, driven by design thinking, we’re watch the opportunity to exploit these economies of learning at the front lines whereevery customer engagement, every operationalexecution is an opportunity to gather not only more data, but to gather more memorizes, to sanction the humans at the front lines of the organization toconstantly be seeking, to try different things, to explore and to learn fromeach of these involvements. I think it’s, AI to meis incredibly powerful.And I think about it as asource of driving more learning, a perpetual learningand continuously adapting an organization whereit’s not just the machines that are doing this, but it’s the human rights who’ve been are equipped to do that. And my period nine in my brand-new bible, Jeff, is all about team empowerment, because good-for-nothing you do withAI is going to matter of doodly-squat if you don’t have empoweredteams who know how to take and leverage that continuouslearning opportunity at the front lines of customerand functional commitment. >> Bill, I couldn’t gave a better, I think we’ll leave it there. That’s a great close, whenis the next record coming out? >> So today I do my secondto last final review. Then it goes back to theeditor and he does a review and we start looking at formatting. So I think we’re probablyfour to six weeks out. >> Okay, well , thank you very much, congratulations on all the success.I just adoration how the Deanis really the Dean now, teaching all over theworld, sharing the knowledge and attacking some of these big problems. And like all huge financials questions, often the answer is not fiscals at all. It’s completely really twist the lens and don’t think of it inthat, all that construct. >> Exactly. >> All freedom, Bill. Thanks again and have a great week. >> Thanks, Jeff. >> All claim. He’s Bill Schmarzo, I’m Jeff Frick. You’re watching theCUBE. Thanks for watching, we’ll see you next time.( soothing music ).
