(upbeat music) >> Welcome to transforming ideas into insights, presented with theCUBE and made possible by Cloudera. My name is Dave Vellante from theCUBE and I'll be your host for today. In the next hundred minutes, you're going to hear how to turn your best ideas into action using data and we're going to share the real-world examples of 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing efficiencies, better forecast retail demand, transform analytics, improve public sector service and so much more. How we use data is rapidly evolving. That is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to, rather we use terms like, digital transformation and digital business. But you think about it. What is a digital business? How is that different from just a business? Well, a digital business is a data business and it differentiates itself by the way it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such, the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings, but increasingly insights are leading to the development of data products and services that can be monetized. Or as you'll hear in our industry examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. Self-service investing, filing insurance claims on our smart phones and so many examples. IOT systems that communicate and act machine to machine. Real-time pricing actions, these are all examples of products and services that drive revenue, cut costs or create other value and they all rely on data. Now, while many business leaders sometimes express frustration that their investments in data, people and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data transformation and leadership. One thing is certain, the next 10 years of data and digital transformation won't be like the last 10. So let's into it. Please join us in the chat. You can ask questions. You can share your comments. Hit us up on Twitter. Right now, it's my pleasure to welcome Mick Holliston and he's the president of Cloudera. Mick, great to see you. >> Great to see you as well, Dave. >> Hey, so I call it the new abnormal, right? The world is kind of out of whack. Offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations, line cooks at restaurants. Everything that we consumers have missed, but here's the one thing, it seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling, their pricing algorithms, commodity prices, we don't know. Is inflation transitory? Is it a long-term threat, trying to forecast GDP? It seems like we have to reset all of our assumptions and Mick, I feel a quality data is going to be a key here. How do you see the current state of the industry in the role data plays to get us into a more predictable and stable future? >> Well, I can sure tell you this, Dave, out of whack is definitely right. I don't know if you know or not, but I happened to be coming to you live today from Atlanta and as a native of Atlanta, I can tell you there's a lot to be known about the airport here. It's often said that whether you're going to heaven or hell, you got to change planes in Atlanta and after 40 minutes waiting on an algorithm to be right for baggage claim last night, I finally managed to get some bag and to be able to show up, dressed appropriately for you today. Here's one thing that I know for sure though, Dave. Clean, consistent and safe data will be essential to getting the world and businesses as we know it back on track again. Without well-managed data, we're certain to get very inconsistent outcomes. Quality data will be the normalizing factor because one thing really hasn't changed about computing since the dawn of time, back when I was taking computer classes at Georgia Tech here in Atlanta and that's what we used to refer to as garbage in, garbage out. In other words, you'll never get quality data-driven insights from a poor dataset. This is especially important today for machine learning and AI. You can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid. As AI is increasingly used in every business app, you must build a solid data foundation. >> Mick, let's talk about hybrid. Every CXO that I talked to, they're trying to get hybrid right. Whether it's hybrid work, hybrid events, which is our business, hybrid cloud. How are you thinking about the hybrid everything, what's your point of view? >> With all those prescriptions of hybrid and everything, there was one item you might not have quite hit on, Dave and that's hybrid data. >> Oh yeah, you're right, Mick, I did miss that. What do you mean by hybrid data? >> Well, Dave, in Cloudera, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now, every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises, in a private cloud, in public cloud or perhaps even in a new open data exchange. Now, this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud. But either way, security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch or read a recent news story. Data breaches are at an all time high and the ethics of AI applications are being called into question everyday. And understanding lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know, Dave, that's the business Cloudera is in. On a serious note, from Cloudera's perspective, adopting a hybrid data strategy is central to every business' digital transformation. It will enable rapid adoption of new technologies and optimize economic models, while ensuring the security and privacy of every bit of data. >> Okay, Mick, I'm glad you brought in that notion of hybrid data because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You got the economics, the physics, the local laws come into play, so what about the rest of hybrid? >> Yeah, that's a great question, Dave and certainly, Cloudera itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind, the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office ethernet may not be happening quite so fast in somebody's rural home in the middle of Nebraska somewhere. So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, involve us kind of slowly coming back to work, beginning this fall. And we're looking forward to being able to shake hands and see one another again for the first time, in many cases, for more than a year and a half. But yes, hybrid work and hybrid data are playing an increasingly important role for every kind of business. >> Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, here's how I think about it. I mean, some industries have transformed. You think about retail, for example, it's pretty clear. Although, every physical retail brand I know has not only beefed up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence. And reverse, we see Amazon building out physical assets, so there's more hybrid going on. But when you look at healthcare, for example, it's just starting with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control of payment systems. In manufacturing, the pandemic highlighted, America's reliance on China as a manufacturing partner and supply chain. And so my point is, it seems at different industries, they're in different stages of transformation, but two things look really clear. One, you got to put data at the core of the business model, that's compulsory. It seems like embedding AI into the applications, the data, the business process, that's going to become increasingly important. So how do you see that? >> Wow, there's a lot packed into that question there, Dave. But yeah, at Cloudera, I happened to be leading our own digital transformation as a technology company and what I would tell you there that's been an interesting process. The shift from being largely a subscription-based model to a consumption-based model requires a completely different level of instrumentation in our products and data collection that takes place in real-time, both for billing for our customers and to be able to check on the health and wellness, if you will, of their Cloudera implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed the rate and pace of getting vaccines to market or we've been assisting with testing process that's taken place. Because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and healthy and safe outcomes for everyone. And Cloudera has been underneath a great deal of that type of work. And the financial services industry you pointed out, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are helping those kinds of organizations get through those difficult challenges. You also happened to mention public sector and in public sector, we're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. And while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, you can't build great ML apps unless you've got a strong data foundation underneath. It's back to that garbage in, garbage out comment that I made previously. And so, in order to do that, you've got to have a great hybrid data management platform at your disposal to ensure that your data is clean and organized and up to date. Just as importantly from that, that's kind of the freedom side of things. On the security side of things, you've got to ensure that you can see who's touched not just the data itself, Dave, but actually the machine learning models. And organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage in addition to data lineage. In other words, who's had access to the machine learning models? When and where and at what time and what decisions were made perhaps, by the humans, perhaps by the machines that may have led to a particular outcome? So, every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models, just as they can track the lineage of data. So, lots going on there across industries. Lots going on as those various industries think about how AI can be applied to their businesses. >> It's a pretty interesting concept you're bringing into the discussion, the hybrid data, sort of, I think new to a lot of people. And this idea of model lineage is a great point because people want to talk about AI ethics, transparency of AI. When you start putting those models into machines to do real-time inferencing at the edge, it starts to get really complicated. I wonder if we could talk, we're still on that theme of industry transformation. I felt like coming into the pre-pandemic, there was just a lot of complacency. Yeah, digital transformation and a lot of buzz words and then we had this forced march to digital, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? >> There definitely is a lot of a POC limbo or I think some of us internally have referred to as POC purgatory, just getting in that phase, not being able to get from point A to point B in digital transformation. And for every industry, transformation, change in general, is difficult and it takes time and money and thoughtfulness. But like with all things, what we've found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts. To say it another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts, as it relates to the underlying technology here and to bring it home a little bit more practically, I guess I would say. At Cloudera, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, right close to home. In place, they can kind of experiment a little bit more safely and economically and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplished, but kind of starting small and then drawing fast from there on customer's journey to the cloud. >> Well Mick, we've covered a lot of ground. Last question, what do you want people to leave this event, this session with and thinking about sort of the next era of data that we're entering? >> Well, it's a great question, but I think it could be summed up in two words. I want them to think about a hybrid data strategy. So, really hybrid data is a concept that we're bringing forward on this show really, for the first time, arguably. And we really do think that it enables customers to experience what we refer to, Dave, as the power of ANT. That is freedom and security and in a world where we're all still trying to decide whether each day when we walk out, each building we walk into, whether we're free to come in and out with a mask, without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe for ourselves and for others. And the same is true of organization's IT strategies. They want the freedom to choose, to run workloads and applications in the best and most economical place possible, but they also want to do that with certainty that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So, hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole. >> Nick, thanks so much, great conversation. I really appreciate the insights that you're bringing to this event, into the industry, really. Thank you for your time. >> You bet, Dave, pleasure being with you.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Worldwide Public sector Welcome to the cubes Coverage of AWS 2020. This is specialized programming for the worldwide public sector. I'm Lisa Martin, and I'm joined by Mick Boccaccio, the security advisor at Splunk Met. Welcome to the Q Virtual Oh, >>thank you for having me. It's great to be here. >>So you have a really interesting background that I wanted to share with our audience. You were the first see so in the history of U. S presidential campaigns with Mayor Pete, you were also branch shape of Threat intelligence at the executive office of the President. Tell us something about about your background is so interesting. >>Uh, yeah, those and I'm a gonna Def con and I teach lock picking for funds. Ease working for Mayor Pete A. C. So the campaign was really, really unique opportunity and I'm glad I did it. I'm hoping that, you know, on both sides of the aisle, no matter what your political preference, people realize that security and campaigns can only be married together. That was an incredible experience and worked with Mayor P. And I learned so much about how campaigns work and just the overall political process. And then previous to that being at the White House and a threat intelligence, role of branch chief they're working over the last election, the 2016 election. I think I learned probably more than any one person wants Thio about elections over that time. So, you know, I'm just a security nerd. That kind of fell into those things. And and and here I am and really, really, really just fortunate to have had those experiences. >>Your phone and your email must have been blowing up the last couple of weeks in the wake of the US presidential election, where the word fraud has brought up many times everyday. But election security. When I saw that you were the first, see so for Pete Buddha Judge, that was so recent, I thought, Really, Why? Why are they just now getting folks like yourself? And you are a self described a cybersecurity nerd? Why are they Why were they just recently starting to catch on to this? >>I think it's, uh like security on the campaign and security anywhere else on credit to the Buddha Judge campaign. There is no federal or mandate or anything like that that says your campaign has toe have a security person at the head of it or any standards to implement those security. So you know that the Buddha Judge campaign kind of leaned into it. We wanna be secure. We saw everything that happened in 2016. We don't want that to be us. And I think Mawr campaigns are getting on that plane. Definitely. You know, you saw recently, uh, Trump's campaign, Biden's campaign. They all had a lot of security folks in, and I think it's the normal. Now people realize how important security is. Uh, not only a political campaign, but I guess the political process overall, >>absolutely. We've seen the rise of cyber attacks and threats and threat vectors this year alone, Ransomware occurring. Everyone attack every 11 seconds or so I was reading recently. So give me an other view of what the biggest threats are right now. >>Two elections and I think the election process in general. You know, like I said, I'm just a security nerd. I've just got a weird background and done some really unique things. Eso I always attack the problems like I'm a security nerd and it comes down to, you know that that triumvirate, the people process and technology people need had to have faith in the process. Faith in the technology. You need to have a a clear source to get their information from the process. To me, I think this year, more than previous elections highlighted the lack of a federal uniforms standard for federal elections. State the state. We have different, different standards, and that kind of leads to confusion with people because, hey, my friend in Washington did it this way. But I'm in Texas and we do it this way. And I think that that standard would help a lot in the faith in the system. And then the last part of that. The technology, uh, you know, voting machines campaigns like I mentioned about campaigns. There's nothing that says a campaign has toe have a security person or a security program, and I think those are the kind of standards for, you know, just voting machines. Um, that needs to be a standard across the board. That's uniforms, so people will will have more faith because It's not different from state to state, and it's a uniformed process. >>E think whole country could have benefited from or uniformed processes in 2020. But one of the things that I like I did my first male and fellow this year always loved going and having that in person voting experience and putting on my sticker. And this year I thought in California we got all of our But there was this massive rise in mainland ballots. I mean, think about that and security in terms of getting the public's confidence. What are some of the things that you saw that you think needs to be uniforms going forward >>again? I think it goes back to when When you look at, you know, you voted by mail and I voted absentee and your ballot was due by this date. Um, you know where I live? Voting absentee. It's Dubai. This state needs we received by the state. Andi, I think this year really highlighted the differences between the states, and I'm hoping that election security and again everyone has done a super fantastic job. Um, sister has done incredible. If you're all their efforts for the working with election officials, secretaries of states on both sides of the aisle. It's an incredible work, and I hope it continues. I think the big problem election security is you know, the election is over, so we don't care again until 2022 or 2024. And I think putting something like a federalized standard, whether it be technology or process putting that in place now so that we're not talking about this in two or four years. I'm hoping that moment, um, continues, >>what would your recommendation be from building security programs to culture and awareness? How would you advise that they start? >>So, uh, one of the things that when I was on the Buddha Judge campaign, you know, like I said, we was the first person to do security for a campaign. And a lot of the staffers didn't quite have the background of professional background of work with security person. No, you know why? What I was doing there Eso my hallmark was You know, I'm trying to build a culture heavy on the cult. Um, you got to get people to buy in. I think this year when you look at what What Krebs and siesta and where the team over there have done is really find a way to tell us. Security story and every facet of the election, whether it be the machines themselves, the transporting the votes, counting the votes, how that information gets out to people websites I started like rumor control, which were were amazing amazing efforts. The public private partnerships that were there I had a chance to work with, uh, MJ and Tanya from from AWS some election project. I think everyone has skin in the game. Everyone wants to make it better. And I hope that moment, um, continues. But I think, you know, embracing that there needs to be a centralized, uniformed place, uh, for every state. And I think that would get rid of a lot of confusion >>when you talk about culture and you mentioned specifically called Do you think that people and agencies and politicians are ready to embrace the culture? Is there enough data to support that? This is really serious. We need to embrace this. We need to buy in a You said, um >>I hope right. I don't know what it could take. I'm hoping so after seeing everything you know, being at the White House from that aperture in 2016. Seeing all of that, I would, you know, think right away. Oh, my gosh. 2018, The midterms, We're gonna be on the ball. And that really didn't happen like we thought it would. 2020. We saw a different kind of technical or I guess, not as technical, uh, security problem. And I think I'm kind of shifting from that to the future. People realize. And I think, uh, both sides of the aisle are working towards security programs and security posture. I think there's a lot of people that have bought into the idea. Um, but I think it kind of starts from the top, and I'm hoping it becomes a standard, so there's not really an option. You will do this just for the security and safety of the campaigns and the electoral process. But I do see a lot more people leaning into it, and a lot more resource is available for those people that are >>talk to me about kind of the status of awareness of security. Needing to combat these issues, be able to remediate them, be able to defend against them where our folks in that awareness cycle, >>I think it ebbs and flows like any other process. Any other you know, incident, event. That happens. And from my experience in the info SEC world, normally there's a compromise. There's an incident, a bunch of money gets thrown at it and then we forget about it a year or two later. Um, I think that culture, that awareness comes in when you have folks that would sustain that effort. And again, you know, on the campaign, um, even at the White House, we try to make everyone apart of security. Security is and all the time thing that everyone has a stake in. Um, you know, I can lock down your email at work. I can make sure this system is super super secure, but it's your personal threat model. You know, your personal email account, your personal social media, putting more security on those and being aware of those, I think that's that awareness is growing. And I Seymour folks in the security community just kind of preaching that awareness more and more and something I'm really, really excited about. >>Yeah, the biggest thing I always think when we talk about security is people that were the biggest threat vector and what happened 89 months ago when so many businesses, um, in any, you know, public sector and private went from on site almost maybe 100% on site to 100% remote people suddenly going, I've got to get connected through my home network. Maybe I'm on my own personal device and didn't really have the time of so many distractions to recognize a phishing email just could come in and propagate. So it's that the people challenge e always seems to me like that might be the biggest challenge. Besides, the technology in the process is what do you think >>I again it goes back. I think it's all part of it. I think. People, um, I've >>looked at it >>slightly. Ah, friend of mine made a really good point. Once he was like, Hey, people gonna click on the link in the email. It's just I think 30% of people dio it's just it's just the nature of people after 20 some odd years and info sec, 20 some odd years and security. I think we should have maybe done a better job of making that link safer, to click on, to click on to make it not militias. But again it goes back, Thio being aware, being vigilant and to your point. Since earlier this year, we've seen a tax increase exponentially specifically on remote desktop protocols from Cove. It related themes and scams and, you know, ransomware targeting healthcare systems. I think it's just the world's getting smaller and we're getting more connected digitally. That vigilance is something you kind of have to building your threat model and build into the ecosystem. When we're doing everything, it's just something you know. I quit a lot, too. You've got junk email, your open your mailbox. You got some junk mail in there. You just throw it out. Your email inbox is no different, and just kind of being aware of that a little more than we are now might go a long way. But again, I think security folks want to do a better job of kind of making these things safer because malicious actors aren't going away. >>No, they're definitely not going away that we're seeing the threat surfaces expanding. I think it was Facebook and TIC Tac and Instagram that were hacked in September. And I think it was unsecured cloud database that was the vehicle. But talking about communication because we talk about culture and awareness communication from the top down Thio every level is imperative. How how do we embrace that and actually make it a standard as possible? >>Uh, in my experience, you know, from an analyst to a C So being able to communicate and communicate effectively, it's gonna save your butt, right? It's if you're a security person, you're You're that cyber guy in the back end, something just got hacked or something just got compromised. I need to be able to communicate that effectively to my leadership, who is gonna be non technical people, and then that leadership has to communicate it out to all the folks that need to hear it. I do think this year just going back to our elections, you saw ah lot of rapid communication, whether it was from DHS, whether it was from, you know, public partners, whether was from the team over Facebook or Twitter, you know, it was ah, lot of activity that they detected and put out as soon as they found it on it was communicated clearly, and I thought the messaging was done beautifully. When you look at all the work that you know Microsoft did on the block post that came out, that information is put out as widely as possible on. But I think it just goes back to making sure that the people have access to it whenever they need it, and they know where to get it from. Um, I think a lot of times you have compromised and that information is slow to get out. And you know that DeLay just creates a confusion, so it clearly concisely and find a place for people, could get it >>absolutely. And how do you see some of these challenges spilling over into your role as the security advisor for Splunk? What are some of the things that you're talking with customers about about right now that are really pressing issues? >>I think my Rolex Plunkett's super super weird, because I started earlier in the year, I actually started in February of this year and a month later, like, Hey, I'm hanging out at home, Um, but I do get a chance to talk to ah, lot of organizations about her security posture about what they're doing. Onda about what they're seeing and you know everything. Everybody has their own. Everybody's a special snowflakes so much more special than others. Um, credit to Billy, but people are kind of seeing the same thing. You know, everybody's at home. You're seeing an increase in the attack surface through remote desktop. You're seeing a lot more fishing. You're singing just a lot. People just under computer all the time. Um, Zoom WebEx I've got like, I don't know, a dozen different chat clients on my computer to talk to people. And you're seeing a lot of exploits kind of coming through that because of that, people are more vigilant. People are adopting new technologies and new processes and kind of finding a way to move into a new working model. I see zero trust architecture becoming a big thing because we're all at home. We're not gonna go anywhere. And we're online more than we're not. I think my circadian rhythm went out the window back in July, so all I do is sit on my computer more often than not. And that caused authentication, just, you know, make sure those assets are secure that we're accessing from our our work resource is I think that gets worse and worse or it doesn't. Not worse, rather. But that doesn't go away, no matter what. Your model is >>right. And I agree with you on that circadian rhythm challenge. Uh, last question for you. As we look at one thing, we know this uncertainty that we're living in is going to continue for some time. And there's gonna be some elements of this that air gonna be permanent. We here execs in many industries saying that maybe we're going to keep 30 to 50% of our folks remote forever. And tech companies that air saying Okay, maybe 50% come back in July 2021. As we look at moving into what we all hope will be a glorious 2021 how can businesses prepare now, knowing some amount of this is going to remain permanent? >>It's a really interesting question, and I'll beyond, I think e no, the team here. It's Plunkett's constantly discussions that start having are constantly evaluating, constantly changing. Um, you know, friends in the industry, it's I think businesses and those executives have to be ready to embrace change as it changes. The same thing that the plans we would have made in July are different than the plans we would have made in November and so on. Andi, I think, is having a rough outline of how we want to go. The most important thing, I think, is being realistic with yourself. And, um, what, you need to be effective as an organization. I think, you know, 50% folks going back to the office works in your model. It doesn't, But we might not be able to do that. And I think that constant ability Thio, adjust. Ah, lot of company has kind of been thrown into the fire. I know my backgrounds mostly public sector and the federal. The federal Space has done a tremendous shift like I never well, rarely got to work, uh, vert remotely in my federal career because I did secret squirrel stuff, but like now, the federal space just leaning into it just they don't have an option. And I think once you have that, I don't I don't think you put Pandora back in that box. I think it's just we work. We work remote now. and it's just a new. It's just a way of working. >>Yep. And then that couldn't be more important to embrace, change and and change over and over again. Make. It's been great chatting with you. I'd love to get dig into some of that secret squirrel stuff. I know you probably have to shoot me, so we will go into that. But it's been great having you on the Cube. Thank you for sharing your thoughts on election security. People processes technology, communication. We appreciate it. >>All right. Thanks so much for having me again. >>My pleasure for McClatchy. Oh, I'm Lisa Martin. You're watching the Cube virtual.

(lively peaceful music) >> Live, from New York, it's The Cube. Covering "The Cube New York City 2018." Brought to you by SiliconANGLE Media and its ecosystem partners. >> Well, everyone, welcome back to The Cube special conversation here in New York City. We're live for Cube NYC. This is our ninth year covering the big data ecosystem, now evolved into AI, machine learning, cloud. All things data in conjunction with Strata Conference, which is going on right around the corner. This is the Cube studio. I'm John Furrier. Dave Vellante. Our next guest is Mick Hollison, who is the CMO, Chief Marketing Officer, of Cloudera. Welcome to The Cube, thanks for joining us. >> Thanks for having me. >> So Cloudera, obviously we love Cloudera. Cube started in Cloudera's office, (laughing) everyone in our community knows that. I keep, keep saying it all the time. But we're so proud to have the honor of working with Cloudera over the years. And, uh, the thing that's interesting though is that the new building in Palo Alto is right in front of the old building where the first Palo Alto office was. So, a lot of success. You have a billboard in the airport. Amr Awadallah is saying, hey, it's a milestone. You're in the airport. But your business is changing. You're reaching new audiences. You have, you're public. You guys are growing up fast. All the data is out there. Tom's doing a great job. But, the business side is changing. Data is everywhere, it's a big, hardcore enterprise conversation. Give us the update, what's new with Cloudera. >> Yeah. Thanks very much for having me again. It's, it's a delight. I've been with the company for about two years now, so I'm officially part of the problem now. (chuckling) It's been a, it's been a great journey thus far. And really the first order of business when I arrived at the company was, like, welcome aboard. We're going public. Time to dig into the S-1 and reimagine who Cloudera is going to be five, ten years out from now. And we spent a good deal of time, about three or four months, actually crafting what turned out to be just 38 total words and kind of a vision and mission statement. But the, the most central to those was what we were trying to build. And it was a modern platform for machine learning analytics in the cloud. And, each of those words, when you unpack them a little bit, are very, very important. And this week, at Strata, we're really happy on the modern platform side. We just released Cloudera Enterprise Six. It's the biggest release in the history of the company. There are now over 30 open-source projects embedded into this, something that Amr and Mike could have never imagined back in the day when it was just a couple of projects. So, a very very large and meaningful update to the platform. The next piece is machine learning, and Hilary Mason will be giving the kickoff tomorrow, and she's probably forgotten more about ML and AI than somebody like me will ever know. But she's going to give the audience an update on what we're doing in that space. But, the foundation of having that data management platform, is absolutely fundamental and necessary to do good machine learning. Without good data, without good data management, you can't do good ML or AI. Sounds sort of simple but very true. And then the last thing that we'll be announcing this week, is around the analytics space. So, on the analytic side, we announced Cloudera Data Warehouse and Altus Data Warehouse, which is a PaaS flavor of our new data warehouse offering. And last, but certainly not least, is just the "optimize for the cloud" bit. So, everything that we're doing is optimized not just around a single cloud but around multi-cloud, hybrid-cloud, and really trying to bridge that gap for enterprises and what they're doing today. So, it's a new Cloudera to say the very least, but it's all still based on that core foundation and platform that, you got to know it, with very early on. >> And you guys have operating history too, so it's not like it's a pivot for Cloudera. I know for a fact that you guys had very large-scale customers, both with three letter, letters in them, the government, as well as just commercial. So, that's cool. Question I want to ask you is, as the conversation changes from, how many clusters do I have, how am I storing the data, to what problems am I solving because of the enterprises. There's a lot of hard things that enterprises want. They want compliance, all these, you know things that have either legacy. You guys work on those technical products. But, at the end of the day, they want the outcomes, they want to solve some problems. And data is clearly an opportunity and a challenge for large enterprises. What problems are you guys going after, these large enterprises in this modern platform? What are the core problems that you guys knock down? >> Yeah, absolutely. It's a great question. And we sort of categorize the way we think about addressing business problems into three broad categories. We use the terms grow, connect, and protect. So, in the "grow" sense, we help companies build or find new revenue streams. And, this is an amazing part of our business. You see it in everything from doing analytics on clickstreams and helping people understand what's happening with their web visitors and the like, all the way through to people standing up entirely new businesses based simply on their data. One large insurance provider that is a customer of ours, as an example, has taken on the challenge and asked us to engage with them on building really, effectively, insurance as a service. So, think of it as data-driven insurance rates that are gauged based on your driving behaviors in real time. So no longer simply just using demographics as the way that you determine, you know, all 18-year old young men are poor drivers. As it turns out, with actual data you can find out there's some excellent 18 year olds. >> Telematic, not demographics! >> Yeah, yeah, yeah, exactly! >> That Tesla don't connect to the >> Exactly! And Parents will love this, love this as well, I think. So they can find out exactly how their kids are really behaving by the way. >> They're going to know I rolled through the stop signs in Palo Alto. (laughing) My rates just went up. >> Exactly, exactly. So, so helping people grow new businesses based on their data. The second piece is "Connect". This is not just simply connecting devices, but that's a big part of it, so the IOT world is a big engine for us there. One of our favorite customer stories is a company called Komatsu. It's a mining manufacturer. Think of it as the ones that make those, just massive mines that are, that are all over the world. They're particularly big in Australia. And, this is equipment that, when you leave it sit somewhere, because it doesn't work, it actually starts to sink into the earth. So, being able to do predictive maintenance on that level and type and expense of equipment is very valuable to a company like Komatsu. We're helping them do that. So that's the "Connect" piece. And last is "Protect". Since data is in fact the new oil, the most valuable resource on earth, you really need to be able to protect it. Whether that's from a cyber security threat or it's just meeting compliance and regulations that are put in place by governments. Certainly GDPR is got a lot of people thinking very differently about their data management strategies. So we're helping a number of companies in that space as well. So that's how we kind of categorize what we're doing. >> So Mick, I wonder if you could address how that's all affected the ecosystem. I mean, one of the misconceptions early on was that Hadoop, Big Data, is going to kill the enterprise data warehouse. NoSQL is going to knock out Oracle. And, Mike has always said, "No, we are incremental". And people are like, "Yeah, right". But that's really, what's happened here. >> Yes. >> EDW was a fundamental component of your big data strategies. As Amr used to say, you know, SQL is the killer app for, for big data. (chuckling) So all those data sources that have been integrated. So you kind of fast forward to today, you talked about IOT and The Edge. You guys have announced, you know, your own data warehouse and platform as a service. So you see this embracing in this hybrid world emerging. How has that affected the evolution of your ecosystem? >> Yeah, it's definitely evolved considerably. So, I think I'd give you a couple of specific areas. So, clearly we've been quite successful in large enterprises, so the big SI type of vendors want a, want a piece of that action these days. And they're, they're much more engaged than they were early days, when they weren't so sure all of this was real. >> I always say, they like to eat at the trough and then the trough is full, so they dive right in. (all laughing) They're definitely very engaged, and they built big data practices and distinctive analytics practices as well. Beyond that, sort of the developer community has also begun to shift. And it's shifted from simply people that could spell, you know, Hive or could spell Kafka and all of the various projects that are involved. And it is elevated, in particular into a data science community. So one of additional communities that we sort of brought on board with what we're doing, not just with the engine and SPARK, but also with tools for data scientists like Cloudera Data Science Workbench, has added that element to the community that really wasn't a part of it, historically. So that's been a nice add on. And then last, but certainly not least, are the cloud providers. And like everybody, they're, those are complicated relationships because on the one hand, they're incredibly valuable partners to it, certainly both Microsoft and Amazon are critical partners for Cloudera, at the same time, they've got competitive offerings. So, like most successful software companies there's a lot of coopetition to contend with that also wasn't there just a few years ago when we didn't have cloud offerings, and they didn't have, you know, data warehouse in the cloud offerings. But, those are things that have sort of impacted the ecosystem. >> So, I've got to ask you a marketing question, since you're the CMO. By the way, great message UL. I like the, the "grow, connect, protect." I think that's really easy to understand. >> Thank you. >> And the other one was modern. The phrase, say the phrase again. >> Yeah. It's the "Cloudera builds the modern platform for machine learning analytics optimized for the cloud." >> Very tight mission statement. Question on the name. Cloudera. >> Mmhmm. >> It's spelled, it's actually cloud with ERA in the letters, so "the cloud era." People use that term all the time. We're living in the cloud era. >> Yes. >> Cloud-native is the hottest market right now in the Linux foundation. The CNCF has over two hundred and forty members and growing. Cloud-native clearly has indicated that the new, modern developers here in the renaissance of software development, in general, enterprises want more developers. (laughs) Not that you want to be against developers, because, clearly, they're going to hire developers. >> Absolutely. >> And you're going to enable that. And then you've got the, obviously, cloud-native on-premise dynamic. Hybrid cloud and multi-cloud. So is there plans to think about that cloud era, is it a cloud positioning? You see cloud certainly important in what you guys do, because the cloud creates more compute, more capabilities to move data around. >> Sure. >> And (laughs) process it. And make it, make machine learning go faster, which gives more data, more AI capabilities, >> It's the flywheel you and I were discussing. >> It's the flywheel of, what's the innovation sandwich, Dave? You know? (laughs) >> A little bit of data, a little bit of machine itelligence, in the cloud. >> So, the innovation's in play. >> Yeah, Absolutely. >> Positioning around Cloud. How are you looking at that? >> Yeah. So, it's a fascinating story. You were with us in the earliest days, so you know that the original architecture of everything that we built was intended to be run in the public cloud. It turns out, in 2008, there were exactly zero customers that wanted all of their data in a public cloud environment. So the company actually pivoted and re-architected the original design of the offerings to work on-prim. And, no sooner did we do that, then it was time to re-architect it yet again. And we are right in the midst of doing that. So, we really have offerings that span the whole gamut. If you want to just pick up you whole current Cloudera environment in an infrastructure as a service model, we offer something called Altus Director that allows you to do that. Just pick up the entire environment, step it up onto AWUS, or Microsoft Azure, and off you go. If you want the convenience and the elasticity and the ease of use of a true platform as a service, just this past week we announced Altus Data Warehouse, which is a platform as a service kind of a model. For data warehousing, we have the data engineering module for Altus as well. Last, but not least, is everybody's not going to sign up for just one cloud vendor. So we're big believers in multi-cloud. And that's why we support the major cloud vendors that are out there. And, in addition to that, it's going to be a hybrid world for as far out as we can see it. People are going to have certain workloads that, either for economics or for security reasons, they're going to continue to want to run in-house. And they're going to have other workloads, certainly more transient workloads, and I think ML and data science will fall into this camp, that the public cloud's going to make a great deal of sense. And, allowing companies to bridge that gap while maintaining one security compliance and management model, something we call a Shared Data Experience, is really our core differentiator as a business. That's at the very core of what we do. >> Classic cloud workload experience that you're bringing, whether it's on-prim or whatever cloud. >> That's right. >> Cloud is an operating environment for you guys. You look at it just as >> The delivery mechanism. In effect. Awesome. All right, future for Cloudera. What can you share with us. I know you're a public company. Can't say any forward-looking statements. Got to do all those disclaimers. But for customers, what's the, what's the North Star for Cloudera? You mentioned going after a much more hardcore enterprise. >> Yes. >> That's clear. What's the North Star for you guys when you talk to customers? What's the big pitch? >> Yeah. I think there's a, there's a couple of really interesting things that we learned about our business over the course of the past six, nine months or so here. One, was that the greatest need for our offerings is in very, very large and complex enterprises. They have the most data, not surprisingly. And they have the most business gain to be had from leveraging that data. So we narrowed our focus. We have now identified approximately five thousand global customers, so think of it as kind of Fortune or Forbes 5000. That is our sole focus. So, we are entirely focused on that end of the market. Within that market, there are certain industries that we play particularly well in. We're incredibly well-positioned in financial services. Very well-positioned in healthcare and telecommunications. Any regulated industry, that really cares about how they govern and maintain their data, is really the great target audience for us. And so, that continues to be the focus for the business. And we're really excited about that narrowing of focus and what opportunities that's going to build for us. To not just land new customers, but more to expand our existing ones into a broader and broader set of use cases. >> And data is coming down faster. There's more data growth than ever seen before. It's never stopping.. It's only going to get worse. >> We love it. >> Bring it on. >> Any way you look at it, it's getting worse or better. Mick, thanks for spending the time. I know you're super busy with the event going on. Congratulations on the success, and the focus, and the positioning. Appreciate it. Thanks for coming on The Cube. >> Absolutely. Thank you gentlemen. It was a pleasure. >> We are Cube NYC. This is our ninth year doing all action. Everything that's going on in the data world now is horizontally scaling across all aspects of the company, the society, as we know. It's super important, and this is what we're talking about here in New York. This is The Cube, and John Furrier. Dave Vellante. Be back with more after this short break. Stay with us for more coverage from New York City. (upbeat music)

>> Live, from The Wigwam, in Phoenix, Arizona, it's theCube, covering Data Platforms 2017. Brought to you by Cue Ball. Hey, welcome back everybody. Jeff Frick here with theCube. Welcome back to Data Platforms 2017, at the historic Wigwam Resort, just outside of Phoenix, Arizona. I'm here all day with George Gilbert from Wikibon, and we're excited to be joined by our next guest. He's Mick Bass, the CEO of 47Lining. Mick, welcome. >> Welcome, thanks for having me, yes. >> Absolutely. So, what is 47Lining, for people that aren't familiar? >> Well, you know every cloud has a silver lining, and if you look at the periodic table, 47 is the atomic number for silver. So, we are a consulting services company that helps customers build out data platforms and ongoing data processes and data machines in Amazon web services. And, one of the primary use cases that we help customers with is to establish data lakes in Amazon web services to help them answer some of their most valuable business questions. >> So, there's always this question about own vs buy, right, with Cloud and Amazon, specifically. >> Mm-hmm, mm-hmm. >> And, with a data lake, the perception right... That's huge, this giant cost. Clearly that's from benefits that come with putting your data lake in AWS vs having it on Primm. What are some of the things you take customers through, and kind of the scenario planning and the value planning? >> Well, just a couple of the really important aspects, one, is this notion of elastic and on-demand pricing. In a Cloud based data lake, you can start out with actually a very small infrastructure footprint that's focused on maybe just one or two business use cases. You can pay only for the data that you need to get your data leg bootstrapped, and demonstrate the business benefit from one of those use cases. But, then it's very easy to scale that up, in a pay as you go kind of a way. The second, you know, really important benefit that customers experience in a platform that's built on AWS, is the breadth of the tools and capabilities that they can bring to bare for their predictive analytics and descriptive analytics, and streaming kinds of data problems. So, you need Spark, you can have it. You need Hive, you can have it. You need a high performance, close to the metal, data warehouse, on a cluster database, you can have it. So, analysts are really empowered through this approach because they can choose the right tool for the right job, and reduce the time to business benefit, based on what their business owners are asking them for. >> You touched on something really interesting, which was... So, when a customer is on Primm, and let's say is evaluating Cloudera, MaPr, Hortonworks, there's a finite set of services or software components within that distro. Once they're on the Cloud, there's a thousand times more... As you were saying, you could have one of 27 different data warehouse products, you could have many different sequel products, some of which are really delivered as services. >> Mm-hmm >> How does the consideration of the customer's choice change when they go to the Cloud? >> Well, I think that what they find is that it's much more tenable to take an agile, iterative process, where they're trying to align the outgoing cost of the data lake build to keep that in alignment with the business benefits that come from it. And, so if you recognize the need for a particular kind of analytics approach, but you're not going to need that until down the road, two or three quarters from now. It's easy to get started with simple use cases, and then like add those incremental services, as the need manifests. One of the things that I mention in my talk, that I always encourage our customers to keep in mind, is that a data lake is more than just a technology construct. It's not just an analysis set of machinery, it's really a business construct. Your data lake has a profit and loss statement, and the way that you interact with your business owners to identify this specific value sources, that you're going to make pop for you company, can be made to align with the cost footprint, as you build your data lake out. >> So I'm curious, when you're taking customers though the journey to start kind of thinking of the data lake and AWS, are there any specific kind of application spaces, or vertical spaces where you have pretty high confidence that you can secure an early, and relatively easy, win to help them kind of move down the road? >> Absolutely. So, you know, many of our customers, in a very common, you know, business need, is to enhance the set of information that they have available for a 360 degree view of the customer. In many cases, this information and data, it's available in different parts of the enterprises, but it might be siloed. And, a data lake approach in AWS really helps you to pull it together in an agile fashion based on particular, quarter by quarter, objectives or capabilities that you're trying to respond to. Another very common example is predictive analytics for things like fraud detection, or mechanical failure. So, in eCommerce kinds of situations, being able to pull together semi-structured information that might be coming from web servers or logs, or like what cookies are associated with this particular user. It's very easy to pull together a fraud oriented predictive analytic. And, then the third area that is very common is internet of things use cases. Many enterprises are augmenting their existing data warehouse with sensor oriented time series data, and there's really no place in the enterprise for that data currently to land. >> So, when you say they are augmenting the data warehouse, are they putting it in the data warehouse, or they putting it in a sort of adjunct, time series database, from which they can sort of curate aggregates, and things like that to put in the data warehouse? >> It's very much the latter, right. And, the time series data itself may come from multiple different vendors and the input formats, in which that information lands, can be pretty diverse. And so, it's not really a good fit for a typical kind of data warehouse ingest or intake process. >> So, if you were to look at, sort of, maturity models for the different use cases, where would we be, you know, like IOT, Customer 360, fraud, things like that? >> I think, you know, so many customers have pretty rich fraud analytics capabilities, but some of the pain points that we hear is that it's difficult for them to access the most recent technologies. In some cases the order management systems that those analytics are running on are quite old. We just finished some work with a customer where literally the order management system's running on a mainframe, even today. Those systems have the ability to accept steer from like a sidecar decision support predictive analytic system. And, one of the things that's really cool about the Cloud is you could build a custom API just for that fraud analytics use case so that you can inject exactly the right information that makes it super cheap and easy for the ops team, that's running that mainframe, to consume the fraud improvement decision signal that you're offering. >> Interesting. And so, this may be diving into the weeds a little bit, but if you've got an order management system that's decades old and you're going to plug-in something that has to meet some stringent performance requirements, how do you, sort of, test... It's not just the end to end performance once, but you know for the 99th percentile, that someone doesn't get locked out for five minutes while he's to trying to finish his shopping cart. >> Exactly. And I mean, I think this is what is important about the concept of building data machines, in the Cloud. This is not like a once and done kind of process. You're not building an analytic that produces a print out that an executive is going to look at (laughing) and make a decision. (laughing) You're really creating a process that runs at consumer scale, and you're going to apply all of the same kinds of metrics of percentile performance that you would apply at any kind of large scale consumer delivery system. >> Do you custom-build, a fraud prevention application for each customer? Or, is there a template and then some additional capabilities that you'll learn by running through their training data? >> Well, I think largely, there are business by business distinctions in the approach that these customers take to fraud detection. There's also business by business direction distinction in their current state. But, what we find is that the commonalities in the kinds of patterns and approaches that you tend to apply. So, you know... We may have extra data about you based on your behavior on the web, and your behavior on a mobile app. The particulars of that data might be different for Enterprise A vs Enterprise B, but this pattern of joining up mobile data plus web data plus, maybe, phone-in call center data. Putting those all together, to increase the signal that can be made available to a fraud prevention algorithm, that's very common across all enterprises. And so, one of the roles that we play is to set up the platform, so that it's really easy to mobilize each of these data sources. So in many cases, it's the customer's data scientist that's saying, I think I know how to do a better job for my business. I just need to be unleashed to be able to access this data, and if I'm blocked, I need a platform where the answer that I get back is oh, you could have that, like, second quarter of 2019. Instead, you want to say, oh, we can onboard that data in an agile fashion pay, and increment a little bit of money because you've identified a specific benefit that could be made available by having that data. >> Alright Mick, well thanks for stopping by. I'm going to send Andy Jassy a note that we found the silver lining to the Cloud (laughing) So, I'm excited for that, if nothing else, so that made the trip well worth while, so thanks for taking a few minutes. >> You bet, thanks so much, guys. >> Alright Mick Bass, George Gilbert, Jeff Frick, you're watching theCube, from Data Platforms 2017. We'll be right back after this short break. Thanks for watching. (computer techno beat)

>>Okay, welcome back to Docker. Main stage is the cube coverage of DockerCon 2022. I'm John FRA host of the cube. We're here with a special segment with sneak. We've been partnering with Docker going back to the early days, Nate cloud native container vulnerability scanning within Docker desktop in 2020. We' it Mick McCulley field strategist sneak Mick. Thanks for coming on the cube. >>Thanks for having me glad to glad to be here. Excited to have this, this, this conversation. >>Yeah, love the background. Got I. Big football fan myself, and love that little mention. There love the sneak logo too. Good, good plug there. Uh, but I want to get into that. The security you guys were of the first conversations when shift left was hot, when it just started to come and it's never going away, but now there's been a huge focus and an increase of concerns around vulnerabilities, uh, within and within the supply chain of security software. So in open source software. So what are you guys doing now? Cause this is a new focus in the industry. Everyone's talking about it, your company's making changes and mitigate that risk. What do you guys have? >>Yeah, that's, it's, it's a great question. And, and shift left is definitely a big focus of ours, right? It's it's what sort of our core foundation is what we based. Um, our whole approach to software supply chain definitely has made its way to the top of the spectrum as far as conversations. And I think it plays very well into our focus. Um, you know, one of the things that, uh, I believe a lot of organizations are focused on is trying to get a hold of understanding a lot of the implicit trust and risk associated with everything that goes into building any sort of modern application. And that's all of the components that are being used. Everything from the open source to the containers that are consumed to the process, into all of the ecosystem and tooling, that's consumed a lot of the trust layers in there. It's, it's extremely important to understand what that is. What's, what's the risk, right? And from a sneak perspective, taking that, that intelligence and trust and giving it back to the developers when they're making these decisions, is, is our focus like that, that whole concept of taking all of that security expertise and pushing it back to the individuals, making those decisions, I think is probably one of the more powerful ways that you can start to implement some more security controls and get some trust and understand your risk process, um, throughout that software supply chain. >>Okay. So you said trust three times, I'm gonna come back to that because shifting left is all about empowering developers, but what good at shifting left? If you gotta stop and then go back and research something that, that wasn't in your pipeline or something else happened. So open source obviously is growing like a weed it's continuing to exponentially grow and more people are doing it commercialization as well, but the word trust is not zero trust. You're hearing, people's use the word zero trust security, that's different, right? They're talking about developers looking for trusted code. So it's interesting, you got hackers and, and zero trust and you got developers and trust and you got software in between. This is kind of the, kind of the core issue here. Isn't it? >>It, it is, um, because of that using, I mean, there's, there's huge advantages with all of these new approaches, right? Leveraging the open source and the containers and the, and the software packages and these ecosystems to automate a lot of those software processes, but doing so means that you've got this implicit trust that's there. And so, um, taking and trying to identify and, and, and share those details with the developers when they're making those decisions, but it doesn't stop there, right? Like that's, that's one of the other important aspects of this is what organizations have to do is to not only provide that and help those individuals when they're making those decisions, but then constantly understand if that posture changes at any given time, right. And knowing where it's happening, what is it, how do I prove and have some of the Providence details of the origination of the information, how can I trust to make sure that the security was, uh, accounted for, for all the components that I'm actually leveraging and using, and then making sure that you have that visibility through that the entire life cycle. That's probably one of the other important areas. So it's not only sort of giving that information in details and trying to take advantage of all of that, that early detection response and decision making process. But it's also maintaining that understanding of what that is, and that trust plays into that, right? There's so much implicit trust associated with it. And the more that you can understand it, comprehend it, take control of it, the better your organization from a security posture's gonna be, >>Yeah. I mean, you got builders and attackers. I mean, it's clearly the spectrum and the builders want the a hundred percent trust. Um, and I think this is gonna be such an important game changing topic that has to be addressed. It's the only way with the scale you're seeing in the growth of software. And by the way, open source become much more than just open source it's community. It's social people kind of hang out and build code together and then ventures are being started over. So this is a nice progression. Makes a lot of sense. I have to ask you though, on what are some of the what's some of the data say on the attacks, is it increasing at what rate what's the complexity look like? What's it look like as it evolves, because, you know, even though it's zero geo trust on one side and trust on the other, the attackers also adjust too. >>Yeah. >>So >>What's, that's, I think it's the staff. >>It's >>A very, yeah, it's a very good question. I think that's what we're seeing is, um, and this is just a natural evolution. I think there's been, you know, an historic focus on a lot of the security associated with, with running applications and locking them down. And I was reading blog just by Docker the other day about how it's like this hardened sort of outside layer, but there's this soft squishy inside that soft squishy inside is all of those building components that are inside of there. And because of that hardened layer, it, it makes those attack vectors a little bit more difficult, right. When you're trying to, to, to penetrate those. And so what we've seen is this natural evolution is say, well, let's go find the weak link. Let's go understand if there's a way to actually bypass these security controls. And sometimes the ways to do that is to simply go into the process in which the application's being built. >>If I can go upstream and actually change some of those components and implement my attack inside of the application, it automatically gets embedded instead of trying to attack it directly. And so we're seeing that, and, and it's, what's banking a lot of the news and why some of the conversations around software supply chain are becoming very prominent, it's this ecosystem. And, um, unfortunately, you know, in a lot of organizations that, that I think some of that development area hasn't had that security focus as a lot of the traditional areas associated with applications and exposure of your organization, because of that it's left a little bit more exposed, right? That, that trust that we talked about in addition to the processes has to have a little bit more of that security ingrained inside of those processes to make sure that it's not being left open. It's not an open door, an open window that's giving sort of an easy route into the application. >>Yeah, totally. I totally see that in the next, in the last couple minutes we have left. I want to get into what you guys are doing with your customers and what our company's doing to mitigate the risks in the software supply chain. Obviously open source is not going away. It's only gonna be part of it what's going on with the customers. >>Yeah, it's, it's a great question. And a big focus of ours is to, um, help organizations understand all of those areas as much as possible, right. And to provide them that guidance. And part of this is not only the solution and how we deploy it and how we can deliver it, but it's some of the security intelligence associated with it instead of putting the burden on our customers of trying to stay on top of all of that risk. Right? What, what, where is all of these different moving parts and something changes from being completely fine one day to, you know, a high vulnerability and risk posture. How do you react to that? And so providing as much of that insight, guidance and prioritization and the details to those organizations in, in an actionable format, um, that's probably one of the more core elements to this. >>It's not just the, Hey, here's a whole list of all your problems. It's what do you do? Like how do you take all of that information, those details, those risks, how do you prioritize them? How do you then what, what's the steps that you take from an action perspective in order to address those, right. If I've got a container with some problems, what is sort of the recommended approach to solving that? What should I upgrade to? What is the guides associated with those? And so a lot of it is focused on providing not only the insight and the ability to react and understand that risk at any given time, but also more focused on what do you gotta do, right? How do you actually take steps to alleviate or remediate that risk as much as possible? Can't not, that's >>The point what's so I gotta have to ask you, what's the difference between getting it right and getting it wrong, or in other words, why do some, um, supply chain vulnerable remain fixed, uh, unfixed and, and deprioritize? What's the, why isn't it going faster? >>Yeah. And, and some of that there's there's reasons across the board, right? Some of it crossed from the perspective that there, there might not be fixes. And so in some of those cases, just being aware of what that risk is. So you can put in other mitigating controls in order to accommodate those. In other cases, it's, it's prioritizing where your risk is most important, right. And part of this also stems from the fact that I, if you fall into sort of that reactionary bucket, then, then you have to be in sort of that prioritization reactive mode. The more that you can push this back to that early process, the less that that has to occur, because you have the ability to actually make the best decision possible with the information you have during that early process. So some of it's just, you know, predicated on the fact that there's not always solutions to all of the problems. Um, and then a part of this too, is where in the, where in the phase are you actually starting to attack and handle it? >>All right, Mick. Thanks. So for coming on, really appreciate it. Business is good at sneak. Thanks for sharing your insights here on the, on the main stage. Okay. This is the queue back to the DockerCon main stage. We'll be back more. See you soon.

(upbeat music) >> Welcome back to theCUBE everyone Lisa Martin here. On the ground in Las Vegas at COUPA INSPIRE 2022. This is our second day of coverage here. There's been about 2,400 to 2,500 folks at the event. This year people are ready to come back. I've been happy to talk with lots Coupa folks, their partners, their customers and I've got both a customer and a partner here with me. Heidi Banks joins us, the Senior director of Global Procurement at Jabil. Heidi it's great to have you on the program. >> Thank you for having me give. >> Give the audience an overview of Jabil and what you guys do. >> So Jabil is a $30 billion manufacturing solutions partner that provides contract manufacturing services for 450 of the world's largest and most premier brands around the globe. Most people don't know our name but we're the wonderful face behind the name. >> Well you guys had, I was looking at some stats, over 260,000 employees across 100 locations. Very customer centric you guys are, as is Coupa, this good obviously synergy there but you had some objectives from a global procurement perspective. What were those? What were some of the challenges that you wanted to solve? >> So about seven years ago, Jabil went on a journey to identify what challenges we had out in the indirect procurement space. Being such a large global company, we had no idea what we were spending on indirect at the of time. After a little bit of digging, we found out that we had over 2 billion in spend that was untapped from a category management perspective. And so we knew that we needed to grow as a company and PaaS technology as a foundation, as our goal and our mission in the company is to be the most technologically advanced manufacturer solutions partner for our customers. >> Was there any sort of one thing or a compelling event seven years ago that caused you guys to go, "We need to be really getting our hands around this indirect spend?" >> So we started off by bringing in category managers and they were doing amazing job delivering savings in our contracts, but we had no way to deliver that out to the company. And the company being so big in so many different jurisdictions in countries around the world, you could negotiate the best contract in the world, but if you couldn't communicate it out to your users then it was a challenge to really capture that savings and make sure we were delivering bottom line savings to the company. >> And you guys are, we're talking about three different SAP ERP systems so a lot of technology in the environment. What were some of the core technology requirements that Jabil had when it came looking for a business plan management solution? >> Yeah, so we were looking for something that was very user friendly. Of course, Coupa takes that box very well. Also something that could drive governance and policy controls again challenging being such a global organization and making sure that things were going according to our policy into our global category managers to be sourced and negotiated for the company. We looked for one that was end to end from a business spend management platform perspective. We wanted something that was integrated and could cover three ERP systems from one pane of glass across the company. So we could get great analytics without having to search in so many different places. >> That is so key. I was talking with Rob, I was talking with Raja and they were all talking about how those silos still exist and how they're helping organizations like Jabil break those down and give them that single pane of glass, as you mentioned, to be able to see, to get that visibility into indirect spend, for example. Talk to me about the solutions that you implemented from Coupa. >> So we started off with Coupa's procure to pay system. Really our focus was to get off of our old system as quickly as we could and get everyone managing on the same policy controls approval flows. We then also had analytics, so we had Coupa AIC and brought in analytics and in the last year and a half I've also deployed strategic and tactical sourcing through Coupa as well, and spend guard from a audit control and compliance perspective. >> So then that the phrase "sweet synergy" that actually probably means a lot to you Coupa was talking about that during the keynote this morning. Your Jabil is living that sweet synergy kind of experience through Coupa >> That's right. As we source in Coupa and we can see, are there different behaviors that we need to look into maybe suppliers that are bidding at the last minute and winning or less than that desirable number of suppliers coming in or duplicate invoices and being able to really look through that and see spend patterns that we would never otherwise uncover is highly important to us from a compliance standpoint, we've gotten a great value out of that solution. >> And in terms of value, one of the things I know that was important to you when you were looking for the right technology partner, was you wanted to involve other folks within the organization across IT, other lines of business. Talk to me about how important that was to bring in that cross-functional team to help make the right decision. >> Yeah, that was one of the most critical things that we did. We needed to make sure, especially being an SAP shop right, we needed to make sure that we were standing back and really being impartial in our decision and driving a non-biased decision in that RFP process. And so we got our executives together, talked to them about the value drivers and the ROI that we could do if we had all of the right support from the right departments, so that we could avoid resistance as we tried to deploy in such a rapid way. So we brought IT, legal, users together, procurement and in advance did a balanced scorecard approach to say these were the important factors that we had whether it was IT infrastructure, whether it was capabilities to make sure that when we came out of that decision and we picked a solution, we could all look at each other and have a handshake and say it was the right decision for us as a company, and so no departments had push back at that point because of that approach that we took. >> An objective approach that you took. >> That's right. >> Let's talk about some of the outcomes look at, actually let's not, let's talk about your deployment first, 'cause you guys started with probably your most challenging sites whereas other folks might go. Let's start with the low hanging fruit and kind of work our way up. Jabil said, "Nope, we're going to flip the script on that." >> That's right. So we, we went with what we call an east to west strategy. We are heavily concentrated in our Asia markets and so we were also wanted to deliver our ROI as quickly as possible and get our spend into the system as quickly as possible. So we we went live with 12 sites, 11 mega sites in China and our corporate headquarters in St. Petersburg in order to get that spend in as quickly as possible and get our ROI delivered. So we started in China and the US then in our second phase deployed the rest of Asia and then the US and North America and then over to Europe. So we went regional from a time zone perspective but also just I say, go bold. I hear a lot of people that start small and then grow but if you want to deliver that ROI and get your money out of that system as soon as possible go big or go home. >> I like that go big, go home. It's like Mick Ebeling was talking about this morning from not impossible labs commit and then figure it out. >> That's right. >> That's right. >> You know what? That's actually brilliant advice because it's probably the opposite that a lot of us want to be we want to be able to figure this out and then go, okay we can do that. And he said no >> Yeah >> To the opposite. >> To the opposite >> Did you have to get buy-in from those cross-functional folks to say we want to start with our most challenging sites first, was that a team decision? >> That was a decision that we did just basically to get that ROI delivered. And we also had a really strong team that still partners with our Coupa admins today that were really invested in getting onto a solution where they can automate and drive control and compliance. And so not only do we involve the team in the solution selection, but also in the global design. So we brought different cross-functional departments together into one location together, we made all of our decisions on how we were going to configure Coupa So that way again all of our divisions and departments had buy-in to how we were going to move forward and then we went from there. >> Well then, and in that case everybody feels like they have a stake >> That's right >> In the issue they have a vested interest >> That's right. >> Which is critical for these types of large projects to be successful. >> That's right. So they were involved in the RFP process so they knew why we were doing it and they were then involved and the design and how we were going to set it up so that they knew that they had a vested interest in how it was going to perform in the end. And then of course there were things that we had to tweak. So we needed to have a design committee that we could come back to and make changes as we needed to, make changes throughout the projects. You don't always get every single decision right. The first time, but you need to be nimble and make changes first and get consensus across the company. >> Right. Talk to me about some of the outcomes I know I've seen a lot of stats in your case study and I always love those numbers always jump out at me. Talk to me about some of those metrics based business outcomes that Jabil is achieving so far. >> Yeah. So in the last four years we've had a heavy focus on catalog. So actually in the last few months, we've gone from 20 to 30% by using Coupa analytics and drilling really into the details and putting really great category strategies in order to drive more catalog penetration. We've got great stats around electronic invoicing especially in certain countries where people think it's not possible. >> Right. >> There's a great change management story we have for what we've achieved in our Asian markets around electronic invoicing and from an ROI perspective, we were able to deliver 3X our ROI by the end of year two which we projected would take three years to do and 7X by year four. So we had a very conservative and achievable ROI that got the buy-in and then we were able to accelerate it by being aggressive, but also with a great solution it was easy to then get that done. >> Can you talk a little bit about the change management that you were able to achieve in the Asian market change management is the difficult thing to do. People are resistant to change, one of the things we've learned in the last two years is sometimes the change comes in there's nothing you can do about it but how did you affect that change management within that culture in the Asian market? >> Yeah. So with the executive buy-in that we had because they knew that there was high potential for us to deliver an ROI. We had executive sponsorship that helped us get through some of those barriers. So if we decided not to bring certain users into the system, for example and there was pushback that they needed to have access we had executive messaging as to why from a policy governance and control standpoint we couldn't break that. So we used our executives' voice and their support to do that. But also we brought in a great system that was user are friendly and so we didn't get a lot of resistance in, in that sense. So they actually embraced the change compared to the solution we had in place before. So by making the right selection from a user centric company we also didn't get as much resistance there as well. >> That's nice the path of least resistance is good especially if you're not exactly sure if you're going to find it, but verifying that and getting that ROI is is probably a big, a big win. Talk to me a little bit about you guys liked Coupa so much you had such, you mentioned 3X ROI within, you said the first year? >> With after year two >> After year two >> Yeah. >> 3X ROI, you liked it so much you decided to become a Coupa partner. Talk to me about that. What does that mean? What are you guys doing as partner? >> Yeah, so this is a super exiting thing for us to adventure into. So we pride ourselves on our theme as built for practitioners by practitioners. We've run the system every single day. We've been running it for years. So my team members are deep in the knowledge and capabilities of Coupa it's functionality, how to manage it every day, how to get the most you out of it and we want to share that knowledge with other Coupa customers to get the most value out of their system as well. So whether that's optimization and helping them get more out of their system or whether it's roadmap or assessments in our perspective, or even doing net new implementations we're excited to venture into that area of services with Coupa as a partner. >> Or have you guys started doing that yet? >> Today is our first Coupa inspire as a partner, which is exciting. And we literally just got started in the last few months. So we are working on getting our first customer here hopefully very shortly and have had a lot of of really great conversations with customers at the show so far. >> That's one of the great things that Coupa took the risk to bring us all together because there's they have a phenomenal community of which you guys have been a part now you said I believe about seven years, but there's nothing that replaces the connections that you make in the community that is grown from doing events like this. I imagine that you've gotten to talk with a lot of prospect >> Yes. >> Prospective customers who, what, how did you do this? This seems like an impossible feat that you've gotten to share with them. This is doable, here's how we did it. >> That's right. So fortunately I've been at previous inspires as well. So I've gotten to talk to people that I haven't seen in a couple of years, which is always exciting. I've been able to talk to customers that I've done, referrals for with Coupa before that are now Coupa customers and we get to talk about that and also those perspective customers and helping them know that it is doable, it is achievable you can get consensus in a decentralized company where all the sites if you have lots, lots of sites and countries have their own autonomy, you can do it. You can do it fast. You can do it effective if you take the right approach. And so it's exciting to get here and share that opportunity and our adventure and our journey with Coupa and the journey is only just beginning. >> Right, what are some of the things that you are excited about in terms of the innovations that they've announced at the event? I know Coupa is very much symbiotic with its customers that the community very much generates a lot of the direction in which the technology goes. But what are some of the things that you've heard announced that you thought, yes, they're going they continue to go in the right direction. >> Yeah. So there's some actual foundational capabilities around things like payment agreements and group carts and things that actually we've contributed through either customer cabs or VP sessions with design, just doing collaboration together but I'm also excited to see some of their price benchmarking that they're doing so that we can know how well are we doing and from our pricing standpoint and also where they're going supply chain I'm excited to see where they're going with that. Being a big supply chain company ourselves, we're hoping that all turns out to be something that we can innovate with Coupa on and hopefully have in the future as well. >> Well, as they said, Rob said it to me just an hour ago, they're tip of the iceberg but what its seems that you've become Heidi yourself and Jabil is really kind of an influencer within the Coupa community. We appreciate you coming by theCUBE, sharing with us what you've accomplished and how you're expanding your Coupa partnership into helping other companies. >> Great. Thank you again for having me today. >> My pleasure. >> All right. >> For Heidi Banks, I'm Lisa Martin and you're watching theCUBE's coverage of COUPA INSPIRE 2022 from Las Vegas. Stick around my next guest joins me momentarily. (upbeat music)

(upbeat music) >> Hey guys and girls. Welcome back to theCUBE's coverage of Coupa Inspire 2022, from the Cosmopolitan, in bustling Las Vegas. Lisa Martin here, and as I mentioned, day two of our coverage and fresh from the main stage, Raja Hammoud joins me, the Executive Vice President of products at Coupa. Raja, welcome back to theCUBE and happy 10th anniversary at Coupa. >> Oh, thank you, thank you, thank you, and welcome back to Inspire. >> Thank you. It's so great- >> We're so happy you're here. >> It's great to be here. So you're just about coming up on your 10 anniversary with Coupa. You showed some great photos of your time there but you've seen, you've lived the evolution that is this rocket ship that's Coupa. >> Raja: It's been incredible journey. I really couldn't believe at first it's been 10. This is the longest I've ever been anywhere. And I honestly feel more refreshed and excited than even when I joined back in the day 10 years ago. And so much has changed, but also so much has not. >> Lisa: Yeah. >> The size of course. We were like 60 people when I joined, the product development team was one person in, in a product, roughly 12 engineers, and fast forward to the scale that's today, it's phenomenal difference. But what has not changed is the, the core values, how, the hustle, how people love working with each other, how we support customers, how we keep stepping up our game how we believe none of us is as smart as all of us, and the community keeps getting stronger and stronger. It's been, it's been really exciting journey. >> The theme of none of us is as smarter as all of us, I'm not sure if I got that right, but the idea is you feel that when you're talking to Coupa partners, I've had the opportunity to talk with Coupa partners and customers and Coupa folks that, that is not just a value statement, people are living that. >> Raja: Yeah. It's, it's everywhere. In the, in the company walls, outside the company walls, you often see product people in different organizations where, they start living in an ivory tower, they think they know everything, I mean, back to what we were discussing earlier about Barbara, when she talked about, get out of your doors, right? A lot of people can tend to do that. We always, from the beginning, believed in the best ideas are out there and you collaborate with each other. And I truly, truly believe that the success that we have achieved today to our community is in a large, large part, because we believed in that. So like on Monday, we hosted, I can't keep track of the number now, so, so many in-parallel Community Advisory Board meetings, and just talking to the products managers and everybody is buzzing with new ideas. And when we go back, there's so much new innovation that has just been co-created here in this conference, and this keeps going on and on and on. >> Lisa: Yeah. I like how you call it, the Community Advisory Board. I'm still used to hearing CAB as Customer Advisory Board, but what Coupa has built, especially with the launch of the Moonshot, the, the community AI, is, is just that. >> Yes. >> It's a very collaborative community. One of the things that's around here, hashtags everywhere, but #United by the Power of Spend. >> Yes. >> What does that mean to you as the EVP of products, and what do you think that means to the community? >> When I think... What we are doing, we're building this platform that is powering all these businesses out there. And the reality of it is you can only, only do so much when you try to do things alone. When we are doing things together, we are way more successful, we are more profitable, we are more sustainable, we are more efficient. And community.ai from a technology standpoint, is making that happen, because what we are doing is taking AI, applying it to all this 3.3 trillion in data, and then bringing back prescriptions that we give back to each and every customer so that everybody can see where they are, how they up their games, and we connect them with other people like them. Now, people love coming to conferences like this, but even in conferences like this, if you think about it, the people you're going to meet, it's, some people are going to do matchmaking but you are also losing an opportunities of meeting the maximum number of people who've done exactly the thing that you did. But when you have the ability to look at all of that data and you can match make people. So we did that already with, for sourcing professionals. So if you are somebody who source a certain category, we can tell somebody else has done something like this in this geography and we offer you to connect to each other. >> Lisa: Wow. >> So this is incredibly powerful way where we are really uniting the whole community by spend, making everybody truly stronger together. >> Lisa: Matchmaker in, in a sense. >> It is matchmaking. >> But it's, but it's- >> It's Spend matchmaking. >> Spend matchmaking, but it's also the opportunity to unite professionals across sourcing, procurement- >> Raja: Yes. >> ... finance, treasury. >> Raja: Yes. >> To your point, and, and Rob said this in his keynote, and he said it here on theCUBE, you know, we've got to break down these silos. >> Raja: Yes. >> People and companies functioning in silos are not going to be successful. >> Raja: Yes. This has been one of the, probably one of the things that we were talking earlier, what has changed, what hasn't. This is one of the fundamental things that has never changed since I've joined. The vision has been very clear. The execution on it, of how we drive successful business spend management program is by breaking down the silos and this idea of sweet synergy, where in product, you start building these capabilities that helps these professionals in the different organizations to actually connect on the touch points, where, where things really matter. >> Lisa: Sweet synergy, was that thing from a concept perspective, did that come from the community, in terms of Coupa going, this is actually what's happening, this synergy across the BSM suite? >> Yes. So in the very beginning, it was early idea. I would say in the first two Inspires that we did, we hadn't given it actually the name itself, and we used to call it unified capabilities, and it started with the first silos we broke down. The first silos we broke down were procurement and AP. And they didn't even used to talk in the same room or even want to care about each other. So we started building so many capabilities that brought these teams together and little by little the community started to feel that and see the value of that. And then the community started to ask us to go break down more silos. So in the beginning, I would say the, the vision before I even joined, the company was on that trajectory. And the early customers saw that and they championed it and then they drove us to do more. So they came to us and said could you please do what you did here in contract? Could you please do what you did here in sourcing? And I was in a meeting last week, a leadership meeting, and one question was asked to leaders in the services team about what are they hearing about, from the customers, about a particular area. And it was music to our ears when we heard the customers are asking for more synergy, right? So, they even have the name for it and they're asking for more and more, and we have built hundreds of these already, but the reality is there is so much opportunity. >> Lisa: Right. >> The world is siloed, no technology has attempted to do that. And I think that's what's a exciting is to go and forge new grounds and do something very special to unite everyone together. >> You guys talked about the waves. Rob talked about the waves yesterday. You talked about it again this morning. And when I think of Inspired community, as that third wave, I see it on both sides. I see the Inspired community that is the Coupa community, but also what you just talked about, that flywheel of that sort of symbiotic relationship that you guys have with your customers as Coupa in and of itself being in a community inspired by the community that it has built. >> Raja: Yes, it's, it's very, very, it's a circular effect. Like it, we inspire one another, and we strengthen one another, and it's, it's just a beautiful, beautiful thing. One of the special things that we are starting to do is we want to take the whole product experience itself, to be a complete community experience. So anywhere you are going to Coupa, when it makes sense, of course, you are not only looking at your data, you are getting connected with people for that particular thing. So we've done that already for 15 different product areas and we're constantly doing more and more and more and more. You can imagine one day we can, where we can start within the product pages themselves, where we host community experts to talk via video and connect with others. So you bring that whole community experience alive in a product in enterprise software, which has not been done. >> Kind of like creating your own influencer network. >> Yes, yes, yes. And give people their voice and, and, and it becomes exciting. It is very different when you're just working on your own and driving goals, and you have no idea how good that can pass on the world. And then when right then and there, you get to learn that some people have hit that, some people have achieved these goals, you just get excited, "I want to hit that goal too. Who are these people? Connect me with these leaders. Let's have a conversation. How did they do it?" And they start creating best practices together. We even have started places where they collaborate on actual documents and templates, and they put them in the community exchange as a way for people to share with others, even taking templates from the product putting them back into a community exchange. So it is sharing, being enabled on the platform, platform itself. >> Lisa: How did you guys function during the pandemic, the last two years when we couldn't get together? >> Raja: Yeah. >> I know that your customers are really the lifeblood of Coupa and vice versa. >> Raja: Yes. >> But talk to me about some of the things that Coupa did with its customers, you know, by video conferencing, for example, that really helped the evolution and some of the innovations that you announced this morning. >> When we first... when the pandemic first hit I think like we all didn't believe what, what is going on. And there was this, I would call it a beautiful period in a way, despite how horrific that was, and that period was where everyone rose to the occasion, everybody wanted to help one another. Across Coupa everywhere, we started having documents of how can step up and help our customers, help our communities. We started to look at how we get PPE, and get it in the hands of our customers. We have access to suppliers. We started looking at helping suppliers with digital payments to speed things up. So, so many things we started doing as a community to just help each other. And then as we got to the next level, then we started, of course, starting to do things over, over zoom. And the big surprise, was we were incredibly productive. If anything, we were worried about people feeling burnt out. >> Yeah. >> Because they were just in it, completely in it. And it created a lot of new avenues for us because often you go and do these meetings in person. Now you could have a user experience session with a customer very easily, they're available more often than they used to. >> Lisa: Right. >> So we did not miss a beat with the community. We moved into virtual caps. We had the advantage of having them recorded as well, where we could have the global development teams learn and see exactly what the, what the customers are are co-creating together. And our goal lives accelerated, because a lot of these implementations, they used to happen in person, so schedules, they actually got accelerated- >> Lisa: Right. >> ...through that. Now of course, there is nothing that matches to this. You can do it, you can do a lot, but a ton of the collaboration comes from real life dialogue and kind of conversation. So it's that balance between the two that I think will be great. >> Lisa: What are some of the things that you've heard the last few days? You mentioned the Partners Summit and, and the Community Advisory Boards on Monday, yesterday, everything kicked off today. What are some of the things that you've heard in your meetings that really inspire you on say the next 10 years at Coupa? >> Raja: By far, by far, by far, it's a validation of, that what we are doing is, we're absolutely on target with it, and that, we just can do so much more. The silos are massive and there are so so many opportunities that you hear in every different areas that we could be doing this, we could be doing this together. So we can break down more and more silos. And using community.ai is just the tip of the iceberg of what we are, what we are doing. Yes, we created tens and tens of capabilities, helping, helping the community with all of that, but data drives everything. And when you look at that, every single process in every single silo can be informed by the power of data within your own company, and then even better, data across. And, and to the point where we're talking about concepts that customers are really excited about, even thinking about this community, they're customers of each other. And when you are a customer of each other what are the different ways as a community, you can help one another more. So we're talking about community netting as new types of concepts. >> Lisa: Talk to me a little about that. You mentioned the community netting this morning but I didn't quite... Help me understand. >> Raja: It is very simple terms is if, if we are buying from each other and we have to do money movements every time I have to pay you, I have to incur fees and likewise, but since we are part of this community we can manage that relationship. So we just pay the Delta, we net it out. So it, it saves reconciliation times it saves money movement. And these are tip of the icebergs of these very cool things that we're doing together. >> Wow. That's fantastic. Last question for you, as you talk with prospects who are in the early stages, or, or still determining, do we go through like a supply chain digital transformation? I mean, I think of companies that probably haven't now or need to get on the bandwagon. >> Raja: Yeah. >> What are some of the things that you advise to those customers to be able to do what Mick Ebeling talked about this morning and that is, commit and then figure it out? >> Raja: Yes. The number one thing is just make sure you don't do the analysis paralysis. There are just so many opportunities so many opportunities start with a project, get going, and it creates incredible momentum, and then you can move on from one to another, to another, to another, instead of trying to just go for a year or two, trying to look at how the world has changed in that process. And so often you could see that projects pay for themselves within the first month of go life. You do that, you'll create another one. And it's not like you are coming in to do something so new nobody has done. Hundreds and hundreds and thousands as a matter of fact, of other community members have done that. It is proven. So get started with those and then continue. Other things I will be talking to them about is to make sure that they understand the way we work is all about partnerships spread. Often people who haven't worked with us in the enterprise software, they're used to working with vendors. We are not that. We never were that. Like the number one, if we're not going to be real partners, honest, transparent and work with each other, we don't waste each other's time. >> Lisa: Well, Raja, it's been great having you on the program. I've really enjoyed your keynote this morning. Congratulations on your 10 years at Coupa. >> Raja: Thank you. >> I'm excited to see what the next 10 years brings for you. We appreciate your insites and everything that Coupa is doing in partnership with its customers is very evident in an event like this. >> Raja: Thank you. And thank you for coming and covering us as well. We really appreciate it. >> Lisa: It's our pleasure to be here. >> Thank you. >> For Raja Hammoud, I'm Lisa Martin. You're watching theCUBE's coverage, day two of Coupa Inspire 2022, from Las Vegas. (upbeat music)

>> Okay. We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid right. So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manuvir Das who's the head of enterprise computing at NVIDIA. And before I hand it off to Rob, I just want to say for those of you who follow me at the Cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise and it's being driven by the emergence of data intensive applications and workloads. No longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like NVIDIA. So let's learn more about this collaboration and what it means to your data business. Rob, take it away. >> Thanks Mick and Dave. That was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy in accelerating the path to value and hybrid environments. And I want to drill down on this aspect. Today, every business is facing accelerating change. Everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor now. Every engagement with coworkers, customers and partners is virtual. From website metrics to customer service records and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? At Cloudera, we believe this onslaught of data offers an opportunity to make better business decisions faster and we want to make that easier for everyone, whether it's fraud detection, demand forecasting, preventative maintenance, or customer churn. Whether the goal is to save money or produce income, every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit that cloud provides. And with security and edge to AI data intimacy, that's why the partnership between Cloudera and NVIDIA together means so much. And it starts with a shared vision, making data-driven decision-making a reality for every business. And our customers will now be able to leverage virtually unlimited quantities and varieties of data to power an order of magnitude faster decision-making. And together we turbo charged the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. We're joined today by NVIDIA's Manduvir Das, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, Manuvir, thank you for joining us. Over to you now. >> Thank you Rob, for having me. It's a pleasure to be here on behalf of NVIDIA. We're so excited about this partnership with Cloudera. You know, when, when NVIDIA started many years ago, we started as a chip company focused on graphics. But as you know, over the last decade, we've really become a full stack, accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, AI being a prime example. And when we think about Cloudera, and your company, your great company, there's three things we see Rob. The first one is that for the companies that were already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing we've seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about NVIDIA's mission going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies to date who have been the early adopters using the power acceleration by changing their technology and their stacks. But more and more we see the opportunity of meeting customers where they are with tools that they're familiar with, with partners that they trust. And of course, Cloudera being a great example of that. The second part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through. But as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. And so again, the power of your platform is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute, the machine learning compute, needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And, and Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where, literally, they took the workflow they had, they took the servers they had, they added GPUs into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >> How you doing? My name's Joe Ansaldi. I'm the branch chief of the technical branch in RAS. It's actually the research division, research and statistical division of the IRS. Basically, the mission that RAS has is we do statistical and research on all things related to taxes, compliance issues, fraud issues, you know, anything that you can think of basically, we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those algorithms, the number of parameters each of those algorithms have. So that's, that's really been our challenge now. The expectation was that with NVIDIA and Cloudera's help and with the cluster, we actually build out to test this on the actual fraud detection algorithm. Our expectation was we were definitely going to see some speed up in computational processing times. And just to give you context, the size of the data set that we were, the SME was actually working her algorithm against was around four terabytes. If I recall correctly, we had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them. It was really, really quick. The definite now term, short term, what's next is going to be the subject matter expert is actually going to take our algorithm run with that. So that's definitely the now term thing we want to do. Going down, go looking forward, maybe out a couple of months, we're also looking at procuring some A-100 cards to actually test those out. As you guys can guess, our datasets are just getting bigger and bigger and bigger, and it demands to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward and then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run a, you know, run to our heart's desire, wherever our imaginations takes our SMEs to actually develop solutions. Now have the platforms to run them on. Just kind of to close out, we really would be remiss, I've worked with a lot of companies through the year and most of them been spectacular. And you guys are definitely in that category, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't thank you guys. So thank you for the opportunity. Doing fantastic. and I'd have to also, I want to thank my guys. my staff, Raul, David worked on this, Richie worked on this, Lex and Tony just, they did a fantastic job and I want to publicly thank them for all the work they did with you guys and Chev, obviously also is fantastic. So thank you everyone. >> Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and NVIDIA? Is it primarily go to market or are you doing engineering work? What's the story there? >> It's really both. It's both go to market and engineering The engineering focus is to optimize and take advantage of NVIDIA's platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both. >> Great. Thank you. Manuvir, maybe you could talk a little bit more about why can't we just use existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do NVIDIA and Cloudera bring to the table that goes beyond the conventional systems that we've known for many years? >> Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So, the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now NVIDIA has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform so that regardless of the technique the customer is using to get insight from the data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >> So, I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going towards doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start. You think about AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems, and real-time AI inference, at least even at the edge, huge potential for business value. In a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the liking. So you're putting AI into these data intensive apps within the enterprise. The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >> Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint. And new platforms like these being developed by Cloudera and NVIDIA will dramatically lower the cost of enabling this type of workload to be done. And what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation engine, supply chain management, drug province. And increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots. That AR, VR and manufacturing so driving better quality. The power grid management, automated retail, IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >> I mean, Manufir, this is like your wheelhouse. Maybe you could add something to that. >> Yeah. I mean, I agree with Rob. I mean he listed some really good use cases, you know, The way we see this at NVIDIA, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled use cases, particular use cases like a chat bot from the ground up with the hardware and the software. Almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. Now, I think we are in the first phase of the democratization. For example, the work we do with Cloudera, which is for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. And you still come home and assemble it, but all the parts are there, the instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought a table and it showed up and somebody placed it in the right spot. Right? And they didn't really have to learn how to do AI. So these are the phases. And I think we're very excited to be going there. >> You know, Rob, the great thing about, for your customers is they don't have to build out the AI. They can, they can buy it. And just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations, and Mick I talked about this, the GIGO problem that we've all, you know, studied in college, you know, garbage in, garbage out. But, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob, over the next several years? >> So yeah, the combination of massive amounts of data that had been gathered across the enterprise in the past 10 years with an open APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency. And that's allowing us as an industry to democratize the data access while at the same time delivering the federated governance and security models. And hybrid technologies are playing a key role in making this a reality and enabling data access to be quote, hybridized, meaning access and treated in a substantially similar way, irrespective of the physical location of where that data actually resides. >> And that's great. That is really the value layer that you guys are building out on top of all this great infrastructure that the hyperscalers have have given us. You know, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you could go first and then Manuvir, you could bring us home. Where do you guys want to see the relationship go between Cloudera and NVIDIA? In other words, how should we as outside observers be, be thinking about and measuring your project, specifically in the industry's progress generally? >> Yes. I think we're very aligned on this and for Cloudera, it's all about helping companies move forward, leverage every bit of their data and all the places that it may be hosted and partnering with our customers, working closely with our technology ecosystem of partners, means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >> Yeah and I agree with Rob and for us at NVIDIA, you know, we, this partnership started with data analytics. As you know, Spark is a very powerful technology for data analytics. People who use Spark rely on Cloudera for that. And the first thing we did together was to really accelerate Spark in a seamless manner. But we're accelerating machine learning. We're accelerating artificial intelligence together. And I think for NVIDIA it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity.

>>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got a particular expertise in, in, in data and finance and insurance. I mean, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We, we talk more about digital, you know, or, or, or data-driven when you think about sort of where we've come from and where we're going, what are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital transformation journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third-party real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on, on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That data. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? >>Absolutely. I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of they having multiple, uh, distributors, what did they have in stock? So there are millions of data points that you need to drill down, down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their businesses and >>The ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting in? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Mick Halston about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict a, they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, w what do you see in that regard? >>Yeah, I think it's, I mean, we're definitely not at a point where when I talk to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? Where you can get machines to solve general knowledge problems, where they can solve one problem, and then a distinctly different problem, right? That's still many years away, but narrow AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So, for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience and pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer, and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this address actually, you know, a business that's a restaurant with indoor dining, does it have a bar is an outdoor dining, and it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do, even with narrow AI that can really drive top line of business results. >>Yeah. I like that term narrow AI because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. >>I mean, I think for most right, most fortune 500 companies, they can't just their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're half they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to, to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh, on-premise and public cloud as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought about? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. Then the salespeople, they know the CRM data and, you know, logistics folks. There they're very much in tune with ERP. I almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. >>I mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience. And that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really, >>I think data as a product is a very powerful concept. And I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data, and that's not necessarily what you mean. You mean thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea of I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my, my data architecture is, is that kind of thinking starting to really hit the marketplace. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware, and is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we, you know, collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies are doing >>Great examples of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss. Exactly. And it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight as to yeah. So, >>Um, I I'm in the executive sponsor for, um, the Accenture cloud era partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud errors, the right data platform for that. So, um, >>That'd be Cloudera ushered in the modern big data era. We, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, >>Absolutely. Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role apply. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Thank you.

>>Have you ever wondered how we sequence the human genome, how your smartphone is so well smart, how we will ever analyze all the patient data for the new vaccines or even how we plan to send humans to Mars? Well, at Cloudera, we believe that data can make what is impossible today possible tomorrow we are the enterprise data cloud company. In fact, we provide analytics and machine learning technology that does everything from making your smartphone smarter, to helping scientists ensure that new vaccines are both safe and effective, big data, no problem out era, the enterprise data cloud company. >>So I think for a long time in this country, we've known that there's a great disparity between minority populations and the majority of population in terms of disease burden. And depending on where you live, your zip code has more to do with your health than almost anything else. But there are a lot of smaller, um, safety net facilities, as well as small academic medical colleges within the United States. And those in those smaller environments don't have the access, you know, to the technologies that the larger ones have. And, you know, I call that, uh, digital disparity. So I'm, Harry's in academic scientist center and our mission is to train diverse health care providers and researchers, but also provide services to underserved populations. As part of the reason that I think is so important for me hearing medical college, to do data science. One of the things that, you know, both Cloudera and Claire sensor very passionate about is bringing those height in technologies to, um, to the smaller organizations. >>It's very expensive to go to the cloud for these small organizations. So now with the partnership with Cloudera and Claire sets a clear sense, clients now enjoy those same technologies and really honestly have a technological advantage over some of the larger organizations. The reason being is they can move fast. So we were able to do this on our own without having to, um, hire data scientists. Uh, we probably cut three to five years off of our studies. I grew up in a small town in Arkansas and is one of those towns where the railroad tracks divided the blacks and the whites. My father died without getting much healthcare at all. And as an 11 year old, I did not understand why my father could not get medical attention because he was very sick. >>Since we come at my Harry are looking to serve populations that reflect themselves or affect the population. He came from. A lot of the data you find or research you find health is usually based on white men. And obviously not everybody who needs a medical provider is going to be a white male. >>One of the things that we're concerned about in healthcare is that there's bias in treatment already. We want to make sure those same biases do not enter into the algorithms. >>The issue is how do we get ahead of them to try to prevent these disparities? >>One of the great things about our dataset is that it contains a very diverse group of patients. >>Instead of just saying, everyone will have these results. You can break it down by race, class, cholesterol, level, other kinds of factors that play a role. So you can make the treatments in the long run. More specifically, >>Researchers are now able to use these technologies and really take those hypotheses from, from bench to bedside. >>We're able to overall improve the health of not just the person in front of you, but the population that, yeah, >>Well, the future is now. I love a quote by William Gibson who said the future is already here. It's just not evenly distributed. If we think hard enough and we apply things properly, uh, we can again take these technologies to, you know, underserved environments, um, in healthcare. Nobody should be technologically disadvantage. >>When is a car not just a car when it's a connected data driven ecosystem, dozens of sensors and edge devices gathering up data from just about anything road, infrastructure, other vehicles, and even pedestrians to create safer vehicles, smarter logistics, and more actionable insights. All the data from the connected car supports an entire ecosystem from manufacturers, building safer vehicles and fleet managers, tracking assets to insurers monitoring, driving behaviors to make roads safer. Now you can control the data journey from edge to AI. With Cloudera in the connected car, data is captured, consolidated and enriched with Cloudera data flow cloud Dara's data engineering, operational database and data warehouse provide the foundation to develop service center applications, sales reports, and engineering dashboards. With data science workbench data scientists can continuously train AI models and use data flow to push the models back to the edge, to enhance the car's performance as the industry's first enterprise data cloud Cloudera supports on-premise public and multi-cloud deployments delivering multifunction analytics on data anywhere with common security governance and metadata management powered by Cloudera SDX, an open platform built on open source, working with open compute architectures and open data stores all the way from edge to AI powering the connected car. >>The future has arrived. >>The Dawn of a retail Renaissance is here and shopping will never be the same again. Today's connected. Consumers are always on and didn't control. It's the era of smart retail, smart shelves, digital signage, and smart mirrors offer an immersive customer experience while delivering product information, personalized offers and recommendations, video analytics, capture customer emotions and gestures to better understand and respond to in-store shopping experiences. Beacons sensors, and streaming video provide valuable data into in-store traffic patterns, hotspots and dwell times. This helps retailers build visual heat maps to better understand custom journeys, conversion rates, and promotional effectiveness in our robots automate routine tasks like capturing inventory levels, identifying out of stocks and alerting in store personnel to replenish shelves. When it comes to checking out automated e-commerce pickup stations and frictionless checkouts will soon be the norm making standing in line. A thing of the past data and analytics are truly reshaping. >>The everyday shopping experience outside the store, smart trucks connect the supply chain, providing new levels of inventory visibility, not just into the precise location, but also the condition of those goods. All in real time, convenience is key and customers today have the power to get their goods delivered at the curbside to their doorstep, or even to their refrigerators. Smart retail is indeed here. And Cloudera makes all of this possible using Cloudera data can be captured from a variety of sources, then stored, processed, and analyzed to drive insights and action. In real time, data scientists can continuously build and train new machine learning models and put these models back to the edge for delivering those moment of truth customer experiences. This is the enterprise data cloud powered by Cloudera enabling smart retail from the edge to AI. The future has arrived >>For is a global automotive supplier. We have three business groups, automotive seating in studios, and then emission control technologies or biggest automotive customers are Volkswagen for the NPSA. And we have, uh, more than 300 sites. And in 75 countries >>Today, we are generating tons of data, more and more data on the manufacturing intelligence. We are trying to reduce the, the defective parts or anticipate the detection of the, of the defective part. And this is where we can get savings. I would say our goal in manufacturing is zero defects. The cost of downtime in a plant could be around the a hundred thousand euros. So with predictive maintenance, we are identifying correlations and patterns and try to anticipate, and maybe to replace a component before the machine is broken. We are in the range of about 2000 machines and we can have up to 300 different variables from pressure from vibration and temperatures. And the real-time data collection is key, and this is something we cannot achieve in a classical data warehouse approach. So with the be data and with clouded approach, what we are able to use really to put all the data, all the sources together in the classical way of working with that at our house, we need to spend weeks or months to set up the model with the Cloudera data lake. We can start working on from days to weeks. We think that predictive or machine learning could also improve on the estimation or NTC patient forecasting of what we'll need to brilliance with all this knowledge around internet of things and data collection. We are applying into the predictive convene and the cockpit of the future. So we can work in the self driving car and provide a better experience for the driver in the car. >>The Cloudera data platform makes it easy to say yes to any analytic workload from the edge to AI, yes. To enterprise grade security and governance, yes. To the analytics your people want to use yes. To operating on any cloud. Your business requires yes to the future with a cloud native platform that flexes to meet your needs today and tomorrow say yes to CDP and say goodbye to shadow it, take a tour of CDP and see how it's an easier, faster and safer enterprise analytics and data management platform with a new approach to data. Finally, a data platform that lets you say yes, >>Welcome to transforming ideas into insights, presented with the cube and made possible by cloud era. My name is Dave Volante from the cube, and I'll be your host for today. And the next hundred minutes, you're going to hear how to turn your best ideas into action using data. And we're going to share the real world examples and 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing, efficiencies, better forecast, retail demand, transform analytics, improve public sector service, and so much more how we use data is rapidly evolving as is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to rather we use terms like digital transformation and digital business, but you think about it. What is a digital business? How is that different from just a business? >>Well, digital business is a data business and it differentiates itself by the way, it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings. But increasingly insights are leading to the development of data, products and services that can be monetized, or as you'll hear in our industry, examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real-time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. >>Self-service investing, filing insurance claims and our smart phones. And so many examples, IOT systems that communicate and act machine and machine real-time pricing actions. These are all examples of products and services that drive revenue cut costs or create other value. And they all rely on data. Now while many business leaders sometimes express frustration that their investments in data, people, and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data, transformation and leadership. One thing is certain the next 10 years of data and digital transformation, won't be like the last 10. So let's get into it. Please join us in the chat. >>You can ask questions. You can share your comments, hit us up on Twitter right now. It's my pleasure to welcome Mick Holliston in he's the president of Cloudera mic. Great to see you. Great to see you as well, Dave, Hey, so I call it the new abnormal, right? The world is kind of out of whack offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations line cooks at restaurants. Everything that we consumers have missed, but here's the one thing. It seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling their pricing algorithms, you know, commodity prices we don't know is inflation. Transitory. Is it a long-term threat trying to forecast GDP? It's just seems like we have to reset all of our assumptions and make a feel a quality data is going to be a key here. How do you see the current state of the industry and the role data plays to get us into a more predictable and stable future? Well, I >>Can sure tell you this, Dave, uh, out of whack is definitely right. I don't know if you know or not, but I happen to be coming to you live today from Atlanta and, uh, as a native of Atlanta, I can, I can tell you there's a lot to be known about the airport here. It's often said that, uh, whether you're going to heaven or hell, you got to change planes in Atlanta and, uh, after 40 minutes waiting on algorithm to be right for baggage claim when I was not, I finally managed to get some bag and to be able to show up dressed appropriately for you today. Um, here's one thing that I know for sure though, Dave, clean, consistent, and safe data will be essential to getting the world and businesses as we know it back on track again, um, without well-managed data, we're certain to get very inconsistent outcomes, quality data will the normalizing factor because one thing really hasn't changed about computing since the Dawn of time. Back when I was taking computer classes at Georgia tech here in Atlanta, and that's what we used to refer to as garbage in garbage out. In other words, you'll never get quality data-driven insights from a poor data set. This is especially important today for machine learning and AI, you can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid as AI is increasingly used in every business app, you must build a solid data foundation mic. Let's >>Talk about hybrid. Every CXO that I talked to, they're trying to get hybrid, right? Whether it's hybrid work hybrid events, which is our business hybrid cloud, how are you thinking about the hybrid? Everything, what's your point of view with >>All those descriptions of hybrid? Everything there, one item you might not have quite hit on Dave and that's hybrid data. >>Oh yeah, you're right. Mick. I did miss that. What, what do you mean by hybrid data? Well, >>David in cloud era, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises in a private cloud and public cloud, or perhaps even in a new open data exchange. Now this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud, but either way security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch her read a recent news story. Data breaches are at an all time high. And the ethics of AI applications are being called into question every day and understanding the lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know Dave? That's the business cloud era? Is it on a serious note from cloud era's perspective? Adopting a hybrid data strategy is central to every business's digital transformation. It will enable rapid adoption of new technologies and optimize economic models while ensuring the security and privacy of every bit of data. What can >>Make, I'm glad you brought in that notion of hybrid data, because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You've got the economics, the physics, the local laws come into play. So what about the rest of hybrid? Yeah, >>It's a great question, Dave and certainly cloud era itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office. Uh, ether net may not be happening quite so fast in somebody's rural home in, uh, in, in the middle of Nebraska somewhere. Right? So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it. Uh, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, uh, involve us kind of slowly coming back to work, begin in this fall. And we're looking forward to being able to shake hands and see one another again for the first time in many cases for more than a year and a half, but, uh, yes, hybrid work, uh, and hybrid data are playing an increasingly important role for every kind of business. >>Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, and the case. Here's how I think about it. It makes, I mean, some industries have transformed. You think about retail, for example, it's pretty clear, although although every physical retail brand I know has, you know, not only peaked up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence, uh, and ended up reverse. We see Amazon building out physical assets so that there's more hybrid going on. But when you look at healthcare, for example, it's just starting, you know, with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control, mint control of payment systems in manufacturing, you know, the pandemic highlighted America's reliance on China as a manufacturing partner and, and supply chain. Uh it's so my point is it seems that different industries they're in different stages of transformation, but two things look really clear. One, you've got to put data at the core of the business model that's compulsory. It seems like embedding AI into the applications, the data, the business process that's going to become increasingly important. So how do you see that? >>Wow, there's a lot packed into that question there, Dave, but, uh, yeah, we, we, uh, you know, at Cloudera I happened to be leading our own digital transformation as a technology company and what I would, what I would tell you there that's been arresting for us is the shift from being largely a subscription-based, uh, model to a consumption-based model requires a completely different level of instrumentation and our products and data collection that takes place in real, both for billing, for our, uh, for our customers. And to be able to check on the health and wellness, if you will, of their cloud era implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed, the rate and pace of getting vaccines, uh, to market, uh, or we've been assisting with testing process. >>That's taken place because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and, and healthy and safe outcomes for everyone. And cloud era has been underneath a great deal of that type of work and the financial services industry you pointed out. Uh, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are really helping those kinds of organizations get through those difficult challenges. You, you also happened to mention, uh, you know, public sector and in public sector. We're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. >>Um, and while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, can't grow, build great ML apps, unless you've got a strong data foundation underneath is back to that garbage in garbage out comment that I made previously. And so in order to do that, you've got to have a great hybrid dated management platform at your disposal to ensure that your data is clean and organized and up to date. Uh, just as importantly from that, that's kind of the freedom side of things on the security side of things. You've got to ensure that you can see who just touched, not just the data itself, Dave, but actually the machine learning models and organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage. >>In addition to data lineage. In other words, who's had access to the machine learning models when and where, and at what time and what decisions were made perhaps by the humans, perhaps by the machines that may have led to a particular outcome. So every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models just as they can track the lineage of data. So lots going on there across industries, lots going on as those various industries think about how AI can be applied to their businesses. Pretty >>Interesting concepts. You bring it into the discussion, the hybrid data, uh, sort of new, I think, new to a lot of people. And th this idea of model lineage is a great point because people want to talk about AI, ethics, transparency of AI. When you start putting those models into, into machines to do real time inferencing at the edge, it starts to get really complicated. I wonder if we could talk about you still on that theme of industry transformation? I felt like coming into the pandemic pre pandemic, there was just a lot of complacency. Yeah. Digital transformation and a lot of buzz words. And then we had this forced March to digital, um, and it's, but, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? It definitely >>Is a lot of a POC limbo or a, I think some of us internally have referred to as POC purgatory, just getting stuck in that phase, not being able to get from point a to point B in digital transformation and, um, you know, for every industry transformation, uh, change in general is difficult and it takes time and money and thoughtfulness, but like with all things, what we found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts set. Another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts as it relates to the underlying technology here. And to bring it home a little bit more practically, I guess I would say at cloud era, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. >>And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, uh, right close to home in place. They can kind of experiment a little bit more safely and economically, and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplish, but kind of starting small and then drawing fast from there on customer's journey to the we'll make we've >>Covered a lot of ground. Uh, last question. Uh, w what, what do you want people to leave this event, the session with, and thinking about sort of the next era of data that we're entering? >>Well, it's a great question, but, uh, you know, I think it could be summed up in, uh, in two words. I want them to think about a hybrid data, uh, strategy. So, uh, you know, really hybrid data is a concept that we're bringing forward on this show really for the, for the first time, arguably, and we really do think that it enables customers to experience what we refer to Dave as the power of, and that is freedom, uh, and security, and in a world where we're all still trying to decide whether each day when we walk out each building, we walk into, uh, whether we're free to come in and out with a mask without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe, uh, for ourselves and for others. And the same is true of organizations. It strategies. They want the freedom to choose, to run workloads and applications and the best and most economical place possible. But they also want to do that with certainty, that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole, >>Nick, thanks so much great conversation and really appreciate the insights that you're bringing to this event into the industry. Really thank you for your time. >>You bet Dave pleasure being with you. Okay. >>We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid, right? So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manny veer, DAS. Who's the head of enterprise computing at Nvidia. And before I hand it off to Robin, I just want to say for those of you who follow me at the cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise, and it's being driven by the emergence of data, intensive applications and workloads no longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like Nvidia. So let's learn more about this collaboration and what it means to your data business. Rob, thanks, >>Mick and Dave, that was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy and accelerating the path to value and hybrid environments. And I want to drill down on this aspect today. Every business is facing accelerating everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor. Now, every engagement with coworkers, customers and partners is virtual from website metrics to customer service records, and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? A cloud era? We believe this onslaught of data offers an opportunity to make better business decisions faster. >>And we want to make that easier for everyone, whether it's fraud, detection, demand, forecasting, preventative maintenance, or customer churn, whether the goal is to save money or produce income every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit they cloud provides and with security and edge to AI data intimacy. That's why the partnership between cloud air and Nvidia together means so much. And it starts with the shared vision making data-driven, decision-making a reality for every business and our customers will now be able to leverage virtually unlimited quantities of varieties, of data, to power, an order of magnitude faster decision-making and together we turbo charge the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. >>We're joined today by NVIDIA's Mandy veer dos, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, man, you're veer. Thank you for joining us over the unit. >>Thank you, Rob, for having me. It's a pleasure to be here on behalf of Nvidia. We are so excited about this partnership with Cloudera. Uh, you know, when, when, uh, when Nvidia started many years ago, we started as a chip company focused on graphics, but as you know, over the last decade, we've really become a full stack accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, uh, AI being a prime example. And when we think about Cloudera, uh, and your company, a great company, there's three things we see Rob. Uh, the first one is that for the companies that will already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. >>And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about in various mission, going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies today who have been the early adopters, uh, using the power acceleration by changing the technology in their stacks. But more and more, we see the opportunity of meeting customers, where they are with tools that they're familiar with with partners that they trust. And of course, Cloudera being a great example of that. Uh, the second, uh, part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through, but as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. >>And so again, the power of your platform, uh, is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute the machine learning compute needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where literally they took the workflow. They had, they took the servers, they had, they added GPS into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >>My name's Joanne salty. I'm the branch chief of the technical branch and RAs. It's actually the research division research and statistical division of the IRS. Basically the mission that RAs has is we do statistical and research on all things related to taxes, compliance issues, uh, fraud issues, you know, anything that you can think of. Basically we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So it's our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those Agra algorithms, the of parameters each of those algorithms have. >>So that's, that's really been our challenge. Now the expectation was that with Nvidia in cloud, there is help. And with the cluster, we actually build out the test this on the actual fraud, a fraud detection algorithm on our expectation was we were definitely going to see some speed up in prom, computational processing times. And just to give you context, the size of the data set that we were, uh, the SMI was actually working, um, the algorithm against Liz around four terabytes. If I recall correctly, we'd had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them? It was really, really quick. Uh, the definite now term short term what's next is going to be the subject matter expert is actually going to take our algorithm run with that. >>So that's definitely the now term thing we want to do going down, go looking forward, maybe out a couple of months, we're also looking at curing some, a 100 cards to actually test those out. As you guys can guess our datasets are just getting bigger and bigger and bigger, and it demands, um, to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward. And then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run them, you know, come onto our art's desire, wherever imagination takes our skis to actually develop solutions, know how the platforms to run them on just kind of the close out. >>I rarely would be very missed. I've worked with a lot of, you know, companies through the year and most of them been spectacular. And, uh, you guys are definitely in that category. The, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't. Thank you guys. So thank you for the opportunity to, and fantastic. And I'd have to also, I want to thank my guys. My, uh, my staff, David worked on this Richie worked on this Lex and Tony just, they did a fantastic job and I want to publicly thank him for all the work they did with you guys and Chev, obviously also. Who's fantastic. So thank you everyone. >>Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and Nvidia? Is it primarily go to market or you do an engineering work? What's the story there? >>It's really both. It's both go to market and engineering and engineering focus is to optimize and take advantage of invidious platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both >>Great. Thank you. Many of Eric, maybe you could talk a little bit more about why can't we just existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do Nvidia and cloud era bring to the table that goes beyond the conventional systems that we've known for many years? >>Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now Nvidia has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform. So that regardless of the technique, the customer's using to get insight from that data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >>So I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going toward doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive, uh, things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start, you think about the AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems and real time, AI inference, at least even at the edge, huge potential for business value and a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the like, and so you're putting AI into these data intensive apps within the enterprise. >>The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >>Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint and new platforms like these being developed by cloud air and Nvidia will dramatically lower the cost, uh, of enabling this type of workload to be done. Um, and what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation, engine chain management, drug province, and increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots that AR VR and manufacturing. So driving quality, better quality in the power grid management, automated retail IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >>I mean, this is like your wheelhouse. Maybe you could add something to that. >>Yeah. I mean, I agree with Rob. I mean he listed some really good use cases. You know, the way we see this at Nvidia, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled, uh, use cases, particular use cases like a chat bot, uh, uh, from the ground up with the hardware and the software almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. I think we are in the first phase of the democratization, uh, for example, the work we did with Cloudera, which is, uh, for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. >>And you still come home and assemble it, but all the parts are there. The instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought, bought a table and it showed up and somebody placed it in the right spot. Right. And they didn't really have to learn, uh, how to do AI. So these are the phases. And I think they're very excited to be going there. Yeah. You know, >>Rob, the great thing about for, for your customers is they don't have to build out the AI. They can, they can buy it. And, and just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations and Mick and I talked about this, the guy go problem that we've all studied in college, uh, you know, garbage in, garbage out. Uh, but, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob over the next several years? >>So yeah, the combination of massive amounts of data that have been gathered across the enterprise in the past 10 years with an open API APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency, you know, and that's allowing us as an industry to democratize the data access while at the same time, delivering the federated governance and security models and hybrid technologies are playing a key role in making this a reality and enabling data access to be hybridized, meaning access and treated in a substantially similar way, your respect to the physical location of where that data actually resides. >>That's great. That is really the value layer that you guys are building out on top of that, all this great infrastructure that the hyperscalers have have given us, I mean, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you can go first and then manufacture. You could bring us home. Where do you guys want to see the relationship go between cloud era and Nvidia? In other words, how should we, as outside observers be, be thinking about and measuring your project specifically and in the industry's progress generally? >>Yeah, I think we're very aligned on this and for cloud era, it's all about helping companies move forward, leverage every bit of their data and all the places that it may, uh, be hosted and partnering with our customers, working closely with our technology ecosystem of partners means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >>Yeah. And I agree with Robin and for us at Nvidia, you know, we, this partnership started, uh, with data analytics, um, as you know, a spark is a very powerful technology for data analytics, uh, people who use spark rely on Cloudera for that. And the first thing we did together was to really accelerate spark in a seamless manner, but we're accelerating machine learning. We accelerating artificial intelligence together. And I think for Nvidia it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity. >>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got particular expertise in, in data and finance and insurance. I mean, you know, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We talk more about digital, you know, or, or, or data driven when you think about sort of where we've come from and where we're going. What are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third party, real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we, we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That day. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, is it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? Absolutely. >>I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of the, having multiple distributors, what did they have in stock? So there are millions of data points that you need to drill down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of it, like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their business >>Is performing good, the ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration. W where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Nicolson about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict that they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, what do you see in that regard? Yeah, I think it's, >>I mean, we're definitely not at a point where, when I talked to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? You can get machines to solve general knowledge problems, where they can solve one problem and then a distinctly different problem, right? That's still many years away, but narrow why AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience in pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this a address actually, you know, a business that's a restaurant with indoor dining, does it have a bar? Is it outdoor dining? And it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do even with narrow AI that can really drive top line of business results. >>Yeah. I liked that term, narrow AI, because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. I mean, I think for most right, most >>Fortune 500 companies, they can't just snap their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're have, they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh on-premise and, um, public as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought of? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. And then the salespeople, they know the CRM data and, you know, logistics folks there they're very much in tune with ERP, almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership, if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. I >>Mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience and that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a, a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really >>Important. I think data as a product is a very powerful concept and I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data and that's not necessarily what you mean, thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my data architecture. Is that kind of thinking starting to really hit the marketplace? Absolutely. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware. And is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies, you're doing great examples >>Of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss and exactly. Then it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight. >>Yeah. So, um, I, I'm in the executive sponsor for, um, the Accenture Cloudera partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud era is the right data platform for that. So, um, >>Having a cloud Cloudera ushered in the modern big data era, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, absolutely. >>Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role to play. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Yes. Thank you so much. Okay. We continue now with the theme of turning ideas into insights. So ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only, and a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal. We heard, or at least semi normal as we begin to better understand and forecast demand and supply and balances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processes, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz. Who's a Kuba woman, VP and principal analyst at Forrester, and doing some groundbreaking work in this area. And Cindy, Mikey, who is the vice president of industry solutions and value management at Cloudera. Welcome to both of >>You. Welcome. Thank you. Thanks Dave. >>All right, Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >>It's really about democratization. If you can't make your data accessible, um, it's not usable. Nobody's able to understand what's happening in the business and they don't understand, um, what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships, their customers, um, due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and Peck around within your ecosystem to find what it is that's important. Great. >>Thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, does, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >>Yeah, there's, there's quite a few. And especially as we look across, um, all the industries that we're actually working with customers in, you know, a few that stand out in top of mind for me is one is IQ via and what they're doing with real-world evidence and bringing together data across the entire, um, healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making the ed accessible by both internally, as well as for their, um, the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, there are a European car manufacturer and how do they make sure that they have, you know, efficient and effective processes when it comes to, uh, fixing equipment and so forth. And then also, um, there's, uh, an Indonesian based, um, uh, telecommunications company tech, the smell, um, who's bringing together, um, over the last five years, all their data about their customers and how do they enhance our customer experience? How do they make information accessible, especially in these pandemic and post pandemic times, um, uh, you know, just getting better insights into what customers need and when do they need it? >>Cindy platform is another core principle. How should we be thinking about data platforms in this day and age? I mean, where does, where do things like hybrid fit in? Um, what's cloud era's point >>Of view platforms are truly an enabler, um, and data needs to be accessible in many different fashions. Um, and also what's right for the business. When, you know, I want it in a cost and efficient and effective manner. So, you know, data needs to be, um, data resides everywhere. Data is developed and it's brought together. So you need to be able to balance both real time, you know, our batch historical information. It all depends upon what your analytical workloads are. Um, and what types of analytical methods you're going to use to drive those business insights. So putting and placing data, um, landing it, making it accessible, analyzing it needs to be done in any accessible platform, whether it be, you know, a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing, being the most successful. >>Great. Thank you, Michelle. Let's move on a little bit and talk about practices and practices and processes as the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >>Yeah, it's a really great question because it's pretty complex. When you have to start to connect your data to your business, the first thing to really gravitate towards is what are you trying to do? And what Cindy was describing with those customer examples is that they're all based off of business goals off of very specific use cases that helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in, you know, near time or real time, or later on, as you're doing your strategic planning, what that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, well, can I also measure the outcomes from those processes and using data and using insight? >>Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my, um, analytic capabilities that are allowing me to be effective in those environments, but everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it, but coming in more from that business perspective to then start to be, data-driven recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions, and ultimately getting to the point of being insight driven, where you're able to both, uh, describe what you want your business to be with your data, using analytics, to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize, and you can innovate because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >>I like how you said near time or real time, because it is a spectrum. And you know, one of the spectrum, autonomous vehicles, you've got to make a decision in real time, but, but, but near real-time, or real-time, it's, it's in the eyes of the holder, if you will, it's it might be before you lose the customer before the market changes. So it's really defined on a case by case basis. Um, I wonder Michelle, if you could talk about in working with a number of organizations, I see folks, they sometimes get twisted up and understanding the dependencies that technology generally, and the technologies around data specifically can have on critical business processes. Can you maybe give some guidance as to where customers should start, where, you know, where can we find some of the quick wins and high return, it >>Comes first down to how does your business operate? So you're going to take a look at the business processes and value stream itself. And if you can understand how people and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process, or are you collecting information asking for information that is going to help satisfy a downstream process step or a downstream decision. So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize. Do you need that real time near real time, or do you want to start grading greater consistency by bringing all of those signals together, um, in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process and the decision points and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time, uh, streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >>Got it. Let's, let's bring Cindy back into the conversation in your city. We often talk about people process and technology and the roles they play in creating a data strategy. That's that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? Yeah. >>And that's, uh, you know, kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but you know, the fuel behind the process, um, and how do you actually become insight-driven? And, you know, you look at the capabilities that you're needing to enable from that business process, that insight process, um, you're extended ecosystem on, on how do I make that happen? You know, partners, um, and, and picking the right partner is important because a partner is one that actually helps under or helps you implement what your decisions are. Um, so, um, looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data, um, you know, for within process on that, if you need to do it in a time fashion, that they can actually meet those needs of the business, um, and enabling on those, those process activities. So the ecosystem looking at how you, um, look at, you know, your vendors are, and fundamentally they need to be that trusted partner. Um, do they bring those same principles of value of being insight driven? So they have to have those core values themselves in order to help you as a, um, an end of business person enable those capabilities. So, so yeah, I'm >>Cool with fuel, but it's like super fuel when you talk about data, cause it's not scarce, right? You're never going to run out. So Michelle, let's talk about leadership. W w who leads, what does so-called leadership look like in an organization that's insight driven? >>So I think the really interesting thing that is starting to evolve as late is that organizations enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be data driving into the insight or the raw data itself has the ability to set in motion. What's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, um, your CRO coming back and saying, I need better data. I need information. That's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come, not just, you know, one month, two months, three months or a year from now, but in a week or tomorrow. >>And so that's, how is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity, you have your chief data officer that is shaping the exp the experiences, uh, with data and with insight and reconciling, what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities, and either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data driven, but ultimately to be insight driven, you're seeing way more, um, participation, uh, and leadership at that C-suite level. And just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >>Right. Thank you. Let's wrap. And I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. You know, a lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a, of a maturity model. Uh, you know, I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on, on insight driven organization, city, what do you see as the major characteristics that define the differences between sort of the, the early, you know, beginners, the sort of fat middle, if you will, and then the more advanced, uh, constituents. >>Yeah, I'm going to build upon, you know, what Michelle was talking about as data as an asset. And I think, you know, also being data where, and, you know, trying to actually become, you know, insight driven, um, companies can also have data and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, um, you know, you're not going to be insight driven. So you've got to move beyond that, that data awareness, where you're looking at data just from an operational reporting, but data's fundamentally driving the decisions that you make. Um, as a business, you're using data in real time. You're, um, you're, you know, leveraging data to actually help you make and drive those decisions. So when we use the term you're, data-driven, you can't just use the term, you know, tongue in cheek. It actually means that I'm using the recent, the relevant and the accuracy of data to actually make the decisions for me, because we're all advancing upon. We're talking about, you know, artificial intelligence and so forth. Being able to do that, if you're just data where I would not be embracing on leveraging artificial intelligence, because that means I probably haven't embedded data into my processes. It's data could very well still be a liability in your organization. So how do you actually make it an asset? Yeah, I think data >>Where it's like cable ready. So, so Michelle, maybe you could, you could, you could, uh, add to what Cindy just said and maybe add as well, any advice that you have around creating and defining a data strategy. >>So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? You know, bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset. It has value, but you may not necessarily know what that value is. Yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more, um, proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. >>So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away. Um, the gap between when you see it, know it and then get the most value and really exploit what that insight is at the time when it's right. So in the moment we talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. >>So are we just introducing it as data-driven organizations where we could see, you know, spreadsheets and PowerPoint presentations and lots of mapping to help make sort of longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder. If I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight. There is none it's all coming together for the best outcomes, >>Right? So people are acting on perfect or near perfect information or machines or, or, uh, doing so with a high degree of confidence, great advice and insights. And thank you both for sharing your thoughts with our audience today. It's great to have you. Thank you. Thank you. Okay. Now we're going to go into our industry. Deep dives. There are six industry breakouts, financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments that each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session for choice of choice or for more information, click on the agenda page and take a look to see which session is the best fit for you. And then dive in, join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community and enjoy the rest of the day.

>>mhm >>mm >>Hello and welcome back to HPD discovered 2021. My name is Dave Volonte and we're going to dig into H P E. Green Lake, we've heard a lot about this, we want to find out how real it is and test a little bit of how how can help solve your business problems. We also want to understand Green Lake relative to the competition. HPV was the first, as you probably know to declare it all in with an as a service model and virtually every major infrastructure player has now followed suit. So we want to hear from HP directly how it's different from the competition, where it's innovating and that means we're gonna poke a little bit of customer examples and how the partner ecosystem is adopting and responding to Green Lake and with me is the right person to do this is keith White, who is the senior Vice President General Manager of the Green Lake cloud services business unit at HP, keith, great to see you, thanks for coming back to the cube. >>Okay, fantastic to see you as always. So thanks so much for having me. >>Yeah, it's our pleasure. So look, we're hearing a lot leading up to discover and at this event about Green Lake you got momentum now, everybody's excited about it. What's driving demand? Where's the excitement coming from? >>No, it's a great question. And you know, the reality is customers are expecting this cloud experience, right? So they they've been using the public cloud, they've been engaging on that front and this cloud experience is really driven, a pretty high amount of customer expectations, make itself served, make it automated, make it easy to consume, only want to pay for what I'm using and then manage it all for me on the back end. But 60 to 70% of apps and data will stay on prem per Gardner and I D. C. And so give me that experience on prem. And so that's why I think Green Lake has gotten so much interest, so much positive growth and momentum is because we're bringing that cloud experience to our customers in their data center, in their Coehlo or at the edge and that's where they want to see it just as much. And so since the world is now hybrid, we have a fantastic solution for folks. >>So you, you were first in this game and so you took some arrows and I'm interested in how Green Lake has evolved, Take us through the journey maybe what were some of the bumps in the road that you had to overcome? Maybe how it compares with the competition. Maybe some of the things that they're going to have to go through as well to get to the point where you are. >>No, it's true. And you know, the great thing is HP as a company is really moving to be much more of a cloud services and software company. And you know, we're seeing this from our competition, as you mentioned, have followed suit. But in essence, you know, you have to move from just sort of providing lease type financing type scenarios for our customers into truly delivering that cloud experience. And that's what's been so exciting over this last year is we've gone from just the basic cloud services, compute storage, networking and VMS to really providing containers as a service, bare metal as a service. Uh, machine learning ops, S. A P V. D I. You know, we've now created a set of workloads and as you heard it discover we're now delivering industry solutions, so electronic medical records for hospitals or high delivery payment transaction processing for, for financial, so that the challenge of moving from just sort of leasing basic capabilities to a true cloud experience that again pay as I go, fully automated self serve, all managed for me has really been a challenge and it's exciting, it's exciting to see customers jump on and really sort of lean in and see the business value that comes from having that level of solution >>keith, am I correct in that pretty much every large tech company has a services arm and they could, they could sort of brute force, some kind of cloud like experience and that's kind of what people have done historically the layer in a financial like leasing financial as you said and and but every situation was unique, it was kind of a snowflake if you will and you guys are probably there a few years ago as well and so I'm interested in sort of how you evolved beyond that. Was it a mindset was a technology, was it sort of cultural? You know, it came from the top as well, but maybe you could describe that a little bit. >>Yeah, the ship comes from our customers because what's happening is customers no longer trying to buy component parts. They're saying it's really about Tesla's like, hey, I want you to deliver this for me. In essence, we're running the data center for them now. We're running their machine learning operations environment for them. Now, you know, we're migrating their mainframe over now. And so what's happening is these sls are really, what matters to customers like that? It's not so much about, hey, what are the speeds and feeds and this and that? And so yes, you can sort of brute force that piece of it. But what you really are having to do is create this deep partnership and relationship with your customer to truly understand their business challenges and then provide them with that capability. Now I think the things that's exciting is yes, the public cloud gives you some some significant benefits for certain workloads and certain capabilities. But what we're hearing from customers is hey, I want to have much more control over my data center. I want to ensure that it has the security required. I want to make sure that I can make the adjustments necessary and so you're doing all that at a lower cost with open platform that I can use a variety of tools and other applications just makes it that much more powerful. So I think that's what we're seeing is we're getting into what our customers really requiring and then you know the most interesting thing is how do you make it work with my entire environment because I am running Azure and I am running A W. S. And I am running google and I'm running some other things. And so how does this cloud really helped me bring all those together to really govern that hybrid estate? And that's where I think Green Lake has really shine. >>So it kind of part of the secret sauce is automation because you've got to be, you still have, you have to be competitive, you know, at least within reason to cloud cost, sometimes it's going to be less expensive, maybe sometimes it can be more expensive. You've got some advantages in certain cases where, you know, there's government governance things and and you know, we don't have to go through all that, but there's the automation but you've got to be profitable at this too. So there's the automation, there's the tooling, there's the openness. So, so that was really a key part of it. Is it not that sort of automating? >>That's right. Automation is key as is really understanding what that customer environment is and optimizing for that piece of it. And so as you heard, we're really excited to announce our Green Lake Lighthouse, which is really providing workload optimized systems that are fully managed for them that provide that capability to run multiple workloads for that customer. But at the same time, to your point, there's a lot of charges that happened on the public cloud side. So, you know, data is the new, you know, gold if you will right, everyone's trying to monetize their data, trying to use it to make decisions and really understand what's happening across their environment and in the cloud. You know, if you put it up in the cloud, you have to pay to get it out. The egress charges can be significant and it's also a bit slower at times because of the latency that happens across that that that connection. And so we are now in a situation where we're seeing a lot of customers that are really trying to analyze their data, leveraging our HPC systems, leveraging our machine learning operation systems in order to really get that data happening, Getting the dancers out much, much faster and a much lower cost than what it would cost them to do that in the cloud. >>So you have some experience at this now. I wonder if we could dig into the customers how customers are using Green Lake. Maybe you can give some examples of success. >>Yeah. Yeah, no. You know it's exciting because you know first off everyone's looking at their digital transformation and that means something different for every single customer, so really understanding what they're trying to do from a transformation standpoint and then saying, okay, well how can we bring a solution to help accelerate that? To help be uh, you know, more connected to your customers to help improve your product delivery. We went to Lyondellbasell for example, one of the largest manufacturers in the world. And you know, they said, hey look, we don't want to run our data center anymore. Most most customers are trying to get out of the data center management business and they're saying, hey, run this for me, uh let me free up resources to go focus on things that really can drive additional value for our customers instead of keeping the lights on patching, blah blah blah. So we have taken their entire environment and moved it to a Coehlo and we're managing it now for them. And so in essence we freed up not just a ton of resources, but they have also been able to drop their carbon footprint, which is also this whole sustainability push is significant as well. And then you look at a customer like care stream, one of the largest medical diagnostic companies in the world, saying hey we gotta be able to allow our doctors to be able to um analyze and diagnose things much much faster through our X ray systems and through our diagnostic machines. And so they have implemented our machine learning operations scenario to dramatically speed up those types of capabilities. So as you go down the list and you start to see these customers really um leveraging technology to meet that digital transformation, saving costs, moving their business forward, creating new business models. It's just, it's really exciting. >>What about partners keith? How how have they responded? I mean, on the one hand, you know, that's great opportunities for them, you know, they're they're transforming their own business model. On the other hand, you know, maybe they were comfortable with the old model, they got a big house, nice, nice boat, you >>know? >>But how are they changing their their their business and how are they leaning in >>similar to what we're seeing? The opportunity for partners is dramatic, right? Because what happens is you have to have a very different relationship with your customer to truly understand their digital transformation. Their business challenges the problems that they're having to address. And so where we're seeing partners really, really sort of the opportunity is where there's the services and that sort of deeper relationship piece of it. So in essence, it's creating much more opportunity because the white spaces dramatic we're seeing, I want to say it's in the 30 to $40 billion worth of market opportunity as we move into an as a service on prem world. So they're seeing that opportunity. They're seeing the ability to add services on top of that and deepen the relationship with our customers. And you know, it's it's from my SVS. We're working closely with S. A. P. For example, to deliver their new rise private cloud customer edition. We're working closely with loosest, for example, who is doing a lot of payment processing type scenarios Nutanix and their database as a service scenario and Splunk because again, we went back to the data piece and these guys are doing so much big data type implementations for risk analytics and and regulatory type scenarios. It's just significant. And so because there's such a push to keep things on prem to have the security to reduce the latency to get rid of the egress charges and everything else. There's just a significant white space for both our partners and then from our distributors and resellers, they're getting to change their business model again, to get much deeper in that relationship with our customers >>to be Green Lake is, I mean it's H. P. E. As a service, it's your platform. And so I wonder if you can think about how you're thinking about uh, share with us, How you think about platform innovation? Um, you've got the pricing model, you know, flex up, flex down. Is there other technology we should know about and other things that are going to move you forward in this battle for the next great hybrid cloud and edge platform? >>Yeah, it's a great push because if you think about it, we are Green Lake is the edge to cloud platform And in essence because we have such a strong edge capability with the arab acquisition we made a few years back. That's really significant momentum with the Silver Peak acquisition to give us SD when you've got that edge connectivity all the way up to our high performance computing. And so you'll see us deliver high performance computing as a service. We're announcing that here at discover um you'll see us announced, you know, machine learning ops I mentioned ASAP, but also a virtual desktops. I think the pandemic has brought a lot more work from home type scenarios and customers really want to have that secure desktop. And so, working with partners like Citrix and Nutanix and and VM ware and Crew were able to provide that again, unique scenario for our customers. And so, um, yeah, the innovation is going to keep coming. You know, I mentioned bare metal as a service because many people are starting to really leverage the metal that's out there. You're seeing us also engaged with folks like intel on our silicon on demand. So this is a really exciting technology because what it allows us to do is turn on cores when we need them. So hey, I need additional capacity. I need some power. Let's turn on some cores. But then I turn off those cores when I'm not using them. You go to a software core based software pricing model, like an oracle or a sequel server. I'm saving dramatic cost now because I don't have to pay for all the cores that are on the system. I'm only paying the licenses for the ones that I use. And so that should bring dramatic cost savings to our customers as well. So we're looking from the silicon all the way up. Uh you know, you hear us talk about project Aurora, which is our security capability. We're looking at the silicon level, but we're also looking at the the container and bare metal and then obviously the workloads in the industry solution. So we're sprinting forward. We're listening to our customers were taking their feedback. We're seeing what they're prioritizing and because we have that tight relationship with them as we help move them to the direction they want to go, it's giving us a ton of fantastic inside information for what really matters. >>Right, Thank you for that. So, I want to ask you about data. A lot of organizations are kind of rethinking their ideal data architecture, their organization. They're they're they're seeing the amount of data that is potentially going to be created at the edge, thinking about ai inference and influencing at the edge and maybe reimagining their data organization in this age of insight. I wonder how Green Lake fits into that. How are you thinking about the new era of data and specifically Green Lakes role? >>Yeah, you mentioned the age of insights and and it really is right. So we've moved sort of as the next phase of digital transformation is basically saying, hey look, I've got all this data. I've got to first get my arms around my data estate because in essence it's in all these different pockets around. And so Green Lake gives you that ability to really get that data estate established. Then I want to take and get the answers in the analytics out of it. And then I want to monetize that data either out to my customer set or out to my industry or out to other scenarios as well. And so as we start to deliver our develops capability, our ai and analytics capabilities through HPC. And it's an open platform. So it allows data scientists to easy boot up easily boot up a cluster with which to do their models and their training and their algorithms. But we can also then use and Estancia at that into the business decisions that our customers are trying to make again without the significant cost that they're seeing on that on the public cloud side and in a very secure way because they have the data exactly where they need it. You'll see us continue to do sort of disaster recovery and data protection and those types of scenarios both with our partners and from H P E. So it's exciting to just understand that now you're going to have the tools and resources so you can actually focus on those business outcomes versus how do I protect the data? Where do I start, how do I get my model set up, etcetera. All that becomes automated and self service. You mentioned earlier >>When you talk to customers Keith one of the big sort of challenges that you're addressing. What's the typical, there was no typical but the but the real nuts that they're trying to crack is it financial? We want to move from Capex to opec's is that hey we want this cloud model but we can't do it in the public cloud for a variety of reasons, edicts, organization leaders or we want to modernize our our state. What are the real sort of sticking points that you're addressing with Green Lake? >>Yeah, I think it's threefold and you sort of touched on those. So one is, it really does start with modernization. Hey, you know, we've got to take costs out of the equation. We've got to reduce our carbon footprint. We've got to automate these things because we have limited resources and how do we maximize the ones that we have? And so I mentioned earlier, getting out of the data center, modernizing our apps, really monetizing our data. So I think that's number one. Number two is what you said as well, which is, hey look, I don't need to have all these capital assets. I don't want to be in charge of managing all all these assets. I just want the capability and so being able to sell them that service that says, hey, we can, we can do X number of desktops for your V. D. I. We can run your S. A. P. Environment or we can make sure that you have the, the analytics structure set up to be able to run your models that becomes super compelling and it frees up a lot of resources in cash on that front as well. And then I think the third thing is what you said, which is the world is hybrid. And so I need to find out what's going to run best in my on prem environment and what's going to run best up in the cloud. And I want to be able to optimize that so that I'm not wasting costs in one place or the other, and I want to be able to govern and govern that holistically. So I have the ability to see what's happening end to end across that so I can manage my business most effectively. So I think those are the three big things that people are really excited about with Green Lake as they enable those things. Um and you know, the reality is that it also means that they have a new partner to help them really think through how can they move forward? So it's not them by themselves. Uh It's really in a one plus one equals three type scenario and then you bring the ecosystem in and now you've got, you know, things working really well. So, >>so big enterprise tech, it's like, it's like the NFL is a sort of a copycat league. And so what, you know what I'm saying? But you guys all got >>big, yeah, >>you've got great resources, hey, this West Coast office exactly is gonna work. We're gonna get a short passing game going. And so that happened. So I feel like, okay, you've raised the bar now on as a service and that's gonna become table stakes. Um you know, it's got a lot of work to get there. I know, and it's a it's a journey, but but when you think about the future uh for H. P. E. Uh what's exciting you the most? >>I think what's exciting me the most is this the reaction that we're seeing with customers because in essence it gets them out of the bits and bytes and speeds and feeds and you know, um >>you >>know, component goo and really gets into business value, business outcomes sls and, and that's what they're looking for because what they're trying to do is break out of, you know that day to day and be able to really focus on the future and where they're going. So I think that's one, I think the second big thing is as you see all these things come together, um you know, we're able to basically provide customers with, I would say a mindset that's like, hey, I can do this holistically, but I can always pick and choose the best that I want and if I ramp up, I have capacity. If I ramp down, I don't have to pay for first scenarios. And so I'm getting the best of both worlds across that piece of it. And then third is I mentioned it earlier. But this whole relationship thing is so important because you know, this isn't about technology anymore. As much as it it is about what's the value that you're going to get out of that technology. And how does that help us move the company and the world forward? Like I love the fact that H. P. E. Was so involved in this pandemic. >>You know, >>with our systems were able to actually uh to run a set of of algorithms and analysis on how to, you know, find a vaccine on how to how to address the things that are going forward. You've seen us now up in space and as we, we broaden our frontier and so as a company you're seeing technology turned into things that are truly helping the world go forward. I think that's exciting as well. >>Yeah. Space. It's like the ultimate edge. >>I >>like you said to me if I take it, it's not not about ports and Mick, nips and gigabytes anymore. It's about the outcome. You mentioned before the S L. A. Um, you know, the thing about, you know, think about virtual, it's great. We have to get in the plane. Its downside. We all know we can't hang out, you know, afterwards, you know, have a drink or you know, chit chat about what's going on in the world, but we can't reach a lot more people. But the other downside of virtual is, you know, you don't have the hallway track. It's not like, hey, did you check out that, that demo on IOT? It's really cool. Where is that? So give us the hallway track. How can folks learn more about discover where would you direct folks? >>You bet. You know, I'm doing a full spot. Obviously let me start with at the top right Antonio Neri our ceo he's going to lay out the whole strategy and then I'll have a spotlight. It's about a 30 minute deep dive on all of these things that that you and I just talked about and then we've got a bunch of breakout sessions were doing some with our partners like Nutanix and others, um, Microsoft as well as we talk about, we didn't really touch on that, but you know, we have a strong partnership with the hyper scholars with Microsoft and with others because in essence customers are expecting an integrated solution that's hybrid. And so, you know, we're showcasing all of that with the with the discover breakouts as well and they're available on demand. We have a huge opportunity with respect to that, so really excited and you know, frankly we're here to help, like I hope people understand this is our opportunity to help you be successful and so please know that our ears are wide open to hear what the challenges are and we're ready to help customers as they needed. >>I'm glad you mentioned the partnership with Microsoft and other hyper skills. I feel like keith, the the Hyper scale is giving us a gift. They've spent last year they spent over $100 billion on Capex build out. That is like, it's like the internet. Thank you. >>Now we're gonna build on >>top of it, we're gonna build an abstraction layer that hides all that underlying complexity. We're gonna connect things and and that's really your job. That's really kind of what you're bringing to the table I think with Green Lake and some of these innovations. So >>I really >>appreciate it. Go ahead please. >>I appreciate the time as well. It's always a pleasure and it's always exciting to get a chance to share with you and and as always, any time you don't want me back, I'm happy to happy to join. Alright, >>would love to do that. So appreciate that. And thank you for spending some time with us. Stay tuned for more great coverage from HPD discovered 21 everything is available on demand as well as the that is the other good thing about virtually go back and watch all this content. This is Dave Volonte for the cube the leader in enterprise tech coverage. Be right back

>>from around the globe. It's the Cube with digital coverage of biz ops Manifesto unveiled. Brought to you by Biz Ops Coalition. Hey, Welcome back, everybody. Jeffrey here with the Cube. We're coming to you from our Palo Alto studios. And welcome back to this event. Is the biz Opps Manifesto unveiling? So the biz Opps manifesto and the biz Opps coalition have been around for a little while, But today's the big day. That's kind of the big public unveiling are excited to have some of the foundational people that put their put their name on the dotted line, if you will, to support this initiative to talk about why that initiative is so important. And so the next guest, we're excited to have his doctor, Mick Kirsten. He is the founder and CEO of Task Top. Make great to see you coming in from Vancouver, Canada, I think. Right. >>Yes. Great to be here, Jeff. Thank you. Absolutely. >>I hope your air is a little better out there. I know you had some of the worst air of all of us a couple a couple of weeks back, so hopefully things air, uh, getting a little better. And we get those fires under control? >>Yeah, Things have cleared up now, so yeah, it's good. It's good to be close to the U. S. And it's gonna have the Arabic clean as well. >>Absolutely. So let's let's jump into it. So you you've just been an innovation guy forever Starting way back in the day and Xerox Park. I was so excited to do an event at Xerox Park for the first time last year. I mean that that to me represents along with Bell Labs and and some other, you know, kind of foundational innovation and technology centers. That's got to be one of the greatest one. So I just wonder if you could share some perspective of getting your start there at Xerox Parc. You know, some of the lessons you learn and what you've been ableto kind of carry forward from those days. >>Yeah, I was fortunate. Joined Xerox Park in the computer science lab there at a very early point in my career, and to be working on open source programming languages. So back then, and the computer science lab where some of the inventions around programming around software development names such as Object of programming and ah, lot of what we had around really modern programming levels construct. Those were the teams that had the fortune of working with and really our goal waas. And of course, there's a Z. You know, this, uh, there's just this DNA of innovation and excitement and innovation in the water. And really, it was the model that was all about changing the way that we work was looking at for how we could make it 10 times easier to write. Code like this is back in 99 we were looking at new ways of expressing especially business concerns, especially ways of enabling people who are who want to innovate for their business, to express those concerns in code and make that 10 times easier than what that would take. So we created a new open source programming language, and we saw some benefits, but not quite quite what we expected. I then went and actually joined Charles Stephanie that former chief actor Microsoft, who is responsible for I actually got a Microsoft word as a out of Xerox Parc and into Microsoft and into the hands of Bill Gates and the company I was behind the whole office suite and his vision and the one I was trying to execute with working for him was to, you know, make Power point like a programming language, make everything completely visual. And I realized none of this was really working, that there was something else fundamentally wrong that programming languages or new ways of building software like Let's try to do with Charles around intentional programming. That was not enough. >>That was not enough. So you know, the agile movement got started about 20 years ago, and we've seen the rise of Dev ops and really this kind of embracing of of, of sprints And, you know, getting away from M. R. D s and P. R. D s and these massive definitions of what we're gonna build and long billed cycles to this iterative process. And that's been going on for a little while. So what was still wrong? What was still missing? Why the Biz Ops Coalition? Why the biz ops manifesto? >>Yeah, so I basically think we nailed some of the things that the programming language levels of teams can have. Effective languages deployed softened the club easily now right and at the kind of process and collaboration and planning level agile two decades decades ago was formed. We were adopting all the all the teams I was involved with on. It's really become a solved problem. So agile tools, agile teams actually of planning are now very mature and the whole challenges when organizations try to scale that. And so what I realized is that the way that Agile was scaling across teams and really scaling from the Technology Party organization to the business was just completely flawed. The agile teams had one set of doing things. One set of metrics, one set of tools and the way that the business was working was planning was investing in technology was just completely disconnected and using a a whole different set of measures. It's pretty interesting because I think it's >>pretty clear from the software development teams in terms of what they're trying to deliver, because they've got a feature set right and they've got bugs and it's easy. It's easy to see what they deliver, but it sounds like what you're really honing in on is is disconnect on the business side in terms of, you know, is it the right investment you know. Are we getting the right business? R o I on this investment? Was that the right feature? Should we be building another feature or shall we building a completely different products? That so it sounds like it's really a core piece of this is to get the right measurement tools, the right measurement data sets so that you can make the right decisions in terms of what you're investing, you know, limited resource is you can't Nobody has unlimited resources and ultimately have to decide what to do, which means you're also deciding what not to dio. It sounds like that's a really big piece of this of this whole effort. >>Yeah, Jeff, that's exactly it. Which is the way that the adult measures their own way of working is very different from the way that you measure business outcomes. The business outcomes are in terms of how happy your customers are. Are you innovating fast enough to keep up with the pace of, ah, rapidly changing economy, rapidly changing market and those are those are all around the customer. And so what? I learned on this long journey of supporting many organizations transformations and having them trying to apply those principles vigilant develops that those are not enough. Those measures technical practices, those measures, technical excellence of bringing code to the market. They don't actually measure business outcomes. And so I realized that really was much more around having these entwined flow metrics that are customer centric and business centric and market centric where we needed to go. So I want to shift gears >>a little bit and talk about your book because you're also a best selling author project a product, and and you you brought up this concept in your book called The Flow Framework. And it's really interesting to me because I know, you know, flow on one hand is kind of a workflow in the process flow, and you know that's how things get done and and embrace the flow. On the other hand, you know, everyone now in a little higher level, existential way is trying to get into the flow right into the workflow and, you know not be interrupted and get into a state where you're kind of your highest productivity, you know, kind of your highest comfort. Which floor you talking about in your book, or is it a little bit of both. >>That's a great question, is it's not what I gotta ask very often, cause me, it's It's absolutely both. So the thing that we want to get that we've learned how toe and, uh, master individual flow, that there's this beautiful book by me Holly teachings mentality. There's a beautiful Ted talk about him as well, about how we can take control of our own flow. So my question with the book with project surprise, How can we bring that to entire teams and really entire organizations? How come we have everyone contributing to a customer outcome? And this is really what if you go to the bazaar manifesto? It says, I focus on Out comes on using data to drive, whether we're delivering those outcomes rather than a focus on proxy metrics such as How quickly did we implement this feature? And now it's really how much value did the customs of the future and how quickly did we learn? And how quickly did you use that data to drive to that next outcome? Really, that with companies like Netflix on, like Amazon, have mastered, how do we get that every large organization, every idea, organization and make everyone be a softer innovator. So it's to bring that on the concept of flow to these entering value streams. And the fascinating thing is, we've actually seen the data. We've been able to study a lot of value streams. We see when flow increases, when organizations deliver value to a customer faster developers actually become more happy. So things like that implying that promotes course rise. And we've got empirical data for this. So that beautiful thing to me is that we've actually been able thio, combine these two things and and see the results in the data that you increased flow to the customer, your development or more happy. I >>love it. I love it, right, because we're all more. We're all happier when we're in the flow and we're all more productive winner in the flow. So I that is a great melding of two concepts. But let's jump into the into the manifesto itself a little bit. And you know, I love that you know, that took this approach really of having kind of four key values, and he gets 12 key principles and I just want to read a couple these values because when you read them, it sounds pretty brain dead, right? Of course. Right. Of course, you should focus on business outcomes. Of course, you should have trust and collaboration. Of course, you should have data based decision making processes and not just intuition or, you know, whoever is the loudest person in the room on toe, learn and respond and pivot. But >>what's the >>value of actually just putting them on a piece of paper? Because again, this is not this. These are all good positive things, right? When when somebody reads these to you or tells you these or sticks it on the wall? Of course. But unfortunately, of course, isn't always enough. >>No, I think what's happened is some of these core principles originally from the agile manifested two decades ago. The whole Dev ops movement of the last decade off flow feedback and continue learning has been key. But a lot of organizations, especially the ones undergoing transformations, have actually gone a very different way, right? The way that they measure value in technology innovation is through costs For many organizations, the way that they actually are looking at at their moving to cloud is actually is a reduction in costs, whereas the right way of looking at moving the cloud is how much more quickly can we get to the value to the customer? How quickly can we learn from that? And how could quickly can we drive the next business outcome? So, really, the key thing is to move away from those old ways of doing things that funding projects and call centers to actually funding and investing in outcomes and measuring outcomes through these flow metrics, which in the end are your fast feedback for how quickly you're innovating for your customer. So these things do seem, you know, very obvious when you look at them. But the key thing is what you need to stop doing. To focus on these, you need to actually have accurate real time data off how much value your phone to the customer every week, every month, every quarter. And if you don't have that, your decisions are not given on data. If you don't know what your bottle like, it's. And this is something that in the decades of manufacturing car manufacturers, other manufacturers master. They always know where the bottom back in their production processes you ask, uh, random. See, I all want a global 500 company where the bottleneck is, and you won't get it there. Answer. Because there's not that level of understanding. So have to actually follow these principles. You need to know exactly where you follow like is because that's what's making your developers miserable and frustrated on having them context, which on thrash So it. The approach here is important, and we have to stop doing these other things right. >>There's so much. They're a pack. I love it, you know, especially the cloud conversation, because so many people look at it wrong as a cost saving device as opposed to an innovation driver, and they get stuck, they get stuck in the literal. And, you know, I think the same thing always about Moore's law, right? You know, there's a lot of interesting riel tech around Moore's law and the increasing power of microprocessors. But the real power, I think in Moore's laws, is the attitudinal change in terms of working in a world where you know that you've got all this power and what will you build and design? E think it's funny to your your comment on the flow in the bottleneck, right? Because because we know manufacturing assumes you fix one bottleneck. You move to your next one, right, You always move to your next point of failure. So if you're not fixing those things, you know you're not. You're not increasing that speed down the line unless you can identify where that bottleneck is, or no matter how Maney improvements you make to the rest of the process, it's still going to get hung up on that one spot. >>That's exactly, and you also make it sound so simple. But again, if you don't have the data driven visibility of where the bottleneck is. And but these bottlenecks are just as you said, if it's just lack, um, all right, so we need to understand is the bottleneck, because our security use air taking too long and stopping us from getting like the customer. If it's that automate that process and then you move on to the next bottleneck, which might actually be that deploy yourself through the clouds is taking too long. But if you don't take that approach of going flow first rather than again the sort of way cost production first you have taken approach of customer centric city, and you only focus on optimizing cost. Your costs will increase and your flow will slow down. And this is just one, these fascinating things. Whereas if you focus on getting back to the customer and reducing your cycles on getting value your flow time from six months to two weeks or 21 week or two event as we see with tech giants, you actually could both lower your costs and get much more value. Of course, get that learning going. So I think I've I've seen all these cloud deployments and modernizations happen that delivered almost no value because there was such a big ball next up front in the process. And actually the hosting and the AP testing was not even possible with all of those inefficiencies. So that's why going flow first rather than costs. First, there are projects versus Sochi. >>I love that and and and and it begs, repeating to that right within a subscription economy. You know you're on the hook to deliver value every single month because they're paying you every single month. So if you're not on top of how you delivering value, you're going to get sideways because it's not like, you know, they pay a big down payment and a small maintenance fee every month. But once you're in a subscription relationship, you know you have to constantly be delivering value and upgrading that value because you're constantly taking money from the customers. It's it's such a different kind of relationship, that kind of the classic, you know, Big Bang with the maintenance agreement on the back end really important. >>Yeah, and I think in terms of industry ship, that's it. That's what catalyzed this industry shift is in this SAS that subscription economy. If you're not delivering more and more value to your customers, someone else's and they're winning the business, not you. So one way we know is that divide their customers with great user experiences. Well, that really is based on how many features you delivered or how much. How about how many quality improvements or scaler performance improvements you delivered? So the problem is, and this is what the business manifesto was was the forefront of touch on is, if you can't measure how much value delivered to a customer, what are you measuring? You just back again measuring costs, and that's not a measure of value. So we have to shift quickly away from measuring costs to measuring value to survive in in the subscription economy. Mick, >>we could go for days and days and days. I want to shift gears a little bit into data and and a data driven, um, decision making a data driven organization. Because right day has been talked about for a long time. The huge big data mean with with Hadoop over over several years and data warehouses and data lakes and data, oceans and data swamps and you go on and on, it's not that easy to do right. And at the same time, the proliferation of data is growing exponentially were just around the corner from from I, O. T and five G. So now the accumulation of data at machine scale again this is gonna overwhelm, and one of the really interesting principles that I wanted to call out and get your take right is today's organizations generate mawr data than humans can process. So informed decisions must be augmented by machine learning and artificial intelligence. I wonder if you can again, you've got some great historical perspective reflect on how hard it is to get the right data to get the data in the right context and then to deliver to the decision makers and then trust the decision makers to actually make the data and move that down. You know, it's kind of this democratization process into more and more people and more and more frontline jobs, making more and more of these little decisions every day. >>Yeah, and Jeff, I think the front part of what you said are where the promises of big data have completely fallen on their face into these swamps. As you mentioned, because if you don't have the data and the right format, you can connect, collected that the right way, you're not. Model it that way the right way. You can't use human or machine learning on it effectively. And there have been the number of data, warehouses and a typical enterprise organization, and the sheer investment is tremendous. But the amount of intelligence being extracted from those is a very big problem. So the key thing that I've known this is that if you can model your value streams so you actually understand how you're innovating, how you're measuring the delivery value and how long that takes. What is your time to value through these metrics? Like for the time you can actually use both. You know the intelligence that you've got around the table and push that balance as it the assay, far as you can to the organization. But you can actually start using that those models to understand, find patterns and detect bottlenecks that might be surprising, Right? Well, you can detect interesting bottle next one you shift to work from home. We detected all sorts of interesting bottlenecks in our own organization that we're not intuitive to me that had to do with more senior people being overloaded and creating bottlenecks where they didn't exist. Whereas we thought we were actually organization. That was very good at working from home because of our open source route. So the data is highly complex. Software Valley streams are extremely complicated, and the only way to really get the proper analysts and data is to model it properly and then to leverage these machine learning and AI techniques that we have. But that front, part of what you said, is where organizations are just extremely immature in what I've seen, where they've got data from all the tools, but not modeled in the right way. >>Well, all right, so before I let you go, you know? So you get a business leader he buys in. He reads the manifesto. He signs on the dotted line. He says, Mick, how do I get started? I want to be more aligned with With the development teams, you know, I'm in a very competitive space. We need to be putting out new software features and engage with our customers. I want to be more data driven. How do I get started? Well, you know, what's the biggest inhibitor for most people to get started and get some early winds, which we know is always the key to success in any kind of a new initiative, >>right? So I think you can reach out to us through the website. Uh, on the is a manifesto, but the key thing is just it's exactly what you said, Jeff. It's to get started and get the key wins. So take a probably value stream. That's mission critical. It could be your new mobile Web experiences, or or part of your cloud modernization platform where your analysts pipeline. But take that and actually apply these principles to it and measure the entire inflow of value. Make sure you have a volumetric that everyone is on the same page on, right. The people on the development teams that people in leadership all the way up to the CEO and one of the where I encourage you to start is actually that enter and flow time, right? That is the number one metric. That is how you measure whether you're getting the benefit of your cloud modernization. That is the one metric that even Cockcroft when people I respect tremendously put in his cloud for CEOs Metric 11 way to measure innovation. So basically, take these principles, deployed them on one product value stream measure into and flow time on. Then you'll actually you well on your path to transforming and to applying the concepts of agile and develops all the way to the business to the way in your operating model. >>Well, Mick, really great tips, really fun to catch up. I look forward to a time when we can actually sit across the table and and get into this, because I just I just love the perspective. And, you know, you're very fortunate to have that foundational, that foundational base coming from Xerox parc. And it's, you know, it's a very magical place with a magical history. So the to incorporate that and to continue to spread that wealth, you know, good for you through the book and through your company. So thanks for sharing your insight with us today. >>Thanks so much for having me, Jeff. Absolutely. >>Alright. And go to the biz ops manifesto dot org's Read it. Check it out. If you want to sign it, sign it. They'd love to have you do it. Stay with us for continuing coverage of the unveiling of the business manifesto on the Cube. I'm Jeffrey. Thanks for watching. See you next time.

>>from around the globe. It's the Cube with digital coverage of biz ops Manifesto unveiled Brought to you by Biz Ops Coalition >>Hey, Welcome back, everybody. Jeffrey here with the Cube. Welcome back to our ongoing coverage of the biz. Opps manifesto Unveil Something has been in the works for a little while. Today's a formal unveiling and we're excited to have three of the core founding members of the manifesto Authors of the manifesto, if you will joining us again. We've had them all on individually. Now we're gonna have a great power panel. First up, we have met Kirsten returning. He's the founder and CEO of Task Top make good to see again. Where you dialing in from? >>Great to see you again, Jeff. I'm dialing from Vancouver, Canada. >>Vancouver, Canada. One of my favorite cities in the whole wide world. Also, we've got Tom Davenport coming from across the country. He's a distinguished professor and author from Babson College. Tom, great to see. And I think you said you're a fund Exotic place on the East Coast. >>Falmouth, Massachusetts, on Cape Cod. >>Nice. Great to see you again. And also joining surge. Lucio. He is the VP and general manager Enterprise Software division at Broadcom Surge. Great to see you again. Where you coming in from? >>From Boston. Right next to kick off. Terrific. >>So welcome back, everybody again. Congratulations. On this day I know it's It's been a lot of work to get here for this unveil, but let's just jump into it. Biz Opps Manifesto What was the initial reason to do this? And how did you decide to do it in a kind of a coalition way, bringing together a group of people versus just making it an internal company, uh, initiative that, you know, you could do better stuff within your own company Surge. Why don't we start with you? >>Yeah, so? So I think we are really a critical juncture, right? Many large enterprises are basically struggling with their digital transformation. In fact, many recognized that the the Business Society collaboration has been one of the major impediments to drive that kind of transformation. And if we look at the industry today, many people or whether we're talking about vendors or system integrators, consulting firms are talking about the same kind of concepts but using very different language. And so we believe that bringing a lot these different players together assed part of the coalition and formalizing, uh, basically the core principles and values in a busy office manifesto. We can really start to have kind of a much bigger movement where we can all talk about kind of the same concepts. And we can really start to provide kind of a much better support for large organizations to transform eso, whether it is technology or services or trading. I think that that's really the value of bringing all of these players together >>and Mick to you. Why did you get involved in this in this effort? >>So I've been closely involved the actual movement since it started two decades ago with that manifesto, and I think we got a lot of improvement at the team level. And I think, as started just noted, we really need to improving. At the business level, every companies trying to become a software innovator trying to make sure that they can pick them, adapt quickly in the changing market economy and what everyone's dealing with in terms of needing to deliver value to customers. Sooner. However, agile practices have really focused that these metrics these measures and understanding processes that help teams be productive. Those things now need to be elevated to the business as a whole, and that just hasn't happened. Organizations are agile. Transmissions are actually failing because they're measuring activities and how they're becoming more agile. Have teams air functioning, not how much quickly they're delivering value to the customer. So we need to now move. Asked that. And that's exactly what the buzz off there's also manifested. Provides, >>right? Right And Tom to you. You've been covering tech for a very long time. You've been looking at really hard challenges and and a lot of work around analytics and data on data evolution. So there's a definitely a data angle here. I wonder if you kind of share your perspective of of what you got excited Thio to sign on to this manifesto? >>Sure. Well, I have. You know, for the past 15 or 20 years, I've been focusing on Data Analytics and AI. But before that, I was a process management guy and a knowledge management guy and a in general. I think, you know, we've just kind of optimized at too narrow a level. Whether you're talking about agile or Dev ops or, um, ml ops. Any of these kind of obs oriented movements, we're making individual project, um, performance and productivity better, But we're not changing the business effectively enough. And that's the thing that appealed to me about the biz ops idea that we're finally creating a closer connection between what we do with technology and how it changes the business and provides value to it. >>That's great, uh, surge back to you, right? I mean, people have been talking about digital transformation for a long time, and it's been, you know, kind of trucking along. And then Cove it hit, and it was instant lights, which everyone's working from home. You've got a lot more reliance on your digital tools, digital communication both within your customer base and your partner base, but also then you're employees. When if you could share how that really pushed this all along, right, because now suddenly the acceleration of digital transformation is higher. Even more importantly, you've got much more critical decisions to make into what you do next. So kind of your portfolio management of projects has been elevated significantly. When maybe revenues are down on. Do you really have Thio prioritize and get it right? >>Yeah. I mean, I'll just start by quoting city An Adele basically recently said that there's bean two years of digital transformation just last two months, and in many ways that's true. Um, but But yet when we look at large enterprises, they're still struggling with kind of a changes in culture that they really need Thio drive to be able to describe themselves. And not surprisingly, you know, when we look at certain parts of the industry, you know, we see some things which are very disturbing, right. About 40% of the personal loans today are being origin dated by Finn tax of a like of Sophie or or ah Lendingclub, right, Not your traditional brick and worked for bank. And so the while there is kind of ah, much more of an appetite, and it's it's more of a survival type driver these days. The reality is that in order for these large enterprises to truly transform and engage on these digital transformation, they need to start to really aligned the business 90 you know, in many ways and make cover that actually really emerge from the court desire to really improve software predictability, but we've which we have really missed is all the way. Start to aligning the software predictability to business predictability and to be able to have continuously continuous improvement and measurement off business outcomes. So by lining, but of this dis kind of inward metrics that I t is typically being using to business outcomes, we think we can start to really help different stakeholders within the organization to collaborate. So I think there is more than ever. There is an imperative to act now, um, and and results. I think it's kind of the right approach to drive that kind of transformation, >>right? I want to follow up on the culture comment with Utah because you've talked before about kind of process, flow and process flow throughout a whore, unorganized ation. And, you know, we talk about people process and tech all the time, and I think the tech is the easy part compared to actually changing the people the way they think and then the actual processes that they put in place. It's a much more difficult issue than just a tech issue to get this digital transformation in your organization. >>Yeah, you know, I've always found that the soft stuff the you know, the culture of the behavior of the values is the hard stuff to change and mawr and mawr. We we realized that to be successful with any kind of digital transformation, you have tow change, people's behaviors and attitudes. Um, we haven't made much progress in that area as we might have. I mean, I've done some surveys suggesting that most organizations still don't have data driven cultures, and in many cases, there is a lower percentage of companies that say they have that then did a few years ago. So we're kind of moving in the wrong direction, which means I think that we have to start explicitly addressing that cultural behavioral dimension and not just assuming that it will happen if we if we build a system, you know, if if we build it, they won't necessarily come right, >>right? So I want to go toe to you, Nick, because, you know, we're talking about work flows and flow. Andi, you've written about flow both in terms of, you know, moving things along a process and trying to find bottlenecks, identify bottlenecks which is now even more important again when these decisions are much more critical because you have a lot less ah, wiggle room in tough times. But you also talked about flow from the culture side on the people's side. So I wonder if you could just share your thoughts on, you know, using flow as a way to think about things to get the answers better? >>Yeah, absolutely. And I'll refer back to what Tom just said. If you're optimize, you need to optimize your system. You need to optimize how you innovate and how you deliver value to the business into the customer. Now what we've noticed in the data since that that we've learned from customers value streams, enterprise organizations, value streams is that what's taking six months and to and to deliver that value, the flow is that slow. You've got a bunch of unhappy developers. Unhappy customers, when you're innovating, have so high performing organizations we can measure their intent flow time in days. All of a sudden, that feedback loop the satisfaction your developers measurably goes up. So not only do you have people context switching last year, delivering so much more value to customers at a lower cost because you've optimized for flow rather than optimizing for these thes other approximately six that we use, which is how efficient my annual team. How quickly can we deploy software? Those are important, but they do not provide the value of agility, of fast learning, of adaptability, of the business. And that's exactly what the bishops manifesto pushes your organization to. You need to put in place this new operate model that's based on flow on the delivery of business value and on bringing value to market much more quickly than you were before, >>right? I love that. I'm gonna go back to Utah, him on that to follow up, because I think I don't think people think enough about how they prioritize what they're optimizing for. Because, you know, if you're optimizing for a vs B, you know you could have a very different product that that you kick out. You know, my favorite example with With Clayton Christensen and Innovator's Dilemma talking about the three inch our drive, if you optimize it for power, you know, is one thing. If you optimize it for vibration is another thing, and sure enough, you know they missed it on the Palm because it was the it was the game council which which drove that whole business. So when you're talking to customers and we think we're here with cloud all the time people optimizing for cost efficiency instead of thinking about it as an innovation tool, how do you help them? Kind of rethink and really, you know, forced them to to look at the at the prioritization and make sure they're prioritizing on the right thing is make just that. What do you optimizing for? >>Oh yeah, you have one of the most important aspects of any decision or attempt to resolve a problem in an organization is the framing process. And, um, you know, it's it's a difficult aspect toe of the decision toe frame it correctly in the first place. Um, there. It's not a technology issue. In many cases, it's largely a human issue. But if you frame that decision or that problem incorrectly too narrowly, say, or you frame it as an either or situation when you could actually have some of both, um, it's very difficult for the process toe work out correctly. So in many cases, I think we need to think mawr at the beginning about how we frame this issue or this decision in the best way possible before we charge off and build a system to support it. You know, it's worth that extra time to think. Think carefully about how the decision has been structured. Right? >>Surge. I wanna go back to you and talk about the human factors because we just discussed you could put in great technology. But if the culture doesn't adopt it and people don't feel good about it, you know it's not gonna be successful. And that's going to reflect poorly on the technology. Even if I had nothing to do with it. And you know, when you look at the core values of the best hopes manifesto, you know a big one is trust and collaboration. You know, learn, responded pivot. Wonder if you can share your thoughts on trying to get that cultural shift s so that you can have success with the people or excuse me with the technology in the process and helping customers, you know, take this more trustworthy and kind of proactive position. >>So I think I think at the ground level, it really starts with the realization that we were all different. We come from different backgrounds. Oftentimes we tend Thio. Blame the data. It's not uncommon my experience that we spend the first, you know, 30 minutes of any kind of one hour the conversation to debate the ability of the data on DSO, one of the first kind of probably manifestations that we've had. Our revelations as we start to engage with our customers is by just exposing high Fidelity data set two different stakeholders from their different lands. We start to enable these different stakeholders to not debate the data, but that's really collaborate to find a solution. So in many ways, when when when we think about kind of the types of changes were trying Thio truly affect around data driven decision making? It's all about bringing the data in context the context that is relevant and understandable for different stakeholders. Whether we're talking about an operator developed for a business analyst, so that's that's the first thing. The second layer, I think, is really to provide context to what people are doing in their specific silo. So I think one of the best examples I have is if you start to be able to align business k p i. Whether you are accounting, you know, with sales per hour or the engagement of your users on your mobile application, whatever it is, if you start to connect that k p I business K p I to the key piece that developers might be looking at, whether it is now the number of defects or velocity or whatever over metrics that they are used to to actually track, you start to be able to actually contextual eyes in what we are. The affecting, basically metric that that is really relevant. And what we see is that this is a much more systematic way to approach the transformation than say, You know, some organizations kind of creating some of these new products or services or initiatives to to drive engagement, right? So, you know, if you look at zoom, for instance, Zoom giving away, it's service thio education is all about. I mean, there's obviously a marketing aspect in there, but it's it's fundamentally about trying to drive also the engagement of their own teams and because now they're doing something for good, and many organizations are trying to do that. But you only can do this kind of things in a limited way. And so you really want to start to rethink? Oh, you connect everybody kind of business objective fruit data. And I always start to get people to stare at the same data from their own lands and collaborating on the data. Right? >>Right. That's good. Uh, Tom, I want to go back to you. You've been studying I t for a long time writing lots of books and and getting into it. Um, Why now? You know what? Why now? Are we finally aligning business objectives with objects? You know, why didn't this happen before? And, you know, what are the factors that are making now? The time for this. This this move with the with the biz ops. >>Well, in much of the past, I t waas sort of a back office related activity. You know, it was important for, um, producing your paychecks and, uh, capturing the customer orders, but the business wasn't built around it. Now, every organization needs to be a software business of data business. A digital business. The anti has been raised considerably. And if you aren't making that connection between your business objectives and the technology that supports it. You run a pretty big risk of, you know, going out of business or losing out to competitors. Totally so. And even if you're in a new industry that hasn't historically been terribly, um, technology oriented customer expectations flow from, you know, the digital native companies that they work with to basically every industry. So you're compared against the best in the world. So we don't really have the luxury anymore of screwing up our I T projects or building things that don't really work for the business. Um, it's mission critical that we do that well, almost every time, >>right. And I just I just wanna fall by that time, in terms of the you've talked extensively about kind of these evolutions of data analytics, from artisanal stage to the big data stage, the data economy stage the ai driven stage. What I find interesting about all the stages you always put a start date. You never put it in date. Um, so you know, is the is the big data. I'm just gonna use that generically moment in time. Finally, here. Where were, you know, off mahogany row with the data scientists, But actually could start to see the promise of delivering the right insight to the right person at the right time to make that decision. >>Well, I think it is true that in general, these previous stages never seem to go away. The three artisanal stuff is still being done, but we would like for less and less of it to be artisanal. We can't really afford for everything to be artisanal anymore. It's too labor and time consuming to do things that way. So we shift Mawr and Mawr of it to be done through automation and be to be done with a higher level of productivity. And, um, you know, at some point maybe we we reached the stage where we don't do anything artisanal e anymore. I'm not sure we're there yet, but, you know, we are We are making progress, >>right? Right. And make back to you in terms of looking at agile because you're you're such a student of agile. When when you look at the opportunity with biz ops, um, and taking the lessons from agile, you know what's been the inhibitor to stop this in the past. And what are you so excited about? you know, taking this approach will enable >>Yeah, I think both surgeon Tom hit on this. Is that in agile? What's happened is that we've been, you know, measuring tiny subsets of the value stream, right? We need to elevate the data's there, developers air working on these tools of delivering features. The foundations for for great culture are there. I spent two decades as a developer, and when I was really happy when I was able to deliver value to customers, the quicker is able to do that. The fewer impediments are in my way, the quicker was deployed and running the cloud, the happier I waas and that's exactly what's happening if we could just get the right data elevated to the business, not just to the agile teams, but really these values of ours are to make sure that you've got these data driven decisions with meaningful data that's oriented and delivering value to customers. None of these legacies that Tom touched on, which has cost center metrics from a nightie, came from where for I t being a cost center and something that provided email on back office systems. So we need thio rapidly shift to those new, meaningful metrics, their customer and business centric. And make sure that every development organization is focused on those as well as the business itself that we're measuring value. And they were helping that value flow without interruption. >>I love that because if you don't measure it, you can't improve on it. And you gotta but you gotta be measuring the right thing. So, gentlemen, thank you again for for your time. Uh, congratulations on the on the unveil of the biz ops manifesto and bringing together this coalition of industry experts to get behind this. And, you know, there's probably never been a more important time than now to make sure that your prioritization is in the right spot. And you're not wasting resource is where you're not gonna get the r. O. I. So, uh, congratulations again. And thank you for sharing your thoughts with us here on the Cube. Thank >>you. All >>right, So we have surged. Tom and Mick. I'm Jeff. You're watching the Cube. It's a biz ops manifesto unveiled. Thanks for watching. We'll see you next time. Yeah,

>>From around the globe. It's the cube with digital coverage, a BizOps manifesto unveiled brought to you by biz ops coalition. >>Hey, welcome back everybody. Jeff Frick here with the cube. Welcome back to our ongoing coverage of the biz ops manifesto. Unveil. Something has been in the works for a little while. Today's the formal unveiling, and we're excited to have three of the core founding members of the manifesto authors of the manifesto. If you will, uh, joining us again, we've had them all on individually. Now we're going to have a great power panel. First up. We're gonna have Mitt Kirsten returning he's the founder and CEO of Tasktop mic. Good to see you again. Where are you dialing in from? >>Great to see you again, Jeff I'm dialing from Vancouver, >>We're Canada, Vancouver, Canada. One of my favorite cities in the whole wide world. Also we've got Tom Davenport come in from across the country. He's a distinguished professor and author from Babson college, Tom. Great to see you. And I think you said you're at a fun, exotic place on the East coast >>Realm of Memphis shoes. That's on Cape Cod. >>Great to see you again and also joining surge Lucio. He is the VP and general manager enterprise software division at Broadcom surge. Great to see you again, where are you coming in from? >>Uh, from Boston right next to Cape Cod. >>Terrific. So welcome back, everybody again. Congratulations on this day. I know it's been a lot of work to get here for this unveil, but let's just jump into it. The biz ops manifesto, what was the initial reason to do this? And how did you decide to do it in a kind of a coalition, a way bringing together a group of people versus just making it an internal company, uh, initiative that, you know, you can do better stuff within your own company, surge, why don't we start with you? >>Yeah, so, so I think we were at a really critical juncture, right? Many, um, large enterprises are basically struggling with their digital transformation. Um, in fact, um, many recognized that, uh, the, the business side, it collaboration has been, uh, one of the major impediments, uh, to drive that kind of transformation. That, and if we look at the industry today, many people are, whether we're talking about vendors or, um, you know, system integrators, consulting firms are talking about the same kind of concepts, but using very different language. And so we believe that bringing all these different players together, um, as part of the coalition and formalizing, uh, basically the core principles and values in a BizOps manifesto, we can really start to F could have a much bigger movement where we can all talk about kind of the same concepts and we can really start to provide, could have a much better support for large organizations to, to transform. Uh, so whether it is technology or services or, um, or training, I think that that's really the value of bringing all of these players together, right. >>And mic to you. Why did you get involved in this, in this effort? >>So I've been closely involved the agile movement since it started two decades with that manifesto. And I think we got a lot of improvement at the team level, and I think that was just no. Did we really need to improve at the business level? Every company is trying to become a software innovator, trying to make sure that they can pivot quickly and the changing market economy and what everyone's dealing with in terms of needing to deliver value to customers sooner. However, agile practices have really focused on these metrics, these measures and understanding processes that help teams be productive. Those things now need to be elevated to the business as a whole. And that just hasn't happened. Uh, organizations are actually failing because they're measuring activities and how they're becoming more agile, how teams are functioning, not how much quickly they're delivering value to the customer. So we need to now move past that. And that's exactly what the manifesto provides. Right, >>Right, right. And Tom, to you, you've been covering tech for a very long time. You've been looking at really hard challenges and a lot of work around analytics and data and data evolution. So there's a definitely a data angle here. I wonder if you could kind of share your perspective of what you got excited to, uh, to sign onto this manifesto. >>Sure. Well, I have, you know, for the past 15 or 20 years, I've been focusing on data and analytics and AI, but before that I was a process management guy and a knowledge management guy. And in general, I think, you know, we've just kind of optimize that to narrow a level, whether you're talking about agile or dev ops or ML ops, any of these kinds of ops oriented movements, we're making individual project, um, performance and productivity better, but we're not changing the business, uh, effectively enough. And that's the thing that appealed to me about the biz ops idea, that we're finally creating a closer connection between what we do with technology and how it changes the business and provides value to it. >>Great. Uh, surge back to you, right? I mean, people have been talking about digital transformation for a long time and it's been, you know, kind of trucking along and then covert hit and it was instant Lightswitch. Everyone's working from home. You've got a lot more reliance on your digital tools, digital communication, uh, both within your customer base and your partner base, but also then your employees when you're, if you could share how that really pushed this all along. Right? Because now suddenly the acceleration of digital transformation is higher. Even more importantly, you got much more critical decisions to make into what you do next. So kind of your portfolio management of projects has been elevated significantly when maybe revenues are down, uh, and you really have to, uh, to prioritize and get it right. >>Yeah. Maybe I'll just start by quoting Satina Nello basically recently said that they're speeding the two years of digital preservation just last two months in any many ways. That's true. Um, but yet when we look at large enterprises, they're still struggling with a kind of a changes in culture. They really need to drive to be able to disrupt themselves. And not surprisingly, you know, when we look at certain parts of the industry, you know, we see some things which are very disturbing, right? So about 40% of the personal loans today are being, uh, origin data it's by fintechs, uh, of a like of Sophie or, uh, or a lending club, right? Not to a traditional brick and mortar for BEC. And so the, well, there is kind of a much more of an appetite and it's a, it's more of a survival type of driver these days. >>Uh, the reality is that's in order for these large enterprises to truly transform and engage on this digital transformation, they need to start to really align the business nightie, you know, in many ways and make cover. Does agile really emerge from the core desire to truly improve software predictability between which we've really missed is all the way we start to aligning the software predictability to business predictability, and to be able to have continual sleep continuous improvement and measurement of business outcomes. So by aligning that of these, uh, discuss inward metrics, that's, it is typically being using to business outcomes. We think we can start to really ELP, uh, different stakeholders within the organization to collaborate. So I think there is more than ever. There's an imperative to acts now. Um, and, and resolves, I think is kind of the right approach to drive that kind of transformation. Right. >>I want to follow up on the culture comment, uh, with you, Tom, because you've talked before about kind of process flow and process flow throughout a whore and an organization. And, you know, we talk about people process and tech all the time. And I think the tech is the easy part compared to actually changing the people the way they think. And then the actual processes that they put in place. It's a much more difficult issue than just the tech issue to get this digital transformation in your organization. >>Yeah. You know, I've always found that the soft stuff about, you know, the culture of a behavior, the values is the hard stuff to change and more and more, we, we realized that to be successful with any kind of digital transformation you have to change people's behaviors and attitudes. Um, we haven't made as much progress in that area as we might have. I mean, I've done some surveys suggesting that most organizations still don't have data driven cultures. And in many cases there is a lower percentage of companies that say they have that then, um, did a few years ago. So we're kind of moving in the wrong direction, which means I think that we have to start explicitly addressing that, um, cultural, behavioral dimension and not just assuming that it will happen if we, if we build system, if we build it, they won't necessarily come. Right. >>Right. So I want to go to you Nick. Cause you know, we're talking about workflows and flow, um, and, and you've written about flow both in terms of, um, you know, moving things along a process and trying to find bottlenecks, identify bottlenecks, which is now even more important again, when these decisions are much more critical. Cause you have a lot less, uh, wiggle room in tough times, but you also talked about flow from the culture side and the people side. So I wonder if you can just share your thoughts on, you know, using flow as a way to think about things, to get the answers better. >>Yeah, absolutely. And I'll refer back to what Tom has said. If you're optimized, you need to optimize your system. You need to optimize how you innovate and how you deliver value to the business and the customer. Now, what we've noticed in the data, since that we've learned from customers, value streams, enterprise organizations, value streams, is that when it's taking six months at the end to deliver that value with the flow is that slow. You've got a bunch of unhappy developers, unhappy customers when you're innovating half so high performing organizations, we can measure third and 10 float time and dates. All of a sudden that feedback loop, the satisfaction your developer's measurably goes up. So not only do you have people context, switching glass, you're delivering so much more value to customers at a lower cost because you've optimized for flow rather than optimizing for these other approximate tricks that we use, which is how efficient is my agile team. How quickly can we deploy software? Those are important, but they do not provide the value of agility of fast learning of adaptability to the business. And that's exactly what the biz ops manifesto pushes your organization to do. You need to put in place this new operating model that's based on flow on the delivery of business value and on bringing value to market much more quickly than you were before. Right. >>I love that. And I'm going back to you, Tom, on that to follow up. Cause I think, I don't think people think enough about how they prioritize what they're optimizing for. Cause you know, if you're optimizing for a versus B, you know, you can have a very different product that you kick out and let you know. My favorite example is with Clayton Christianson and innovator's dilemma talking about the three inch hard drive. If you optimize it for power, you know, is one thing, if you optimize it for vibration is another thing and sure enough, you know, they missed it on the poem because it was the, it was the game console, which, which drove that whole business. So when you, when you're talking to customers and we think we hear it with cloud all the time, people optimizing for cost efficiency, instead of thinking about it as an innovation tool, how do you help them kind of rethink and really, you know, force them to, to look at the, at the prioritization and make sure they're prioritizing on the right thing is make just said, what are you optimizing for? >>Oh yeah. Um, you have one of the most important aspects of any decision or, um, attempt to resolve a problem in an organization is the framing process. And, um, you know, it's, it's a difficult aspect of the decision to frame it correctly in the first place. Um, there, it's not a technology issue. In many cases, it's largely a human issue, but if you frame that decision or that problem incorrectly to narrowly say, or you frame it as an either or situation where you could actually have some of both, um, it, it's very difficult for the, um, process to work out correctly. So in many cases that I think we need to think more at the beginning about how we bring this issue or this decision in the best way possible before we charge off and build a system to support it. You know, um, it's worth that extra time to think, think carefully about how the decision has been structured, right >>Surgery. I want to go back to you and talk about the human factors because as we just discussed, you can put it in great technology, but if the culture doesn't adopt it and people don't feel good about it, you know, it's not going to be successful and that's going to reflect poorly on the technology, even if it had nothing to do with it. And you know, when you look at the, the, the core values, uh, of the Bezos manifesto, you know, a big one is trust and collaboration, you know, learn, respond and pivot. I wonder if you can share your thoughts on, on trying to get that cultural shift, uh, so that you can have success with the people or excuse me, with the technology in the process and helping customers, you know, take this more trustworthy and kind of proactive, uh, position. >>So I think, I think at the ground level, it truly starts with the realization that we're all different. We come from different backgrounds. Um, oftentimes we tend to blame the data. It's not uncommon my experiments that we spend the first 30 minutes of any kind of one hour conversation to debate the validity of the data. Um, and so, um, one of the first kind of, uh, probably manifestations that we've had or revelations as we start to engage with our customers is spike, just exposing, uh, high-fidelity data sets to different stakeholders from their different lens. We start to enable these different stakeholders to not debate the data. That's really collaborate to find a solution. So in many ways, when, when, when we think about kind of the types of changes we're trying to, to truly affect around data driven decision making, it's all about bringing the data in context, in the context that is relevant and understandable for, for different stakeholders, whether we're talking about an operator or develop for a business analyst. >>So that's, that's the first thing. The second layer I think, is really to provide context to what people are doing in their specific cycle. And so I think one of the best examples I have is if you start to be able to align business KPI, whether you are counting, you know, sales per hour, or the engagements of your users on your mobile applications, whatever it is, you can start to connect that PKI to the business KPI, to the KPIs that developers might be looking at, whether it is the number of defects or a velocity or whatever, you know, metrics that they are used to to actually track you start to, to be able to actually contextualize in what we are the effecting, basically a metric that is really relevant in which we see is that DC is a much more systematic way to approach the transformation than say, you know, some organizations kind of creating, uh, some of these new products or services or initiatives, um, to, to drive engagements, right? >>So if you look at zoom, for instance, zoom giving away a it service to, uh, to education, he's all about, I mean, there's obviously a marketing aspect in therapists. It's fundamentally about trying to drive also the engagement of their own teams. And because now they're doing something for good and the organizations are trying to do that, but you only can do this kind of things in a limited way. And so you really want to start to rethink how you connect to, everybody's kind of a business objective fruit data, and now you start to get people to stare at the same data from their own lens and collaborate on all the data. Right, >>Right. That's a good, uh, Tom, I want to go back to you. You've been studying it for a long time, writing lots of books and getting into it. Um, why now, you know, what w why now are we finally aligning business objectives with, with it objectives? You know, why didn't this happen before? And, you know, what are the factors that are making now the time for this, this, this move with the, uh, with the biz ops? >>Well, and much of the past, it was sort of a back office related activity. And, you know, it was important for, um, uh, producing your paychecks and, uh, um, capturing the customer orders, but the business wasn't built around it now, every organization needs to be a software business, a data business, a digital business, the auntie has been raised considerably. And if you aren't making that connection between your business objectives and the technology that supports it, you run a pretty big risk of, you know, going out of business or losing out to competitors. Totally. So, um, and, uh, even if you're in a, an industry that hasn't historically been terribly, um, technology oriented customer expectations flow from, uh, you know, the digital native, um, companies that they work with to basically every industry. So you're compared against the best in the world. So we don't really have the luxury anymore of screwing up our it projects or building things that don't really work for the business. Um, it's mission critical that we do that well. Um, almost every time, I just want to follow up by that, Tom, >>In terms of the, you've talked extensively about kind of these evolutions of data and analytics from artismal stage to the big data stage, the data economy stage, the AI driven stage and what I find diff interesting that all those stages, you always put a start date. You never put an end date. Um, so you know, is the, is the big data I'm just going to use that generically a moment in time finally here, where we're, you know, off mahogany row with the data scientists, but actually can start to see the promise of delivering the right insight to the right person at the right time to make that decision. >>Well, I think it is true that in general, these previous stages never seemed to go away. The, um, the artisinal stuff is still being done, but we would like for less than less of it to be artisinal, we can't really afford for everything to be artisinal anymore. It's too labor and time consuming to do things that way. So we shift more and more of it to be done through automation and B to be done with a higher level of productivity. And, um, you know, at some point maybe we reached the stage where we don't do anything artisanally anymore. I'm not sure we're there yet, but, you know, we are, we are making progress. Right, >>Right. And Mick, back to you in terms of looking at agile, cause you're, you're such a, a student of agile when, when you look at the opportunity with ops, um, and taking the lessons from agile, you know, what's been the inhibitor to stop this in the past. And what are you so excited about? You know, taking this approach will enable. >>Yeah. I think both Sergeant Tom hit on this is that in agile what's happened is that we've been measuring tiny subsets of the value stream, right? We need to elevate the data's there. Developers are working on these tools that delivering features that the foundations for, for great culture are there. I spent two decades as a developer. And when I was really happy is when I was able to deliver value to customers, the quicker I was able to do that the fewer impediments are in my way, that quicker was deployed and running in the cloud, the happier I was, and that's exactly what's happening. If we can just get the right data, uh, elevated to the business, not just to the agile teams, but really these values of ours are to make sure that you've got these data driven decisions with meaningful data that's oriented around delivering value to customers. Not only these legacies that Tom touched on, which has cost center metrics from an ITK, from where, for it being a cost center and something that provided email and then back office systems. So we need to rapidly shift to those new, meaningful metrics that are customized business centric and make sure that every development the organization is focused on those as well as the business itself, that we're measuring value and that we're helping that value flow without interruptions. >>I love that mic. Cause if you don't measure it, you can't improve on it and you gotta, but you gotta be measuring the right thing. So gentlemen, uh, thank you again for, for your time. Congratulations on the, uh, on the unveil of the biz ops manifesto and together this coalition >>Of, of, uh, industry experts to get behind this. And, you know, there's probably never been a more important time than now to make sure that your prioritization is in the right spot and you're not wasting resources where you're not going to get the ROI. So, uh, congratulations again. And thank you for sharing your thoughts with us here on the cube. Alright, so we had surge, Tom and Mick I'm. Jeff, you're watching the cube, it's a biz ops manifesto and unveil. Thanks for watching. We'll see you next time >>From around the globe. It's the cube with digital coverage of BizOps manifesto, unveiled brought to you by biz ops coalition and welcome back Friday, Jeff Frick here with the cube we're in our Palo Alto studios. And we'd like to welcome you back to our continuing coverage of biz ops manifesto, unveil exciting day to really, uh, kind of bring this out into public. There's been a little bit of conversation, but today's really the official unveiling and we're excited to have our next guest to share a little bit more information on it. He's Patrick tickle. He's a chief product officer for planned view. Patrick. Great to see you. Yeah, it's great to be here. Thanks for the invite. So why the biz ops manifesto, why the biz optical edition now when you guys have been at it, it's relatively mature marketplace businesses. Good. What was missing? Why, why this, uh, why this coalition? >>Yeah, so, you know, again, why is, why is biz ops important and why is this something I'm, you know, I'm so excited about, but I think companies as well, right. Well, you know, in some ways or another, this is a topic that I've been talking to, you know, the market and our customers about for a long time. And it's, you know, I really applaud, you know, this whole movement, right. And, um, in resonates with me, because I think one of the fundamental flaws, frankly, of the way we've talked about technology and business literally for decades, uh, has been this idea of, uh, alignment. Those who know me, I occasionally get off on this little rant about the word alignment, right. But to me, the word alignment is, is actually indicative of the, of the, of the flaw in a lot of our organizations and biz ops is really, I think now trying to catalyze and expose that flaw. >>Right. Because, you know, I always say that, you know, you know, alignment implies silos, right. Instantaneously, as soon as you say there's alignment, there's, there's obviously somebody who's got a direction and other people that have to line up and that, that kind of siloed, uh, nature of organizations. And then frankly, the passive nature of it. Right. I think so many technology organizations are like, look, the business has the strategy you guys need to align. Right. And, and, you know, as a product leader, right. That's where I've been my whole career. Right. I can tell you that I never sit around. I almost never use the word alignment. Right. I mean, whether I never sit down and say, you know, the product management team has to get aligned with Deb, right. Or the dev team has to get aligned with the delivery and ops teams. I mean, what I say is, you know, are we on strategy, right? >>Like we've, we have a strategy as a, as a full end to end value stream. Right. And that there's no silos. And I mean, look, every on any given day we got to get better. Right. But the context, the context we operate is not about alignment. Right. It's about being on strategy. And I think I've talked to customers a lot about that, but when I first read the manifesto, I was like, Oh yeah, this is exactly. This is breaking down. Maybe trying to eliminate the word alignment, you know, from a lot of our organizations, because we literally start thinking about one strategy and how we go from strategy to delivery and have it be our strategy, not someone else's that we're all aligning to it. And it's a great way to catalyze that conversation. That I've, it's been in my mind for years, to be honest. Right. >>So, so much to unpack there. One of the things obviously, uh, stealing a lot from, from dev ops and the dev ops manifesto from 20 years ago. And as I look through some of the principles and I looked through some of the values, which are, you know, really nicely laid out here, you know, satisfy customers, do continuous delivery, uh, measure, output against real results. Um, the ones that, that jumps out though is really about, you know, change, change, right? Requirements should change frequently. They do change frequently, but I'm curious to get your take from a, from a software development point, it's easy to kind of understand, right. We're making this widget and our competitors, beta widget plus X, and now we need to change our plans and make sure that the plus X gets added to the plan. Maybe it wasn't in the plan, but you talked a lot about product strategy. So in this kind of continuous delivery world, how does that meld with, I'm actually trying to set a strategy, which implies the direction for a little bit further out on the horizon and to stay on that while at the same time, you're kind of doing this real time continual adjustments. Cause you're not working off a giant PRD or MRD anymore. >>Yeah, yeah, totally. Yeah. You know, one of the terms, you know, that we use internally a lot and even with my customers, our customers is we talked about this idea of rewiring, right. And I think, you know, it's kind of a, now an analogy for transformation. And I think a lot of us have to rewire the way we think about things. Right. And I think at Planview where we have a lot of customers who live in that, you know, who operationalize that traditional PPM world. Right. And are shifting to agile and transforming that rewire is super important. And, and to your point, right, it's, you've just, you've got to embrace this idea of, you know, just iterative getting better every day and iterating, iterating, iterating as to building annual plans or, you know, I get customers occasionally who asked me for two or three year roadmap. >>Right. And I literally looked at them and I go, there's no, there's no scenario where I can build a two or three year roadmap. Right. You, you, you think you want that, but that's not, that's not the way we run. Right. And I will tell you the biggest thing that for us, you know, that I think is matched the planning, uh, you know, patents is a word I like to use a lot. So the thing that we've like, uh, that we've done from a planning perspective, I think is matched impedance to continuous delivery is instituting the whole program, implement, you know, the program, increment planning, capabilities and methodologies, um, in the scaled agile world. Right. And over the last 18 months to two years, we really have now, you know, instrumented our company across three value streams. You know, we do quarterly PI program increment 10 week planning, you know, and that becomes, that becomes the Terra firma of how we plant. >>Right. And it's, what are we doing for the next 10 weeks? And we iterate within those 10 weeks, but we also know that 10 weeks from now, we're going to, we're going to adjust iterate again. Right. And that shifting of that planning model, you know, to being as cross-functional is that as that big room planning kind of model is, um, and also, uh, you know, on that shorter increment, when you get those two things in place, all sudden the impedance really starts to match up, uh, with continuous delivery and it changes, it changes the way you plan and it changes the way you work. Right? >>Yeah. Their thing. Right. So obviously a lot of these things are kind of process driven, both within the values, as well as the principles, but there's a whole lot, really about culture. And I just want to highlight a couple of the values, right? We already talked about business outcomes, um, trust and collaboration, uh, data driven decisions, and then learn, respond and pivot. Right. A lot of those are cultural as much as they are process. So again, is it the, is it the need to really kind of just put them down on paper and you know, I can't help, but think of, you know, the hammering up the, uh, the thing in the Lutheran church with their, with their manifesto, is it just good to get it down on paper? Because when you read these things, you're like, well, of course we should trust people. And of course we need an environment of collaboration and of course we want data driven decisions, but as we all know saying it and living, it are two very, very different things. >>Yeah. Good question. I mean, I think there's a lot of ways you bring that to life you're right. And just hanging up, you know, I think we've all been through the hanging up posters around your office, which these days, right. Unless you're going to hang a poster and everybody's home office. Right. You can't even, you can't even fake it that you think that might work. Right. So, um, you know, you really, I think we've attacked that in a variety of ways. Right. And you definitely have to, you know, you've got to make the shift to a team centric culture, right. Empowered teams, you know, that's a big deal. Right. You know, a lot of, a lot of the people that, you know, we lived in a world of quote unquote, where we were lived in a deep resource management world for a long, long time. >>And right. A lot of our customers still do that, but you know, kind of moving to that team centric world is, uh, is really important and core the trust. Um, I think training is super important, right. We've, you know, we've internally, right. We've trained hundreds employees over the last a year and a half on the fundamentals really of safe. Right. Not necessarily, you know, we've had, we've had teams delivering in scrum and the continuous delivery for, you know, for years, but the scaling aspect of it, uh, is where we've done a lot of training and investment. Um, and then, you know, I think, uh, leadership has to be bought in. Right. You know? And so when we pie plan, you know, myself and Cameron and the other members of our leadership, you know, we're NPI planning, you know, for, for four days. Right. I mean, it's, it's, you've got to walk the walk, you know, from top to bottom and you've got to train on the context. Right. And then you, and then, and, and then once you get through a few cycles where you've done a pivot, right. Or you brought a new team in, and it just works, it becomes kind of this virtuous circle where he'll go, man, this really works so much better than what we used to do. Right. >>Right. The other really key principle to this whole thing is, is aligning, you know, the business leaders and the business prioritization, um, so that you can get to good outcomes with the development and the delivery. Right. And we, we know again, and kind of classic dev ops to get the dev and the production people together. So they can, you know, quickly ship code that works. Um, but adding the business person on there really puts, puts a little extra responsibility that they, they understand the value of a particular feature or particular priority. Uh, they, they can make the, the, the trade offs and that they kind of understand the effort involved too. So, you know, bringing them into this continuous again, kind of this continuous development process, um, to make sure that things are better aligned and really better prioritize. Cause ultimately, you know, we don't live in an infinite resources situation and people got to make trade offs. They got to make decisions as to what goes and what doesn't go in for everything that goes. Right. I always say you pick one thing. Okay. That's 99 other things that couldn't go. So it's really important to have, you know, this, you said alignment of the business priorities as well as, you know, the execution within, within the development. >>Yeah. I think that, you know, uh, you know, I think it was probably close to two years ago. Forester started talking about the age of the customer, right. That, that was like their big theme at the time. Right. And I think to me what that, the age of the customer actually translates to and Mick, Mick and I are both big fans of this whole idea of the project and product shift, mixed book, you know, it was a great piece on a, you're talking about, you know, as part of the manifesto is one of the authors as well, but this shift from project to product, right? Like the age of the customer, in my opinion, the, the embodiment of that is the shift to a product mentality. Right. And, and the product mentality in my opinion, is what brings the business and technology teams together, right? >>Once you, once you're focused on a customer experience is delivered through a product or a service. That's when I that's, when I started to go with the alignment problem goes away, right. Because if you look at software companies, right, I mean, we run product management models yeah. With software development teams, customer success teams, right. That, you know, the software component of these products that people are building is obviously becoming bigger and bigger, you know, in an, in many ways, right. More and more organizations are trying to model themselves over as operationally like software companies. Right. Um, they obviously have lots of other components in their business than just software, but I think that whole model of customer experience equaling product, and then the software component of product, the product is the essence of what changes that alignment equation and brings business and teams together because all of a sudden, everyone knows what the customer's experiencing. Right. And, and that, that, that makes a lot of things very clear, very quickly. >>Right. I'm just curious how far along this was as a process before, before COBIT hit, right. Because serendipitous, whatever. Right. But the sudden, you know, light switch moment, everybody had to go work from home and in March 15th compared to now we're in October and this is going to be going on for a while. And it is a new normal and whatever that whatever's going to look like a year from now, or two years from now is TBD, you know, had you guys already started on this journey cause again, to sit down and actually declare this coalition and declare this manifesto is a lot different than just trying to do better within your own organization. >>Yeah. So we had started, uh, you know, w we definitely had started independently, you know, some, some, you know, I think people in the community know that, uh, we, we came together with a company called lean kit a handful of years ago, and I give John Terry actually one of the founders LeanKit immense credit for, you know, kind of spearheading our cultural change and not, and not because of, we were just gonna be, you know, bringing agile solutions to our customers, but because, you know, he believed that it was going to be a fundamentally better way for us to work. Right. And we kind of, you know, we started with John and built, you know, out of concentric circles of momentum and, and we've gotten to the place where now it's just part of who we are, but, but I do think that, you know, COVID has, you know, um, I think pre COVID a lot of companies, you know, would, would adopt, you know, the would adopt digital slash agile transformation. >>Um, traditional industries may have done it as a reaction to disruption. Right. You know, and in many cases, the disruption to these traditional industries was, I would say a product oriented company, right. That probably had a larger software component, and that disruption caused a competitive issue, uh, or a customer issue that caused companies and tried to respond by transforming. I think COVID, you know, all of a sudden flatten that out, right. We literally all got disrupted. Right. And so all of a sudden, every one of us is dealing with some degree of market uncertainty, customer uncertainty, uh, and also, you know, none of us were insulated from the need to be able to pivot faster, deliver incrementally, you know, and operate in a different, completely more agile way, uh, you know, post COVID. Right. Yeah. That's great. >>So again, a very, very, very timely, you know, a little bit of serendipity, a little bit of planning. And, you know, as, as with all important things, there's always a little bit of lock in, uh, and a lot of hard work involved. So a really interesting thank you for, for your leadership, Patrick. And, you know, it really makes a statement. I think when you have a bunch of leaderships across an industry coming together and putting their name on a piece of paper, uh, that's aligned around us some principles and some values, which again, if you read them who wouldn't want to get behind these, but if it takes, you know, something a little bit more formal, uh, to kind of move the ball down the field, and then I totally get it and a really great work. Thanks for, uh, thanks for doing it. >>Oh, absolutely. No. Like I said, the first time I read it, I was like, yep. Like you said, this is all, it's all makes complete sense, but just documenting it and saying it and talking about it moves the needle. I'll tell you as a company, you gotta, we're pushing really hard on, uh, you know, on our own internal strategy on diversity and inclusion. Right. And, and like, once we wrote the words down about what, you know, what we aspire to be from a diversity and inclusion perspective, it's the same thing. Everybody reads the words that goes, why wouldn't we do this? Right. But until you write it down and kind of have again, a manifesto or a Terra firma of what you're trying to accomplish, you know, then you can rally behind it. Right. As opposed to it being something that's, everybody's got their own version of the flavor. Right. And I think it's a very analogous, you know, kind of, uh, initiative. Right. And, uh, and it's happening, both of those things right. Are happening across the industry these days. Right. >>And measure it too. Right. And measure it, measure, measure, measure, get a baseline. Even if you don't like to measure, even if you don't like what the, even if you can argue against the math, behind the measurement, measure it. And at least you can measure it again and you can, and you've got some type of a comp and that is really the only way to, to move it forward. We're Patrick really enjoyed the conversation. Thanks for, uh, for taking a few minutes out of your day. >>It's great to be here. It's an awesome movement and we're glad to be a part of it. >>All right. Thanks. And if you want to check out the biz ops, Manifesta go to biz ops, manifesto.org, read it. You might want to sign it. It's there for you. And thanks for tuning in on this segment will continuing coverage of the biz op manifesto unveil you're on the cube. I'm Jeff, thanks for watching >>From around the globe. It's the cube with digital coverage of biz ops manifesto unveiled brought to you by biz ops coalition. >>Hey, welcome back, everybody Jeffrey here with the cube. We're coming to you from our Palo Alto studios. And welcome back to this event is the biz ops manifesto unveiling. So the biz ops manifesto and the biz ops coalition had been around for a little while, but today's the big day. That's kind of the big public unveiling, or we're excited to have some of the foundational people that, you know, have put their, put their name on the dotted, if you will, to support this initiative and talk about why that initiative is so important. And so the next guest we're excited to have is dr. Mick Kirsten. He is the founder and CEO of Tasktop mic. Great to see you coming in from Vancouver, Canada, I think, right? Yes. Great to be here, Jeff. Thank you. Absolutely. I hope your air is a little better out there. I know you had some of the worst air of all of us, a couple, a couple of weeks back. So hopefully things are, uh, are getting a little better and we get those fires under control. Yeah. >>Things have cleared up now. So yeah, it's good. It's good to be close to the U S and it's going to have the Arabic cleaner as well. >>Absolutely. So let's, let's jump into it. So you you've been an innovation guy forever starting way back in the day and Xerox park. I was so excited to do an event at Xerox park for the first time last year. I mean, that, that to me represents along with bell labs and, and some other, you know, kind of foundational innovation and technology centers, that's gotta be one of the greatest ones. So I just wonder if you could share some perspective of getting your start there at Xerox park, you know, some of the lessons you learned and what you've been able to kind of carry forward from those days. >>Yeah. I was fortunate to join Xerox park in the computer science lab there at a fairly early point in my career, and to be working on open source programming languages. So back then in the computer science lab, where some of the inventions around programming around software development games, such as object programming, and a lot of what we had around really modern programming levels constructs, those were the teams I had the fortunate of working with, and really our goal was. And of course, there's, as, as you noticed, there's just this DNA of innovation and excitement and innovation in the water. And really it was the model that was all about changing the way that we work was looking at for how we can make it 10 times easier to white coat. But this is back in 99. And we were looking at new ways of expressing, especially business concerns, especially ways of enabling people who are wanting to innovate for their business to express those concerns in code and make that 10 times easier than what that would take. >>So we create a new open source programming language, and we saw some benefits, but not quite quite what we expected. I then went and actually joined Charles Stephanie, that former to fucking from Microsoft who was responsible for, he actually got Microsoft word as a sparking into Microsoft and into the hands of bill Gates and that company that was behind the whole office suite and his vision. And then when I was trying to execute with, working for him was to make PowerPoint like a programming language to make everything completely visual. And I realized none of this was really working, that there was something else, fundamentally wrong programming languages, or new ways of building software. Like let's try and do with Charles around intentional programming. That was not enough. >>That was not enough. So, you know, the agile movement got started about 20 years ago, and we've seen the rise of dev ops and really this kind of embracing of, of, of sprints and, you know, getting away from MRDs and PRDs and these massive definitions of what we're going to build and long build cycles to this iterative process. And this has been going on for a little while. So what was still wrong? What was still missing? Why the biz ops coalition, why the biz ops manifesto? >>Yeah, so I basically think we nailed some of the things that the program language levels of teams can have effective languages deployed to soften to the cloud easily now, right? And at the kind of process and collaboration and planning level agile two decades, decades ago was formed. We were adopting and all the, all the teams I was involved with and it's really become a self problem. So agile tools, agile teams, agile ways of planning, uh, are now very mature. And the whole challenge is when organizations try to scale that. And so what I realized is that the way that agile was scaling across teams and really scaling from the technology part of the organization to the business was just completely flawed. The agile teams had one set of doing things, one set of metrics, one set of tools. And the way that the business was working was planning was investing in technology was just completely disconnected and using a whole different set of measures. Pretty >>Interesting. Cause I think it's pretty clear from the software development teams in terms of what they're trying to deliver. Cause they've got a feature set, right. And they've got bugs and it's easy to, it's easy to see what they deliver, but it sounds like what you're really honing in on is this disconnect on the business side, in terms of, you know, is it the right investment? You know, are we getting the right business ROI on this investment? Was that the right feature? Should we be building another feature or should we building a completely different product set? So it sounds like it's really a core piece of this is to get the right measurement tools, the right measurement data sets so that you can make the right decisions in terms of what you're investing, you know, limited resources. You can't, nobody has unlimited resources. And ultimately you have to decide what to do, which means you're also deciding what not to do. And it sounds like that's a really big piece of this, of this whole effort. >>Yeah. Jeff, that's exactly it, which is the way that the agile team measures their own way of working is very different from the way that you measure business outcomes. The business outcomes are in terms of how happy your customers are, but are you innovating fast enough to keep up with the pace of a rapidly changing economy, roughly changing market. And those are, those are all around the customer. And so what I learned on this long journey of supporting many organizations transformations and having them try to apply those principles of agile and dev ops, that those are not enough, those measures technical practices, uh, those measured sort of technical excellence of bringing code to the market. They don't actually measure business outcomes. And so I realized that it really was much more around having these entwined flow metrics that are customer centric and business centric and market centric where we need it to go. Right. >>So I want to shift gears a little bit and talk about your book because you're also a bestselling author from project to product and, and, and you, you brought up this concept in your book called the flow framework. And it's really interesting to me cause I know, you know, flow on one hand is kind of a workflow and a process flow and, and you know, that's how things get done and, and, and embrace the flow. On the other hand, you know, everyone now in, in a little higher level existential way is trying to get into the flow right into the workflow and, you know, not be interrupted and get into a state where you're kind of at your highest productivity, you know, kind of your highest comfort, which flow are you talking about in your book? Or is it a little bit of both? >>That's a great question. It's not one I get asked very often cause to me it's absolutely both. So that the thing that we want to get, that we've learned how to master individual flow, that there's this beautiful book by me, how you teaches me how he does a beautiful Ted talk by him as well about how we can take control of our own flow. So my question with the book with question replies, how can we bring that to entire teams and really entire organizations? How can we have everyone contributing to a customer outcome? And this is really what if you go to the biz ops manifesto, it says, I focus on outcomes on using data to drive whether we're delivering those outcomes rather than a focus on proxy metrics, such as, how quickly did we implement this feature? No, it's really how much value did the customer go to the future? >>And how quickly did you learn and how quickly did you use that data to drive to that next outcome? Really that with companies like Netflix and Amazon have mastered, how do we get that to every large organization, every it organization and make everyone be a software innovator. So it's to bring that, that concept of flow to these end to end value streams. And the fascinating thing is we've actually seen the data. We've been able to study a lot of value streams. We see when flow increases, when organizations deliver value to a customer faster, developers actually become more happy. So things like that and point out promoter scores, rise, and we've got empirical data for this. So that the beautiful thing to me is that we've actually been able to combine these two things and see the results and the data that you increase flow to the customer. Your developers are more, >>I love it. I love it, right, because we're all more, we're all happier when we're in the flow and we're all more productive when we're in the flow. So I, that is a great melding of, of two concepts, but let's jump into the, into the manifesto itself a little bit. And you know, I love that you took this approach really of having kind of four key values and then he gets 12 key principles. And I just want to read a couple of these values because when you read them, it sounds pretty brain dead. Right? Of course. Right. Of course you should focus on business outcomes. Of course you should have trust and collaboration. Of course you should have database decision making processes and not just intuition or, you know, whoever's the loudest person in the room, uh, and to learn and respond and pivot. But what's the value of actually just putting them on a piece of paper, because again, this is not this, these are all good, positive things, right? When somebody reads these to you or tells you these are sticks it on the wall, of course. But unfortunately of course isn't always enough. >>No. And I think what's happened is some of these core principles originally from the agile manifesto in two decades ago, uh, the whole dev ops movement of the last decade of flow feedback and continue learning has been key. But a lot of organizations, especially the ones undergoing digital transformations have actually gone a very different way, right? The way that they measure value, uh, in technology and innovation is through costs for many organizations. The way that they actually are looking at that they're moving to cloud is actually as a reduction in cost. Whereas the right way of looking at moving to cloud is how much more quickly can we get to the value to the customer? How quickly can we learn from that? And how quickly can we drive the next business outcome? So really the key thing is, is to move away from those old ways of doing things of funding projects and cost centers, to actually funding and investing in outcomes and measuring outcomes through these flow metrics, which in the end are your fast feedback and how quickly you're innovating for your customer. >>So these things do seem very obvious when you look at them. But the key thing is what you need to stop doing to focus on these. You need to actually have accurate realtime data of how much value you fund to the customer every week, every month, every quarter. And if you don't have that, your decisions are not driven on data. If you don't know what your bottleneck is. And this is something that in decades of manufacturing, a car manufacturers, other manufacturers, master, they always know where the bottom back in their production processes. You ask a random CIO when a global 500 company where their bottleneck is, and you won't get a clear answer because there's not that level of understanding. So have to actually follow these principles. You need to know exactly where you fall. And I guess because that's, what's making your developers miserable and frustrated, then having them context, which I'm trash. So the approach here is important and we have to stop doing these other things, >>Right? There's so much there to unpack. I love it. You know, especially the cloud conversation because so many people look at it wrong as, as, as a cost saving a device, as opposed to an innovation driver and they get stuck, they get stuck in the literal. And I, you know, I think at the same thing, always about Moore's law, right? You know, there's a lot of interesting real tech around Moore's law and the increasing power of microprocessors, but the real power, I think in Moore's laws is the attitudinal change in terms of working in a world where you know that you've got all this power and what you build and design. I think it's funny to your, your comment on the flow and the bottleneck, right? Cause, cause we know manufacturing, as soon as you fix one bottleneck, you move to your next one, right? You always move to your next point of failure. So if you're not fixing those things, you know, you're not, you're not increasing that speed down the line, unless you can identify where that bottleneck is or no matter how many improvements you make to the rest of the process, it's still going to get hung up on that one spot. >>That's exactly it. And you also make it sound so simple, but again, if you don't have the data driven visibility of where the bottom line is, and these bottlenecks are adjusted to say, it's just whack-a-mole right. So we need to understand is the bottleneck because our security reviews are taking too long and stopping us from getting value for the customer. If it's that automate that process. And then you move on to the next bottleneck, which might actually be that deploying yourself into the cloud was taking too long. But if you don't take that approach of going flow first, rather than again, that sort of cost reduction. First, you have to think of that approach of customer centricity and you only focused on optimizing costs. Your costs will increase and your flow will slow down. And this is just one of these fascinating things. Whereas if you focus on getting back to the customer and reducing your cycles on getting value, your flow time from six months to two weeks or two, one week or two event, as we see with, with tech giants, you actually can both lower your costs and get much more value that for us to get that learning loop going. >>So I think I've seen all of these cloud deployments and one of the things that's happened that delivered almost no value because there was such big bottlenecks upfront in the process and actually the hosting and the AP testing was not even possible with all of those inefficiencies. So that's why going float for us rather than costs where we started our project versus silky. >>I love that. And, and, and, and it, it begs repeating to that right within the subscription economy, you know, you're on the hook to deliver value every single month because they're paying you every single month. So if you're not on top of how you're delivering value, you're going to get sideways because it's not like, you know, they pay a big down payment and a small maintenance fee every month, but once you're in a subscription relationship, you know, you have to constantly be delivering value and upgrading that value because you're constantly taking money from the customer. So it's such a different kind of relationship than kind of the classic, you know, big bang with a maintenance agreement on the back end really important. Yeah. >>And I think in terms of industry shifts that that's it that's, what's catalyzed. This interesting shift is in this SAS and subscription economy. If you're not delivering more and more value to your customers, someone else's and they're winning the business, not you. So one way we know is to delight our customers with great user experiences. Well, that really is based on how many features you delivered or how much, how big, how many quality improvements or scalar performance improvements you delivered. So the problem is, and this is what the business manifesto, as well as the full frame of touch on is if you can't measure how much value you delivered to a customer, what are you measuring? You just backed again, measuring costs and that's not a measure of value. So we have to shift quickly away from measuring cost to measuring value, to survive in the subscription economy. >>We could go for days and days and days. I want to shift gears a little bit into data and, and, and a data driven, um, decision making a data driven organization cause right day has been talked about for a long time, the huge big data meme with, with Hadoop over, over several years and, and data warehouses and data lakes and data oceans and data swamps, and can go on and on and on. It's not that easy to do, right? And at the same time, the proliferation of data is growing exponentially. We're just around the corner from, from IOT and 5g. So now the accumulation of data at machine scale, again, this is going to overwhelm and one of the really interesting principles, uh, that I wanted to call out and get your take right, is today's organizations generate more data than humans can process. So informed decisions must be augmented by machine learning and artificial intelligence. I wonder if you can, again, you've got some great historical perspective, um, reflect on how hard it is to get the right data, to get the data in the right context, and then to deliver it to the decision makers and then trust the decision makers to actually make the data and move that down. You know, it's kind of this democratization process into more and more people and more and more frontline jobs making more and more of these little decisions every day. >>Yeah. I definitely think the front parts of what you said are where the promises of big data have completely fallen on their face into the swamps as, as you mentioned, because if you don't have the data in the right format, you've cannot connect collected at the right way. You want that way, the right way you can't use human or machine learning effectively. And there've been the number of data warehouses in a typical enterprise organization. And the sheer investment is tremendous, but the amount of intelligence being extracted from those is, is, is a very big problem. So the key thing that I've noticed is that if you can model your value streams, so yes, you understand how you're innovating, how you're measuring the delivery of value and how long that takes. What is your time to value these metrics like full time? You can actually use both the intelligence that you've got around the table and push that down as well, as far as getting to the organization, but you can actually start using that those models to understand and find patterns and detect bottlenecks that might be surprising, right? >>Well, you can detect interesting bottlenecks when you shift to work from home. We detected all sorts of interesting bottlenecks in our own organization that were not intuitive to me that had to do with, you know, more senior people being overloaded and creating bottlenecks where they didn't exist. Whereas we thought we were actually an organization that was very good at working from home because of our open source roots. So that data is highly complex. Software value streams are extremely complicated. And the only way to really get the proper analyst and data is to model it properly and then to leverage these machine learning and AI techniques that we have. But that front part of what you said is where organizations are just extremely immature in what I've seen, where they've got data from all their tools, but not modeled in the right way. Right, right. >>Right. Well, all right. So before I let you go, you know, let's say you get a business leader, he buys in, he reads the manifesto, he signs on the dotted line and he says, Mick, how do I get started? I want to be more aligned with, with the development teams. You know, I'm in a very competitive space. We need to be putting out new software features and engaging with our customers. I want to be more data-driven how do I get started? Well, you know, what's the biggest inhibitor for most people to get started and get some early wins, which we know is always the key to success in any kind of a new initiative. >>Right? So I think you can reach out to us through the website, uh, there's the manifesto, but the key thing is just to get you set up it's to get started and to get the key wins. So take a probably value stream that's mission critical. It could be your new mobile and web experiences or, or part of your cloud modernization platform or your analytics pipeline, but take that and actually apply these principles to it and measure the end to end flow of value. Make sure you have a value metric that everyone is on the same page on the people, on the development teams, the people in leadership all the way up to the CEO. And one of the, what I encourage you to start is actually that content flow time, right? That is the number one metric. That is how you measure it, whether you're getting the benefit of your cloud modernization, that is the one metric that Adrian Cockcroft. When the people I respect tremendously put into his cloud for CEOs, the metric, the one, the one way to measure innovation. So basically take these principles, deploy them on one product value stream, measure, sentiment, flow time, and then you'll actually be well on your path to transforming and to applying the concepts of agile and dev ops all the way to, to the business, to the way >>You're offering model. >>Well, Mick really great tips, really fun to catch up. I look forward to a time when we can actually sit across the table and, and get into this. Cause I just, I just love the perspective and, you know, you're very fortunate to have that foundational, that foundational base coming from Xerox park and they get, you know, it's, it's a very magical place with a magical history. So to, to incorporate that into, continue to spread that well, uh, you know, good for you through the book and through your company. So thanks for sharing your insight with us today. >>Thanks so much for having me, Jeff. >>All right. And go to the biz ops manifesto.org, read it, check it out. If you want to sign it, sign it. They'd love to have you do it. Stay with us for continuing coverage of the unveiling of the business manifesto on the cube. I'm Jeff. Rick. Thanks for watching. See you next time >>From around the globe. It's the cube with digital coverage of biz ops manifesto unveiled brought to you by biz ops coalition. >>Hey, welcome back everybody. Jeff Frick here with the cube come due from our Palo Alto studios today for a big, big reveal. We're excited to be here. It's the biz ops manifesto unveiling a thing's been in the works for awhile and we're excited to have our next guest. One of the, really the powers behind this whole effort. And he's joining us from Boston it's surge, Lucio, the vice president, and general manager enterprise software division at Broadcom surge. Great to see you. >>Hi, good to see you, Jeff. Glad to be here. >>So you've been in this business for a very long time. You've seen a lot of changes in technology. What is the biz ops manifesto? What is this coalition all about? Why do we need this today and in 2020? >>Yeah. So, so I've been in this business for close to 25 years, right? So about 20 years ago, the agile manifesto was created. And the goal of the agile manifesto was really to address the uncertainty around software development and the inability to predict the efforts to build software. And, uh, if you, if you roll that kind of 20 years later, and if you look at the current state of the industry, uh, the product, the project management Institute, estimates that we're wasting about a million dollars, every 20 seconds in digital transformation initiatives that do not deliver on business results. In fact, we were recently served a third of the, uh, a number of executives in partnership with Harvard business review and 77% of those executives think that one of the key challenges that they have is really at the collaboration between business and it, and that that's been kind of a case for, uh, almost 20 years now. >>Um, so the, the, the key challenge we're faced with is really that we need a new approach and many of the players in the industry, including ourselves, I've been using different terms, right? Some are being, are talking about value stream management. Some are talking about software delivery management. If you look at the site, reliability engineering movement, in many ways, it embodies a lot of these kind of concepts and principles. So we believed that it became really imperative for us to crystallize around, could have one concept. And so in many ways, the, uh, the BizOps concept and the business manifesto are bringing together a number of ideas, which have been emerging in the last five years or so, and, and defining the key values and principles to finally help these organizations truly transform and become digital businesses. And so the hope is that by joining our forces and defining public key principles and values, we can help the industry, uh, not just, uh, by, you know, providing them with support, but also, uh, tools and consulting that is required for them to truly achieve the kind of transformation that everybody's seeking. >>Right, right. So COVID now we're six months into it, approximately seven months into it. Um, a lot of pain, a lot of bad stuff still happening. We've got a ways to go, but one of the things that on the positive side, right, and you've seen all the memes and social media is, is a driver of digital transformation and a driver of change. Cause we had this light switch moment in the middle of March and there was no more planning. There was no more conversation. You've suddenly got remote workforces, everybody's working from home and you got to go, right. So the reliance on these tools increases dramatically, but I'm curious, you know, kind of short of, of the beginnings of this effort in short of kind of COVID, which, you know, came along unexpectedly. I mean, what were those inhibitors because we've been making software for a very long time, right? The software development community has, has adopted kind of rapid change and, and iterative, uh, delivery and, and sprints, what was holding back the connection with the business side to make sure that those investments were properly aligned with outcomes. >>Well, so, so you have to understand that it is, is kind of a its own silos. And traditionally it has been treated as a cost center within large organizations and not as a value center. And so as a result could have a traditional dynamic between it and the business is basically one of a kind of supplier up to kind of a business. Um, and you know, if you, if you go back to, uh, I think you'll unmask a few years ago, um, basically at this concept of the machines to build the machines and you went as far as saying that, uh, the machines or the production line is actually the product. So, um, meaning that the core of the innovation is really about, uh, building, could it be engine to deliver on the value? And so in many ways, you know, we have missed on this shift from, um, kind of it becoming this kind of value center within the enterprises. >>And, and he talks about culture. Now, culture is a, is a sum total of beavers. And the reality is that if you look at it, especially in the last decade, uh, we've agile with dev ops with, um, I bring infrastructures, uh, it's, it's way more volatile today than it was 10 years ago. And so the, when you start to look at the velocity of the data, the volume of data, the variety of data to analyze this system, um, it's, it's very challenging for it to actually even understand and optimize its own processes, let alone, um, to actually include business as sort of an integral part of kind of a delivery chain. And so it's both kind of a combination of, of culture, um, which is required as well as tools, right? To be able to start to bring together all these data together, and then given the volume variety of philosophy of the data, uh, we have to apply some core technologies, which have only really, truly emerged in the last five to 10 years around machine learning and analytics. And so it's really kind of a combination of those freaks, which are coming together today to really help organizations kind of get to the next level. Right, >>Right. So let's talk about the manifesto. Let's talk about, uh, the coalition, uh, the BizOps coalition. I just liked that you put down these really simple, you know, kind of straightforward core values. You guys have four core values that you're highlighting, you know, business outcomes, over individual projects and outputs, trust, and collaboration, oversight, load teams, and organizations, data driven decisions, what you just talked about, uh, you know, over opinions and judgment and learned, respond and pivot. I mean, surgery sounds like pretty basic stuff, right? I mean, aren't, isn't everyone working to these values already. And I think he touched on it on culture, right? Trust and collaboration, data driven decisions. I mean, these are fundamental ways that people must run their business today, or the person that's across the street, that's doing it. It's going to knock them out right off their blog. >>Yeah. So that's very true. But, uh, so I'll, I'll mention in our survey, we did, uh, I think about six months ago and it was in partnership with, uh, with, uh, an industry analyst and we serve at a, again, a number of it executives to understand how many we're tracking business outcomes I'm going to do with the software executives. It executives we're tracking business outcomes. And the, there were less than 15% of these executives were actually tracking the outcomes of a software delivery. And you see that every day. Right? So in my own teams, for instance, we've been adopting a lot of these core principles in the last year or so, and we've uncovered that 16% of our resources were basically aligned around initiatives, which are not strategic for us. Um, I take, you know, another example, for instance, one of our customers in the, uh, in the airline industry and Harvard, for instance, that a number of, uh, um, that they had software issues that led to people searching for flights and not returning any kind of availability. >>And yet, um, you know, the, it teams, whether it's operations, software environments were completely oblivious to that because they were completely blindsided to it. And so the connectivity between kind of the inwards metrics that RT is using, whether it's database time, cycle time, or whatever metric we use in it are typically completely divorced from the business metrics. And so at its core, it's really about starting to align the business metrics with what the, the software delivery chain, right? This, uh, the system, which is really a core differentiator for these organizations. It's about connecting those two things and, and starting to, um, infuse some of the agile culture and principles. Um, that's emerged from the software side into the business side. Um, of course the lean movement and other movements have started to change some of these dynamic on the, on the business side. And so I think this, this is the moment where we are starting to see kind of the imperative to transform. Now, you know, Covina obviously has been a key driver for that. The, um, the technology is right to start to be able to weave data together and really kind of, uh, also the cultural shifts, uh, Prue agile through dev ops through, uh, the SRE movement, uh frulein um, business transformation, all these things are coming together and that are really creating kind of the conditions for the BizOps manifesto to exist. >>So, uh, Clayton Christianson, great, uh, Harvard professor innovator's dilemma might still my all time favorite business books, you know, talks about how difficult it is for incumbents to react to, to disruptive change, right? Because they're always working on incremental change because that's what their customers are asking for. And there's a good ROI when you talk about, you know, companies not measuring the right thing. I mean, clearly it has some portion of their budget that has to go to keeping the lights on, right. That that's always the case, but hopefully that's an, an ever decreasing percentage of their total activity. So, you know, what should people be measuring? I mean, what are kind of the new metrics, um, in, in biz ops that drive people to be looking at the right things, measuring the right things and subsequently making the right decisions, investment decisions on whether they should do, you know, move project a along or project B. >>So there, there are only two things, right? So, so I think what you're talking about is portfolio management, investment management, right. And, um, which, which is a key challenge, right? Um, in my own experience, right? Uh, driving strategy or a large scale kind of software organization for years, um, it's very difficult to even get kind of a base data as to who is doing what, uh, um, I mean, some of our largest customers we're engaged with right now are simply trying to get a very simple answer, which is how many people do I have and that specific initiative at any point in time, and just tracking that information is extremely difficult. So, and again, back to a product project management Institute, um, there, they have estimated that on average, it organizations have anywhere between 10 to 20% of their resources focused on initiatives, which are not strategically aligned. >>So, so that's one dimensional portfolio management. I think the key aspect though, that we are, we're really keen on is really around kind of the alignment of a business metrics to the it metrics. Um, so I'll use kind of two simple examples, right? And my background is around quality and I've always believed that the fitness for purpose is really kind of a key, um, uh, philosophy if you will. And so if you start to think about quality as fitness for purpose, you start to look at it from a customer point of view, right. And fitness for purpose for a core banking application or mobile application are different, right? So the definition of a business value that you're trying to achieve is different. Um, and so the, and yeah, if you look at our, it, operations are operating there, we're using kind of a same type of, uh, kind of inward metrics, uh, like a database off time or a cycle time, or what is my point of velocity, right? >>And so the challenge really is this inward facing metrics that it is using, which are divorced from ultimately the outcome. And so, you know, if I'm, if I'm trying to build a poor banking application, my core metric is likely going to be uptight, right? If I'm trying to build a mobile application or maybe your social, a mobile app, it's probably going to be engagement. And so what you want is for everybody across it, to look at these metric and what are the metrics within the software delivery chain, which ultimately contribute to that business metric. And some cases cycle time may be completely irrelevant, right? Again, my core banking app, maybe I don't care about cycle time. And so it's really about aligning those metrics and be able to start to, um, Charles you mentioned, uh, around the, the, um, uh, around the disruption that we see is, or the investors is the dilemma now is really around the fact that many it organizations are essentially applying the same approaches of, for innovation, like for basically scrap work, then they would apply to kind of over more traditional projects. And so, you know, there's been a lot of talk about two-speed it, and yes, it exists, but in reality are really organizations, um, truly differentiating, um, all of the operate, their, their projects and products based on the outcomes that they're trying to achieve. And this is really where BizOps is trying to affect. >>I love that, you know, again, it doesn't seem like brain surgery, but focus on the outcomes, right. And it's horses for courses, as you said, this project, you know, what you're measuring and how you define success, isn't necessarily the same as, as on this other project. So let's talk about some of the principles we talked about the values, but, you know, I think it's interesting that, that, that the BizOps coalition, you know, just basically took the time to write these things down and they don't seem all that super insightful, but I guess you just got to get them down and have them on paper and have them in front of your face. But I want to talk about, you know, one of the key ones, which you just talked about, which is changing requirements, right. And working in a dynamic situation, which is really what's driven, you know, this, the software to change in software development, because, you know, if you're in a game app and your competitor comes out with a new blue sword, you got to come out with a new blue sword. >>So whether you had that on your Kanban wall or not. So it's, it's really this embracing of the speed of change and, and, and, and making that, you know, the rule, not the exception. I think that's a phenomenal one. And the other one you talked about is data, right? And that today's organizations generate more data than humans can process. So informed decisions must be generated by machine learning and AI, and, you know, in the, the big data thing with Hadoop, you know, started years ago, but we are seeing more and more that people are finally figuring it out, that it's not just big data, and it's not even generic machine learning or artificial intelligence, but it's applying those particular data sets and that particular types of algorithms to a specific problem, to your point, to try to actually reach an objective, whether that's, you know, increasing the, your average ticket or, you know, increasing your checkout rate with, with, with shopping carts that don't get left behind in these types of things. So it's a really different way to think about the world in the good old days, probably when you got started, when we had big, giant, you know, MRDs and PRDs and sat down and coded for two years and came out with a product release and hopefully not too many patches subsequently to that. >>It's interesting. Right. Um, again, back to one of these surveys that we did with, uh, with about 600, the ITA executives, and, uh, and, and we, we purposely designed those questions to be pretty open. Um, and, and one of them was really wrong requirements and, uh, and it was really a wrong, uh, kind of what do you, what is the best approach? What is your preferred approach towards requirements? And if I were to remember correctly, over 80% of the it executives set that the best approach they'll prefer to approach these core requirements to be completely defined before software development starts, let me pause there we're 20 years after the agile manifesto, right? And for 80% of these idea executives to basically claim that the best approach is for requirements to be fully baked before salt, before software development starts, basically shows that we still have a very major issue. >>And again, our hypothesis in working with many organizations is that the key challenge is really the boundary between business and it, which is still very much contract based. If you look at the business side, they basically are expecting for it deliver on time on budget, right. But what is the incentive for it to actually delivering on the business outcomes, right? How often is it measured on the business outcomes and not on an SLA or on a budget type criteria? And so that's really the fundamental shift that we need to, we really need to drive up as an industry. Um, and you know, we, we talk about kind of this, this imperative for organizations to operate that's one, and back to the, the, um, you know, various Doris dilemna the key difference between these larger organization is, is really kind of, uh, if you look at the amount of capital investment that they can put into pretty much anything, why are they losing compared to, um, you know, startups? What, why is it that, uh, more than 40% of, uh, personal loans today or issued not by your traditional brick and mortar banks, but by, um, startups? Well, the reason, yes, it's the traditional culture of doing incremental changes and not disrupting ourselves, which Christiansen covered the length, but it's also the inability to really fundamentally change kind of a dynamic picture. We can business it and, and, and partner right. To, to deliver on a specific business outcome. >>All right. I love that. That's a great, that's a great summary. And in fact, getting ready for this interview, I saw you mentioning another thing where, you know, the, the problem with the agile development is that you're actually now getting more silos. Cause you have all these autonomous people working, you know, kind of independently. So it's even a harder challenge for, for the business leaders to, to, as you said, to know, what's actually going on, but, but certainly I w I want to close, um, and talk about the coalition. Um, so clearly these are all great concepts. These are concepts you want to apply to your business every day. Why the coalition, why, you know, take these concepts out to a broader audience, including either your, your competition and the broader industry to say, Hey, we, as a group need to put a stamp of approval on these concepts, these values, these principles. >>So first I think we, we want, um, everybody to realize that we are all talking about the same things, the same concepts. I think we were all from our own different vantage point, realizing that things after change, and again, back to, you know, whether it's value stream management or site reliability engineering, or biz ops, we're all kind of using slightly different languages. Um, and so I think one of the important aspects of BizOps is for us, all of us, whether we're talking about, you know, consulting agile transformation experts, uh, whether we're talking about vendors, right, provides kind of tools and technologies or these large enterprises to transform for all of us to basically have kind of a reference that lets us speak around kind of, um, in a much more consistent way. The second aspect is for, to me is for, um, DS concepts to start to be embraced, not just by us or trying, or, you know, vendors, um, system integrators, consulting firms, educators, thought leaders, but also for some of our old customers to start to become evangelists of their own in the industry. >>So we, our, our objective with the coalition needs to be pretty, pretty broad. Um, and our hope is by, by starting to basically educate, um, our, our joint customers or partners, that we can start to really foster these behaviors and start to really change some of dynamics. So we're very pleased at if you look at, uh, some of the companies which have joined the, the, the, the manifesto. Um, so we have vendors such as desktop or advance, or, um, uh, PagerDuty for instance, or even planned view, uh, one of my direct competitors, um, but also thought leaders like Tom Davenport or, uh, or cap Gemini or, um, um, smaller firms like, uh, business agility, institutes, or agility elf. Um, and so our goal really is to start to bring together, uh, fall years, people would have been LP, large organizations, do digital transformation vendors. We're providing the technologies that many of these organizations use to deliver on this digital preservation and for all of us to start to provide the kind of, uh, education support and tools that the industry needs. Yeah, >>That's great surge. And, uh, you know, congratulations to you and the team. I know this has been going on for a while, putting all this together, getting people to sign onto the manifesto, putting the coalition together, and finally today getting to unveil it to the world in, in a little bit more of a public, uh, opportunity. So again, you know, really good values, really simple principles, something that, that, uh, shouldn't have to be written down, but it's nice cause it is, and now you can print it out and stick it on your wall. So thank you for, uh, for sharing this story and again, congrats to you and the team. >>Thank you. Thanks, Jeff. Appreciate it. >>Oh, my pleasure. Alrighty, surge. If you want to learn more about the BizOps manifest to go to biz ops manifesto.org, read it and you can sign it and you can stay here for more coverage. I'm the cube of the biz ops manifesto unveiled. Thanks for watching. See you next >>From around the globe. It's the cube with digital coverage of this ops manifesto unveiled brought to you by bill. >>Hey, welcome back, everybody Jeffrey here with the cube. Welcome back to our ongoing coverage of the biz ops manifesto unveiling. It's been in the works for awhile, but today's the day that it actually kind of come out to the, to the public. And we're excited to have a real industry luminary here to talk about what's going on, why this is important and share his perspective. And we're happy to have from Cape Cod, I believe is Tom Davenport. He is a distinguished author and professor at Babson college. We could go on, he's got a lot of great titles and, and really illuminary in the area of big data and analytics Thomas. Great to see you. >>Thanks Jeff. Happy to be here with you. >>Great. So let's just jump into it, you know, and getting ready for this. I came across your LinkedIn posts. I think you did earlier this summer in June and right off the bat, the first sentence just grabbed my attention. I'm always interested in new attempts to address longterm issues, uh, in how technology works within businesses, biz ops. What did you see in biz ops, uh, that, that kind of addresses one of these really big longterm problems? >>Well, yeah, but the longterm problem is that we've had a poor connection between business people and it people between business objectives and the, it solutions that address them. This has been going on, I think since the beginning of information technology and sadly it hasn't gone away. And so biz ops is a new attempt to deal with that issue with, you know, a new framework, eventually a broad set of solutions that increase the likelihood that we'll actually solve a business problem with an it capability. >>Right. You know, it's interesting to compare it with like dev ops, which I think a lot of people are probably familiar with, which was, you know, built around, uh, agile software development and a theory that we want to embrace change that that changes. Okay. Uh, and we want to be able to iterate quickly and incorporate that. And that's been happening in the software world for, for 20 plus years. What's taken so long to get that to the business side, because as the pace of change has changed on the software side, you know, that's a strategic issue in terms of execution on the business side that they need now to change priorities. And, you know, there's no PRDs and MRDs and big, giant strategic plans that sit on the shelf for five years. That's just not the way business works anymore. It took a long time to get here. >>Yeah, it did. And you know, there have been previous attempts to make a better connection between business and it, there was the so called alignment framework that a couple of friends of mine from Boston university developed, I think more than 20 years ago, but you know, now we have better technology for creating that linkage. And the, you know, the idea of kind of ops oriented frameworks is pretty pervasive now. So I think it's time for another serious attempt at it. Right. >>And do you think doing it this way, right. With the, with the biz ops coalition, you know, getting a collection of, of, of kind of likeminded individuals and companies together, and actually even having a manifesto, which we're making this declarative statement of, of principles and values, you think that's what it takes to kind of drive this kind of beyond the experiment and actually, you know, get it done and really start to see some results in, in, uh, in production in the field. >>I think certainly, um, no one vendor organization can pull this off single handedly. It does require a number of organizations collaborating and working together. So I think our coalition is a good idea and a manifesto is just a good way to kind of lay out what you see as the key principles of the idea. And that makes it much easier for everybody to understand and act on. >>I think it's just, it's really interesting having, you know, having them written down on paper and having it just be so clearly articulated both in terms of the, of the values as well as, as the, uh, the principles and the values, you know, business outcomes matter trust and collaboration, data driven decisions, which is the number three or four, and then learn, respond and pivot. It doesn't seem like those should have to be spelled out so clearly, but, but obviously it helps to have them there. You can stick them on the wall and kind of remember what your priorities are, but you're the data guy. You're the analytics guy, uh, and a big piece of this is data and analytics and moving to data-driven decisions. And principle number seven says, you know, today's organizations generate more data than humans can process and informed decisions can be augmented by machine learning and artificial intelligence right up your alley. You know, you've talked a number of times on kind of the mini stages of analytics. Um, and how has that's evolved over, over time, you know, as you think of analytics and machine learning, driving decisions beyond supporting decisions, but actually starting to make decisions in machine time. What's that, what's that thing for you? What does that make you, you know, start to think, wow, this is, this is going to be pretty significant. >>Yeah. Well, you know, this has been a longterm interest of mine. Um, the last generation of AI, I was very interested in expert systems. And then, um, I think, uh, more than 10 years ago, I wrote an article about automated decision-making using what was available then, which was rule-based approaches. Um, but you know, this addresses an issue that we've always had with analytics and AI. Um, you know, we, we tended to refer to those things as providing decision support. The problem is that if the decision maker didn't want their support, didn't want to use them in order to make a decision, they didn't provide any value. And so the nice thing about automating decisions, um, with now contemporary AI tools is that we can ensure that data and analytics get brought into the decision without any possible disconnection. Now, I think humans still have something to add here, and we often will need to examine how that decision is being made and maybe even have the ability to override it. But in general, I think at least for, you know, repetitive tactical decisions, um, involving a lot of data, we want most of those, I think to be at least recommended if not totally made by an algorithm or an AI based system, and that I believe would add to the quality and the precision and the accuracy of decisions and in most organizations, >>No, I think, I think you just answered my next question before I, before Hey, asked it, you know, we had dr. Robert Gates on a former secretary of defense on a few years back, and we were talking about machines and machines making decisions. And he said at that time, you know, the only weapon systems, uh, that actually had an automated trigger on it were on the North Korea and South Korea border. Um, everything else, as you said, had to go through a sub person before the final decision was made. And my question is, you know, what are kind of the attributes of the decision that enable us to more easily automated? And then how do you see that kind of morphing over time, both as the data to support that as well as our comfort level, um, enables us to turn more and more actual decisions over to the machine? >>Well, yeah, it's suggested we need, um, data and, um, the data that we have to kind of train our models has to be high quality and current. And we, we need to know the outcomes of that data. You know, um, most machine learning models, at least in business are supervised. And that means we need to have labeled outcomes in the, in the training data. But I, you know, um, the pandemic that we're living through is a good illustration of the fact that, that the data also have to be reflective of current reality. And, you know, one of the things that we're finding out quite frequently these days is that, um, the data that we have do not reflect, you know, what it's like to do business in a pandemic. Um, I wrote a little piece about this recently with Jeff cam at wake forest university, we called it data science quarantined, and we interviewed with somebody who said, you know, it's amazing what eight weeks of zeros will do to your demand forecast. We just don't really know what happens in a pandemic. Um, our models maybe have to be put on the shelf for a little while and until we can develop some new ones or we can get some other guidelines into making decisions. So I think that's one of the key things with automated decision making. We have to make sure that the data from the past and that's all we have of course, is a good guide to, you know, what's happening in the present and the future as far as we understand it. Yeah. >>I used to joke when we started this calendar year 2020, it was finally the year that we know everything with the benefit of hindsight, but it turned out 20, 20 a year. We found out we actually know nothing and everything thought we knew, but I wanna, I wanna follow up on that because you know, it did suddenly change everything, right? We got this light switch moment. Everybody's working from home now we're many, many months into it, and it's going to continue for a while. I saw your interview with Bernard Marr and you had a really interesting comment that now we have to deal with this change. We don't have a lot of data and you talked about hold fold or double down. And, and I can't think of a more, you know, kind of appropriate metaphor for driving the value of the BizOps when now your whole portfolio strategy, um, these to really be questioned and, and, you know, you have to be really, uh, well, uh, executing on what you are, holding, what you're folding and what you're doubling down with this completely new environment. >>Well, yeah, and I hope I did this in the interview. I would like to say that I came up with that term, but it actually came from a friend of mine. Who's a senior executive at Genpact. And, um, I, um, used it mostly to talk about AI and AI applications, but I think you could, you could use it much more broadly to talk about your entire sort of portfolio of digital projects. You need to think about, well, um, given some constraints on resources and a difficult economy for a while, which of our projects do we want to keep going on pretty much the way we were and which ones are not that necessary anymore? You see a lot of that in AI, because we had so many pilots, somebody told me, you know, we've got more pilots around here than O'Hare airport and AI. Um, and then, but the ones that involve double down they're even more important to you. They are, you know, a lot of organizations have found this out in the pandemic, on digital projects. It's more and more important for customers to be able to interact with you, um, digitally. And so you certainly wouldn't want to, um, cancel those projects or put them on hold. So you double down on them and get them done faster and better. >>Right, right. Uh, another, another thing that came up in my research that you quoted, um, was, was from Jeff Bezos, talking about the great bulk of what we do is quietly, but meaningfully improving core operations. You know, I think that is so core to this concept of not AI and machine learning and kind of the general sense, which, which gets way too much buzz, but really applied right. Applied to a specific problem. And that's where you start to see the value. And, you know, the, the BizOps, uh, manifesto is, is, is calling it out in this particular process. But I'd love to get your perspective as you know, you speak generally about this topic all the time, but how people should really be thinking about where are the applications where I can apply this technology to get direct business value. >>Yeah, well, you know, even talking about automated decisions, um, uh, the kind of once in a lifetime decisions, uh, the ones that, um, ag Lafley, the former CEO of Procter and gamble used to call the big swing decisions. You only get a few of those. He said in your tenure as CEO, those are probably not going to be the ones that you're automating in part because, um, you don't have much data about them. You're only making them a few times and in part, because, um, they really require that big picture thinking and the ability to kind of anticipate the future, that the best human decision makers, um, have. Um, but, um, in general, I think where they are, the projects that are working well are, you know, when I call the low hanging fruit ones, the, some people even report to it referred to it as boring AI. >>So, you know, sucking data out of a contract in order to compare it to a bill of lading for what arrived at your supply chain companies can save or make a lot of money with that kind of comparison. It's not the most exciting thing, but AI, as you suggested is really good at those narrow kinds of tasks. It's not so good at the, at the really big moonshots, like curing cancer or, you know, figuring out well what's the best stock or bond under all circumstances or even autonomous vehicles. Um, we, we made some great progress in that area, but everybody seems to agree that they're not going to be perfect for quite a while. And we really don't want to be driving around on them very much unless they're, you know, good and all kinds of weather and with all kinds of pedestrian traffic and you know, that sort of thing, right? That's funny you bring up contract management. >>I had a buddy years ago, they had a startup around contract management and was like, and this was way before we had the compute power today and cloud proliferation. I said, you know, how, how can you possibly build software around contract management? It's language, it's legal, ease. It's very specific. And he's like, Jeff, we just need to know where's the contract. And when does it expire? And who's the signatory. And he built a business on those, you know, very simple little facts that weren't being covered because their contracts contractor in people's drawers and files and homes, and Lord only knows. So it's really interesting, as you said, these kind of low hanging fruit opportunities where you can extract a lot of business value without trying to, you know, boil the ocean. >>Yeah. I mean, if you're Amazon, um, uh, Jeff Bezos thinks it's important to have some kind of billion dollar projects. And he even says it's important to have a billion dollar failure or two every year. But I think most organizations probably are better off being a little less aggressive and, you know, sticking to, um, what AI has been doing for a long time, which is, you know, making smarter decisions based on, based on data. >>Right? So Tom, I want to shift gears one more time before, before we let you go on on kind of a new topic for you, not really new, but you know, not, not a, the vast majority of, of your publications and that's the new way to work, you know, as, as the pandemic hit in mid March, right. And we had this light switch moment, everybody had to work from home and it was, you know, kind of crisis and get everybody set up. Well, you know, now we're five months, six months, seven months. A number of companies have said that people are not going to be going back to work for a while. And so we're going to continue on this for a while. And then even when it's not what it is now, it's not going to be what it was before. So, you know, I wonder, and I know you, you, uh, you teased, you're working on a new book, you know, some of your thoughts on, you know, kind of this new way to work and, and the human factors in this new, this new kind of reality that we're kind of evolving into, I guess. >>Yeah. I missed was an interest of mine. I think, um, back in the nineties, I wrote an article called, um, a coauthored, an article called two cheers for the virtual office. And, you know, it was just starting to emerge. Then some people were very excited about it. Some people were skeptical and, uh, we said two cheers rather than three cheers because clearly there's some shortcomings. And, you know, I keep seeing these pop up. It's great that we can work from our homes. It's great that we can accomplish most of what we need to do with a digital interface, but, um, you know, things like innovation and creativity and certainly, um, uh, a good, um, happy social life kind of requires some face to face contact every now and then. And so I, you know, I think we'll go back to an environment where there is some of that. >>Um, we'll have, um, times when people convene in one place so they can get to know each other face to face and learn from each other that way. And most of the time, I think it's a huge waste of people's time to commute into the office every day and to jump on airplanes, to, to, um, give every little, um, uh, sales call or give every little presentation. Uh, we just have to really narrow down what are the circumstances where face to face contact really matters. And when can we get by with, with digital, you know, I think one of the things in my current work I'm finding is that even when you have AI based decision making, you really need a good platform in which that all takes place. So in addition to these virtual platforms, we need to develop platforms that kind of structure the workflow for us and tell us what we should be doing next, then make automated decisions when necessary. And I think that ultimately is a big part of biz ops as well. It's not just the intelligence of an AI system, but it's the flow of work that kind of keeps things moving smoothly throughout your organization. >>Yeah. I think such, such a huge opportunity as you just said, cause I forget the stats on how often we're interrupted with notifications between email texts, Slack, a sauna, Salesforce, the list goes on and on. So, you know, to put an AI layer between the person and all these systems that are begging for attention, and you've written a book on the attention economy, which is a whole nother topic, we'll say for another day, you know, it really begs, it really begs for some assistance because you know, you just can't get him picked, you know, every two minutes and really get quality work done. It's just not, it's just not realistic. And you know what? I don't think that's a feature that we're looking for. I agree. Totally. Alright, Tom. Well, thank you so much for your time. Really enjoyed the conversation. I gotta dig into the library. It's very long. So I might start at the attention economy. I haven't read that one in to me. I think that's the fascinating thing in which we're living. So thank you for your time and, uh, great to see you. >>My pleasure, Jeff. Great to be here. >>All right. Take care. Alright. He's Tom I'm Jeff. You are watching the continuing coverage of the biz ops manifesto and Vale. Thanks for watching the cube. We'll see you next time.

>>From around the globe. It's the cube with digital coverage, a BizOps manifesto unveiled brought to you by biz ops coalition. >>Hey, welcome back everybody. Jeff Frick here with the cube. Welcome back to our ongoing coverage of the biz ops manifesto. Unveil. Something has been in the works for a little while. Today's the formal unveiling, and we're excited to have three of the core of founding members of the manifesto authors of the manifesto. If you will, uh, joining us again, we've had them all on individually. Now we're going to have a great power panel first up. We're gab Mitt, Kirsten returning he's the founder and CEO of Tasktop mic. Good to see you again. Where are you dialing in from? >>Great to see you again, Jeff I'm dialing from Vancouver, >>We're Canada, Vancouver, Canada. One of my favorite cities in the whole wide world. Also we've got Tom Davenport come in from across the country. He's a distinguished professor and author from Babson college, Tom. Great to see you. And I think you said you're at a fun, exotic place on the East coast >>Realm of Memphis shoe sits on Cape Cod. >>Great to see you again and also joining surge Lucio. He is the VP and general manager enterprise software division at Broadcom surge. Great to see you again, where are you coming in from? >>Uh, from Boston right next to kickoff. >>Terrific. So welcome back, everybody again. Congratulations on this day. I know it's, it's been a lot of work to get here for this unveil, but let's just jump into it. The biz ops manifesto, what was the initial reason to do this? And how did you decide to do it in a kind of a coalition, a way bringing together a group of people versus just making it an internal company, uh, initiative that, you know, you can do better stuff within your own company, surge, why don't we start with you? >>Yeah, so, so I think we were at a really critical juncture, right? Many, um, large enterprises are basically struggling with their digital transformation. Um, in fact, um, many recognize that, uh, the, the business side, it collaboration has been, uh, one of the major impediments, uh, to drive that kind of transformation. And if we look at the industry today, many people are, whether we're talking about vendors or, um, you know, system integrators, consulting firms are talking about the same kind of concepts, but using very different language. And so we believe that bringing all these different players together, um, as part of the coalition and formalizing, uh, basically the core principles and values in a BizOps manifesto, we can really start to F could have a much bigger movement where we can all talk about kind of the same concepts and we can really start to provide, could have a much better support for large organizations to transform. Uh, so whether it is technology or services or, um, we're training, I think that that's really the value of bringing all of these players together, right. >>And Nick to you, why did you get involved in this, in this effort? >>So Ben close and follow the agile movement since it started two decades ago with that manifesto. >>And I think we got a lot of improvement at the team level, and I think as satisfies noted, uh, we really need to improve at the business level. Every company is trying to become a software innovator, uh, trying to make sure that they can adapt quickly and the changing market economy and what everyone's dealing with in terms of needing to deliver the customer sooner. However, agile practices have really focused on these metrics, these measures and understanding processes that help teams be productive. Those things now need to be elevated to the business as a whole. And that just hasn't happened. Uh, organizations are actually failing because they're measuring activities and how they're becoming more agile, how teams are functioning, not how much quickly they're delivering value to the customer. So we need to now move past that. And that's exactly what the that's manifested provides. Right, >>Right, right. And Tom, to you, you've been covering tech for a very long time. You've been looking at really hard challenges and a lot of work around analytics and data and data evolution. So there's a definitely a data angle here. I wonder if you could kind of share your perspective of what you got excited to, uh, to sign onto this manifesto. >>Sure. Well, I have, you know, for the past 15 or 20 years, I've been focusing on data and analytics and AI, but before that I was a process management guy and a knowledge management guy. And in general, I think, you know, we've just kind of optimized that to narrow a level, whether you're talking about agile or dev ops or ML ops, any of these kinds of ops oriented movements, we're making individual project, um, performance and productivity better, but we're not changing the business, uh, effectively enough. And that's the thing that appealed to me about the biz ops idea that we're finally creating a closer connection between what we do with technology and how it changes the business and provides value to it. >>Great. Uh, surge back to you, right? I mean, people have been talking about digital transformation for a long time and it's been, you know, kind of trucking along and then covert hit and it was instant lights, which everyone's working from home. You've got a lot more reliance on your digital tools, digital communication, uh, both within your customer base and your partner base, but also then your employees when you're, if you could share how that really pushed this all along. Right? Because now suddenly the acceleration of digital transformation is higher. Even more importantly, you got much more critical decisions to make into what you do next. So kind of your portfolio management of projects has been elevated significantly when maybe revenues are down, uh, and you really have to, uh, to prioritize and get it right. >>Yeah. Maybe I'll just start by quoting Satina Nello basically recently said that they're speeding the two years of digital preservation just last two months in any many ways. That's true. Um, but, but yet when we look at large enterprises, they're >>Still struggling with the kind of a changes in culture that they really need to drive to be able to disrupt themselves. And not surprisingly, you know, when we look at certain parts of the industry, you know, we see some things which are very disturbing, right? So about 40% of the personal loans today, or being, uh, origin data it's by fintechs, uh, of a like of Sophie or, uh, or a lending club, right? Not to a traditional brick and mortar for BEC. And so the, well, there is kind of a much more of an appetite and it's a, it's more of a survival type of driver these days. Uh, the reality is that's in order for these large enterprises to truly transform and engage with this digital transformation, they need to start to really align the business. And it, you know, in many ways, uh, make covered that agile really emerged from the core desire to truly improve software predictability between which we've really missed is all that we, we start to aligning the software predictability to business predictability and to be able to have continual sleep continuous improvement and measurement of business outcomes. So by aligning kind of these, uh, kind of inward metrics, that's, it is typically being using to business outcomes. We think we can start to really ELP different stakeholders within the organization to collaborate. So I think there is more than ever. There's an imperative to act now. Um, and, and resolves, I think is kind of the right approach to drive that transformation. Right. >>I want to follow up on the culture comment, uh, with Utah, because you've talked before about kind of process flow and process flow throughout a whore and an organization. And, you know, we talk about people process and tech all the time. And I think the tech is the easy part compared to actually changing the people the way they think. And then the actual processes that they put in place. It's a much more difficult issue than just the tech issue to get this digital transformation in your organization. >>Yeah. You know, I've always found that the soft stuff about, you know, the culture of the behavior, the values is the hard stuff to change and more and more, we, we realized that to be successful with any kind of digital transformation you have to change people's behaviors and attitudes. Um, we haven't made as much progress in that area as we might have. I mean, I've done some surveys suggesting that, um, most organizations still don't have data-driven cultures. And in many cases there is a lower percentage of companies that say they have that then, um, did a few years ago. So we're kind of moving in the wrong direction, which means I think that we have to start explicitly addressing that, um, cultural, behavioral dimension and not just assuming that it will happen if we, if we build a system, >>If we build it, they won't necessarily come. Right. >>Right. So I want to go to, to you Nick cause you know, we're talking about workflows and flow, um, and, and you've written about flow both in terms of, um, you know, moving things along a process and trying to find bottlenecks, identify bottlenecks, which is now even more important again, when these decisions are much more critical. Cause you have a lot less, uh, wiggle room in tough times, but you also talked about flow from the culture side and the people side. So I wonder if you can just share your thoughts on, you know, using flow as a way to think about things, to get the answers better. >>Yeah, absolutely. And I'll refer back to what Tom has said. If you're optimized, you need to optimize your system. You need to optimize how you innovate and how you deliver value to the business and the customer. Now, what we've noticed in the data, since that we've learned from customers, value streams, enterprise organizations, value streams, is that when it's taking six months at the end to deliver that value with the flow is that slow. You've got a bunch of unhappy developers, unhappy customers when you're innovating house. So high performing organizations we can measure at antenna flow time and dates. All of a sudden that feedback loop, the satisfaction, your developers measurably, it goes up. So not only do you have people context, switching glass, you're delivering so much more value to customers at a lower cost because you've optimized for flow rather than optimizing for these, these other approximate tricks that we use, which is how efficient is my adult team. How quickly can we deploy software? Those are important, but they do not provide the value of agility of fast learning of adaptability to the business. And that's exactly what the biz ops manifesto pushes your organization to do. You need to put in place this new operating model that's based on flow on the delivery of business value and on bringing value to market much more quickly than you were before. Right. >>I love that. And I'm gonna back to you Tom, on that to follow up. Cause I think, I don't think people think enough about how they prioritize what they're optimizing for, because you know, if you're optimizing for a versus B, you know, you can have a very different product that, that you kick out. And, you know, my favorite example is with Clayton Christianson and innovator's dilemma talking about the three inch hard drive, if you optimize it for power, you know, is one thing, if you optimize it for vibration is another thing and sure enough, you know, they missed it on the poem because it was the, it was the game console, which, which drove that whole business. So when you're talking to customers and we think we hear it with cloud all the time, people optimizing for a cost efficiency, instead of thinking about it as an innovation tool, how do you help them kind of rethink and really, you know, force them to, to look at the, at the prioritization and make sure they're prioritizing on the right thing is make just that, what are you optimizing for? >>Oh yeah. Um, you have one of the most important aspects of any decision or attempt to resolve a problem in an organization is the framing process. And, um, you know, it's, it's a difficult aspect to have the decision to confirm it correctly in the first place. Um, there, it's not a technology issue. In many cases, it's largely a human issue, but if you frame >>That decision or that problem incorrectly to narrowly say, or you frame it as an either or situation where you could actually have some of both, um, it, it's very difficult for the, um, process to work out correctly. So in many cases, I think we need to think more at the beginning about how we bring this issue or this decision in the best way possible before we charge off and build a system to support it. You know, um, it's worth that extra time to think, think carefully about how the decision has been structured. Right, >>Sir, I want to go back to you and talk about the human factors because as we just discussed, you can put it in great technology, but if the culture doesn't adopt it and people don't feel good about it, you know, it's not going to be successful and that's going to reflect poorly on the technology, even if that had nothing to do with it. And you know, when you look at the, the, the, the core values, uh, of the Bezos manifesto, you know, a big one is trust and collaboration, you know, learn, respond, and pivot. Wonder if you can share your thoughts on, on trying to get that cultural shift, uh, so that you can have success with the people, or excuse me, with the technology in the process and helping customers, you know, take this more trustworthy and kind of proactive, uh, position. >>So I think, I think at the ground level, it truly starts with the realization that we're all different. We come from different backgrounds. Uh, oftentimes we tend to blame the data. It's not uncommon my experiments that we spend the first 30 minutes of any kind of one hour conversation to debate the validity of the data. Um, and so, um, one of the first kind of, uh, probably manifestations that we've had or revelations as we start to engage with our customers is spoke just exposing, uh, high-fidelity data sets to different stakeholders from their different lens. We start to enable these different stakeholders to not debate the data. That's really collaborate to find a solution. So in many ways, when, when, when we think about kind of the types of changes we're trying to, to truly affect around data driven decision making, he told about bringing the data in context and the context that is relevant and understandable for, for different stakeholders, whether we're talking about an operator or develop for a business analyst. >>So that's, that's the first thing. The second layer I think, is really to provide context to what people are doing in their specific silo. And so I think one of the best examples I have is if you start to be able to align business KPI, whether you are counting, you know, sales per hour, or the engagements of your users on your mobile applications, whatever it is, you can start to connect that PKI to business KPI, to the KPIs that developers might be looking at, whether it is all the number of defects or velocity or whatever over your metrics that you're used to, to actually track you start to be able to actually contextualize in what we are, the effecting, basically a metric of that that is really relevant. And then what we see is that this is a much more systematic way to approach the transformation than say, you know, some organizations kind of creating some of these new products or services or initiatives, um, to, to drive engagements, right? >>So if you look at zoom, for instance, zoom giving away a it service to, uh, to education, he's all about, I mean, there's obviously a marketing aspect in there, but it's, it's fundamentally about trying to drive also the engagement of their own teams. And because now they're doing something for good and many organizations are trying to do that, but you only can do this kind of things in the limited way. And so you really want to start to rethink how you connect to, everybody's kind of a business objective fruit data, and now you start to get people to stare at the same data from their own lens and collaborate on all the data. Right, >>Right. That's a good, uh, Tom, I want to go back to you. You've been studying it for a long time, writing lots of books and getting into it. Um, why now, you know, what, why, why now are we finally aligning business objectives with, with it objectives? You know, why didn't this happen before? And, you know, what are the factors that are making now the time for this, this, this move with the, uh, with the biz ops? >>Well, and much of a past, it was sort of a back office related activity. And, you know, it was important for, um, uh, producing your paychecks and, uh, capturing the customer orders, but the business wasn't built around it now, every organization needs to be a software business, a data business, a digital business, the auntie has been raised considerably. And if you aren't making that connection between your business objectives and the technology that supports it, you run a pretty big risk of, you know, going out of business or losing out to competitors. Totally. So, um, and even if you're in, uh, an industry that hasn't historically been terribly, um, technology oriented customer expectations flow from, uh, you know, the digital native, um, companies that they work with to basically every industry. So you're compared against the best in the world. So we don't really have the luxury anymore of screwing up our it projects or building things that don't really work for the business. Um, it's mission critical that we do that well. Um, almost every time, I just want to fall by that, Tom, >>In terms of the, you've talked extensively about kind of these evolutions of data and analytics from artismal stage to the big data stage, the data economy stage, the AI driven stage and what I find diff interesting that all those stages, you always put a start date, you never put an end date. Um, so you know, is the, is the big data I'm just going to use that generically a moment in time finally here where we're, you know, off mahogany row with the data scientists, but actually can start to see the promise of delivering the right insight to the right person at the right time to make that decision. >>Well, I think it is true that in general, these previous stages never seemed to go away. The, um, the artisinal stuff is still being done, but we would like for less and less of it to be artisinal, we can't really afford for everything to be artisinal anymore. It's too labor and, and time consuming to do things that way. So we shift more and more of it to be done through automation and B to be done with a higher level of productivity. And, um, you know, at some point maybe we reached the stage where we don't do anything artisanally anymore. I'm not sure we're there yet, but we are, we are making progress. Right. >>Right. And Mick, back to you in terms of looking at agile, cause you're, you're such a student of agile. When, when you look at the opportunity with biz ops and taking the lessons from agile, you know, what's been the inhibitor to stop this in the past. And what are you so excited about? You know, taking this approach will enable. >>Yeah. I think both search and Tom hit on this is that in agile what's happened is that we've been measuring tiny subsets of the value stream, right? We need to elevate the data's there. Developers are working on these tools that delivering features that the foundations for for great culture are there. I spent two decades as a developer. And when I was really happy is when I was able to deliver value to customers, the quicker I was able to do that the fewer impediments are in my way, that quicker was deployed and running in the cloud, the happier I was, and that's exactly what's happening. If we can just get the right data, uh, elevated to the business, not just to the agile teams, but really this, these values of ours are to make sure that you've got these data driven decisions with meaningful data that's oriented around delivering value to customers. Not only these legacies that Tom touched on, which has cost center metrics. So when, from where for it being a cost center and something that provided email and then back office systems. So we need to rapidly shift to those new, meaningful metrics that are customized business centric and make sure that every development the organization is focused on those as well as the business itself, that we're measuring value. And that will help you that value flow without interruptions. >>I love that mic. Cause if you don't measure it, you can't improve on it and you gotta, but you gotta be measuring the right thing. So gentlemen, uh, thank you again for, for your time. Uh, congratulations on the, uh, on the unveil of the biz ops manifesto and bringing together this coalition, uh, of, of, uh, industry experts to get behind this. And, you know, there's probably never been a more important time than now to make sure that your prioritization is in the right spot and you're not wasting resources where you're not going to get the ROI. So, uh, congratulations again. And thank you for sharing your thoughts with us here on the cube. >>Thank you. >>Alright, so we had surge Tom and Mick I'm. Jeff, you're watching the cube. It's a biz ops manifesto unveil. Thanks for watching. We'll see you next time >>From around the globe. It's the cube with digital coverage of biz ops manifesto unveiled brought to you by biz ops coalition. >>Hey, welcome back. Variety. Jeff Frick here with the cube. We're in our Palo Alto studios, and we'd like to welcome you back to our continuing coverage of biz ops manifesto unveil some exciting day to really, uh, kind of bring this out into public. There's been a little bit of conversation, but today's really the official unveiling and we're excited to have our next guest is share a little bit more information on it. He's Patrick tickle. He's a chief product officer for planned view. Patrick. Great to see you. >>Yeah, it's great to be here. Thanks for the invite. So why >>The biz ops manifesto, why the biz ops coalition now when you guys have been at it, it's relatively mature marketplace businesses. Good. What was missing? Why, why this, why this coalition? >>Yeah. So, you know, again, why is, why is biz ops important and why is this something that I'm, you know, I'm so excited about, but I think companies as well, right? Well, no, in some ways or another, this is a topic that I've been talking to the market and our customers about for a long time. And it's, you know, I really applaud this whole movement. Right. And, um, it resonates with me because I think one of the fundamental flaws, frankly, of the way we have talked about technology and business literally for decades, uh, has been this idea of, uh, alignment. Those who know me, I occasionally get off on this little rant about the word alignment, right. But to me, the word alignment is, is actually indicative of the, of the, of the flaw in a lot of our organizations and biz ops is really, I think now trying to catalyze and expose that flaw. >>Right. Because, you know, I always say that, you know, you know, alignment implies silos, right. Instantaneously, as soon as you say there's alignment, there's, there's obviously somebody who's got a direction and other people that have to line up and that kind of siloed, uh, nature of organizations then frankly, the passive nature of it. Right. I think so many technology organizations are like, look, the business has the strategy you guys need to align. Right. And, and, you know, as a product leader, right. That's where I've been my whole career. Right. I can tell you that I never sit around. I almost never use the word alignment. Right. I mean, whether, you know, I never sit down and say, you know, the product management team has to get aligned with dev, right. Or the dev team has to get aligned with the delivery and ops teams. I mean, what I say is, you know, are we on strategy, right? >>Like we've, we have a strategy as a, as a full end to end value stream. Right. And that there's no silos. And I mean, look, every on any given day we got to get better. Right. But the context, the context we operate is not about alignment. Right. It's about being on strategy. And I think I've talked to customers a lot about that, but when I first read the manifesto, I was like, Oh yeah, this is exactly. This is breaking down. Maybe trying to eliminate the word alignment, you know, from a lot of our organizations, because we literally start thinking about one strategy and how we go from strategy to delivery and have it be our strategy, not someone else's that we're all aligning to. And I, and it's a great way to catalyze that conversation that I've, it's been in my mind for years, to be honest. Right. >>So, so much to unpack there. One of the things obviously, uh, stealing a lot from, from dev ops and the dev ops manifesto from 20 years ago. And, and as I look through some of the principles and I looked through some of the values, which are, you know, really nicely laid out here, you know, satisfy customer, do continuous delivery, uh, measure, output against real results. Um, the ones that, that jumps out though is really about, you know, change, change, right? Requirements should change frequently. They do change frequently, but I'm curious to get your take from a, from a software development point, it's easy to kind of understand, right. We're making this widget and our competitors, beta widget plus X, and now we need to change our plans and make sure that the plus X gets added to the plan. Maybe it wasn't in the plan, but you talked a lot about product strategy. So in this kind of continuous delivery world, how does that meld with, I'm actually trying to set a strategy, which implies the direction for a little bit further out on the horizon and to stay on that while at the same time, you're kind of doing this real time continual adjustments because you're not working off a giant PRD or MRD anymore. >>Yeah, yeah, totally. Yeah. You know, one of the terms, you know, that we use internally a lot and even with my customers, our customers is we talk about this idea of rewiring, right. And I think, you know, it's kind of a, now an analogy for transformation. And I think a lot of us have to rewire the way we think about things. Right. And I think at Planview where we have a lot of customers who live in that, you know, who operationalize that traditional PPM world. Right. And are shifting to agile and transforming that rewire is super important. And, and to your point, right, it's, you've just, you've got to embrace this idea of, you know, just iterative getting better every day and iterating, iterating, iterating as opposed to building annual plans or, you know, I get customers occasionally who asked me for two or three year roadmap. >>Right. And I literally looked at them and I go, there's no, there's no scenario where I can build a two or three year roadmap. Right. You, you, you think you want that, but that's not, that's not the way we run. Right. And I will tell you the biggest thing that for us, you know, that I think is matched the planning, uh, you know, patents is a word I like to use a lot. So the thing that we've like, uh, that we've done from a planning perspective, I think is matched impedance to continuous delivery is instituting the whole program, implement, you know, the program, increment planning, capabilities, and methodologies, um, in the scaled agile world. Right. And over the last 18 months to two years, we really have now, you know, instrumented our company across three value streams. You know, we do quarterly PI program increment 10 week planning, you know, and that becomes, that becomes the Terra firma of how we plan. >>Right. And it's, what are we doing for the next 10 weeks? And we iterate within those 10 weeks, but we also know that 10 weeks from now, we're gonna, we're gonna adjust iterate again. Right. And that shifting of that planning model to, you know, to being as cross-functional is that as that big room planning kind of model is, um, and also, uh, you know, on that shorter increment, when you get those two things in place, also the impedance really starts to match up, uh, with continuous delivery and it changes, it changes the way you plan and it changes the way you work. Right? >>Yeah. Their thing. Right. So obviously a lot of these things are kind of process driven, both within the values, as well as the principles, but there's a whole lot, really about culture. And I just want to highlight a couple of the values, right? We already talked about business outcomes, um, trust and collaboration, uh, data driven decisions, and then learn, respond and pivot. Right. A lot of those are cultural as much as they are process. So again, is it the, is it the need to really kind of just put them down on paper and, you know, I can't help, but think of, you know, the hammer and up the, a, the thing in the Lutheran church with it, with their manifesto, is it just good to get it down on paper? Because when you read these things, you're like, well, of course we should trust people. And of course we need an environment of collaboration and of course we want data driven decisions, but as we all know saying it and living, it are two very, very different things. >>Yeah. Good question. I mean, I think there's a lot of ways to bring that to life you're right. And just hanging up, you know, I think we've all been through the hanging up posters around your office, which these days, right. Unless you're going to hang a poster in everybody's home office. Right. You can't even, you can't even fake it that you think that might work. Right. So, um, you know, you really, I think we've attacked that in a variety of ways. Right. And you definitely have to, you know, you've got to make the shift to a team centric culture, right. Empowered teams, you know, that's a big deal. Right. You know, a lot of, a lot of the people that, you know, we lived in a world of quote, unquote work. We lived in a deep resource management world for a long, long time, and right. >>A lot of our customers still do that, but, you know, kind of moving to that team centric world is, uh, is really important and core to the trust. Um, I think training is super important, right. I mean, we've, you know, we've internally, right. We've trained hundreds employees over the last a year and a half on the fundamentals really of safe. Right. Not necessarily, you know, we've had, we've had teams delivering in scrum and the continuous delivery for, you know, for years, but the scaling aspect of it, uh, is where we've done a lot of training investment. Um, and then, you know, I think a leadership has to be bought in. Right. You know? And so when we pie plan, you know, myself and Cameron and the other members of our leadership, you know, we're NPI planning, you know, for, for four days. Right. I mean, it's, it's, you've got to walk the walk, you know, from top to bottom and you've got to train on the context. Right. And then you, and then, and, and then once you get through a few cycles where you've done a pivot, right. Or you brought a new team in, and it just works, it becomes kind of this virtuous circle where he'll go, man, this really works so much better than what we used to do. Right. >>Right. The other really key principle to this whole thing is, is aligning, you know, the business leaders and the business prioritization, um, so that you can get to good outcomes with the development and the delivery. Right. And we know again, and kind of classic dev ops to get the dev and the production people together. So they can, you know, quickly ship code that works. Um, but adding the business person on there really puts, puts a little extra responsibility that they, they understand the value of a particular feature or particular priority. Uh, they, they can make the, the, the trade offs and that they kind of understand the effort involved too. So, you know, bringing them into this continuous again, kind of this continuous development process, um, to make sure that things are better aligned and really better prioritize. Cause ultimately, you know, we don't live in an infinite resources situation and people gotta make trade offs. They gotta make decisions as to what goes and what doesn't go in for everything that goes. Right. I always say you pick one thing. Okay. That's 99 other things that couldn't go. So it's really important to have, you know, this, you said alignment of the business priorities as well as, you know, the execution within, within the development. >>Yeah. I think that, you know, uh, you know, I think it was probably close to two years ago. Forester started talking about the age of the customer, right. That, that was like their big theme at the time. Right. And I think to me what that, the age of the customer actually translates to and Mick, Mick and I are both big fans of this whole idea of the project, the product shift, mixed book, you know, it was a great piece on a, you're talking to Mick, you know, as part of the manifesto is one of the authors as well, but this shift from project to product, right? Like the age of the customer, in my opinion, the, the, the embodiment of that is the shift to a product mentality. Right. And, and the product mentality in my opinion, is what brings the business and technology teams together, right? >>Once you, once you're focused on a customer experience, that's delivered through a product or a service that's when I that's, when I started to go with the alignment problem goes away, right. Because if you look at software companies, right, I mean, we run product management models, you know, with software development teams, customer success teams, right. That, you know, the software component of these products that people are building is obviously becoming bigger and bigger, you know, in an, in many ways, right. More and more organizations are trying to model themselves over as operationally like software companies. Right. Um, they obviously have lots of other components in their business than just software, but I think that whole model of customer experience equaling product, and then the software component of product, the product is the essence of what changes that alignment equation and brings business and teams together because all of a sudden, everyone knows what the customer's experiencing. Right. And, and that, that, that makes a lot of things very clear, very quickly. >>Right. I'm just curious how far along this was as a process before, before covert hit, right. Because serendipitous, whatever. Right. But th the sudden, you know, light switch moment, everybody had to go work from home and in March 15th compared to now, we're in October, and this is going to be going on for a while, and it is a new normal and whatever that whatever's going to look like a year from now, or two years from now is TBD, you know, had you guys already started on this journey cause again, to sit down and actually declare this coalition and declare this manifesto is a lot different than just trying to do better within your own organization. >>Yeah. So we had started, uh, you know, w we definitely had started independently, you know, some, some, you know, I think people in the community know that, uh, we, we came together with a company called lean kit a handful of years ago, and I give John Terry actually one of the founders leaned to immense credit for, you know, kind of spearheading our cultural change and not, and not because of, we were just going to be, you know, bringing agile solutions to our customers, but because, you know, he believed that it was going to be a fundamentally better way for us to work. Right. And we kind of, you know, when we started with John and built, you know, out of concentric circles of momentum and, and we've gotten to the place where now it's just part of who we are, but, but I do think that, you know, COVID has, you know, um, I think pre COVID a lot of companies, you know, would, would adopt, you know, the, you would adopt digital slash agile transformation. >>Um, traditional industries may have done it as a reaction to disruption. Right. You know, and in many cases, the disruption to these traditional industries was, I would say a product oriented company, right. That probably had a larger software component, and that disruption caused a competitive issue or a customer issue that caused companies and tried to respond by transforming. I think COVID, you know, all of a sudden flatten that out, right. We literally all got disrupted. Right. And, and so all of a sudden, every one of us is dealing with some degree of market uncertainty, customer uncertainty, uh, and also know none of us were insulated from the need to be able to pivot faster, deliver incrementally, you know, and operate in a different, completely more agile way, uh, you know, post COVID. Right. Yeah. That's great. >>So again, a very, very, very timely, you know, a little bit of serendipity, a little bit of, of planning. And, you know, as, as with all important things, there's always a little bit of luck and a lot of hard work involved. So a really interesting thank you for, for your leadership, Patrick. And, you know, it really makes a statement. I think when you have a bunch of leaderships across an industry coming together and putting their name on a piece of paper, uh, that's aligned around us some principles and some values, which again, if you read them who wouldn't want to get behind these, but if it takes, you know, something a little bit more formal, uh, to kind of move the ball down the field, and then I totally get it and a really great work. Thanks for, uh, thanks for doing it. >>Oh, absolutely. No. Like I said, the first time I read it, I was like, yeah, like you said, this is all, this all makes complete sense, but just documenting it and saying it and talking about it moves the needle. I'll tell you as a company, you gotta, we're pushing really hard on, uh, you know, on our own internal strategy on diversity inclusion. Right? And, and like, once we wrote the words down about what, you know, what we aspire to be from a diversity and inclusion perspective, it's the same thing. Everybody reads the words and goes, why wouldn't we do this? Right. But until you write it down and kind of have again, a manifesto or a Terrafirma of what you're trying to accomplish, you know, then you can rally behind it. Right. As opposed to it being something that's, everybody's got their own version of the flavor. Right. And I think it's a very analogous, you know, kind of, uh, initiative, right. And, uh, and this happening, both of those things, right. Are happening across the industry these days. Right. >>And measure it too. Right. And measure it, measure, measure, measure, get a baseline. Even if you don't like to measure, even if you don't like what the, even if you can argue against the math, behind the measurement, measure it, and at least you can measure it again and you can, and you've got some type of a comp and that is really the only way to, to move it forward. Well, Patrick really enjoyed the conversation. Thanks for, uh, for taking a few minutes out of your day. >>It's great to be here. It's an awesome movement and we're glad >>That'd be part of it. All right. Thanks. And if you want to check out the biz ops, Manifesta go to biz ops, manifesto.org, read it. You might want to sign it. It's there for you. And thanks for tuning in on this segment will continuing coverage of the biz op manifesto unveil here on the cube. I'm Jeff, thanks for watching >>From around the globe. It's the cube with digital coverage of biz ops manifesto unveiled brought to you by biz ops coalition. >>Hey, welcome back, everybody Jeffrey here with the cube. We're coming to you from our Palo Alto studios. And welcome back to this event is the biz ops manifesto unveiling. So the biz ops manifesto and the biz ops coalition had been around for a little while, but today's the big day. That's kind of the big public unveiling or excited to have some of the foundational people that, you know, have put their, put their name on the dotted, if you will, to support this initiative and talk about why that initiative is so important. And so the next guest we're excited to have is dr. Mick Kirsten. He is the founder and CEO of Tasktop mic. Great to see you coming in from Vancouver, Canada, I think, right? Yes. Thank you. Absolutely. I hope your air is a little better out there. I know you had some of the worst air of all of us, a couple, a couple of weeks back. So hopefully things are, uh, are getting a little better and we get those fires under control. Yeah. >>Things have cleared up now. So yeah, it's good. It's good to be close to the U S and it's going to have the Arabic cleaner as well. >>Absolutely. So let's, let's jump into it. So you you've been an innovation guy forever starting way back in the day and Xerox park. I was so excited to do an event at Xerox park for the first time last year. I mean, that, that to me represents along with bell labs and, and some other, you know, kind of foundational innovation and technology centers, that's gotta be one of the greatest ones. So I just wonder if you could share some perspective of getting your start there at Xerox park, you know, some of the lessons you learned and what you've been able to kind of carry forward from those days. >>Yeah. I was fortunate to join Xerox park in the computer science lab there at a very early point in my career, and to be working on open source programming languages. So back then in the computer science lab, where some of the inventions around programming around software development teams, such as object oriented programming, and a lot of what we had around really modern programming levels constructs, those were the teams I have the fortune of working with, and really our goal was. And of course there's as, as you know, uh, there's just this DNA of innovation and excitement and innovation in the water. And really it was the model back then was all about changing the way that we work, uh, was looking at for how we could make it 10 times easier to write code. But this is back in 99. And we were looking at new ways of expressing, especially business concerns, especially ways of enabling people who are, who want to innovate for their business to express those concerns in code and make that 10 times easier than what that would take. >>So we create a new open source programming language, and we saw some benefits, but not quite quite what we expected. I then went and actually joined Charles Stephanie, that former to fucking Microsoft who was responsible for, he actually got Microsoft word as a spark and into Microsoft and into the hands of bill Gates on that company. I was behind the whole office suite and his vision. And then when I was trying to execute with, working for him was to make PowerPoint like a programming language, make everything completely visual. And I realized none of this was really working in that there was something else, fundamentally wrong programming languages, or new ways of building software. Like let's try and do with Charles around intentional programming. That was not enough. >>That was not enough. So, you know, the agile movement got started about 20 years ago, and we've seen the rise of dev ops and really this kind of embracing of, of, of sprints and, you know, getting away from MRDs and PRDs and these massive definitions of what we're going to build and long build cycles to this iterative process. And this has been going on for a little while. So what was still wrong? What was still missing? Why the BizOps coalition, why the biz ops manifesto? >>Yeah, so I basically think we nailed some of the things that the program language levels of teams can have effective languages deployed soften to the cloud easily now, right? And at the kind of process and collaboration and planning level agile two decades, decades ago was formed. We were adopting and all the, all the teams I was involved with and it's really become a self problem. So agile tools, agile teams, agile ways of planning, uh, are now very mature. And the whole challenge is when organizations try to scale that. And so what I realized is that the way that agile was scaling across teams and really scaling from the technology part of organization to the business was just completely flawed. The agile teams had one set of doing things, one set of metrics, one set of tools. And the way that the business was working was planning was investing in technology was just completely disconnected and using a whole different set of advisors. >>Interesting. Cause I think it's pretty clear from the software development teams in terms of what they're trying to deliver. Cause they've got a feature set, right. And they've got bugs and it's easy to, it's easy to see what they deliver, but it sounds like what you're really honing in on is this disconnect on the business side, in terms of, you know, is it the right investment? You know, are we getting the right business ROI on this investment? Was that the right feature? Should we be building another feature or should we building a completely different product set? So it sounds like it's really a core piece of this is to get the right measurement tools, the right measurement data sets so that you can make the right decisions in terms of what you're investing, you know, limited resources. You can't, no one has unlimited resources and ultimately have to decide what to do, which means you're also deciding what not to do. And it sounds like that's a really big piece of this, of this whole effort. >>Yeah. Jeff, that's exactly it, which is the way that the agile team measures their own way of working is very different from the way that you measure business outcomes. The business outcomes are in terms of how happy your customers are, but are you innovating fast enough to keep up with the pace of a rapidly changing economy, rapidly changing market. And those are, those are all around the customer. And so what I learned on this long journey of supporting many organizations transformations and having them try to apply those principles of agile and dev ops, that those are not enough, those measures technical practices, those measured sort of technical excellence of bringing code to the market. They don't actually measure business outcomes. And so I realized that it really was much more around having these entwined flow metrics that are customer centric and business centric and market centric where we need it to go. Right. >>So I want to shift gears a little bit and talk about your book because you're also a bestselling author, a project, a product, and, and, and you, you brought up this concept in your book called the flow framework. And it's really interesting to me cause I know, you know, flow on one hand is kind of a workflow and a process flow and, and you know, that's how things get done and, and, and embrace the flow. On the other hand, you know, everyone now in, in a little higher level existential way is trying to get into the flow right into the workflow and, you know, not be interrupted and get into a state where you're kind of at your highest productivity, you know, kind of your highest comfort, which flow are you talking about in your book or is it a little bit about, >>Well, that's a great question. It's not what I get asked very often. Just to me, it's absolutely both. So that the thing that we want to get to, we've learned how to master individual flow. That is this beautiful book by me, how he teaches me how he does a beautiful Ted talk by him as well about how we can take control of our own flow. So my question with the book with project replies, how can we bring that to entire teams and really entire organizations? How can we have everyone contributing to a customer outcome? And this is really what if you go to the biz ops manifesto, it says, I focus on outcomes on using data to drive whether we're delivering those outcomes rather than a focus on proxy metrics, such as, how quickly did we implement this feature? No, it's really how much value did the customer go to the feature and how quickly did you learn and how quickly did you use that data to drive to that next outcome? >>Really that with companies like Netflix and Amazon have mastered, how do we get that to every large organization, every it organization and make everyone be a software innovator. So it's to bring that co that concept of flow to these entwined value streams. And the fascinating thing is we've actually seen the data. We've been able to study a lot of value streams. We see when flow increases, when organizations deliver value to a customer faster, developers actually become more happy. So things like the employee net promoter scores rise, and we've got empirical data for this. So the beautiful thing to me is that we've actually been able to combine these two things and see the results in the data that you increase flow to the customer. Your developers are more happy. >>I love it, right, because we're all more, we're all happier when we're in the flow and we're all more productive when we're in the flow. So I, that is a great melding of, of two concepts, but let's jump into the, into the manifesto itself a little bit. And, you know, I love that, you know, took this approach really of having kind of four key values and then he gets 12 key principles. And I just want to read a couple of these values because when you read them, it sounds pretty brain dead. Right? Of course. Right. Of course you should focus on business outcomes. Of course you should have trust and collaboration. Of course you should have database decision making processes and not just intuition or, you know, whoever's the loudest person in the room, uh, and to learn and respond and pivot. But what's the value of actually just putting them on a piece of paper, because again, this is not this, these are all good, positive things, right? When somebody reads these to you or tells you these are sticks it on the wall, of course. But unfortunately of course isn't always enough. >>No. And I think what's happened is some of these core principles originally from the agile manifesto two decades ago, uh, the whole dev ops movement of the last decade of flow feedback and continue learning has been key. But a lot of organizations, especially the ones that are undergoing digital transformations have actually gone a very different way, right? The way that they measure value in technology and innovation is through costs for many organizations. The way that they actually are looking at that they're moving to cloud is actually as a reduction in cost. Whereas the right way of looking at moving to cloud is how much more quickly can we get to the value to the customer? How quickly can we learn from that? And how quickly can we drive the next business outcome? So really the key thing is, is to move away from those old ways of doing things, a funny projects and cost centers, uh, to actually funding and investing in outcomes and measuring outcomes through these flow metrics, which in the end are your fast feedback and how quickly you're innovating for your customer. >>So these things do seem, you know, very obvious when you look at them. But the key thing is what you need to stop doing to focus on these. You need to actually have accurate realtime data of how much value your phone to the customer every week, every month, every quarter. And if you don't have that, your decisions are not driven on data. If you don't know what your boggling like is, and this is something that in decades of manufacturing, a car manufacturers, other manufacturers, master, they always know where the bottom back in their production processes. You ask a random CIO when a global 500 company where their bottleneck is, and you won't get a clear answer because there's not that level of understanding. So let's, you actually follow these principles. You need to know exactly where you fall. And I guess because that's, what's making your developers miserable and frustrated around having them context, which on thrash. So it, the approach here is important and we have to stop doing these other things, >>Right? There's so much there to unpack. I love it. You know, especially the cloud conversation, because so many people look at it wrong as, as, as a cost saving device, as opposed to an innovation driver and they get stuck, they get stuck in the literal and the, and you know, I think at the same thing, always about Moore's law, right? You know, there's a lot of interesting real tech around Moore's law and the increasing power of microprocessors, but the real power, I think in Moore's laws is the attitudinal change in terms of working in a world where you know that you've got all this power and what you build and design. I think it's funny to your, your comment on the flow and the bottleneck, right? Cause, cause we know manufacturing, as soon as you fix one bottleneck, you move to your next one, right? You always move to your next point of failure. So if you're not fixing those things, you know, you're not, you're not increasing that speed down the line, unless you can identify where that bottleneck is or no matter how many improvements you make to the rest of the process, it's still going to get hung up on that one spot. >>That's exactly it. And you also make it sound so simple, but again, if you don't have the data driven visibility of where that bottom line is, and these bottlenecks are adjusted to say defense just whack them. All right. So we need to understand is the bottleneck because our security reviews are taking too long and stopping us from getting value for the customer. If it's that automate that process. And then you move on to the next bottleneck, which might actually be that deploying yourself into the cloud. It's taking too long. But if you don't take that approach of going flow first, rather than again, that sort of cost reduction. First, you have to think of the approach of customer centricity and you only focused on optimizing costs. Your costs will increase and your flow will slow down. And this is just one of these fascinating things. >>Whereas if you focus on getting closer to the customer and reducing your cycles out on getting value, your flow time from six months to two weeks or two, one week or two event, as we see with the tech giants, you actually can both lower your costs and get much more value for us to get that learning loop going. So I think I've, I've seen all these cloud deployments and one of the things happened that delivered almost no value because there was such big bottlenecks upfront in the process and actually the hosting and the AP testing was not even possible with all of those inefficiencies. So that's why going float us rather than costs when we started our project versus silky. >>I love that. And, and, and, and it, it begs repeating to that right within the subscription economy, you know, you're on the hook to deliver value every single month because they're paying you every single month. So if you're not on top of how you're delivering value, you're going to get sideways because it's not like they pay a big down payment and a small maintenance fee every month. But once you're in a subscription relationship, you know, you have to constantly be delivering value and upgrading that value because you're constantly taking money from the customer. So it's such a different kind of relationship than kind of the classic, you know, big bang with a maintenance agreement on the back end really important. Yeah. >>And I think in terms of industry shifts that that's, it that's, what's catalyzed. This industry shift is in this SAS and subscription economy. If you're not delivering more and more value to your customers, someone else's, and they're winning the business, not you. So, one way we know is to delight our customers with great user experience as well. That really is based on how many features you delivered or how much, how much, how many quality improvements or scalar performance improvements we delivered. So the problem is, and this is what the business manifesto, as well as the flow frame of touch on is if you can't measure how much value you deliver to a customer, what are you measuring? You just backed again, measuring costs, and that's not a measure of value. So we have to shift quickly away from measuring costs to measuring value, to survive. And in the subscription economy, >>We could go for days and days and days. I want to shift gears a little bit into data and, and a data driven decision making a data driven organization cause right day has been talked about for a long time, the huge big data meme with, with Hadoop over, over several years and, and data warehouses and data lakes and data oceans and data swamps. And you can go on and on and on. It's not that easy to do, right? And at the same time, the proliferation of data is growing exponentially. We're just around the corner from, from IOT and five G. So now the accumulation of data at machine scale, again, is this gonna overwhelm? And one of the really interesting principles, uh, that I wanted to call out and get your take right, is today's organizations generate more data than humans can process. So informed decisions must be augmented by machine learning and artificial intelligence. I wonder if you can, again, you've got some great historical perspective, um, reflect on how hard it is to get the right data, to get the data in the right context, and then to deliver it to the decision makers and then trust the decision makers to actually make the data and move that down. You know, it's kind of this democratization process into more and more people and more and more frontline jobs making more and more of these little decisions every day. >>Yeah. I definitely think the front parts of what you said are where the promises of big data have completely fallen on their face into the swamps as, as you mentioned, because if you don't have the data in the right format, you've cannot connect, collected that the right way you want it, that way, the right way you can't use human or machine learning on it effectively. And there've been the number of data where, how has this in a typical enterprise organization and the sheer investment is tremendous, but the amount of intelligence being extracted from those is, is, is a very big problem. So the key thing that I've noticed is that if you can model your value streams, so you actually understand how you're innovating, how you're measuring the delivery of value and how long that takes, what is your time to value through these metrics like full time? >>You can actually use both the intelligence that you've got around the table and push that down as well, as far as getting to the organization, but you can actually start using that those models to understand and find patterns and detect bottlenecks that might be surprising, right? Well, you can detect interesting bottlenecks when you shift to work from home. We detected all sorts of interesting bottlenecks in our own organization that were not intuitive to me that have to do with, you know, more senior people being overloaded and creating bottlenecks where they didn't exist. Whereas we thought we were actually an organization that was very good at working from home because of our open source roots. So the data is highly complex. Software value streams are extremely complicated. And the only way to really get the proper analysts and data is to model it properly and then to leverage these machine learning and AI techniques that we have. But that front part of what you said is where organizations are just extremely immature in what I've seen, where they've got data from all their tools, but not modeled in the right way. Right, right. >>Right. Well, all right. So before I let you go, you know, let's say you get a business leader. He, he buys in, he reads the manifesto, he signs on the dotted line and he says, Mick, how do I get started? I want to be more aligned with the, with the development teams. I know I'm in a very competitive space. We need to be putting out new software features and engage with our customers. I want to be more data-driven how do I get started? Well, you know, what's the biggest inhibitor for most people to get started and get some early wins, which we know is always the key to success in any kind of a new initiative. >>Right? So I think you can reach out to us through the website, uh, for the manifesto. But the key thing is just, it's definitely set up it's to get started and to get the key wins. So take a product value stream. That's mission critical if it'd be on your mobile and web experiences or part of your cloud modernization platform where your analytics pipeline, but take that and actually apply these principles to it and measure the end to end flow of value. Make sure you have a value metric that everyone is on the same page on, but the people on the development teams that people in leadership all the way up to the CEO, and one of the, where I encourage you to start is actually that end to end flow time, right? That is the number one metric. That is how you measure it, whether you're getting the benefit of your cloud modernization, that is the one metric that when the people I respect tremendously put into his cloud for CEOs, the metric, the one, the one way to measure innovation. So basically take these principles, deploy them on one product value stream measure, Antonin flow time, uh, and then you'll actually be well on your path to transforming and to applying the concepts of agile and dev ops all the way to, to the, to the way >>You're offering model. >>Well, Mick really great tips, really fun to catch up. I look forward to a time when we can actually sit across the table and, and get into this. Cause I just, I just love the perspective and, you know, you're very fortunate to have that foundational, that foundational base coming from Xerox park and they get, you know, it's, it's a very magical place with a magical history. So to, to incorporate that into, continue to spread that well, uh, you know, good for you through the book and through your company. So thanks for sharing your insight with us today. >>Thanks so much for having me, Jeff. Absolutely. >>All right. And go to the biz ops manifesto.org, read it, check it out. If you want to sign it, sign it. They'd love to have you do it. Stay with us for continuing coverage of the unveiling of the business manifesto on the cube. I'm Jeff. Rick. Thanks for watching. See you next time >>From around the globe. It's the cube with digital coverage, a biz ops manifesto unveiled brought to you by biz ops coalition. >>Hey, welcome back. You're ready. Jeff Frick here with the cube for our ongoing coverage of the big unveil. It's the biz ops manifesto manifesto unveil. And we're going to start that again from the top three And a Festo >>Five, four, three, two. >>Hey, welcome back everybody. Jeff Frick here with the cube come to you from our Palo Alto studios today for a big, big reveal. We're excited to be here. It's the biz ops manifesto unveiling a thing's been in the works for a while and we're excited to have our next guest. One of the, really the powers behind this whole effort. And he's joining us from Boston it's surge, Lucio, the vice president, and general manager enterprise software division at Broadcom surge. Great to see you. >>Hi, good to see you, Jeff. Glad to be here. >>Absolutely. So you've been in this business for a very long time. You've seen a lot of changes in technology. What is the biz ops manifesto? What is this coalition all about? Why do we need this today and in 2020? >>Yeah. So, so I've been in this business for close to 25 years, right? So about 20 years ago, the agile manifesto was created. And the goal of the agile manifesto was really to address the uncertainty around software development and the inability to predict the efforts to build software. And, uh, if you, if you roll that kind of 20 years later, and if you look at the current state of the industry of the product, the project management Institute, estimates that we're wasting about a million dollars, every 20 seconds in digital transformation initiatives that do not deliver on business results. In fact, we were recently served a third of the, a, a number of executives in partnership with Harvard >>Business review and 77% of those executives think that one of the key challenges that they have is really the collaboration between business and it, and that that's been kind of a case for, uh, almost 20 years now. Um, so the, the, the key challenge that we're faced with is really that we need a new approach. And many of the players in the industry, including ourselves have been using different terms, right? Some are being, are talking about value stream management. Some are talking about software delivery management. If you look at the site, reliability engineering movement, in many ways, it embodies a lot of these kind of concepts and principles. So we believed that it became really imperative for us to crystallize around, could have one concept. And so in many ways, the, a, the BizOps concept and the BizOps manifesto are bringing together a number of ideas, which has been emerging in the last five years or so, and, and defining the key values and principles to finally help these organizations truly transform and become digital businesses. And so the hope is that by joining our forces and defining public key principles and values, we can help the industry, uh, not just, uh, by, you know, providing them with support, but also tools and consulting that is required for them to truly achieve the kind of transformation that everybody's taking. >>Right. Right. So COVID now we're six months into it, approximately seven months into it. Um, a lot of pain, a lot of bad stuff still happening. We've got a ways to go, but one of the things that on the positive side, right, and you've seen all the memes and social media is, is a driver of digital transformation and a driver of change. Cause we had this light switch moment in the middle of March, and there was no more planning. There was no more conversation. You've suddenly got remote workforces, everybody's working from home and you got to go, right. So the reliance on these tools increases dramatically, but I'm curious, you know, kind of short of, of the beginnings of this effort in short of kind of COVID, which, you know, came along unexpectedly. I mean, what were those inhibitors because we've been making software for a very long time, right? The software development community has, has adopted kind of rapid change and, and iterative, uh, delivery and, and sprints, what was holding back the connection with the business side to make sure that those investments were properly aligned with outcomes. >>Well, so, so you have to understand that it is, is kind of a its own silos. And traditionally it has been treated as a cost center within large organizations and not as a value center. And so as a result, kind of a, the traditional dynamic between it and the business is basically one of a kind of supplier up to kind of a business. Um, and you know, if you go back to, uh, I think you'll unmask a few years ago, um, basically at this concept of the machines to build the machines and you went as far as saying that, uh, the, the machines or the production line is actually the product. So, uh, meaning that the core of the innovation is really about, uh, building, could it be engine to deliver on the value? And so in many ways, you know, we, we have missed on this shift from, um, kind of it becoming this kind of value center within the enterprises and end. >>He talks about culture. Now, culture is a, is a sum total of behaviors. And the reality is that if you look at it, especially in the last decade, uh, we've agile with dev ops with, um, I bring infrastructures, uh, it's, it's way more volatile today than it was 10 years ago. And so the, when you start to look at the velocity of the data, the volume of data, the variety of data to analyze the system, um, it's, it's very challenging for it to actually even understand and optimize its own processes, let alone, um, to actually include business as sort of an integral part of kind of a delivery chain. And so it's both kind of a combination of, of culture, um, which is required, uh, as well as tools, right? To be able to start to bring together all these data together, and then given the volume of variety of philosophy of the data. Uh, we have to apply some core technologies, which have only really, truly emerged in the last five to 10 years around machine learning and analytics. And so it's really kind of a combination of those freaks, which are coming together today, truly out organizations kind of get to the next level. Right, >>Right. So let's talk about the manifesto. Let's talk about, uh, the coalition, uh, the BizOps coalition. I just liked that you put down these really simple, you know, kind of straightforward core values. You guys have four core values that you're highlighting, you know, business outcomes, over individual projects and outputs, trust, and collaboration, oversight, load teams, and organizations, data driven decisions, what you just talked about, uh, you know, over opinions and judgment and learned, respond and pivot. I mean, surgery sounds like pretty basic stuff, right? I mean, aren't, isn't everyone working to these values already. And I think he touched on it on culture, right? Trust and collaboration, data driven decisions. I mean, these are fundamental ways that people must run their business today, or the person that's across the street, that's doing it. It's going to knock them out right off their block. >>Yeah. So that's very true. But, uh, so I'll, I'll mention an hour survey. We did, uh, I think about six months ago and it was in partnership with, uh, with, uh, an industry analyst and we serve at a, again, a number of it executives to understand only we're tracking business outcomes. I'm going to get the software executives, it executives we're tracking business outcomes. And the, there were less than 15% of these executives were actually tracking the outcomes of the software delivery. And you see that every day. Right? So in my own teams, for instance, we've been adopting a lot of these core principles in the last year or so, and we've uncovered that 16% of our resources were basically aligned around initiatives, which are not strategic for us. Um, I take another example, for instance, one of our customers in the, uh, in the airline industry and Harvard, for instance, that a number of, uh, um, that they had software issues that led to people searching for flights and not returning any kind of availability. >>And yet, um, you know, the it teams, whether it's operation software environments were completely oblivious to that because they were completely blindsided to it. And so the connectivity between kind of the inwards metrics that RT is using, whether it's database time, cycle time, or whatever metric we use in it are typically completely divorced from the business metrics. And so at its core, it's really about starting to align the business metrics with the, the, the software delivery chain, right? This, uh, the system, which is really a core differentiator for these organizations. It's about connecting those two things and starting to, um, infuse some of the agile culture and principles. Um, that's emerged from the software side into the business side. Um, of course the lean movement and other movements have started to change some of these dynamics on the business side. And so I think this, this is the moment where we are starting to see kind of the imperative to transform. Now, you know, Covina obviously has been a key driver for that. The, um, the technology is right to start to be able to weave data together and really kind of, uh, also the cultural shifts, uh, Prue agile through dev ops through, uh, the SRE movement, uh frulein um, business transformation, all these things are coming together and that are really creating kind of the conditions for the BizOps manifestor to exist, >>Uh, Clayton Christianson, great, uh, Harvard professor innovator's dilemma might steal my all time. Favorite business books, you know, talks about how difficult it is for incumbents to react to, to disruptive change, right? Because they're always working on incremental change cause that's what their customers are asking for. And there's a good ROI when you talk about, you know, companies not measuring the right thing. I mean, clearly it has some portion of their budget that has to go to keeping the lights on, right. That that's always the case, but hopefully that's an ever decreasing percentage of their total activity. So, you know, what should people be measuring? I mean, what are kind of the new metrics, um, in, in biz ops that drive people to be looking at the right things, measuring the right things and subsequently making the right decisions, investment decisions on whether they should do, you know, move project a along or project B. >>So there, there are only two things, right? So, so I think what you're talking about is portfolio management, investment management, right. And, um, which, which is a key challenge, right? Um, in my own experience, right? Uh, driving strategy or a large scale kind of software organization for years, um, it's very difficult to even get kind of a base data as to who is doing what, uh, um, I mean, some of our largest customers we're engaged with right now are simply trying to get a very simple answer, which is how many people do I have and that specific initiative at any point in time and just tracking that information is extremely difficult. So, and, and again, back to a product project management Institute, um, they're, they've estimated that on average, it organizations have anywhere between 10 to 20% of their resources focused on initiatives, which are not strategically aligned. >>So that's one dimension on portfolio management. I think the key aspect though, that we are really keen on is really around kind of the alignment of a business metrics to the it metrics. Um, so I'll use kind of two simple examples, right? And my background is around quality. And so I've always believed that fitness for purpose is really kind of a key, um, uh, philosophy if you will. And so if you start to think about quality as fitness for purpose, you start to look at it from a customer point of view, right. And fitness for purpose for core banking application or mobile application are different, right? So the definition of a business value that you're trying to achieve is different. Um, and so the, and yet, if you look at our, it, operations are operating, they were using kind of a same type of, uh, kind of inward metrics, uh, like a database of time or a cycle time, or what is my point of velocity, right? >>And, uh, and so the challenge really is this inward facing metrics that it is using, which are divorced from ultimately the outcome. And so, you know, if I'm, if I'm trying to build a poor banking application, my core metric is likely going to be uptime, right? If I'm trying to build a mobile application or maybe your social mobile app, it's probably going to be engagement. And so what you want is for everybody across it, to look at these metric, and what's hard, the metrics within the software delivery chain, which ultimately contribute to that business metric and some cases cycle time may be completely irrelevant, right? Again, my core banking app, maybe I don't care about cycle time. And so it's really about aligning those metrics and be able to start to differentiate, um, the key challenges you mentioned, uh, around the, the, um, uh, around the disruption that we see is, or the investors is the dilemma now is really around the fact that many it organizations are essentially applying the same approaches of, for innovation, right, for basically scrap work, then they would apply to kind of over more traditional projects. And so, you know, there's been a lot of talk about two-speed it, and yes, it exists, but in reality are really organizations, um, truly differentiating, um, all of the operate, their, their projects and products based on the outcomes that they're trying to achieve. And this is really where BizOps is trying to affect. >>I love that, you know, again, it doesn't seem like brain surgery, but focus on the outcomes, right. And it's horses for courses, as you said, this project, you know, what you're measuring and how you define success, isn't necessarily the same as, as on this other project. So let's talk about some of the principles we've talked about the values, but, you know, I think it's interesting that, that, that the BizOps coalition, you know, just basically took the time to write these things down and they don't seem all that, uh, super insightful, but I guess you just gotta get them down and have them on paper and have them in front of your face. But I want to talk about, you know, one of the key ones, which you just talked about, which is changing requirements, right. And working in a dynamic situation, which is really what's driven, you know, this, the software to change in software development, because, you know, if you're in a game app and your competitor comes out with a new blue sword, you've got to come out with a new blue sword. >>So whether you had that on your Kanban wall or not. So it's, it's really this embracing of the speed of change and, and, and, and making that, you know, the rule, not the exception. I think that's a phenomenal one. And the other one you talked about is data, right? And that today's organizations generate more data than humans can process. So informed decisions must be generated by machine learning and AI, and, you know, in the, the big data thing with Hadoop, you know, started years ago, but we are seeing more and more that people are finally figuring it out, that it's not just big data, and it's not even generic machine learning or artificial intelligence, but it's applying those particular data sets and that particular types of algorithms to a specific problem, to your point, to try to actually reach an objective, whether that's, you know, increasing the, your average ticket or, you know, increasing your checkout rate with, with, with shopping carts that don't get left behind and these types of things. So it's a really different way to think about the world in the good old days, probably when you got started, when we had big, giant, you know, MRDs and PRDs and sat down and coded for two years and came out with a product release and hopefully not too many patches subsequently to that. >>It's interesting. Right. Um, again, back to one of these surveys that we did with, uh, with about 600, the ITA executives, and, uh, and, and we, we purposely designed those questions to be pretty open. Um, and, and one of them was really role requirements and, uh, and it was really a wrong kind of what do you, what is the best approach? What is your preferred approach towards requirements? And if I remember correctly over 80% of the it executives set that the best approach they'll prefer to approach is for requirements to be completely defined before software development starts. Let me pause there where 20 years after the agile manifesto, right? And for 80% of these idea executives to basically claim that the best approach is for requirements to be fully baked before salt, before software development starts, basically shows that we still have a very major issue. >>And again, our hypothesis in working with many organizations is that the key challenge is really the boundary between business and it, which is still very much contract based. If you look at the business side, they basically are expecting for it deliver on time on budget, right. But what is the incentive for it to actually delivering all the business outcomes, right? How often is it measured on the business outcomes and not on an SLA or on a budget type criteria. And so that, that's really the fundamental shift that we need to, we really need to drive up as an industry. Um, and you know, we, we talk about kind of this, this imperative for organizations to operate that's one, and back to the innovator's dilemma. The key difference between these larger organization is, is really kind of a, if you look at the amount of capital investment that they can put into pretty much anything, why are they losing compared to, um, you know, startups? What, why is it that, uh, more than 40% of, uh, personal loans today or issued not by your traditional brick and mortar banks, but by, um, startups? Well, the reason, yes, it's the traditional culture of doing incremental changes and not disrupting ourselves, which Christiansen covered at length, but it's also the inability to really fundamentally change kind of a dynamic picture. We can business it and, and, and partner right. To, to deliver on a specific business outcome. Right. >>I love that. That's a great, that's a great summary. And in fact, getting ready for this interview, I saw you mentioning another thing where, you know, the, the problem with the agile development is that you're actually now getting more silos because you have all these autonomous people working, you know, kind of independently. So it's even a harder challenge for, for the business leaders to, to, to, as you said, to know, what's actually going on, but, but certainly I w I want to close, um, and talk about the coalition. Um, so clearly these are all great concepts. These are concepts you want to apply to your business every day. Why the coalition, why, you know, take these concepts out to a broader audience, including your, your competition and, and the broader industry to say, Hey, we, as a group need to put a stamp of approval on these concepts, values, these principles. >>So, first I think we, we want, um, everybody to realize that we are all talking about the same things, the same concepts. I think we were all from our own different vantage point, realizing that, um, things after change, and again, back to, you know, whether it's value stream management or site reliability engineering, or biz ops, we're all kind of using slightly different languages. Um, and so I think one of the important aspects of BizOps is for us, all of us, whether we're talking about, you know, consulting agile transformation experts, uh, whether we're talking about vendors, right, provides kind of tools and technologies, or these large enterprises to transform for all of us to basically have kind of a reference that lets us speak around kind of, um, in a much more consistent way. The second aspect is for, to me is for, um, these concepts to start to be embraced, not just by us or trying, or, you know, vendors, um, system integrators, consulting firms, educators, thought leaders, but also for some of our old customers to start to become evangelists of their own in the industry. >>So we, our, our objective with the coalition needs to be pretty, pretty broad. Um, and our hope is by, by starting to basically educate, um, our, our joint customers or partners, that we can start to really foster these behaviors and start to really change, uh, some of dynamics. So we're very pleased at if you look at, uh, some of the companies which have joined the, the, the, the manifesto. Um, so we have vendors and suggest desktop or advance, or, um, uh, PagerDuty for instance, or even planned view, uh, one of my direct competitors, um, but also thought leaders like Tom Davenport or, uh, or cap Gemini or, um, um, smaller firms like, uh, business agility, institutes, or agility elf. Um, and so our, our goal really is to start to bring together, uh, thought leaders, people who have been LP, larger organizations do digital transformation vendors, were providing the technologies that many of these organizations use to deliver on these digital preservation and for all of us to start to provide the kind of, uh, education support and tools that the industry needs. Yeah, >>That's great surge. And, uh, you know, congratulations to you and the team. I know this has been going on for a while, putting all this together, getting people to sign onto the manifesto, putting the coalition together, and finally today getting to unveil it to the world in a little bit more of a public, uh, opportunity. So again, you know, really good values, really simple principles, something that, that, uh, shouldn't have to be written down, but it's nice cause it is, and now you can print it out and stick it on your wall. So thank you for, uh, for sharing this story. And again, congrats to you and the team. Thank you. Appreciate it. My pleasure. Alrighty, surge. If you want to learn more about the biz ops, Manifesta go to biz ops manifesto.org, read it, and you can sign it and you can stay here for more coverage. I'm the cube of the biz ops manifesto unveiled. Thanks for watching. See you next time >>From around the globe. It's the cube with digital coverage of this ops manifesto unveiled and brought to you by >>This obstacle volition. Hey, welcome back, everybody Jeffrey here with the cube. Welcome back to our ongoing coverage of the biz ops manifesto unveiling. It's been in the works for awhile, but today's the day that it actually kind of come out to the, to the public. And we're excited to have a real industry luminary here to talk about what's going on, why this is important and share his perspective. And we're happy to have from Cape Cod, I believe is Tom Davenport. He's a distinguished author and professor at Babson college. We could go on, he's got a lot of great titles and, and really illuminary in the area of big data and analytics Thomas. Great to see you. >>Thanks Jeff. Happy to be here with you. >>Great. So let's just jump into it, you know, and getting ready for this. I came across your LinkedIn posts. I think you did earlier this summer in June and right off the bat, the first sentence just grabbed my attention. I'm always interested in new attempts to address longterm issues, uh, in how technology works within businesses, biz ops. What did you see in biz ops, uh, that, that kind of addresses one of these really big longterm problems? >>Well, yeah, but the longterm problem is that we've had a poor connection between business people and it people between business objectives and the, it solutions that address them. This has been going on, I think since the beginning of information technology and sadly it hasn't gone away. And so biz ops is a new attempt to deal with that issue with a, you know, a new framework, eventually a broad set of solutions that increase the likelihood that will actually solve a business problem with an it capability. >>Right. You know, it's interesting to compare it with like dev ops, which I think a lot of people are probably familiar with, which was, you know, built around, uh, agile software development and a theory that we want to embrace change that that changes. Okay. And we want to be able to iterate quickly and incorporate that. And that's been happening in the software world for, for 20 plus years. What's taken so long to get that to the business side, because as the pace of change has changed on the software side, you know, that's a strategic issue in terms of execution, the business side that they need now to change priorities. And, you know, there's no PRDs and MRDs and big, giant strategic plans that sit on the shelf for five years. That's just not the way business works anymore. It took a long time to get here. >>Yeah, it did. And, you know, there had been previous attempts to make a better connection between business and it, there was the so called strategic alignment framework that a couple of friends of mine from Boston university developed, I think more than 20 years ago, but you know, now we have better technology for creating that linkage. And the, you know, the idea of kind of ops oriented frameworks is pretty pervasive now. So I think it's time for another serious attempt at it. >>And do you think doing it this way, right. With the, with the BizOps coalition, you know, getting a collection of, of, of kind of likeminded individuals and companies together, and actually even having a manifesto, which we're making this declarative statement of, of principles and values, you think that's what it takes to kind of drive this kind of beyond the experiment and actually, you know, get it done and really start to see some results in, in, uh, in production in the field. >>I think certainly no one vendor organization can pull this off single handedly. It does require a number of organizations collaborating and working together. So I think our coalition is a good idea and a manifesto is just a good way to kind of lay out what you see as the key principles of the idea. And that makes it much easier for everybody to understand and act on. >>I, I think it's just, it's really interesting having, you know, having them written down on paper and having it just be so clearly articulated both in terms of the, of the values as well as, as the, uh, the principles and the values, you know, business outcomes matter trust and collaboration, data-driven decisions, which is the number three of four, and then learn, respond and pivot. It doesn't seem like those should have to be spelled out so clearly, but, but obviously it helps to have them there. You can stick them on the wall and kind of remember what your priorities are, but you're the data guy. You're the analytics guy, uh, and a big piece of this is data and analytics and moving to data driven decisions. And principle number seven says, you know, today's organizations generate more data than humans can process and informed decisions can be augmented by machine learning and artificial intelligence right up your alley. You know, you've talked a number of times on kind of the mini stages of analytics. Um, and how has that evolved over over time, you know, as you think of analytics and machine learning, driving decisions beyond supporting decisions, but actually starting to make decisions in machine time. What's that, what's that thing for you? What does that make you, you know, start to think, wow, this is this going to be pretty significant. >>Yeah. Well, you know, this has been a longterm interest of mine. Um, the last generation of AI, I was very interested in expert systems. And then, um, I think, uh, more than 10 years ago, I wrote an article about automated decision-making using what was available then, which was rule-based approaches. Um, but you know, this addresses an issue that we've always had with analytics and AI. Um, you know, we, we tended to refer to those things as providing decision support, but the problem is that if the decision maker didn't want their support, didn't want to use them in order to make a decision, they didn't provide any value. And so the nice thing about automating decisions, um, with now contemporary AI tools is that we can ensure that data and analytics get brought into the decision without any possible disconnection. Now, I think humans still have something to add here, and we often will need to examine how that decision is being made and maybe even have the ability to override it. But in general, I think at least for, you know, repetitive tactical decisions, um, involving a lot of data, we want most of those, I think to be at least, um, recommended if not totally made by an algorithm or an AI based system. And that I believe would add to, um, the quality and the precision and the accuracy of decisions and in most organizations, >>No, I think, I think you just answered my next question before I, before I asked it, you know, we had dr. Robert Gates on the former secretary of defense on a few years back, and we were talking about machines and machines making decisions. And he said at that time, you know, the only weapon systems, uh, that actually had an automated trigger on it were on the North Korea and South Korea border. Um, everything else, as you said, had to go through a sub person before the final decision was made. And my question is, you know, what are kind of the attributes of the decision that enable us to more easily automated? And then how do you see that kind of morphing over time, both as the data to support that as well as our comfort level, um, enables us to turn more and more actual decisions over to the machine? >>Well, yeah, as I suggested we need, um, data and the data that we have to kind of train our models has to be high quality and current, and we need to know the outcomes of that data. You know, um, most machine learning models, at least in business are supervised. And that means we need to have labeled outcomes in the, in the training data. But I, you know, um, the pandemic that we're living through is a good illustration of the fact that, that the data also have to be reflective of current reality. And, you know, one of the things that we're finding out quite frequently these days is that, um, the data that we have do not reflect, you know, what it's like to do business in a pandemic. Um, I wrote a little piece about this recently with Jeff cam at wake forest university, we call it data science quarantined, and we interviewed with somebody who said, you know, it's amazing what eight weeks of zeros will do to your demand forecast. We just don't really know what happens in a pandemic. Um, our models maybe have to be put on the shelf for a little while and until we can develop some new ones or we can get some other guidelines into making decisions. So I think that's one of the key things with automated decision making. We have to make sure that the data from the past and that's all we have of course, is a good guide to, you know, what's happening in the present and the future as far as we understand it. >>Yeah. I used to joke when we started this calendar year 2020, it was finally the year that we know everything with the benefit of hindsight, but I turned down 20, 20 a year. We found out we actually know nothing and everything and thought we knew, but I want to, I want to follow up on that because you know, it did suddenly change everything, right? We've got this light switch moment. Everybody's working from home now we're many, many months into it, and it's going to continue for a while. I saw your interview with Bernard Marr and you had a really interesting comment that now we have to deal with this change. We don't have a lot of data and you talked about hold fold or double down. And, and I can't think of a more, you know, kind of appropriate metaphor for driving the value of the biz ops when now your whole portfolio strategy, um, these to really be questioned and, and, you know, you have to be really, uh, well, uh, executing on what you are, holding, what you're folding and what you're doubling down with this completely new environment. >>Well, yeah, and I hope I did this in the interview. I would like to say that I came up with that term, but it actually came from a friend of mine. Who's a senior executive at Genpact. And, um, I, um, used it mostly to talk about AI and AI applications, but I think you could, you could use it much more broadly to talk about your entire sort of portfolio of digital projects. You need to think about, well, um, given some constraints on resources and a difficult economy for a while, which of our projects do we want to keep going on pretty much the way we were and which ones are not that necessary anymore? You see a lot of that in AI, because we had so many pilots, somebody told me, you know, we've got more pilots around here than O'Hare airport and, and AI. Um, and then, but the ones that involve doubled down, they're even more important to you. They are, you know, a lot of organizations have found this out, um, in the pandemic on digital projects, it's more and more important for customers to be able to interact with you, um, digitally. And so you certainly wouldn't want to cancel those projects or put them on hold. So you double down on them and get them done faster and better. Right, >>Right. Uh, another, another thing that came up in my research that, that you quoted, um, was, was from Jeff Bezos, talking about the great bulk of what we do is quietly, but meaningfully improving core operations. You know, I think that is so core to this concept of not AI and machine learning and kind of the general sense, which, which gets way too much buzz, but really applied right. Applied to a specific problem. And that's where you start to see the value. And, you know, the, the BizOps, uh, manifesto is, is, is calling it out in this particular process. But I'd love to get your perspective as you know, you speak generally about this topic all the time, but how people should really be thinking about where are the applications where I can apply this technology to get direct business value. >>Yeah, well, you know, even talking about automated decisions, um, uh, the kind of once in a lifetime decisions, uh, the ones that, um, ag Lafley, the former CEO of Procter and gamble used to call the big swing decisions. You only get a few of those. He said in your tenure as CEO, those are probably not going to be the ones that you're automating in part because, um, you don't have much data about them. You're only making them a few times and in part, because, um, they really require that big picture thinking and the ability to kind of anticipate the future, that the best human decision makers, um, have. Um, but, um, in general, I think where they, I, the projects that are working well are, you know, what I call the low hanging fruit ones, the, some people even report to it referred to it as boring AI. >>So, you know, sucking data out of a contract in order to compare it to a bill of lading for what arrived at your supply chain companies can save or make a lot of money with that kind of comparison. It's not the most exciting thing, but AI, as you suggested is really good at those narrow kinds of tasks. It's not so good at the, at the really big moonshots, like curing cancer or, you know, figuring out well what's the best stock or bond under all or even autonomous vehicles. Um, we, we made some great progress in that area, but everybody seems to agree that they're not going to be perfect for quite a while, and we really don't want to be driving around on, um, and then very much unless they're, you know, good and all kinds of weather and with all kinds of pedestrian traffic and you know, that sort of thing, right? >>That's funny you bring up contract management. I had a buddy years ago, they had a startup around contract management and I've like, and this was way before we had the compute power today and cloud proliferation. I said, you know, how can you possibly build software around contract management? It's language, it's legal, ease. It's very specific. And he's like, Jeff, we just need to know where's the contract. And when does it expire? And who's the signatory. And he built a business on those, you know, very simple little facts that weren't being covered because their contracts are in people's drawers and files and homes. And Lord only knows. So it's really interesting, as you said, these kind of low hanging fruit opportunities where you can extract a lot of business value without trying to, you know, boil the ocean. >>Yeah. I mean, if you're Amazon, um, uh, Jeff Bezos thinks it's important to have some kind of billion dollar project. And he even says it's important to have a billion dollar failure or two every year. But I think most organizations probably are better off being a little less aggressive and, you know, sticking to, um, what AI has been doing for a long time, which is, you know, making smarter decisions based on, based on data. >>Right? So Tom, I want to shift gears one more time before, before we let you go on, on kind of a new topic for you, not really new, but you know, not, not a, the vast majority of, of your publications and that's the new way to work, you know, as, as the pandemic hit in mid March, right. And we had this light switch moment, everybody had to work from home and it was, you know, kind of crisis and get everybody set up. Well, you know, now we're five months, six months, seven months. A number of companies have said that people are not going to be going back to work for a while. And so we're going to continue on this for a while. And then even when it's not what it is now, it's not going to be what it was before. So, you know, I wonder, and I know you, you, uh, you teased, you're working on a new book, you know, some of your thoughts on, you know, kind of this new way to work and, and, and the human factors in this new, this new kind of reality that we're kind of evolving into, I guess. >>Yeah. I missed was an interest of mine. I think, um, back in the nineties, I wrote an article called, um, a coauthored, an article called two cheers for the virtual office. And, you know, it was just starting to emerge. Then some people were very excited about it. Some people were skeptical and, uh, we said two cheers rather than three cheers because clearly there's some shortcomings. And, you know, I keep seeing these pop up. It's great that we can work from our homes. It's great that we can, most of what we need to do with a digital interface, but, um, you know, things like innovation and creativity, and certainly, um, uh, a good, um, happy social life kind of requires some face to face contact every now and then. And so I, you know, I think we'll go back to an environment where there is some of that. >>Um, we'll have, um, times when people convene in one place so they can get to know each other face to face and learn from each other that way. And most of the time, I think it's a huge waste of people's time to commute into the office every day and to jump on airplanes, to, to, um, give every little, um, uh, sales call or give every little presentation. Uh, we just have to really narrow down what are the circumstances where face to face contact really matters. And when can we get by with digital? You know, I think one of the things in my current work I'm finding is that even when you have AI based decision making, you really need a good platform in which that all takes place. So in addition to these virtual platforms, we need to develop platforms that kind of structure the workflow for us and tell us what we should be doing next, then make automated decisions when necessary. And I think that ultimately is a big part of biz ops as well. It's not just the intelligence of an AI system, but it's the flow of work that kind of keeps things moving smoothly throughout your organization. >>I think such, such a huge opportunity as you just said, cause I forget the stats on how often we're interrupted with notifications between email texts, Slack, a sauna, Salesforce, the list goes on and on. So, you know, to put an AI layer between the person and all these systems that are begging for attention, you've written a book on the attention economy, which is a whole nother topic, we'll say for another day, you know, it, it really begs, it really begs for some assistance because you know, you just can't get him picked, you know, every two minutes and really get quality work done. It's just not, it's just not realistic. And you know what? I don't think that's a feature that we're looking for. >>I agree. Totally >>Tom. Well, thank you so much for your time. Really enjoyed the conversation. I got to dig into the library. It's very long. So I might start at the attention economy. I haven't read that one. And to me, I think that's the fascinating thing in which we're living. So thank you for your time and, uh, great to see you. >>My pleasure, Jeff. Great to be here. >>All right. He's Tom I'm Jeff. You are watching the continuing coverage of the biz ops manifesto and Vail. Thanks for watching the cube. We'll see you next time.

>>from around the globe. It's the Cube with digital coverage of biz ops Manifesto unveiled. Brought to you by Biz Ops Coalition >>Hey, welcome back Variety. Jeff Freak here with the Cube were Palo Alto studios. And we like to welcome you back to our continuing coverage of biz. Opps manifesto. Unveil. Exciting day to really, uh, kind of bring this out into public. There's been a little bit of conversation, but today is really the official unveiling, and we're excited to have our next guest to share a little bit more information on it. He's Patrick Tickle. He's a chief product officer for plan view. Patrick, great to see you. Yeah, it's great to be here. Thanks for the invite. So why the biz? Opps manifesto? Why the bizarre coalition? Now, when you guys have been added, it's relatively mature marketplace. Business is good. What was missing? Why? Why this? Why this coalition? >>Yeah, so you know, again, Why? Why is bizarre is important. And why is this something I'm you know, I'm so excited about, but I think companies as well, right? Well, no. In some ways or another, this is a topic that I've been talking to you know the market in our customers about for a long time. And it's, you know, I really applaud, you know, this whole movement, right? And, um, it resonates with me because I think one of the fundamental flaws, frankly, of the way we have talked about technology and business literally for decades has been this idea of alignment. Those who know me, I occasionally get off on this little rant about the word alignment. Right? But to me, the word alignment is actually indicative of the of the of the flaw in a lot of our organizations. And biz ops is really, I think, now, trying to catalyze and expose that flaw, right? Because, you know, I I always say that, you know, you know, alignment implies silos right the instantaneously with soon as you say, there's alignment. There's there's obviously somebody who's got a direction and other people that have tow line up and that that kind of siloed, uh, nature of organizations. And then, frankly, the passive nature of I think so many technology organizations like look, the business has the strategy. You guys need to align right and and, you know, is a product leader right that's what I've been my whole career, right? I can tell you that I never sit around. I almost never used the word alignment, right. I mean, whether you know, I never sit down and say, you know, the product management team has to get aligned with death, right? Or the Dev team has to get aligned with the delivery and ops teams. I mean, what I say is, are we on strategy? Right? Like we have a strategy, a zey full end to end value stream, right, and that there's no silos, and I mean, look, every on any given day, we got to get better, right? But the context, the context we operate is not about alignment, right? It's about being on strategy. And I think I've talked to customers a lot about that. But when I first read the manifesto, I was like, Oh, you know, this is exactly this. Is breaking down maybe trying to eliminate the word alignment, you know, from a lot of our organizations, because we literally start thinking about one strategy and how we go from strategy to delivery and have it be our strategy, not someone else's, that we're all aligning to it. And it's a great way to catalyze. You know, that conversation that I've It's been in my mind for years, to be honest, >>right? So, so much to unpack there. One of the things, obviously, uh, stealing a lot from from Dev Ops and the Dev Ops manifesto from 20 years ago. And and as I looked through some of the principles and I looked through some of the values which are, you know, really nicely laid out here, you know, satisfied customers do continues delivery, uh, measure output against riel results. Um, the ones that that jumps out that was really about, you know, change change, right requirements should change frequently. They do change frequently, but I'm curious to get your take from A from a software development point. It's easy to kind of understand, right? We're making this widget, and our competitors made a widget plus X, and now we need to change our plans and make sure that the plus X gets added to the plan. Maybe it wasn't in the plan. You talked a lot about product strategy. So in this kind of continuous delivery world, how does that meld with? I'm actually trying to set a strategy which implies a direction for a little bit further out on the horizon. And to stay on that while at the same time you're kind of doing this real time, continual adjustments because you're not working off a giant PRD or M R d anymore. Yeah, >>totally. Yeah. You know, one of the terms you know, that we use internally a lot on even with my customers, our customers is we talked about this idea rewiring right, and I think you know. So it's kind of an analogy for transformation, and I think a lot of us have to rewire the way we think about things right. And I think it planned view where we have a lot of customers who live in that you know who operationalized that traditional ppm world right and are shifting toe agile and transforming that rewire super important and and to your point right, it's You've just you've got to embrace this idea of, you know, just iterative getting better every day and iterating iterating iterating as opposed to building annual plans. Or, you know, I get customers occasionally. Who asked me for two or three year roadmap right and I literally looked at them and I go There is No There is no scenario where I could build a two or three year roadmap. Right? You you think you want that? But that's not That's not the way we run, right. And I will tell you the biggest thing that for us, you know, that I think is matched the planning. You know, impedance is a word I like to use a lot. So the thing that we've like that we've done from a planning perspective, I think is matched impedance to continuous delivery is instituting the whole program. Implement the program in current planning capabilities and methodologies. Um, in the scaled, agile world, right. And over the last 18 months to two years, we really have now, you know, instrumented our company across three value streams. You know, we do Corley p I program increment 10 week planning, you know, And that becomes that becomes the terra firma of how we plan, right? And it's what are we doing for the next 10 weeks? And we iterated within those 10 weeks. But we also know that 10 weeks from now we're gonna we're gonna just generate again, right and that shifting of that planning model, you know, to being is cross functional. Is that as that big room planning kind of model is, um and also, you know, on that shorter increments. Um, when you get those two things in place also, the impedance really starts to match up with continuous delivery, and it changes. It changes the way you plan. And it changes the way you work, right? >>The other thing. Right. So obviously a lot of these things are kind of process driven both within the values as well as the principles. But there's a whole lot really about culture, and I just wanna highlight a couple of the values, right? We already talked about business outcomes, um, trust and collaboration, a data driven decisions and then learned responded Pivot, right. A lot of those air cultural as much as they are processed. So again, is that the Is that the need to really kind of just put them down on paper? And you know, e can't help but think of, you know, the hammer and up the thing in the Lutheran church with it with their manifesto. Is it just good to get it down? on paper, because when you read these things, you're like, Well, of course we should trust people. And, of course, we need an environment of collaboration. And, of course, we want data driven decisions. But as we all know, saying it and living it are two very, very different things. >>Yeah, good question. I mean, I think there's a lot of ways to bring that to life. You're right. And just hanging up. You know, I think we've all been through the hanging up posters around your office, which these days, right, Unless you're gonna hang a poster and everybody's home office, right, you can't even you can't even fake it that you think that might work, right? So, um, you know, you really, I think we have attacked that in a variety of ways, right? And you definitely have to, you know, you've got to make the shift to a team centric culture, right? Empowered teams. You know, that's a big deal, right? You know, a lot of a lot of the people that you know, we lived in a world of unquote where we lived in a deep resource management world for a long, long time and write a lot of our customers still do that, but, you know, kind of moving to that team centric world is eyes really important and court of the trust. I think training is super important, right? We've, you know, we've internally, right. We've trained hundreds employees over the last year and a half on the fundamentals. Really of safe, right? Not necessarily. You know, we've had we've had teams delivering and scrum and continuous delivery for, you know, for years. But this scaling aspect of it eyes where we've done a lot of training investment on Ben. You know, I think leadership has to be bought in, right, you know? And so we p I plan, you know, myself and camera and the other members are leadership, you know, we're in p I planning, you know, for for four days, right? I mean, you've gotta walk the walk, you know, from top to bottom. And you've got a train on the context, right? And then you and then and then once you get through a few cycles where you've done a pivot, right or you brought a new team in and it just works, it becomes kind of this virtuous circle where people go. Man, this really works so much better than what we used to dio, >>right? Right. Theater Really key principle to this whole thing is is aligning the business leaders and the business prioritization s so that you can get to good outcomes with the development and and the delivery, right? And we know again and kind of classic dev ops to get the Dev and the production people together so they can, you know, quickly ship code that works. Um, but adding the business person on there really puts puts a little extra responsibility that they they understand the value of a particular feature, a particular priority. Uh, they can make the trade offs and that they kind of understand the effort involved too. So, you know, bringing them into this continuous again kind of this continuous development process, Um, to make sure that things were better aligned and really better Prioritize, because ultimately, you know, we don't live in an infinite resource is situation and and people got to make tradeoffs. They gotta make decisions, is toe what goes and what doesn't go on for everything that goes right. I always say you pick one thing Okay, that's 99 other things that couldn't go. So it's really important to have you know, this you said alignment of the business priorities as well as you know, the execution within. Within the development. >>Yeah, I think that, you know, You know, I think it was probably close to two years ago, Forrester started talking about the age of the customer, right? That was like their big theme at the time, right? And I think to me what that the age of the customer actually translates to. And Mick, Mick and I are both big fans of this whole idea of the project. The product shift mixed book, you know, is a great piece on your talking assed part of the manifesto is one of the authors as well. But this shift from project to product, right? Like the age of the customer, in my opinion, the mhm, the embodiment that is the shift to a product mentality, right? And? And the product mentality, in my opinion, is what brings the business and technology teams together, right? Once, once you're focused on a customer experience is delivered through a product or service. That's when I That's when I start to go. The alignment problem goes away. Right? Because if you look at software companies, right, I mean, we run product management models with software development teams, customer success teams, right? That, you know, the software component of these products that people are building is obviously becoming bigger and bigger, you know, and in many ways, right. More more organizations, air trying to model themselves over as operationally like software companies, Right? They obviously have lots of other components in their business than just software. But I think that whole model of customer experience equaling product and then the software component of product the product is the essence of what changes that alignment equation and brings business and teams together. Because also, everyone knows what the customers experiencing, right, And that that that makes a lot of things very clear very quick, >>right? I'm just curious how far along this was as a process before, before Cove, it hit right, because serendipitous. Whatever. Right? But the sudden, you know, light switch moment. Everybody had to go work from home in March 15th. Compared to now, we're in October on. This is gonna be going on for a while and it is a new normal and whatever that whatever is gonna look like a year from now or two years from now is T v D. You know, had you guys already started on this journey because again, to sit down and actually declare this coalition and declare this manifesto is a lot different than just trying to do better within your own organization. Yeah, >>So we had started, you know, we definitely had started independently. You know, some some. You know, I think people in the community know that we we came together with a company called Link It Handful years ago. And I give John Terry, actually one of the founders link it immense credit for, you know, kind of spearheading our cultural change. And not and not because of we're just gonna be, you know, bringing agile solutions to our customers. But because, you know, he believed that it was gonna be a fundamentally better way for us to work, right. And we kind of, you know, and we started with John and built, you know, centric circles, momentum. And we've gotten to the place where now it's just part of who we are, but but I do think that you know, Cove it has. You know, I think pre Cove in a lot of companies, you know, would would adopt, you know, the would adopt digital slash agile transformation. Um, traditional industries may have done it as a reaction to disruption. Right, You know, And in many cases, though, the disruption to these traditional industries was, I would say, a product oriented company, right that probably had a larger software component, and that disruption caused a competitive issue or a customer issue that cause companies to try to respond by transforming. I think co vid, you know, all sudden flatten that out, right? We literally all got disrupted, right? And and so all of a sudden, every one of us is dealing with some degree of market uncertainty customer uncertainty on doll. So none of us are insulated from the need to be able to pivot faster, deliver incrementally, you know, and operating a different, completely more agile way. Uh, you know, Post Cove it right? >>Yeah, that's great. So again, very, very, very timely. You know, a little bit of serendipity, a little bit of planning and, you know, a zoo with all important things There's always a little bit of locking a a lot of hard work involved. So really interesting. Thank you for for your leadership, Patrick. And, you know, it really makes a statement. I think when you have a bunch of leaderships across an industry coming together and putting their name on a piece of paper, that's a line around us, um, principles and some values, which again, if you read them, who wouldn't want to get behind these? But if it takes, you know, something a little bit more formal, uh, to kind of move the ball down the field, and then I totally get it. And, uh, really great work. Thanks for Thanks for doing it. >>Oh, absolutely. No. Like I said, the first time I read it, I was like, Yeah, like you said, this is all this all makes complete sense, but just documenting it and saying it and talking about it moves the needle. I'll tell you, as a company, we were pushing really hard on, uh, you know, on our own internal strategy on diversity inclusion, right. And and like once we wrote the words down About what? You know what we aspire to be from a diversity and inclusion perspective. It's the same thing. Everybody reads the words and goes, Why wouldn't we do this right? But until you write it down and kind of have again, you know, a manifesto or a terra firma of what you're trying to accomplish, you know, Then you can rally behind right, as opposed to it being, you know, something that's everybody's got their own version of the flavor, right? And I think it's a very analogous, you know, kind of initiative. And, uh, it's happening. Both of those things right are happening across the industry these days, >>right and measure it to write and measure it. Measure, measure, get a baseline even if you don't like the measure, even if you don't like what the Even if you can argue against the math behind the measurement, measure it, and at least you could measure it again. And you've got some type of a compound that is really the only way toe to move it forward with. Patrick really enjoyed the conversation. Thanks for for taking a few minutes out of your day. It's great to >>be here. It's an awesome movement, and we're glad to be part of it. All >>right. Thanks. And if you want to check out the biz ops manifesto goto biz Opps manifesto dot org's read it. You might want to sign it there for you. And thanks for tuning in on this segment. We'll continuing coverage of the bizarre manifesto unveiled here on the Cube. I'm Jeff. Thanks for watching.