>>The Cube presents Ignite 22, brought to you by Palo Alto Networks. >>Welcome back to Vegas. Guys. We're happy that you're here. Lisa Martin here covering with Dave Valante, Palo Alto Networks Ignite 22. We're at MGM Grand. This is our first day, Dave of two days of cube coverage. We've been having great conversations with the ecosystem with Palo Alto executives, with partners. One of the things that they have is unit 42. We're gonna be talking with them next about cyber intelligence. And the threat data that they get is >>Incredible. Yeah. They have all the data, they know what's going on, and of course things are changing. The state of play changes. Hold on a second. I got a text here. Oh, my Netflix account was frozen. Should I click on this link? Yeah. What do you think? Have you had a, it's, have you had a little bit more of that this holiday season? Yeah, definitely. >>Unbelievable, right? A lot of smishing going on. >>Yeah, they're very clever. >>Yeah, we're very pleased to welcome back one of our alumni to the queue. Wendy Whitmore is here, the SVP of Unit 42. Welcome back, Wendy. Great to have >>You. Thanks Lisa. So >>Unit 42 created back in 2014. One of the things that I saw that you said in your keynote this morning or today was everything old is still around and it's co, it's way more prolific than ever. What are some of the things that Unit 42 is seeing these days with, with respect to cyber threats as the landscape has changed so much the last two years alone? >>You know, it, it has. So it's really interesting. I've been responding to these breaches for over two decades now, and I can tell you that there are a lot of new and novel techniques. I love that you already highlighted Smishing, right? In the opening gate. Right. Because that is something that a year ago, no one knew what that word was. I mean, we, it's probably gonna be invented this year, right? But that said, so many of the tactics that we have previously seen, when it comes to just general espionage techniques, right? Data act filtration, intellectual property theft, those are going on now more than ever. And you're not hearing about them as much in the news because there are so many other things, right? We're under the landscape of a major war going on between Russia and Ukraine of ransomware attacks, you know, occurring on a weekly basis. And so we keep hearing about those, but ultimately these nations aid actors are using that top cover, if you will, as a great distraction. It's almost like a perfect storm for them to continue conducting so much cyber espionage work that like we may not be feeling that today, but years down the road, they're, the work that they're doing today is gonna have really significant impact. >>Ransomware has become a household word in the last couple of years. I think even my mom knows what it is, to some degree. Yeah. But the threat actors are far more sophisticated than they've ever written. They're very motivated. They're very well funded. I think I've read a stat recently in the last year that there's a ransomware attack once every 11 seconds. And of course we only hear about the big ones. But that is a concern that goes all the way up to the board. >>Yeah. You know, we have a stat in our ransomware threat report that talks about how often victims are posted on leak sites. And I think it's once every seven minutes at this point that a new victim is posted. Meaning a victim has had their data, a victim organization had their data stolen and posted on some leak site in the attempt to be extorted. So that has become so common. One of the shifts that we've seen this year in particular and in recent months, you know, a year ago when I was at Ignite, which was virtual, we talked about quadruple extortion, meaning four different ways that these ransomware actors would go out and try to make money from these attacks in what they're doing now is often going to just one, which is, I don't even wanna bother with encrypting your data now, because that means that in order to get paid, I probably have to decrypt it. Right? That's a lot of work. It's time consuming. It's kind of painstaking. And so what they've really looked to do now is do the extortion where they simply steal the data and then threaten to post it on these leak sites, you know, release it other parts of the web and, and go from there. And so that's really a blending of these techniques of traditional cyber espionage with intellectual property theft. Wow. >>How trustworthy are those guys in terms of, I mean, these are hackers, right? In terms of it's really the, the hacker honor system, isn't it? I mean, if you get compromised like that, you really beholden to criminals. And so, you >>Know, so that's one of the key reasons why having the threat intelligence is so important, right? Understanding which group that you're dealing with and what their likelihood of paying is, what's their modus operandi. It's become even more important now because these groups switch teams more frequently than NFL trades, you know, free agents during the regular season, right? Or players become free agents. And that's because their infrastructure. So the, you know, infrastructure, the servers, the systems that they're using to conduct these attacks from is actually largely being disrupted more from law enforcement, international intelligence agencies working together with public private partnerships. So what they're doing is saying, okay, great. All that infrastructure that I just had now is, is burned, right? It's no longer effective. So then they'll disband a team and then they'll recruit a new team and it's constant like mixing and matching in players. >>All that said, even though that's highly dynamic, one of the other areas that they pride themselves on is customer service. So, and I think it's interesting because, you know, when I said they're not wanting to like do all the decryption? Yeah. Cuz that's like painful techni technical slow work. But on the customer service side, they will create these customer service portals immediately stand one up, say, you know, hey it's, it's like an Amazon, you know, if you've ever had to return a package on Amazon for example, and you need to click through and like explain, you know, Hey, I didn't receive this package. A portal window pops up, you start talking to either a bot or a live agent on the backend. In this case they're hu what appeared to be very much humans who are explaining to you exactly what happened, what they're asking for, super pleasant, getting back within minutes of a response. And they know that in order for them to get paid, they need to have good customer service because otherwise they're not going to, you know, have a business. How, >>So what's the state of play look like from between nation states, criminals and how, how difficult or not so difficult is it for you to identify? Do you have clear signatures? My understanding in with Solar Winds it was a little harder, but maybe help us understand and help our audience understand what the state of play is right now. >>One of the interesting things that I think is occurring, and I highlighted this this morning, is this idea of convergence. And so I'll break it down for one example relates to the type of malware or tools that these attackers use. So traditionally, if we looked at a nation state actor like China or Russia, they were very, very specific and very strategic about the types of victims that they were going to go after when they had zero day. So, you know, new, new malware out there, new vulnerabilities that could be exploited only by them because the rest of the world didn't know about it. They might have one organization that they would target that at, at most, a handful and all very strategic for their objective. They wanted to keep that a secret as long as possible. Now what we're seeing actually is those same attackers going towards one, a much larger supply chain. >>So, so lorenzen is a great example of that. The Hafnia attacks towards Microsoft Exchange server last year. All great examples of that. But what they're also doing is instead of using zero days as much, or you know, because those are expensive to build, they take a lot of time, a lot of funding, a lot of patience and research. What they're doing is using commercially available tools. And so there's a tool that our team identified earlier this year called Brute Rael, C4 or BRC four for short. And that's a tool that we now know that nation state actors are using. But just two weeks ago we invested a ransomware attack where the ransomware actor was using that same piece of tooling. So to your point, yak can get difficult for defenders when you're looking through and saying, well wait, they're all using some of the same tools right now and some of the same approaches when it comes to nation states, that's great for them because they can blend into the noise and it makes it harder to identify as >>Quickly. And, and is that an example of living off the land or is that B BRC four sort of a homegrown hacker tool? Is it, is it a, is it a commercial >>Off the shelf? So it's a tool that was actually, so you can purchase it, I believe it's about 2,500 US dollars for a license. It was actually created by a former Red teamer from a couple well-known companies in the industry who then decided, well hey, I built this tool for work, I'm gonna sell this. Well great for Red teamers that are, you know, legitimately doing good work, but not great now because they're, they built a, a strong tool that has the ability to hide amongst a, a lot of protocols. It can actually hide within Slack and teams to where you can't even see the data is being exfiltrated. And so there's a lot of concern. And then now the reality that it gets into the wrong hands of nation state actors in ransomware actors, one of the really interesting things about that piece of malware is it has a setting where you can change wallpaper. And I don't know if you know offhand, you know what that means, but you know, if that comes to mind, what you would do with it. Well certainly a nation state actor is never gonna do something like that, right? But who likes to do that are ransomware actors who can go in and change the background wallpaper on a desktop that says you've been hacked by XYZ organization and let you know what's going on. So pretty interesting, obviously the developer doing some work there for different parts of the, you know, nefarious community. >>Tremendous amount of sophistication that's gone on the last couple of years alone. I was just reading that Unit 42 is now a founding member of the Cyber Threat Alliance includes now more than 35 organizations. So you guys are getting a very broad picture of today's threat landscape. How can customers actually achieve cyber resilience? Is it achievable and how do you help? >>So I, I think it is achievable. So let me kind of parse out the question, right. So the Cyber Threat Alliance, the J C D C, the Cyber Safety Review Board, which I'm a member of, right? I think one of the really cool things about Palo Alto Networks is just our partnerships. So those are just a handful. We've got partnerships with over 200 organizations. We work closely with the Ukrainian cert, for example, sharing information, incredible information about like what's going on in the war, sharing technical details. We do that with Interpol on a daily basis where, you know, we're sharing information. Just last week the Africa cyber surge operation was announced where millions of nodes were taken down that were part of these larger, you know, system of C2 channels that attackers are using to conduct exploits and attacks throughout the world. So super exciting in that regard and it's something that we're really passionate about at Palo Alto Networks in terms of resilience, a few things, you know, one is visibility, so really having a, an understanding of in a real, as much of real time as possible, right? What's happening. And then it goes into how you, how can we decrease operational impact. So that's everything from network segmentation to wanna add the terms and phrases I like to use a lot is the win is really increasing the time it takes for the attackers to get their work done and decreasing the amount of time it takes for the defenders to get their work done, right? >>Yeah. I I call it increasing the denominator, right? And the ROI equation benefit over or value, right? Equals equals or benefit equals value over cost if you can increase the cost to go go elsewhere, right? Absolutely. And that's the, that's the game. Yeah. You mentioned Ukraine before, what have we learned from Ukraine? I, I remember I was talking to Robert Gates years ago, 2016 I think, and I was asking him, yeah, but don't we have the best cyber technology? Can't we attack? He said, we got the most to lose too. Yeah. And so what have we learned from, from Ukraine? >>Well, I, I think that's part of the key point there, right? Is you know, a great offense essentially can also be for us, you know, deterrent. So in that aspect we have as an, as a company and or excuse me, as a country, as a company as well, but then as partners throughout all parts of the world have really focused on increasing the intelligence sharing and specifically, you know, I mentioned Ukrainian cert. There are so many different agencies and other sorts throughout the world that are doing everything they can to share information to help protect human life there. And so what we've really been concerned with, with is, you know, what cyber warfare elements are going to be used there, not only how does that impact Ukraine, but how does it potentially spread out to other parts of the world critical infrastructure. So you've seen that, you know, I mentioned CS rrb, but cisa, right? >>CISA has done a tremendous job of continuously getting out information and doing everything they can to make sure that we are collaborating at a commercial level. You know, we are sharing information and intelligence more than ever before. So partners like Mania and CrowdStrike, our Intel teams are working together on a daily basis to make sure that we're able to protect not only our clients, but certainly if we've got any information relevant that we can share that as well. And I think if there's any silver lining to an otherwise very awful situation, I think the fact that is has accelerated intelligence sharing is really positive. >>I was gonna ask you about this cause I think, you know, 10 or so years ago, there was a lot of talk about that, but the industry, you know, kind of kept things to themselves, you know, a a actually tried to monetize some of that private data. So that's changing is what I'm hearing from you >>More so than ever more, you know, I've, I mentioned I've been in the field for 20 years. You know, it, it's tough when you have a commercial business that relies on, you know, information to, in order to pay people's salaries, right? I think that has changed quite a lot. We see the benefit of just that continuous sharing. There are, you know, so many more walls broken down between these commercial competitors, but also the work on the public private partnership side has really increased some of those relationships. Made it easier. And you know, I have to give a whole lot of credit and mention sisa, like the fact that during log four J, like they had GitHub repositories, they were using Slack, they were using Twitter. So the government has really started pushing forward with a lot of the newer leadership that's in place to say, Hey, we're gonna use tools and technology that works to share and disseminate information as quickly as we can. Right? That's fantastic. That's helping everybody. >>We knew that every industry, no, nobody's spared of this. But did you notice in the last couple of years, any industries in particular that are more vulnerable? Like I think of healthcare with personal health information or financial services, any industries kind of jump out as being more susceptible than others? >>So I think those two are always gonna be at the forefront, right? Financial services and healthcare. But what's been really top of mind is critical infrastructure, just making sure right? That our water, our power, our fuel, so many other parts of right, the ecosystem that go into making sure that, you know, we're keeping, you know, houses heated during the winter, for example, that people have fresh water. Those are extremely critical. And so that is really a massive area of focus for the industry right now. >>Can I come back to public-private partnerships? My question is relates to regulations because the public policy tends to be behind tech, the technology industry as an understatement. So when you take something like GDPR is the obvious example, but there are many, many others, data sovereignty, you can't move the data. Are are, are, is there tension between your desire as our desire as an industry to share data and government's desire to keep data private and restrict that data sharing? How is that playing out? How do you resolve that? >>Well I think there have been great strides right in each of those areas. So in terms of regulation when it comes to breaches there, you know, has been a tendency in the past to do victim shaming, right? And for organizations to not want to come forward because they're concerned about the monetary funds, right? I think there's been tremendous acceleration. You're seeing that everywhere from the fbi, from cisa, to really working very closely with organizations to, to have a true impact. So one example would be a ransomware attack that occurred. This was for a client of ours within the United States and we had a very close relationship with the FBI at that local field office and made a phone call. This was 7:00 AM Eastern time. And this was an organization that had this breach gone public, would've made worldwide news. There would've been a very big impact because it would've taken a lot of their systems offline. >>Within the 30 minutes that local FBI office was on site said, we just saw this piece of malware last week, we have a decryptor for it from another organization who shared it with us. Here you go. And within 60 minutes, every system was back up and running. Our teams were able to respond and get that disseminated quickly. So efforts like that, I think the government has made a tremendous amount of headway into improving relationships. Is there always gonna be some tension between, you know, competing, you know, organizations? Sure. But I think that we're doing a whole lot to progress it, >>But governments will make exceptions in that case. Especially for something as critical as the example that you just gave and be able to, you know, do a reach around, if you will, on, on onerous regulations that, that ne aren't helpful in that situation, but certainly do a lot of good in terms of protecting privacy. >>Well, and I think there used to be exceptions made typically only for national security elements, right? And now you're seeing that expanding much more so, which I think is also positive. Right. >>Last question for you as we are wrapping up time here. What can organizations really do to stay ahead of the curve when it comes to, to threat actors? We've got internal external threats. What can they really do to just be ahead of that curve? Is that possible? >>Well, it is now, it's not an easy task so I'm not gonna, you know, trivialize it. But I think that one, having relationships with right organizations in advance always a good thing. That's a, everything from certainly a commercial relationships, but also your peers, right? There's all kinds of fantastic industry spec specific information sharing organizations. I think the biggest thing that impacts is having education across your executive team and testing regularly, right? Having a plan in place, testing it. And it's not just the security pieces of it, right? As security responders, we live these attacks every day, but it's making sure that your general counsel and your head of operations and your CEO knows what to do. Your board of directors, do they know what to do when they receive a phone call from Bloomberg, for example? Are they supposed supposed to answer? Do your employees know that those kind of communications in advance and training can be really critical and make or break a difference in an attack. >>That's a great point about the testing but also the communication that it really needs to be company wide. Everyone at every level needs to know how to react. Wendy, it's been so great having, >>Wait one last question. Sure. Do you have a favorite superhero growing up? >>Ooh, it's gotta be Wonder Woman. Yeah, >>Yeah, okay. Yeah, so cuz I'm always curious, there's not a lot of women in, in security in cyber. How'd you get into it? And many cyber pros like wanna save the world? >>Yeah, no, that's a great question. So I joined the Air Force, you know, I, I was a special agent doing computer crime investigations and that was a great job. And I learned about that from, we had an alumni day and all these alumni came in from the university and they were in flight suits and combat gear. And there was one woman who had long blonde flowing hair and a black suit and high heels and she was carrying a gun. What did she do? Because that's what I wanted do. >>Awesome. Love it. We >>Blonde >>Wonder Woman. >>Exactly. Wonder Woman. Wendy, it's been so great having you on the program. We, we will definitely be following unit 42 and all the great stuff that you guys are doing. Keep up the good >>Work. Thanks so much Lisa. Thank >>You. Day our pleasure. For our guest and Dave Valante, I'm Lisa Martin, live in Las Vegas at MGM Grand for Palo Alto Ignite, 22. You're watching the Cube, the leader in live enterprise and emerging tech coverage.

(soft electronic music) >> Welcome to this special CUBE Conversation. I'm John Furrier here, in theCUBE's Palo Alto studio. We're here, remotely, with Nick Halsey who's the CEO of OKERA, hot startup doing amazing work in cloud, cloud data, cloud security, policy governance as the intersection of cloud and data comes into real stable operations. That's the number one problem. People are figuring out, right now, is how to make sure that data's addressable and also secure and can be highly governed. So Nick, great to see you. Thanks for coming on theCUBE. >> It's great to be here, John, thank you. >> So you guys have a really hot company going on, here, and you guys are in an intersection, an interesting spot as the market kind of connects together as cloud is going full, kind of, whatever, 3.0, 4.0. You got the edge of the network developing with 5G, you've got space, you've got more connection points, you have more data flowing around. And the enterprises and the customers are trying to figure out, like, okay, how do I architect this thing. And oh, by the way, I got a, like all these compliance issues, too. So this is kind of what you could do. Take a minute to explain what your company's doing. >> Yeah, I'm happy to do that, John. So we're introduced a new category of software that we call universal data authorization or UDA which is really starting to gain some momentum in the market. And there're really two critical reasons why that happening. People are really struggling with how do I enable my digital transformation, my cloud migration while at the same time making sure that my data is secure and that I'm respecting the privacy of my customers, and complying with all of these emerging regulations around data privacy like GDPR, CCPA, and that alphabet soup of regulations that we're all starting to become aware of. >> I want to ask about the market opportunity because, you know, one of the things we see and the cloud covers normal conversations like, "Hey, modern applications are developing." We're starting to see cloud-native. You're starting to see these new use cases so you're starting to see new expectations from users and companies which creates new experiences. And this is throwing off all kinds of new, kinds of data approaches. And a lot of people are scratching their head and they feel like do they slow it down, they speed it up? Do I get a hold of the compliance side first? Do I innovate? So it's like a real kind of conflict between the two. >> Yeah, there's a real tension in most organizations. They're trying to transform, be agile, and use data to drive that transformation. But there's this explosion of the volume, velocity, and variety of data, we've all heard about the three Ds, we'll say there're five Ds. You know, it's really complicated. So you've got the people on the business side of the house and the Chief Data Officer who want to enable many more uses of all of these great data assets. But of course, you've got your security teams and your regulatory and compliance teams that want to make sure they're doing that in the right way. And so you've got to build a zero-trust infrastructure that allows you to be agile and be secure at the same time. And that's why you need universal data authorization because the old manual ways of trying to securely deliver data to people just don't scale in today's demanding environments. >> Well I think that's a really awesome approach, having horizontally scalable data. Like infrastructure would be a great benefit. Take me through what this means. I'd like to get you to define, if you don't mind, what is universal data authorization. What is the definition? What does that mean? >> Exactly and people are like, "I don't understand security. "I do data over here and privacy, "well I do that over here." But the reality is you really need to have the right security platform in order to express your privacy policies, right. And so in the old days, we used to just build it into the database, or we'd build it into the analytic tools. But now, we have too much data in too many platforms in too many locations being accessed by too many, you know, BI applications and A-I-M-L data apps and so you need to centralize the policy definition and policy enforcement so that it can be applied everywhere in the organization. And the example I like to give, John, is we are just like identity access management. Why do I need Okta or Sale Point, or one of those tools? Can't I just log in individually to, you know, SalesForce or to GitHub or? Sure, you can but once you have 30 or 40 systems and thousands of users, it's impossible to manage your employee onboarding and off-boarding policy in a safe and secure way. So you abstract it and then you centralize it and then you can manage and scale it. And that's the same thing you do with OKERA. We do all of the security policy enforcement for all of your data platforms via all of your analytic tools. Anything from Tableau to Databricks to Snowflake, you name it, we support those environments. And then as we're applying the security which says, "Oh, John is allowed access to this data in this format "at this time," we can also make sure that the privacy is governed so that we only show the last four digits of your social security number, or we obfuscate your home address. And we certainly don't show them your bank balance, right? So you need to enable the use of the data without violating the security and privacy rights that you need to enforce. But you can do both, with our customers are doing at incredible scale, then you have sort of digital transformation nirvana resulting from that. >> Yeah, I mean I love what you're saying with the scale piece, that's huge. At AWS's Reinforce Virtual Conference that they had to run because the event was canceled due to the Delta COVID surge, Stephen Schmidt gave a great keynote, I called it a master class, but he mainly focused on cyber security threats. But you're kind of bringing that same architectural thinking to the data privacy, data security piece. 'Cause it's not so much you're vulnerable for hacking, it's still a zero-trust infrastructure for access and management, but-- >> Well you mean you need security for many reasons. You do want to be able to protect external hacks. I mean, every week there's another T-Mobile, you know, you name it, so that's... But 30% of data breaches are by internal trusted users who have rights. So what you needed to make sure is that you're managing those rights and that you're not creating any long tails of data access privilege that can be abused, right? And you also need, one of the great benefits of using a platform like OKERA, is we have a centralized log of what everybody is doing and when, so I could see that you, John, tried to get into the salary database 37 times in the last hour and maybe we don't want to let you do that. So we have really strong stakeholder constituencies in the security and regulatory side of the house because, you know, they can integrate us with Splunk and have a single pane of glass on, weird things are happening in the network and there's, people are trying to hit these secure databases. I can really do event correlation and analysis, I can see who's touching what PII when and whether it's authorized. So people start out by using us to do the enforcement but then they get great value after they've been using us for a while, using that data, usage data, to be able to better manage their environments. >> It's interesting, you know, you bring up the compliance piece as a real added value and I wasn't trying to overlook it but it brings up a good point which is you have, you have multiple benefits when you have a platform like this. So, so take me through like, who's using the product. You must have a lot of customers kicking the tires and adopting it because architecturally, it makes a lot of sense. Take me through a deployment of what it's like in the customer environment. How are they using it? What is some of the first mover types using this approach? And what are some of the benefits they might be realizing? >> Yeah, as you would imagine, our early adopters have been primarily very large organizations that have massive amounts of data. And they tend also to be in more regulated industries like financial services, biomedical research and pharmaceuticals, retail with tons of, you know, consumer information, those are very important. So let me give you an example. We work with one of the very largest global sports retailers in the world, I can't use their name publicly, and we're managing all of their privacy rights management, GDPR, CCPA, worldwide. It's a massive undertaking. Their warehouse is over 65 petabytes in AWS. They have many thousands of users in applications. On a typical day, an average day OKERA is processing and governing six trillion rows of data every single day. On Black Friday, it peaked over 10 trillion rows of data a day, so this is scale that most people really will never get to. But one of the benefits of our architecture is that we are designed to be elastically scalable to sort of, we actually have a capability we call N scale because we can scale to the Nth degree. We really can go as far as you need to in terms of that. And it lets them do extraordinary things in terms of merchandising and profitability and market basket analysis because their teams can work with that data. And even though it's governed and redacted and obfuscated to maintain the individuals' privacy rights, we still let them see the totality of the data and do the kind of analytics that drive the business. >> So large scale, big, big customer base that wants scale, some, I'll say data's huge. What are some of the largest data lakes that you guys have been working with? 'Cause sometimes you hear people saying our data lakes got zettabytes and petabytes of content. What are some of the, give us a taste of the order of magnitude of some of the size of the data lakes and environments that your customers were able to accomplish. >> I want to emphasize that this is really important no matter what size because some of our customers are smaller tech-savvy businesses that aren't necessarily processing huge volumes of data, but it's the way that they are using the data that drives the need for us. But having said that, we're working with one major financial regulator who has a data warehouse with over 200 petabytes of data that we are responsible for providing the governance for. And one thing about that kind of scale that's really important, you know, when you want to have everybody in your organization using data at that scale, which people think of as democratizing your data, you can't just democratize the data, you also have to democratize the governance of the date, right? You can't centralize policy management in IT because then everybody who wants access to the data still has to go back to IT. So you have to make it really easy to write policy and you have to make it very easy to delegate policy management down to the departments. So I need to be able to say this person in HR is going to manage these 50 datasets for those 200 people. And I'm going to delegate the responsibility to them but I'm going to have centralized reporting and auditing so I can trust but verify, right? I can see everything they're doing and I can see how they are applying policy. And I also need to be able to set policy at the macro level at the corporate level that they inherit so I might just say I don't care who you are, nobody gets to see anything but the last four digits of your social security number. And they can do further rules beyond that but they can't change some of the master rules that you're creating. So you need to be able to do this at scale but you need to be able to do it easily with a graphical policy builder that lets you see policy in plain English. >> Okay, so you're saying scale, and then the more smaller use cases are more refined or is it more sensitive data? Regulated data? Or more just levels of granularity? Is that the use case? >> You know, I think there's two things that are really moving the market right now. So the move to remote work with COVID really changed everybody's ideas about how do you do security because you're no longer in a data center, you no longer have a firewall. The Maginot Line of security is gone away and so in a zero-trust world, you know, you have to secure four endpoints: the data, the device, the user, and the application. And so this pretty radical rethinking of security is causing everybody to think about this, big, small, or indifferent. Like, Gartner just came out with a study that said by 2025 75% of all user data in the world is going to be governed by privacy policy. So literally, everybody has to do this. And so we're seeing a lot more tech companies that manage data on behalf of other users, companies that use data as a commodity, they're transacting data. Really, really understand the needs for this and when you're doing data exchange between companies that is really delicate process that have to be highly governed. >> Yeah, I love the security redo. We asked Pat Gelsinger many, many years ago when he was a CEO of VMware what we thought about security and Dave Allante, my co-host at theCUBE said is it a do-over? He said absolutely it's a do-over. I think it was 2013. He mused around that time frame. It's kind of a do-over and you guys are hitting it. This is a key thing. Now he's actually the CEO of Intel and he's still driving forward. Love Pat's vision on this early, but this brings up the question okay, if it's a do-over and these new paradigms are existing and you guys are building a category, okay, it's a new thing. So I have to ask you, I'm sure your customers would say, "Hey, I already got that in another platform." So how do you address that because when you're new you have to convince the customer that this is a new thing. Like, I don't-- >> So, so look, if somebody is still running on Teradata and they have all their security in place and they have a single source of the truth and that's working for them, that's great. We see a lot of our adoption happening as people go on their cloud transformation journey. Because I'm lifting and shifting a lot of data up into the cloud and I'm usually also starting to acquire data from other sources as I'm doing that, and I may be now streaming it in. So when I lift and shift the data, unfortunately, all of the security infrastructure you've built gets left behind. And so a lot of times, that's the forcing function that gets people to realize that they have to make a change here, as well. And we also find other characteristics like, people who are getting proactive in their data transformation initiatives, they'll often hire a CDO, they'll start to use modern data cataloging tools and identity access management tools. And when we see people adopting those things, we understand that they are on a journey that we can help them with. And so we partner very closely with the catalog vendors, with the identity access vendors, you know, with many other parts of the data lake infrastructure because we're just part of the stack, right? But we are the last mile because we're the part of the stack that lets the user connect. >> Well I think you guys are on a wave that's massive and I think it's still, it's going to be bigger coming forward. Again, when you see categories being created it's usually at the beginning of a bigger wave. And I got to ask you because one thing's I've been really kind of harping on on theCUBE and pounding my fist on the table is these siloed approaches. And you're seeing 'em everywhere, I mean, even in the consumer world. LinkedIn's a silo. Facebook's a silo. So you have this siloed mentality. Certainly in the enterprise they're no stranger to silos. So if you want to be horizontally scalable with data you've got to have it free, you've got to break the silos. Are we going to get there? Is this the beginning? Are we breaking down the silos, Nick, or is this the time or what's your reaction to that? >> I'll tell you something, John. I have spent 30 years in the data and analytics business and I've been fortunate enough to help launch many great BI companies like Tableau and Brio Software, and Jaspersoft and Alphablocks we were talking about before the show. Every one of those companies would have been much more successful if they had OKERA because everybody wanted to spread those tools across the organization for better, more agile business analytics, but they were always held back by the security problem. And this was before privacy rights were even a thing. So now with UDA and I think hand-in-hand with identity access management, you truly have the ability to deliver analytic value at scale. And that's key, you need simplicity at scale and that is what lets you let all parts of your organization be agile with data and use it to transform the business. I think we can do that, now. Because if you run in the cloud, it's so easy, I can stand up things like Hadoop in, you know, like Databricks, like Snowflake. I could never do that in my on-prem data center but I can literally press a button and have a very sophisticated data platform, press a button, have OKERA, have enforcement. Really, almost any organization can now take advantage of what only the biggest and most sophisticated organizations use to be able to do it. >> I think Snowflake's an example for all companies that you could essentially build in the shadows of the big clouds and build your own franchise if you nail the security and privacy and that value proposition of scale and good product. So I got, I love this idea of security and privacy managed to a single platform. I'd love to get your final thought while I got you here, on programmability because I'm seeing a lot of regulators and people in the privacy world puttin' down all these rules. You got GDPR and I want to write we forgot and I got all these things... There's a trend towards programmability around extraction of data and managing data where just a simple query could be like okay, I want to know what's goin' on with my privacy and we're a media company and so we record a lot of data too, and we've got to comply with all these like, weird requests, like hey, can you, on June 10th, I want, can you take out my data? And so that's programmatic, that's not a policy thing. It's not like a lawyer with some privacy policy. That's got to be operationalized. So what's your reaction to that as this world starts to be programmable? >> Right, well that's key to our design. So we're an API first approach. We are designed to be a part of a very sophisticated mesh of technology and data so it's extremely simple to just call us to get the information that you need or to express a policy on the fly that might be created because of the current state-based things that are going on. And that's very, very important when you start to do real-time applications that require geo-fencing, you're doing 5G edge computing. It's a very dynamic environment and the policies need to change to reflect the conditions on the ground, so to speak. And so to be callable, programmable, and betable, that is an absolutely critical approach to implementing IUDA in the enterprise. >> Well this is super exciting, I feel you guys are on, again, a bigger wave than it appears. I mean security and privacy operating system, that's what you guys are. >> It is. >> It is what it is. Nick, great to chat with you. >> Couldn't have said it better. >> I love the category creation, love the mojo and I think you guys are on the right track. I love this vision merging data security policy in together into one to get some enablement and get some value creation for your customers and partners. Thanks for coming on to theCUBE. I really appreciate it. >> Now, it's my pleasure and I would just give one piece of advice to our listeners. You can use this everywhere in your organization but don't start with that. Don't boil the ocean, pick one use case like the right to be forgotten and let us help you implement that quickly so you can see the ROI and then we can go from there. >> Well I think you're going to have a customer in theCUBE. We will be calling you. We need this. We've done a lot of digital events now with the pandemic, so locked data that we didn't have to deal with before. But thanks for coming on and sharing, appreciate it. OKERA, hot startup. >> My pleasure, John and thank you so much. >> So OKERA conversation, I'm John Furrier here, in Palo Alto. Thanks for watching. (soft electronic music)

>>from >>around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by A. W s Global Partner Network. >>Welcome back to the cubes. Coverage of reinvent 2020 virtual. Three weeks. We're here covering all the action. Virtually Mister Cube. Virtual normally were in person. This year. We're remote. It's cute. Virtual. We are the cube virtual. And I'm please have a great guest here, man. Who? Car Kumar, Who's the VP of product market Nutanix. Um, the tons of deep coverage on Nutanix over the years we followed the this company since its inception almost over just over 10 years ago. Uh, medic are Welcome to the Cube. Thanks for coming on today. >>Nice to be here, John. >>Were part of the A p. M. Partner experience programming in within. The reinvent is a big day here. Um, you guys are a big part of it. You you have such a great partnership with a W s. You have product on on a W s, which is a high distinction in the in the spirit of their partnership technology rise. Can you tell us real quick? A quick update on the partnership with AWS. What is it? How's it going? What's new? >>So I think about it. We had a dot next John, and as part of that, we announced something called Nutanix clusters. And as part of that cluster that's our hybrid, uh, solution. Basically, what we're saying is we have a lot of customers who certainly had to, you know, take years or maybe even months of digital transformation. And then all of a sudden, they have to now figure out how do I go toe elastic work Lord, in a few weeks. So we were seeing a lot of our customers coming to us and saying, Hey, we really need help with this. We no longer in a situation where we have to go on by a silver and rack and stack that and then, you know, manage all of that over a pair of month. We really need to do something in few weeks, and when we do that, we need some tools that we are really familiar with and something that can help us get toe cloud as quickly as possible. So we were seeing this a lot even before the macro conditions. So sometime around August, we as part of our annual conference. We did announce a partnership with Data Blue s where now you can run an entire Nutanix cluster with all of its products on AWS bare metal as well. And that's the hybrid solution that we're talking about John today. >>That's awesome. And in line with the major themes and waves from the announcements from Andy Jassy and slew of kind of higher level services because the co fit pandemic really highlights this digital transformation of cloud bursting Thio. You know, deploying quicker in the cloud, being more agile and having speed thio value because you need it because of the world's changed. But it's also highlighted. This is a key theme. I want to get your reaction. Teoh is the hybrid Cloud E. I mean, it's been out there. We saw Outpost two years ago, and it's been kind of filling in and and now the environment is clear, right? The enterprises they're saying, I have to operate on premises and in the cloud the same kind of way, but I'm going to do different things. It's not just lift and shift. Throw in the cloud that's been there, done that. It's different. Now it's operating models and environment. Two different environments operate the same. Your reaction. >>That's exactly right. In fact, what we're seeing is from an i d perspective. The new reality is multiple environments on those environments. You know, it could be your, of course, your private data center. It could be your public cloud. Sometimes it could even be the edge and so on. And every time what we see is if you don't have the portability off your workload, you have to kind of redo a whole bunch of things. You have to re factor your applications. You have to go maybe even re skill, your entire workforce. And so there's a lot of overhead involved. Whenever portability is involved in The new reality is that you have to have portability, which is the reason why we see, even with kubernetes, taking such a strong hole in a lot of these organizations. So we've we've been seeing a bunch of different use cases come to us as well. Some customers saying, Hey, that's great that we have all of these multiple tools, but I want consistency. I want consistency in the constructs off the way I manage my i t If I'm managing some workload abs in a different way on Prem, I want to maintain that also in Public Cloud. How do I do that? So clusters really tries to address that gap. In fact, another story I will tell you, John, is that disaster recovery is one of those use cases that we're seeing quite a lot in these conditions as well. We had one customer come to us based in Oregon and they had, of course, you've heard about the fires over there, and they did not really have a disaster recovery plan. So what do you do in situations like that? You have to rely on cloud. So within four hours, we were able to help them to take, you know, their entire infrastructure and have a recovery plan directly into the cloud. So you're seeing a lot off. You use cases like that to, >>you know, that's interesting. The d. R. That recovery is a great one of many use cases, but it highlights the pandemic surge of the change right that the sea change. It's so fast. Okay, Yeah, disaster recovery. We're gonna cloud great solution. But because of the personnel challenges. It also works well, too. So this is the theme. You know, personnel may or may not be available. I got to get to the cloud. I gotta have everything. Software run. Everything is being run by software. So this kind of brings up my favorite topic, which is a big part of the this year's event, which is architecture and edge. And you're starting to see not to pat myself on the back. But I kind of predicted a couple of years ago that there is no edge of its cloud, right. It's cloud public cloud you got on premise Edge data centers a big edge. I mean, it's all the one thing, right? So edges big now, right? And now people working at home, it's an edge, and it highlights all the security issues. So how do you operate that? Yeah, this is a huge challenge. Yeah, >>of course. I think what you touched upon is ah, massive shift that we have seen over the years. As you said, right? Even if you look at things like Calico, for example, first, over a massive shift from hardware specialized hardware to virtualized network functions, for example, which will virtual machines, and I think we are seeing a bigger shift also now where virtual machines are now moving over to containers. And because these are all micro services and very tiny, so to speak, you can run it anywhere and hopefully and commodity hardware. So throughout the years, if you look at if you followed Nutanix, we have followed the path where we started off with hyper hyper converge infrastructure, and that was virtual izing your entire data center stack so you could take storage. Network compute, and now it's completely software defined or virtualized. Whatever you wanna call it, you can run it on any commodity hardware or hardware off your choice. What we see now is that we want toe. Apply that same principle off, being ableto right once, and run anywhere and be agnostic to the underlying layers, even for cloud. So, just as you could take and run your entire Nutanix platform on, create virtual machines and containers on a HP or Dell box, you can now also take that and also run it on Public Cloud, for example. Yeah, that's a great >>point. I mean, I want to just that's the first. That's a great point that's been in your mission from day one. But I wanna ask you if I don't if you don't mind on the edge one topic that's come up a lot, um, this week on we've been reporting on this before. Reinvent I think a VM world that came up a few months ago, um, purpose built edge devices in the old days were purpose built. They were purpose built with, you know, up and down the stack from hardware supply chain all the way. It's software. But when you're kind of getting at is kind of this new use case where you can have a purpose built edge device, whether it's a you know, wearable or machine sensors or whatever machines and still run software on their trusted software suffer defined. This is a key point. Can you can you unpack that this piece? Because I think this is kind of where the rubber meets the road, because if you can be software operated, you can go to that device. It could still be purpose built. >>You still function >>with software >>that that's exactly right. So if you think about it at the end of the day, if you're running some sort of an application or a workload. I always say you need compute, you need storage and you need networking. And we started off with physical hardware than with virtual machines and now with containers. But at the containers level or at the virtual machine level, the application doesn't really care about the underlying pieces right, And that's been our principal when we created the entire Nutanix stack on virtualized everything. So with the Newtown in stock you could take, you know we have our own hyper visor, but we also support others as well, so you can create virtual machines. You can create containers, you could have storage network. And now, because we are agnostic, you can actually run it on hardware off your choice or an environment off your choice. What's more important, though here is that you know the same set of tools that you used to manage. Your data center is now also available available to you to be able to manage it on other environments to in this case it's AWS, or if you decide to run it in any other environment, it would be the exact same. Construct the exact same automation scripts. >>And that, really is what seamless really means. Matt Kuchar. Thanks for coming on and sharing that inside. I want to get your thoughts as we wrap up here. Um, if you could tease out the most important feature or benefit or technology solution up with of the Nutanix on AWS because you know and reinvent, there's a lot of sessions people can go to. You guys have your own. Build your workshop, build your own hybrid cloud workshop. People should check that out. But you know your product marketing your job is to figure out what people really love the most about it. So, you know, here at reinvent this week, what's the most important thing? What should people pay attention to with Nutanix and AWS? >>Yeah, I think it's for us. Uh, I see myself as a developer. Still are our technical person, and for me, what I what really excites me about clusters is through the freedom of choice. I can choose to run it on the environment of my choice in this case is AWS, But there are some Enberg cost benefit features that's in there, you know, as you know, if you create something in the cloud. You don't necessarily think off cloud or cost. You create something that runs all the time, but you often have to worry about Hey, how much is going to cost this? So one of things that we did right as part of clusters is a hibernate feature. And what it allows you to do is that when you're not using clusters, you just like your laptop. You close the screen, you hit the hibernate button on it takes the entire state of your cluster and saves it on s three bucket. And when you're ready, toe reignited. You just hit the resume button. So when you're not using it using the true fundamentals of cloud, you are actually saving costs. That's one of the thing I think is something that will really excite a lot of I. D folks like me. >>Well, you know, being technical, being on the right wave. Software defined software operated infrastructure, automation, speed, consistency, multiple environments operating consistently. This is the Holy Grail is what we want and you guys are doing it. Congratulations. And and have a good Have a good conference. Thanks. >>All right. Thanks. So >>Okay. So cubes coverage of aws reinvent 2023 weeks. We're here. Virtually this. The cube. We are the cube Virtual. I'm John Furry, your host. 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. 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.

>> connecting with thought leaders all around the world, this is a CUBE Conversation. Hi, everybody this is Dave Vellante of theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SEER model, the most popular SEER model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O our open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these great Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Starting the record, Dave in five, four, three. Hi, everybody this is Dave Vellante, theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SaaS model, the most popular SaaS model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O or open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these Greek Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Announcer: Live from New Orleans, Louisiana, it's theCUBE covering .NEXT conference, 2018. Brought to you by Nutanix. >> Welcome back I'm Stu Miniman with my co-host Keith Townsend, and you're watching theCUBE, the leader in live tech coverage. We're at Nutanix NEXT 2018, happy to welcome to the program Brian Stevens, who's the CTO of Google Cloud, had on the program many times. Brian, always a pleasure to catch up with you. >> Thanks, glad to be here. >> Stu: And have a first time guest, Ricardo Jenez, who's the SVP of Development at Nutanix. Thank you so much for joining us. >> Well, thank you for being, thanks for being here. >> Alright, so Ricardo you've only been with Nutanix for three months. I believe this is probably your first .NEXT? >> Ricardo: Yes, it is. >> So give us a little bit about your role and what brings you to us today. >> So I'm responsible for some of the core data path and per some products. So, you know, a lot of it has to do with how do we end up delivering value to our customers and actually end up having predictable, scalable HCI solutions. So, that's really what I'm focused on and focusing on sort of improving our ability to deliver products more quickly. >> So Brian, last year Diane Greene was up on stage talking about the partnership and what was happening here, see Google at the show, obviously a tighter partnership for Nutanix, but give us the update on-- >> We're downgrading. >> Yeah? >> Slumming it. >> Not at all. Not at all. I wish we had enough time to get into the weeds on some of the stuff you're working on, but tell us what brings you here and what kind of stuff you're poking at these days. >> Geez, I think I met Sunil Potti a couple years ago, just at the very beginning of trying to find sort of the intersection between Google Cloud and Nutanix. I mean, Nutanix is largely redefining what IT looks like on premise. We believe we're doing that in cloud, and you really just want to eliminate the impedance between on-premise and public cloud, and so the work with Nutanix is all like what can we do to actually make it more seamless for users that want to use core cloud technology. >> Yeah Brian, you're one of those people that we would say have enterprise DNA in what they've done in their background. People on the outside will always say, Well you know, it's Google, it's Google-y. It's too smart for us. >> Brian: Enterprise DNA is still sexy. >> Yeah, I mean look, there's a lot of enterprises out there, and while yes, the other startups. Maybe we talk a little bit about what that means inside Google. >> Oh my gosh, yeah it was quite a pivot for Google, you know. It was amazing technology, but the customer that you were serving with Google Cloud was already inside of Google. You were serving Surge and YouTube and ads. So you end being up, a really technically, but close relationship. And so what enterprise is, a couple things, it's been a cultural transformation inside of Google, it's been obviously working with enterprise customers globally and building that go to market model and motion that you can sell, but we want a really technical engineered partnership with our customers. So it's not a vendor relationship. So building all that out, we thing we're unique with that. And then the other part I think you were alluding to early before we went on mic, was just around enterprise has a increased set of requirements on what we deliver them from a capabilities perspective, from a security aspect, from a telemetry aspect. And then it's all like how do we actually slipstream into their process, rather than just redefine everything. So to us, that's a big part of what our enterprise pivot's been, for the last three, four years. >> So Ricardo, you have some background at Google. What brought you to Nutanix? What appealed to you? >> Well you know, more than anything else, I think Nutanix has set themselves up, to basically take that experience it has in enterprise, and translate that into the cloud. So when I was at Google, I actually worked on the Google search appliance, which was Google's first-- >> I remember that. >> You remember that. >> I had that one. >> Little yellow boxes, sometimes blue boxes, and that was a great experience. So I'm really happy to hear that Brain talk about the transformation that has happened within Google. But you know, being at Nutanix, the ability to take that experience very close with the work loads that customers are running, and then being able to work with a partner like Google and actually be able to have hybrid clouds where internal private cloud plus having public cloud providers, that really ends up changing the game for a lot of enterprises. >> Yep. Brain, One of the things we've been struggling with as an industry is, you know, it's application mobility. Data, where it goes. Nutanix has been talking about really hybrid cloud from their standpoint. We've talked with you before about where Kubernetes fits into this. Application portability, you just made an acquisition, today was announced, Velostrata. Give us your state on where those things added, it's a big gnarly topic. >> It was just more friction, like public cloud offers great capability that's going to be used not necessarily completely instead of, but in companion to, you know, application services. But there was still that friction around in the early incarnation, it was like it's VMware in this environment or KVM in this environment, and it's a whole nother AMI kind of model here. So the ability to use it, there was a tax. And then there also wasn't that portability and that lightweight aspect that you'd want from an application containerization. I mean, you want what you have on your phone. You want that ability to install apps anywhere. And cloud and IT infrastructure should be exactly the same way. So that's a big part of our investment in containerization. You know Google, back when I was at Red Hat, was investing in cgroups back when there was a kernel, way back then to kind of build that first incarnation of containers in Linux. Along comes Docker to standardize that. I mean, it's an amazing gift to the world. And then Kubernetes is, we're just moving up the stack, on how do you orchestrate it. So sure, companies like Velostrata are really interesting because you have, you know, beyond having Kubernetes platform everywhere, yeah we'll say it's the de facto, but that doesn't mean everybody's running it. And so you're still running on existing systems, you know, largely kind of virtualized. And Velostrata is a technology leader in being virtualization of this type to Google Cloud or other clouds. And then even more so, the technology they have to bring that to containers. So they help you do that migration, transformation process. And I think that's really important for IT organizations. >> Ricardo, you want to comment on some of the hybrid cloud migration stuff? >> So we have our com product, which allows us to actually end up taking workload and moving it to, for instance, Google Cloud or eventually sciCloud and then moving that workload back. So having that sort of Nutanix inside and outside gives it maximum flexibility, and that's a lot of power for IT to have, right? Deciding where it's best to actually run their workloads and be as efficient as possible. >> So as we look at the com, we look at Google Cloud, just the overall pictures, if you're enterprise, you're looking at Google and you're saying man, Google runs at two different speeds. One is 12 factor, micro services, Kubernetes, functions. And then the other side is that, some people just want a VM. They just want a cloud instance and how to make that simple. So let's talk about this relationship. How does Nutanix come together with Google who runs at two different speeds, to make Google Cloud more consumable to the average enterprise? >> Well we're going to talk a little bit more about it later, but the fact that basically we're going to be able to deploy Xi within Google Cloud with nested AHV, and then allow our customers, that'll basically be doing standard workloads to migrate their jobs over to a Google Cloud offering. And as Brian will point out, that basically creates opportunities for them to be able to avail themselves of other capabilities that Google has. So it's not altogether an instant moving path to rewrite, reorient all your apps. It's an ability to kind of do that school migration, if you want to. But you have that capability of being able to go back and forth, in terms of what your workloads are. >> Yeah. >> Brian, want to get your viewpoint on just some of the changing roles that are happening in our industry. We were talking that some of the interviews we've been doing today, it's people talking about infrastructure and code. There was a big hackathon at this event for the first time in, they sold out with over 14 groups, and everything like that. This is a show that started out with people talking about storage, and now we're talking about individual data centers and clouds and all of those things. What are you seeing out in the marketplace? What are some of the challenges and opportunities you're hearing from customers these days? >> I mean it depends on which customers, right? Which region of the world and what their business looks like and I think we all know the holy grail. Infrastructure, as code, is an implementation, but I think what we know that what you really desire is the ability for reproduce ability. The ability to sort of not have state in the IT process. You want to be able to recreate things anywhere. Recreate a whole application, blueprint internally, on public cloud. Tear it down, recreate it. There's no other way to do that without code. So what sort of comes from that SRE model that Google invented, is that what it you didn't have an IT department? And what if you had software engineers that were responsible for IT function? What would that look like? And that's where all of the sudden you realize, everything's APIs and code. So I think that's interesting, and that's sort of where you want to get to, but it's then like, how do you bridge that because a lot of people aren't software developers in IT departments. >> So here's my follow up, 'cause when I go to the Kubernetes show and I talk to users there, 95% of them-- >> They're way over there. >> Had built their own stack, and why do they do that? Because they were ahead of all the platforms. And then I come to the Nutanix show and they're like oh, tensorflow and functions and all that stuff. We're going to put an easy button, and make it easy. I need to take all of these tools and open source and put it together, versus the platform and the easy button. Is this just the early adopters and the majority? >> I think that's okay. That's the open source world, right? I mean think about what's great about open source, is not just creating sort of a venue for collaboration and developers, it's creating access for end users. And so some of the best companies in the world have been built on a DIY model of people just taking open source and integrating it and making the recipe that they want. And so I think you get that whole sort of spectrum and you aren't forced down this model of, here's a COTS product, oh and it happens to be based on open source, but you always have to use technology this way. Open source gives them the freedom to do it as they want. We just need to make sure that we bridge it, so that there's not anybody left behind. That everybody should be able to use the power of Kubernetes, and that means making things super easy to use, and the integration with Nutanix we think is a huge part of making you use that technology stack in a way that's seamlessly operated for an audience like this. >> So a lot of the debate and questions around Kubernetes is how far should it go? Should it go as far as being an opinionated pass? Should it just be a container platform? Where does it start and end? >> Brian: You want my opinion? >> Yeah, opinion that would be awesome. >> Yeah, that was it. Well I think the way the industry started was obviously, there were no PASes, and then we built OpenShift to Red Hat and Google app engine in Roku. And what happened is, those are interesting, but you're right, they are overly opinionated. So you were left either picking a PAS, and you got to change everything to do it this way, and it's great because it delivers value of managed service, but not everything fit in that model. Or you got next to nothing. >> Keith: Right. >> You got a straight IS platform, and then you got to do all the rest. So what we've been doing at Google is tearing that apart and building that architecture from the ground up where you opt into the level that you want. If you want to be able to use IS and the features of IS you use that. If you want to step up and just use containers and IS, you can use containers and IS. If you want to step up and use Kubernetes orchestration, you can do that. If you want to step up and run managing everything in services, than that stacks on top of Kubernetes with STO. If you want to be full on and put in a developer workflow that always has you do deploys this way, then that stacks on top. So I think you're going to get away from this false dichotomy of a choice over here or here, and you're going to all of a sudden get this architectural layering cake that lets you opt into what you want and have IT consistency all the way through it. >> I mean, I used to have a startup that was focused on Hatuputu service, and you know one of the things was basically you didn't have this layering, right? It was, you take the whole stack or you take nothing. And I think the strategy that Google has employed with Kubernetes is just brilliant, to kind of work you way up and basically get people at different levels to be involved. You know, there is a do-it-yourself folks, and they should be allowed to and empowered to do the things that they want to do. And then there are other people who want to have more composed environment. And so we can actually bring that to them as well. And I think that's brilliant. Basically very early on, while Google used a lot of open source internally, it wasn't a strong sort of part of the open source environment. And so I've just enjoyed watching the evolution of Google, sort of leading the open source movement. So, it's been fantastic. I'm right there with you, you know, give them at every level. >> Ricardo, one of the questions coming into this week, people want to know the update of what's happening with Xi. Can you speak about where we are with that and the relationship with Google? What should we be looking for for the rest of this year? >> Well I can't really talk about that, but you know, we are working very closely with Google. And we'll talk a little bit about that at our talk later today. But I won't comment on anything to do with Xi. >> So that gives me the opportunity to ask another controversial question about Kubernetes and getting both of your opinions on it. There's religions and open sourced as religions, enterprise IT, one of which is DevOps. And you look at what companies like Netflix have done with containerizing Java applications and running those legacy Java applications in their container platform. Enterprises are looking at that stuff and thinking, you know what, can I containerize my monolithic application, put it on top of Kubernetes, and drive more efficiency out of my operations from portability to being able to stack up applications in public cloud, general things. Monolithic applications, is that a good thing, bad thing, indifferent? Wrong plate, wrong tool, wrong-- >> No, I think it's just that there's no like one size even for what a monolithic app looks like. Like we don't really have a really proper definition of what it is, but I think people do feel that all of a sudden Kubernetes needs a rewrite and containers needs a rewrite, and actually it doesn't. Because apps are usually sort of separated from the OS already. And so what they're doing is marrying the libraries of the OS, and containers allows them to do that, but just get a higher degree of portability and then with Kubernetes orchestration. So it really depends more around what's the machine resources that that monolithic app needs and are those machine resources still available in a containerized environment. In most cases, the answer is yes. Now the most interesting thing is, what's the escape hatch? Because you can't have a monolithic app that your company, say it's on Mainframe, say it's in the case of something that will not containerize and shouldn't because it's working as designed and there's no use touching it. But that should still participate in the application architecture of the future. And that's why SEO and services are so important. So even if you can't change your runtime stack, you still need to be able to put a services layer in an API in front of that monolithic service, and you'll have a visibility of a service mesh inside of that environment. So now IT sees it just looks like a black box IT service. It doesn't really matter to them that it's not running on the next generation stack because they can still depend on its' services. >> Yeah, I mean I would agree. I look at what Kubernetes offers and containers as sort of an on ramp to creating services, the on ramp to actually taking that monolithic application, assuming that they're resources, and take a step up in terms of the architectures that you can build around it and then be able to break apart that monolithic application. It doesn't have to happen all at once. It's sort of the stepping stones that you can take. So it's a very powerful model for enablement for people who have stuff that they haven't been able to make the most value out of because maybe the application's been around for a while. Now they can actually end up putting it in an environment where they can actually make the most of it and then work on how they're going to end up slowly pulling it apart and making it more service oriented. >> Alright, Ricardo and Brian, thank you so much for joining us. Appreciate the update and look forward to seeing more throughout the show and further in the year. Be sure to check out theCUBE.net where you'll not only find all of this information, but theCUBE is really excited to say that we will be at the Google Cloud show in July. So for Keith Townsend, and I'm Stu Miniman, getting towards the end of day one of two days of live coverage. Thanks so much for watching theCUBE. (upbeat music)

>> Narrator: Live from Las Vegas, it's theCUBE covering ServiceNow Knowledge 2018, brought to you by ServiceNow. >> Welcome back everyone to theCUBE's live coverage of ServiceNow Knowledge 18. We are theCUBE, we are the leader in live tech coverage. I'm your host Rebecca Knight along with my cohost Dave Vellante. We're joined by Jason Wojahn. He is the managing director global ServiceNow practice lead at Accenture. Thanks so much for your, your returning guest. You're a CUBE veteran. >> Yeah, many times. >> Many time CUBE alum. >> Yes, >> Thanks for noticing. >> Back in the early days. >> But for those who have not had the pleasure of watching your CUBE clips, can you explain what your role is and what you do at Accenture? >> Sure, I'm the global ServiceNow practice lead at Accenture, I'm responsible for our global capabilities in ServiceNow for the company of Accenture. So you know, everything to do with ServiceNow from our consulting capability to our training capability. At Accenture we also have, kind of, what we call three estates of ServiceNow. We have the CIO estate, I know you had Andrew Wilson on theCUBE yesterday, and of course we are a fully deployed ServiceNow customer in our CIO's office. One of the top 10 customers of ServiceNow. We also utilize ServiceNow in our AO, IO, and PBO lines of business. Now in that case that's a go to market relationship where we're selling things like HR outsourcing that is platformed and delivered on ServiceNow and of course last but not least our consulting capabilities. Just over 3000 skilled ServiceNow resources across the world What makes us the largest practice for ServiceNow in the world as well. And those are our three estates of ServiceNow in Accenture. >> So don't hate me for saying this but when we first started following ServiceNow I remember Frank Slootman said to me Dave, this thing is a rocketship. We're going to blow through a billion dollars. We're going to be the next great software company. And one of the things Jeff and I said was well, the ecosystem has to grow. There were companies like Cloud Sherpas which nobody ever heard of which were specialists in the space. Now you fast forward five, six, seven years, Accenture gets into the game, other big SI's have gotten into the game and it is the real deal. It feels like the next ERP of the modern era. >> In my view there are three main big surges going on in the ServiceNow ecosystem and you can kind of tie them back to the CEO's. So you had the early day with Fred Luddy of course, kind of the zero to 150 million stage of ServiceNow. of course when Frank Slootman came in in the 2011 time frame you know you have the next big surge, see them getting IPO ready, you see them really ruggedizing their commercial selling capabilities, their delivery methodology capabilities, etc., and then we move all the way to today and with John Donahoe you see the third surge. And here you see every GSI on the planet wanting to do something with ServiceNow for a lot of the reasons that I just discussed. I mean ServiceNow has been a terribly strategic tool in Accenture across multiple aspects. Of course our go to market aspects, our consulting aspects and of course our internal use of the platform as well. >> It's not easy for software companies to reach escape velocity, certainly many of them can become unicorns and have a billion dollar valuation. It's really hard for them to get to a billion dollars of revenue. ServiceNow has blown through that. They'll probably do three billion or close to it this year. So they really are, in many ways, the next great software company, but you know, VMWare got there, Red Hat obviously doing really well. What are your perspectives on the software ecosystem? I mean, personally I think it's great that we see more competition but there seems to be always this pressure to consolidate. What's your sense of what's happening now? >> Well you see a lot of consolidation that ServiceNow is doing to round out their capabilities as a platform and I think that's terribly important. That's how people want to consume technology right now so we spend a ton of time at this event and you've heard ServiceNow as well, talking about experience management, service management, you know trying to get things away from, you know how do I do this and going to why would I do this versus how. And of course you utilize platforms to really set that tenancy. When you got platform like ServiceNow that has the ability to turn on intelligent automation machine learning capabilities across your platform, the ability to turn on chatbox across your platform, analytics across your platform, knowledge across your platform and of course manage your workflow the way they do with portals, etc. I mean there's no reason to go somewhere else but more importantly, the strategy underneath it you know ServiceNow is an outcome of something that's very important. You can't use AI, you can't use Chatbox, you can't automate if you don't have what we call a lake of data, a data lake. You've got to have that kind of single source of information so that you can do those compounded workflows and get that automation benefit and then when you start laying things like AI, machine learning, intelligent automation, chatbox in there, actually you have to have the data in there to make the suggestions, right, to do the modeling and the analyses to find those opportunities. So I think what you're going to see and what you're actually seeing right now is consolidations on platforms. And those platforms are kind of being used as a ubiquitous glue code for everything else behind the infrastructure and really looking at you know, this is the employee first experience. This is where the last yard of the field is being delivered to the individual. >> The red zone. >> Yeah. >> So the timing of the Accenture acquistion was actually fortuitous because it coincided with ServiceNow's push into the rest of the enterprise. Accenture obviously deep into lines of business, board levels, C-Suite, etc. Talk about how that's changed the whole relationship motion with your customers, how you've gone deeper and describe, sort of, that dynamic. >> Yeah, so, obviously within Accenture our diamond clients are paramount to the way we run our business and who we are as a business and what's great is we're seeing more and more of those clients where they have comprehensive relationships with Accenture, bringing ServiceNow to bear in that conversation and actually, again, using it as an overarching capability to help get things done better. You know it can be very austere to sit at a Cebol console or an Oracle console or those types of things. We're actually using ServiceNow to kind of keep that from having to happen but you're doing the same transaction on the back end. And again, like I said, you know, once you get some of those data points in there it tends to kind of start to gain some momentum because you get a little bit of automation here or a little bit of automation there and then suddenly that connects you to other aspects of the enterprise and other consolidation points. >> What makes Accenture different, you got all the SI's are now in, elbowing their way in. We want a piece of the action. Why Accenture? >> Well the ego in me says it's because we're number one. We have the largest single certified pool of resources across the globe. There's nobody bigger than us. There's nobody that does more influence revenue than ServiceNow, than us and there's no one with higher customer satisfaction than us We actually got that award two days ago from ServiceNow. So if you value those things, that's why you should work with Accenture. But more importantly than that we've really spent a lot of time making sure that we're doubling down on our methodologies, we're doubling down on our thought leadership, we're leveraging our capabilities that we're you know, trialing and piloting in our CIO's office across the 450,000 person company called Accenture. We're obviously leveraging the things we learn in our AO, IO, BPO practices where we have embedded ServiceNow into those go to market services. But we're bringing that all back to our consulting practice and it's a creed of to not only the way we handle CIO, AO, IO, BPO, but a way we handle our customers from a consulting perspective as well. >> It's the customercentric approach. >> Jason: It is, it is. >> Well Jason thanks so much for coming on the program. It's always fun to have you on theCUBE. >> Thanks a lot. >> Dave: Great to see you. >> Thanks. >> I'm Rebecca Knight for Dave Vellante. We will have more from theCUBE's live coverage of ServiceNow Knowledge 18 in just a little bit.