>> Narrator: TheCUBE presents Ignite '22, brought to you by Palo Alto Networks. >> Greetings from the MGM Grand Hotel in beautiful Las Vegas. It's theCUBE Live Day two of our coverage of Palo Alto Networks, ignite 22. Lisa Martin, Dave Vellante. Dave, what can I say? This has been a great couple of days. The amount of content we have created and shared with our viewers on theCUBE is second to none. >> Well, the cloud has completely changed the way that people think about security. >> Yeah. You know at first it was like, oh, the cloud, how can that be secure? And they realized, wow actually cloud is pretty secure if we do it right. And so shared responsibility model and partnerships are critical. >> Partnerships are critical, especially as more and more organizations are multicloud by default. Right? These days we're going to be bring Google into the conversation. Josh Haslet joins us. Strategic Partnership Manager at Google. Welcome. Great to have you Josh. >> Hi Lisa, thanks for having me here. >> So you are a secret squirrel from Palo Alto Networks. Talk to me a little bit about your background and about your role at Google in terms of partnership management. >> Sure, I feel like we need to add that to my title. [Lisa] You should, secret squirrel. >> Great. Yeah, so as a matter of fact, I've been at Google for two and a half years. Prior to that, I was at Palo Alto Networks. I was managing the business development relationship with Google, and I was kind of at the inception of when the cash came in and, and decided that we needed to think about how to do security in a new way from a platform standpoint, right? And so it was exciting because when I started with the partnership, we were focusing on still securing you know, workloads in the cloud with next generation firewall. And then as we went through acquisitions the Palo Alto added it expanded the capabilities of what we could do from cloud security. And so it was very exciting, you know, to, to make sure that we could onboard with Google Cloud, take a look at how not only Palo Alto was enhancing their solutions as they built those and delivered those from Google Cloud. But then how did we help customers adopt cloud in a more easy fashion by making things, you know more tightly integrated? And so that's really been a lot of what I've been involved in, which has been exciting to see the growth of both organizations as we see customers shifting to cloud transformation. And then how do they deploy these new methodologies and tools from a security perspective to embrace this new way of working and this new way of, you know creating applications and doing digital transformation. >> Important, since work is no longer a place, it's an activity. Organizations have have to be able to cater to the distributed workforce. Of course, the, the, the workforce has to be able to access everything that they need to, but it has to be done in a secure way regardless of what kind of company you are. >> Yeah, you're right, Lisa. It's interesting. I mean, the pandemic has really changed and accelerated that transformation. I think, you know really remote working has started previous to that. And I think Nikesh called that out in the keynote too right? He, he really said that this has been ongoing for a while, but I think, you know organizations had to figure out how to scale and that was something that they weren't as prepared for. And a lot of the technology that was deployed for VPN connectivity or supporting remote work that was fixed hardware. And so cloud deployment and cloud architecture specifically with Prisma access really enabled this transformation to happen in a much faster, you know, manner. And where we've come together is how do we make sure that customers, no matter what device, what user what application you're accessing. As we take a look at ZTNA, Zero Trust Network Access 2.0, how can we come together to partner to make sure the customers have that wide range of coverage and capability? >> How, how do you how would you describe Josh Google's partner strategy generally and specifically, you know, in the world of cyber and what makes it unique and different? >> Yeah, so that's a great question. I think, you know, from Google Cloud perspective we heard TK mention this in the keynote with Nikesh. You know, we focus on on building a secure platform first and foremost, right? We want to be a trusted cloud for customers to deploy on. And so, you know, we find that as customers do one of two things, they're looking at, you know, reducing cost as they move to cloud and consolidate workloads or as they embrace innovation and look at, you know leveraging things like BigQuery for analytics and you know machine learning for the way that they want to innovate and stay ahead of the competition. They have to think about how do they secure in a new way. And so, not only do we work on how do we secure our own platform, we work with trusted partners to make sure that customers have you mentioned it earlier, Dave the shared security model, right? How do they take a look at their applications and their workloads and this new way of working as they go to CI/CD pipelines, they start thinking about DevSecOps. How do they integrate tooling that is frictionless and seamless for their, for their teams to deploy but allows them to quickly embrace that cloud transformation journey. And so, yes, partners are critical to that. The other thing is, you know we find that, you mentioned earlier, Lisa that customers are multicloud, right? That's kind of the the new normal as we look at enterprises today. And so Google Cloud's going to do a great job at securing our platform, but we need partners that can help customers deploy policy that embraces not only the things that they put in Google Cloud but as they're in their transformation journey. How that embraces the estates that are in data centers the things that are still on-prem. And really this is about making sure that the applications no matter where they are, the databases no matter where they are, and the users no matter where they are are all secure in that new framework of deploying and embracing innovation on public cloud. >> One of the things that almost everybody from Palo Alto Networks talks about is their partnering strategy their acquisition strategy integrations. And I was doing some research. There's over 50 joint integrations that Google Cloud and Palo Alto Networks. Have you talked about Zero Trust Network Access 2.0 that was announced yesterday. >> Correct. >> Give us a flavor of what that is and what does it deliver that 1.0 did not? >> Well, great. And what I'd like to do is touch a little bit on those 50 integrations because it's been, you know, a a building rolling thunder, shall we say as far as how have we taken a look at customers embracing the cloud. The first thing was we took a look at at how do we make sure that Palo Alto solutions are easier for customers to deploy and to orchestrate in Google Cloud making their journey to embracing cloud seamless and easy. The second thing was how could we make that deployment and the infrastructure even more easy to adopt by doing first party integrations? So earlier this year we announced cloud IDS intrusion detection system where we actually have first party directly in our console of customers being able to simply select, they want to turn on inspection of the traffic that's running on Google Cloud and it leverages the threat detection capability from Palo Alto Networks. So we've gone from third party integration alone to first party integration. And that really takes us to, you know, the direction of what we're seeing customers need to embrace now which is, this is your Zero Trusts strategy and Zero Trust 2.0 helps customers do a number of things. The first is, you know, we don't want to just verify a user and their access into the environment once. It needs to be continuous inspection, right? Cause their state could change. I think, you know, the, the teams we're talking about some really good ways of addressing, you know for instance, TSA checkpoints, right? And how does that experience look? We need to make sure that we're constantly evaluating that user's access into the environment and then we need to make sure that the content that's being accessed or, you know, loaded into the environment is inspected. So we need continuous content inspection. And that's where our partnership really comes together very well, is not only can we take care of any app any device, any user, and especially as we take a look at you know, embracing contractor like use cases for instance where we have managed devices and unmanaged devices we bring together beyond Corp and Prisma access to take a look at how can we make sure any device, any user any application is secure throughout. And then we've got content inspection of how that ZTNA 2.0 experience looks like. >> Josh, that threat data that you just talked about. >> Yeah. >> Who has access to that? Is it available to any partner, any customer, how... it seems like there's gold in them, NAR hills, so. >> There is. But, this could be gold going both ways. So how, how do you adjudicate and, how do you make sure that first of all that that data's accessible for, for good and not in how do you protect it against, you know, wrong use? >> Well, this is one of the great things about partnering with Palo Alto because technically the the threat intelligence is coming from their ingestion of malware, known threats, and unknown threats right into their technology. Wildfire, for instance, is a tremendous example of this where unit 42 does, you know, analysis on unknown threats based upon what Nikesh said on stage. They've taken their I think he said 27 days to identification and remediation down to less than a minute, right? So they've been able to take the intelligence of what they ingest from all of their existing customers the unknown vulnerabilities that are identified quickly assessing what those look like, and then pushing out information to the rest of their customers so that they can remediate and protect against those threats. So we get this shared intelligence from the way that Palo Alto leverages that capability and we've brought that natively into Google Cloud with cloud intrusion detection. >> So, okay, so I'm, I'm I dunno why I have high frequency trading in my mind cause it used to be, you know, like the norm was, oh it's going to take a year to identify an intrusion. And, and, and now it's down to, you know take was down to 27 days. Now it's down to a minute. Now it's not. That's best practice. And I'm, again, I'm thinking high frequency trading how do I beat the speed of light? And that's kind of where we're headed, right? >> Right. >> And so that's why he said one minute's not enough. We have to keep going. >> That's right. >> So guys got your best people working on that? >> Well, as a matter of fact, so Palo Alto Networks, you know when we take a look at what Nikesh said from stage, he talked about using machine learning and AI to get ahead of what we what they look at as far as predictability not only about behaviors in the environment so things that are not necessarily known threats but things that aren't behaving properly in the environment. And you can start to detect based on that. The second piece of it then is a lot of that technology is built on Google Cloud. So we're leveraging, their leveraging the capabilities that come together with you know, aggregation of, of logs the file stitching across the entire environment from the endpoint through to cloud operations the things that they detect for network content inspection putting all those files together to understand, you know where has the threat vector entered how has it gone lateral inside the environment? And then how do you make sure that you remediate all of those points of intrusion. And so yeah it's been exciting to see how our product teams have worked together to continue to advance the capabilities for speed for customers. >> And secure speed is critical. We had the opportunity this morning to speak with Lee Claridge, the chief product officer, and you know one of the things that I had heard about Lee is that despite all of the challenges in cybersecurity and the amorphous expansion of the threat network and the sophistication of the adversaries he's really optimistic about what it's going to enable organizations to do. I see you smiling. Do you share that optimism? >> I, I do. I think, you know, when you bring, when you bring leaders together to tackle big problems, I think, you know we've got the right teams working on the right things and we understand the problems that the customers are facing. And so, you know, from a a Google cloud perspective we understand that partnering with Palo Alto Networks helps to make sure that that optimism continues. You know, we work on continuous innovation when it comes to Google Cloud security framework, but then partnering with Palo Alto brings additional capabilities to the table. >> Vision for the, for the partnership. Where do you want to see it go? What's... we're two to five years down the road, what's it look like? Maybe two to three years. Let's go. >> Well, it was interesting. I, I think neer was the one that mentioned on stage about, you know how AI is going to start replacing us in our main jobs, right? I I think there's a lot of truth to that. I think as we look forward, we see that our teams are going to continue to help with automation remediation and we're going to have the humans working on things that are more interesting and important. And so that's an exciting place to go because today the reality is that we are understaffed in cybersecurity across the industry and we just can't hire enough people to make sure that we can detect, remediate and secure, you know every user endpoint and environment out there. So it's exciting to see that we've got a capability to move in a direction to where we can make sure that we get ahead of the threat actors. >> Yeah. So he said within five years your SOC will be AI based and and basically he elaborated saying there's a lot of stuff that you're doing today that you're not going to be doing tomorrow. >> That's true. >> And that's going to continue to be a moving target I would think Google is probably ahead in that game and ahead of most, right? I mean, you guys were there early. I mean, I remember when Hadoop was all the rage like just at the beginning you guys like, yeah, you know Google's like, no, no, no, we're not doing Hadoop anymore. That's like old news. So you tended to be, I don't know, at least five maybe seven years ahead of the industry. So I imagine you using a lot of those AI techniques in your own business today. >> Absolutely. I mean, I think you see it in our consumer products, and you certainly see it in the the capabilities we make available to enterprise as far as how they can innovate on our cloud. And we want to make sure that we continue to provide those capabilities, you know not only for the tools that we build but the tools that customers use. >> What's the, as we kind of get towards the end of our conversation here, we we talk about zero trust as, as a journey, as an approach. It's not a product, it's not a tool. What is the, who's involved in the zero trust journey from the customers perspective? Is this solely with the CSO, CSO, CIOs or is this at the CEO level going, we have to be a data company but we have to be a secure data company 24/7. >> It's interesting as you've seen malware, phishing, ransomware attacks. >> Yeah. >> This is not only just a CSO CIO conversation it's a board level conversation. And so, you know the way to address this new way of working where we have very distributed environments where you can't create a perimeter anymore. You need to strategize with zero trust. And so continuously, when we're talking to customers we're hearing that as a main initiative, you know from the CIO's office and from the board level. >> Got it, last question. The upgrade path for existing customers from 1., ZTNA 1.0 to 2.0. How simple is that? >> It's easy. You know, when we take- >> Is there an easy button? >> So here's the great thing [Dave] If you're feeling lucky. [Lisa] Yeah. (group laughs) >> Well, Palo Alto, right? Billing prisma access has really taken what was traditional security that was an on-prem or a data center deployed strategy to cloud-based. And so we've worked with customers like Princeton University who had to quickly transition from in-person learning to distance learning find a way to ramp their staff their faculty and their students. And we were able to, you know Palo Alto deploy it on Google Cloud's, you know network that solution in very quick order and had those, you know, everybody back up and running. So deployment and upgrade path is, is simple when you look at cloud deployed architectures to address zero trusts network. >> That's awesome. Some of those, some of those use cases that came out of the pandemic were mind blowing but also really set the table for other organizations to go, yes, this can be done. And it doesn't have to take forever because frankly where security is concerned, we don't have time. >> That's right. And it's so much faster than traditional architectures where you had to procure hardware. >> Yeah. >> Deploy it, configure it, and then, you know push agents out to all the endpoints and and get your users provisioned. In this case, we're talking about cloud delivered, right? So I've seen, you know, with Palo Alto deploying for customers that run on Google Cloud they've deployed tens of thousands of users in a very short order. You know, we're talking It was, it's not months anymore. It's not weeks anymore. It's days >> Has to be days. Josh, it's been such a pleasure having you on the program. Thank you for stopping by and talking with Dave and me about Google Cloud, Palo Alto Networks in in addition to secret squirrel. I feel like when you were describing your background that you're like the love child of Palo Alto Networks and Google Cloud, you might put that on your cartoon. >> That is a huge compliment. I really appreciate that, Lisa, thank you so much. >> Thanks so much, Josh. [Josh] It's been a pleasure being here with you. [Dave] Thank you >> Oh, likewise. For Josh Haslett and Dave, I'm Lisa Martin. You're watching theCUBE, the leader in live coverage for emerging and enterprise tech. (upbeat outro music)

>>Hey everyone. Welcome to Las Vegas. The Cube is here, live at the Venetian Expo Center for AWS Reinvent 2022. Amazing attendance. This is day one of our coverage. Lisa Martin here with Day Ante. David is great to see so many people back. We're gonna be talk, we've been having great conversations already. We have a wall to wall coverage for the next three and a half days. When we talk to companies, customers, every company has to be a data company. And one of the things I think we learned in the pandemic is that access to real time data and real time analytics, no longer a nice to have that is a differentiator and a competitive all >>About data. I mean, you know, I love the topic and it's, it's got so many dimensions and such texture, can't get enough of data. >>I know we have a great guest joining us. One of our alumni is back, Evan Kaplan, the CEO of Influx Data. Evan, thank you so much for joining us. Welcome back to the Cube. >>Thanks for having me. It's great to be here. So here >>We are, day one. I was telling you before we went live, we're nice and fresh hosts. Talk to us about what's new at Influxed since the last time we saw you at Reinvent. >>That's great. So first of all, we should acknowledge what's going on here. This is pretty exciting. Yeah, that does really feel like, I know there was a show last year, but this feels like the first post Covid shows a lot of energy, a lot of attention despite a difficult economy. In terms of, you know, you guys were commenting in the lead into Big data. I think, you know, if we were to talk about Big Data five, six years ago, what would we be talking about? We'd been talking about Hadoop, we were talking about Cloudera, we were talking about Hortonworks, we were talking about Big Data Lakes, data stores. I think what's happened is, is this this interesting dynamic of, let's call it if you will, the, the secularization of data in which it breaks into different fields, different, almost a taxonomy. You've got this set of search data, you've got this observability data, you've got graph data, you've got document data and what you're seeing in the market and now you have time series data. >>And what you're seeing in the market is this incredible capability by developers as well and mostly open source dynamic driving this, this incredible capability of developers to assemble data platforms that aren't unicellular, that aren't just built on Hado or Oracle or Postgres or MySQL, but in fact represent different data types. So for us, what we care about his time series, we care about anything that happens in time, where time can be the primary measurement, which if you think about it, is a huge proportion of real data. Cuz when you think about what drives ai, you think about what happened, what happened, what happened, what happened, what's going to happen. That's the functional thing. But what happened is always defined by a period, a measurement, a time. And so what's new for us is we've developed this new open source engine called IOx. And so it's basically a refresh of the whole database, a kilo database that uses Apache Arrow, par K and data fusion and turns it into a super powerful real time analytics platform. It was already pretty real time before, but it's increasingly now and it adds SQL capability and infinite cardinality. And so it handles bigger data sets, but importantly, not just bigger but faster, faster data. So that's primarily what we're talking about to show. >>So how does that affect where you can play in the marketplace? Is it, I mean, how does it affect your total available market? Your great question. Your, your customer opportunities. >>I think it's, it's really an interesting market in that you've got all of these different approaches to database. Whether you take data warehouses from Snowflake or, or arguably data bricks also. And you take these individual database companies like Mongo Influx, Neo Forge, elastic, and people like that. I think the commonality you see across the volume is, is many of 'em, if not all of them, are based on some sort of open source dynamic. So I think that is an in an untractable trend that will continue for on. But in terms of the broader, the broader database market, our total expand, total available tam, lots of these things are coming together in interesting ways. And so the, the, the wave that will ride that we wanna ride, because it's all big data and it's all increasingly fast data and it's all machine learning and AI is really around that measurement issue. That instrumentation the idea that if you're gonna build any sophisticated system, it starts with instrumentation and the journey is defined by instrumentation. So we view ourselves as that instrumentation tooling for understanding complex systems. And how, >>I have to follow quick follow up. Why did you say arguably data bricks? I mean open source ethos? >>Well, I was saying arguably data bricks cuz Spark, I mean it's a great company and it's based on Spark, but there's quite a gap between Spark and what Data Bricks is today. And in some ways data bricks from the outside looking in looks a lot like Snowflake to me looks a lot like a really sophisticated data warehouse with a lot of post-processing capabilities >>And, and with an open source less >>Than a >>Core database. Yeah. Right, right, right. Yeah, I totally agree. Okay, thank you for that >>Part that that was not arguably like they're, they're not a good company or >>No, no. They got great momentum and I'm just curious. Absolutely. You know, so, >>So talk a little bit about IOx and, and what it is enabling you guys to achieve from a competitive advantage perspective. The key differentiators give us that scoop. >>So if you think about, so our old storage engine was called tsm, also open sourced, right? And IOx is open sourced and the old storage engine was really built around this time series measurements, particularly metrics, lots of metrics and handling those at scale and making it super easy for developers to use. But, but our old data engine only supported either a custom graphical UI that you'd build yourself on top of it or a dashboarding tool like Grafana or Chronograph or things like that. With IOCs. Two or three interventions were important. One is we now support, we'll support things like Tableau, Microsoft, bi, and so you're taking that same data that was available for instrumentation and now you're using it for business intelligence also. So that became super important and it kind of answers your question about the expanded market expands the market. The second thing is, when you're dealing with time series data, you're dealing with this concept of cardinality, which is, and I don't know if you're familiar with it, but the idea that that it's a multiplication of measurements in a table. And so the more measurements you want over the more series you have, you have this really expanding exponential set that can choke a database off. And the way we've designed IIS to handle what we call infinite cardinality, where you don't even have to think about that design point of view. And then lastly, it's just query performance is dramatically better. And so it's pretty exciting. >>So the unlimited cardinality, basically you could identify relationships between data and different databases. Is that right? Between >>The same database but different measurements, different tables, yeah. Yeah. Right. Yeah, yeah. So you can handle, so you could say, I wanna look at the way, the way the noise levels are performed in this room according to 400 different locations on 25 different days, over seven months of the year. And that each one is a measurement. Each one adds to cardinality. And you can say, I wanna search on Tuesdays in December, what the noise level is at 2:21 PM and you get a very quick response. That kind of instrumentation is critical to smarter systems. How are >>You able to process that data at at, in a performance level that doesn't bring the database to its knees? What's the secret sauce behind that? >>It's AUM database. It's built on Parque and Apache Arrow. But it's, but to say it's nice to say without a much longer conversation, it's an architecture that's really built for pulling that kind of data. If you know the data is time series and you're looking for a time measurement, you already have the ability to optimize pretty dramatically. >>So it's, it's that purpose built aspect of it. It's the >>Purpose built aspect. You couldn't take Postgres and do the same >>Thing. Right? Because a lot of vendors say, oh yeah, we have time series now. Yeah. Right. So yeah. Yeah. Right. >>And they >>Do. Yeah. But >>It's not, it's not, the founding of the company came because Paul Dicks was working on Wall Street building time series databases on H base, on MyQ, on other platforms and realize every time we do it, we have to rewrite the code. We build a bunch of application logic to handle all these. We're talking about, we have customers that are adding hundreds of millions to billions of points a second. So you're talking about an ingest level. You know, you think about all those data points, you're talking about ingest level that just doesn't, you know, it just databases aren't designed for that. Right? And so it's not just us, our competitors also build good time series databases. And so the category is really emergent. Yeah, >>Sure. Talk about a favorite customer story they think really articulates the value of what Influx is doing, especially with IOx. >>Yeah, sure. And I love this, I love this story because you know, Tesla may not be in favor because of the latest Elon Musker aids, but, but, but so we've had about a four year relationship with Tesla where they built their power wall technology around recording that, seeing your device, seeing the stuff, seeing the charging on your car. It's all captured in influx databases that are reporting from power walls and mega power packs all over the world. And they report to a central place at, at, at Tesla's headquarters and it reports out to your phone and so you can see it. And what's really cool about this to me is I've got two Tesla cars and I've got a Tesla solar roof tiles. So I watch this date all the time. So it's a great customer story. And actually if you go on our website, you can see I did an hour interview with the engineer that designed the system cuz the system is super impressive and I just think it's really cool. Plus it's, you know, it's all the good green stuff that we really appreciate supporting sustainability, right? Yeah. >>Right, right. Talk about from a, what's in it for me as a customer, what you guys have done, the change to IOCs, what, what are some of the key features of it and the key values in it for customers like Tesla, like other industry customers as well? >>Well, so it's relatively new. It just arrived in our cloud product. So Tesla's not using it today. We have a first set of customers starting to use it. We, the, it's in open source. So it's a very popular project in the open source world. But the key issues are, are really the stuff that we've kind of covered here, which is that a broad SQL environment. So accessing all those SQL developers, the same people who code against Snowflake's data warehouse or data bricks or Postgres, can now can code that data against influx, open up the BI market. It's the cardinality, it's the performance. It's really an architecture. It's the next gen. We've been doing this for six years, it's the next generation of everything. We've seen how you make time series be super performing. And that's only relevant because more and more things are becoming real time as we develop smarter and smarter systems. The journey is pretty clear. You instrument the system, you, you let it run, you watch for anomalies, you correct those anomalies, you re instrument the system. You do that 4 billion times, you have a self-driving car, you do that 55 times, you have a better podcast that is, that is handling its audio better, right? So everything is on that journey of getting smarter and smarter. So >>You guys, you guys the big committers to IOCs, right? Yes. And how, talk about how you support the, develop the surrounding developer community, how you get that flywheel effect going >>First. I mean it's actually actually a really kind of, let's call it, it's more art than science. Yeah. First of all, you you, you come up with an architecture that really resonates for developers. And Paul Ds our founder, really is a developer's developer. And so he started talking about this in the community about an architecture that uses Apache Arrow Parque, which is, you know, the standard now becoming for file formats that uses Apache Arrow for directing queries and things like that and uses data fusion and said what this thing needs is a Columbia database that sits behind all of this stuff and integrates it. And he started talking about it two years ago and then he started publishing in IOCs that commits in the, in GitHub commits. And slowly, but over time in Hacker News and other, and other people go, oh yeah, this is fundamentally right. >>It addresses the problems that people have with things like click cows or plain databases or Coast and they go, okay, this is the right architecture at the right time. Not different than original influx, not different than what Elastic hit on, not different than what Confluent with Kafka hit on and their time is you build an audience of people who are committed to understanding this kind of stuff and they become committers and they become the core. Yeah. And you build out from it. And so super. And so we chose to have an MIT open source license. Yeah. It's not some secondary license competitors can use it and, and competitors can use it against us. Yeah. >>One of the things I know that Influx data talks about is the time to awesome, which I love that, but what does that mean? What is the time to Awesome. Yeah. For developer, >>It comes from that original story where, where Paul would have to write six months of application logic and stuff to build a time series based applications. And so Paul's notion was, and this was based on the original Mongo, which was very successful because it was very easy to use relative to most databases. So Paul developed this commitment, this idea that I quickly joined on, which was, hey, it should be relatively quickly for a developer to build something of import to solve a problem, it should be able to happen very quickly. So it's got a schemaless background so you don't have to know the schema beforehand. It does some things that make it really easy to feel powerful as a developer quickly. And if you think about that journey, if you feel powerful with a tool quickly, then you'll go deeper and deeper and deeper and pretty soon you're taking that tool with you wherever you go, it becomes the tool of choice as you go to that next job or you go to that next application. And so that's a fundamental way we think about it. To be honest with you, we haven't always delivered perfectly on that. It's generally in our dna. So we do pretty well, but I always feel like we can do better. >>So if you were to put a bumper sticker on one of your Teslas about influx data, what would it >>Say? By the way, I'm not rich. It just happened to be that we have two Teslas and we have for a while, we just committed to that. The, the, so ask the question again. Sorry. >>Bumper sticker on influx data. What would it say? How, how would I >>Understand it be time to Awesome. It would be that that phrase his time to Awesome. Right. >>Love that. >>Yeah, I'd love it. >>Excellent time to. Awesome. Evan, thank you so much for joining David, the >>Program. It's really fun. Great thing >>On Evan. Great to, you're on. Haven't Well, great to have you back talking about what you guys are doing and helping organizations like Tesla and others really transform their businesses, which is all about business transformation these days. We appreciate your insights. >>That's great. Thank >>You for our guest and Dave Ante. I'm Lisa Martin, you're watching The Cube, the leader in emerging and enterprise tech coverage. We'll be right back with our next guest.

>>Hey, everyone. Welcome back to San Francisco. Lisa Martin and John furrier live on the floor at VMware Explorer, 2022. This is our third day of wall to wall coverage on the cube. But you know that cuz you've been here the whole time. We're pleased to welcome up. First timer to the cubes we saw is here. The CEO and founder of ZDA. Saed welcome to the program. >>Thank you for having me >>Talk to me a little bit about what ZDA does in edge. >>Sure. So ZDA is a company purely focused in edge computing. I started a company about five years ago, go after edge. So what we do is we help customers with orchestrating their edge, helping them to deploy secure monitor application services and devices at the edge. >>What's the business model for you guys. We get that out there. So the targeting the edge, which is everything from telco to whatever. Yeah. What's the business model. Yeah. >>Maybe before we go there, let's talk about edge itself. Cuz edge is complex. There's a lot of companies. I call 'em lens company nowadays, if you're not a cloud company, you're probably an edge company at this point. So we are focusing something called the distributed edge. So distributed edge. When you start putting tiny servers in environments like factory floors, solar farms, wind farms, even inside machines or well sites, et cetera. And a question that people always ask me, like why, why would you want to put, you know, servers there on servers supposed to be in a data center in the cloud? And the answer to the question actually is data gravity. So traditionally wherever the data gets created is where your applications live. But as we're connecting more and more devices to the edge of the network, we basically customers now are required to push the applications to the edge cause they can't go all the data to the cloud. So basically that's where we focus on people call it the far edge as well. You know, that's the term we've heard in the past as well. And what we do in our business model is provide customers a, a software as a service solution where they can basically deploy and monitor these applications at these highly distributed environments. >>Data, gravity comes up a lot and I want you to take a minute to explain the definition as it is today. And people have used that term, you know, with big data, going back to 2010 leads when we covering the Hadoop wave, which ended up becoming, you know, data, data, bricks, and snowflake now, but, but a lots changed, but what does it mean to be data gravity? It means that staying local, it's just what specifically describe and, and define what data gravity is. >>Yeah. So for me, data gravity is where you need to process the data, right? It's where the data usually gets created. So if you think about a web app, where does the data get created? Where people click on buttons, they, they interface with it. They, they upload content to it, et cetera. So that's where the data gravity therefore is therefore that's where you do your analytics. That's where you do your visualization processing, machine learning and all of those pieces. So it's really where that data gets created is where the data gravity in my view says, >>What are some of the challenges that data and opportunities that data gravity presents to customers? >>Well, obviously I think every enterprise in this day is trying to take data and make it a competitive advantage, right? Like faster decisions, better decisions, outcompete your competition by, you know, being first with a product or being first with a product with the future, et cetera. So, so I think, you know, if you're not a data driven enterprise by now, then I think the future may be a little bit bleak. >>Okay. So you're targeting the market distributed edge business model, SAS technology, secret sauce. What's that piece. >>Yeah. So that's, that's what the interesting part comes in. I think, you know, if you kind of look at the data center in the cloud, we've had these virtualization and orchestration stacks create, I mean, we're here in VMware Explorer. And as an example, what we basically, what we saw is that the edge is so unique and so different than what we've seen in the data center, in the cloud that we needed to build a complete brand new purpose-built illustration and virtualization solution. So that's really what we, we set off to do. So there's two components that we do. One end is we built a purpose-built edge operating system for the edge and we actually open sourced it. And the reason we opensource it, we said, Hey, you know, edge is so diverse. You know, depending on the environment you're running in a machine or in a vehicle or in a well site, you have different hardware, different networks, different applications you need to enable. >>And we will never be able to support all of them ourselves. As a matter of fact, we actually think there's a need for standardization at the edge. We need to kind of cut through all these silos that have been created traditionally from the embedded way of thinking. So we created basically an open source project in the Linux foundation in LFS, which is a sister organization through the CNCF it's called project Eve. And the idea is to create the Android of the edge, basically what Android became for mobile computing, an a common operating system. So you build one app. You can run in any phone in the world that runs Android, build an architecture. You build one app. You can run in any Eve powered node in the world, >>So distributed edge and you get the tech here, get the secret sauce. We'll get more into that in a second, but I wanna just tie one kick quick point and get your clarification on edge is becoming much more about the physical side too. I mean, absolutely. So when you talk about Android, you're making the reference of a phone. I get that's metaphor to what you're doing at the edge, wind farms, factories, alarms, light bulbs, buildings. I mean, that's what you're talking about, right? Yes. We're getting down to that very, >>Very physical, dark distributed locations. >>We're gonna come back to the CISO CSO. We're gonna come back to the CISO versus CSO question because is the CISO or CIO or who runs that anyway? So that's true. What's the important thing that's happening because that sounds like old OT world, like yes. Operating technology, not it information technology, is it a complete reset of those worlds or is it a collision? >>It's a great question. So what we're seeing is first of all, there is already compute in these environments, industrial PCs of existed well beyond, you know, an industrial automation has been done for many, many decades. The point is that that stuff has been done. Collect data has been collected, but never connected, right? So with edge computing, we're connecting now this data from an industrial machine and industrial process to the cloud, right? And one of the problems is it's data that comes of that industrial process too much to upload to the cloud. So I gotta analyze, analyze it locally. So one of the, the things we saw early on in edge is there's a lot of brownfield. Most of our customers today actually have applications running on windows and they would love to make in Linux and containers and Kubernetes, but it took them 20, 30 years to build those apps. And they basically are the money makers of the enterprise. So they are in a, in a transitionary phase and they need something that can take them from the brown to the Greenfield. So to your point, you gotta support all of these types of unique brownfield applications. >>So you're, you're saying I don't really care if this is a customer, how you get the data, you wanna start new start fresh. That's cool. But if you wanna take your old data, you'll >>Take that. Yeah. You don't wanna rebuild the whole machine. You're >>Just, they can life cycle it out on their own timetable. Yeah. >>So we had to learn, first of all, how do we take and lift and shift windows based industrial application and make it run at the edge on, on our architecture. Right? And then the second step is how do we then Sen off that data that this application is generating and do we fuse it with cloud native capability? Like, >>So your cloud, so your staff is your open source that you're giving to the Linux foundation as part of that Eve project that's available to everybody. So they can, they can look at the code, which is great by the way. Yeah. So people wanna do that. Yeah. Your self source, I'm assuming, is your hardened version with support? >>Well, we took what we took, what the open source companies did, opensource companies traditionally have sold, you know, basically a support model around the open source. We actually saw another problem. Customers has like, okay, now I have this node running and I can, you know, do this data analytics, but what if I have 15 or 20,000 of these node? And they're all around the world in remote locations on satellite links or wireless connectivity, how do I orchestrate them? So we actually build an orchestration service for these nodes running this open source >>Software. So that's a key secret sauce right there. >>That is the business model that taking open store and a lot. >>And you're taking your own code that you have. Okay. Got it. Cool. And then the customer's customer piece is, is key. So that's the final piece, I guess who's using it. >>Yeah. Well, and, >>And, and one of the business outcomes that they're achieving. Oh >>Yeah. Well, so maybe start with that first. I mean, we are deployed in customers in all and gas, for instance, helping them with the transition to renewable energy, right? So basically we, we have customers for instance, that deploy us in the, how they drill Wells is one use case and doing that better, faster, and cheaper and, and less environmental impacting. But we also have customers that use us in wind farms. We have, and solar farms, like we, one of the leading solar energy companies in the world is using us to bring down the cost of power by predicting failures ahead of time, for >>Instance. And when you're working with customers to create the optimal solution at the distributed edge, who are you working with in, within an organization? Yeah. >>It's usually a mix of OT and it people. Okay. So the OT people typically they're >>Arm wrestling, well, or they're getting along, actually, >>I think they're getting along very well. Okay, good. But they also agree that they have to have swim lanes. The it folks, obviously their job is to make sure, you know, everything is secure. Everything is according to the compliance it's, it's, you know, the, the best TCO on the infrastructure, those type of things, the OT guy, they, they, or girl, they care about the application. They care about the services. They care about the support new business. So how can you create a model that too can coexist? And if you do that, they get along really well. >>You know, we had an event called Supercloud and@theurlsupercloud.world, if you're watching check it out, it's our version of what we think multicloud will merge into including edge cuz edge is just another node in the, in the, in the network. As far as we're concerned, hybrid is the steady state. That's distributed computing on premise, private cloud, public cloud. We know what that looks like. People love that things are happening. Edge is like a whole nother new area. That's blossoming and with disruption, yeah. There's a lot of existing market and incumbents that need to be disrupted. And there's also a new capabilities that are coming that we don't yet see. So we're seeing it with the super cloud idea that these new kinds of clouds are emerging. Like there could be an edge cloud. Yeah. Why isn't there a security cloud, whereas the financial services cloud, whereas the insurance cloud, whereas the, so these become super clouds where the CapEx could be done by the Amazon, whatnot you've been following them is edge cloud. Can you make that a cloud? Is that what you guys are trying to do? And if so, what does that look like? Cause we we're adding a new track to our super cloud site. I mentioned on edge specifically, we're trying to figure out you and if you share your opinion, it'd be great. Can the E can edge clouds exist and be run by companies? Yeah. Or is that what you guys are trying to do? >>I, I, I mean, I think first of all, there is no edge without cloud, right? So when I meet any customer who says, Hey, we're gonna do edge without cloud. Then I'm like, you're probably not gonna do edge computing. Right. And, and the way we built the company and the way we think about it, it's about extending the cloud experience all the way into these embedded distributed environments. That's really, I think what customers are looking for, cuz customers love the simplicity of the cloud. They love the ease of use agility, all of that greatness. And they're like, Hey, I want that. But not in a, you know, in an Amazon or Azure data center. I want that in my factories. I want that in my wealth sites, in my vehicles. And that's really what I think the future >>Is gonna. And how long have you guys been around? What's the, what's the history of the company because you might actually be that cloud. Yeah. And are you on AWS or Azure? You're building your own. What's the, >>Yeah. Yeah. So >>Take it through the, the architecture because yeah, yeah, sure. You're a modern startup. I mean you gotta, and the edges you're going after you gotta be geared up. Yeah. To win that. Yeah. >>So, so the company's about five years old. So we, when we started focusing on edge, people didn't necessarily talk as much about edge. We kind of identified the it's like, you know, how do you find a black hole in, in the universe? Cuz you can't see it, but you sort of look around that's why you in it. And so we were like looking at it, like there's something gonna happen here at the edge of the network, because everybody's saying we're connecting these vice upload the data to the cloud's never gonna work. My background is networking. I worked at companies like Juniper and Ericsson ran several products there. So I know how the internet networks have built. And it was very Evan to me. It's not gonna be possible. My co-founders come from open source companies like pivotal and Cloudera. My auto co-founder was a, an engineer at sun Microsystems built the first network stack in the solar is operating system. So a lot of experience that kind of came together to build this. >>Yeah. Cloudera is a big day. That's where the cube started by the way. Yeah. >>Yeah. So, so we, we, we have, I think a good view on the stack, the cloud stack and therefore a good view of what the ed stack needs to look like. And then I think, you know, to answer your other question, our orchestration service runs in the cloud. We have, we actually are multi-cloud company. So we offer customers choice where they want to orchestrate the node from the nodes themself, never sit in a data center. They always highly embedded. We have customers are putting machines or inside these factory lines, et cetera. Are >>You running your SAS on Amazon web services or which >>Cloud we're running it on several clouds, including Amazon, all of, pretty much the cloud. So some customers say, Hey, I'd prefer to be on the Amazon set. And others customers say, I wanna be on Azure set. >>And you leverage their CapEx on that side. Yes. On behalf of yeah. >>Yeah. We, yes. Yes. But the majority of the customer data and, and all the data that the nodes process, the customer send it to their clouds. They don't send it to us. We don't get a copy of the camera feed analytics or the machine data. We actually decouple those though. So basically the, the team production data go straight to the customer's cloud and that's why they love us. >>And they choose that they can control their own desktop. >>Yeah. So we separate the management plane from the data plane at the edge. Yeah. >>That's a good call >>Actually. Yeah. That was another very important part of the architecture early on. Cause customers don't want us to see their, you know, highly confidential production data and we don't wanna have it either. So >>We had a great chat with Chris Wolf who works with kit culvert about control plane, data, plane. So that seems to be the trend data, plane customers want full yeah. Management of that. Yeah. Control plane. Maybe give multiple >>Versions. Yeah. Yeah. So our cloud consumption what the data we stories about the apps, their behavior, the networking, the security, all of that. That's what we store in our cloud. And then customers can access that and monitor. But the actual machine that I go somewhere else >>Here we are at VMware. Explore. Talk a little bit about the VMware relationship. You just had some big news the other day. >>Yeah. So two days ago we actually made a big announcement with VMware. So we signed an OEM agreement with VMware. So we're part now of VMware's edge compute stack. So VMware customers, as they start using the recently announced edge compute stack 2.0, that was announced here. Basically it's powered by Edda technology. So it's a really exciting partnership as part of this, we actually building integrations with the VMware organization products. So that's basically now extending to more, you know, other groups inside VMware. >>So what's the value in it for VMware customers. >>Yeah. So I think the, the, the benefit of, of VMware customers, I think cus VMware customers want that multi-cloud multi edge orchestration experience. So they wanna be able to deploy workloads in the cloud. They wanna deploy the workloads in the data center. And of course also at the edge. So by us integrating in that vision customers now can have that unified experience from cloud to edge and anywhere in between. >>What's the big vision that you see happening at the edge. I mean, a lot of the VMware customers here, they're classic it that have evolved into ops now, dev ops. Now you've got second data ops coming. The edge is gonna right around the corner for them. They're dealing with it now, probably just kicking the tires, towing the water kind of thing. Where do you see the vision going? Cuz now, no matter what happens with VMware, the Broadcom, this wave is still here. You got AWS, got Azure, got Google cloud, you got Oracle, Alibaba internationally. And the cloud native surges here. How do you see that disrupting the existing edge? Because let's face it the O some of those OT players, a little bit old and antiquated, a little bit outdated. I mean, I was talking to a telco person. They, they puked the word open source. I mean, these people are so dogmatic on, on their architecture. Yeah. They're gonna get disrupted. It's a matter of time. Yeah. Where's the new guard come in. How do you see the configuration changing in the landscape? Because some people will cross over to the right side of the street here. Yeah. Some won't yeah. Open circle. Dominate cloud native will be key. Yeah. >>Well, I mean, I think, again, let's, let's take an example of a vertical that's heavily disrupted now as the automotive market, right? The, so look at Tesla and look at all these companies, they built, they built software first cars, right? Software, first delivery of capabilities and everything else. And the, and the incumbents. They have only two options, right? Either they try to respond by adopting open source cloud, native technologies. Like the, these new entrants have done and really, you know, compete with them at that level, or they can become commodity. Right. So, and I think that's the customers we're seeing the smart customers go like, we need to compete with these guys. We need to figure out how to take this technology in. And they need partners like us and partners like VMware for them. >>Do you see customers becoming cloud super cloud players? If they continue to keep leveraging the CapEx of the clouds and focus all their operational capital on top line revenue, generating activities. >>Yeah. I, so I think the CapEx model of the cloud is a great benefit of the cloud, but I think that is not, what's the longer term future of the cloud. I think the op the cloud operating model is the future. Like the agility, the ability imagine embedded software that, you know, you do an over the year update to fix a bug, but it's very hard to make a, an embedded device smarter over time. And then imagine if you can run cloud native software, you can roll out every two weeks new features and make that thing smarter, intelligent, and continue to help you in your business. That I think is what cloud did ultimately. And I think that is what really these customers are gonna need at their edge. >>Well, we talked about the value within it for customers with the VMware partnership, but what are some of your expectations? Obviously, this is a pretty powerful partnership for you guys. Yeah. What are some of the things that you're expecting that this is gonna drive? Yeah, >>So we, we, we have always operated at the more OT layer, distributed organizations in retail, energy, industrial automotive. Those are the verticals we, so we've developed. I think a lot of experience there, what, what we're seeing as we talk to those customers is they obviously have it organizations and the it organizations, Hey, that's great. You're looking at its computing, but how do we tie this into the existing investments we made with VMware? And how do we kind of take that also to this new environment? And I think that's the expectation I have is that I think we will be able to, to talk to the it folks and say, Hey, you can actually talk to the OT person. And both of you will speak the same language. You probably will both standardize on the same architecture and you'll be together deploying and enabling this new agility at the edge. >>What are some of the next things coming up for ZDA and the team? >>Well, so we've had a really amazing few quarters. We just close a series B round. So we've raised the companies raised over 55 million so far, we're growing very rapidly. We opened up no new international offices. I would say the, the early customers that we started deploying, wait a while back, they're now going into mass scale deployment. So we have now deployments underway in, you know, the 10 to hundred thousands of nodes at certain customers and in amazing environments. And so, so for us, it's continuing to prove the product in more and more verticals. Our, our product is really built for the largest of the largest. So, you know, for the size of the company, we are, we have a high concentration of fortune 500 global 500 customers, and some of them even invested in our rounds recently. So we we've been really, you know, honored with that support. Well, congratulations. Good stuff, edges popping. All right. Thank you. >>Thank you so much for joining us, talking about what you're doing in distributed edge. What's in it for customers, the VMware partnership, and by the way, congratulations on >>That too. Thank you. Thank you so much. Nice to meet you. Thank >>You. All right. Nice to meet you as well for our guest and John furrier. I'm Lisa Martin. You're watching the cube live from VMware Explorer, 22, John and I will be right back with our next guest.

(light techno music) >> Okay, welcome back, everyone from theCUBE's coverage of AWS re:Mars here in Las Vegas. Back in person, I'm John Furrier, host of theCUBE. Re:MARS stands or Machine learning, Automation, Robotics, and Space. And we're covering all the action two days, day two. And we're here with Regina Manfredi, who's the VP of global CSPs, Cloud Service Providers Alliances with Teradata. Great to see you. Cloud service providers or- >> Cloud services providers, the hyperscalers. >> Hyperscalers, the big guys. All the CapEx, Amazon. >> Yes. >> The big guys. >> Indeed, thanks for having me. >> Yeah, Thanks for coming on. So tell about your role. So alliances, you're here with AWS. What's the role with AWS and Teradata? >> So AWS and Teradata have recently entered into a strategic collaboration agreement where we're really focused on building solutions together, leveraging AWS services, as well as Teradata's outstanding architecture, as it relates to the data analytics platform that we provide for our customers in the cloud today. And we're really trying to drive better outcomes for data scientists, business analysts, etc. >> You know, just recently, did a CUBE conversation with Teradata, and I was really surprised to find, not shocked, but kind of surprised, the scale of the computation that's going on in some of the cloud things you're doing. And you have the legacy on-premises data warehouse traditional business as well. >> Regina: We do. >> And there's a huge shift going on. A lot of the kind of upstarts, "Oh, data warehouse, old school. Data warehouse, it's antiquated, old," but that's not true. You guys have a lot of cloud action. >> We do, we have substantial cloud action that's occurring with our customers today. We actually just released earlier this year an announcement around 1,000 node tests in the cloud together with AWS, and had success, no downtime, no failures at all. And so we're pretty proud about that, and excited about what that's going to hold for our customers who need that level of scale. >> Well, Regina, I got to tell you, I have a little bit of a confession here. I'm a cloud data nerd by my training. And, you know, I've always watched all the different kind of levels of transformation with the industry, and you know, this is going to change that, that's going to kill that. Everything's going to be killed and then it never dies, but it just changes. Even today, SQL is still like the prominent language, it's never going to, in fact it's amplified further because that's what people like. So that just proves that things don't always get replaced. And so I wanted to ask you this because as we're here at this event at re:MARS, you have space, you have all these ambitious positive goals, and they just need to do some machine learning. They need some cloud, they need some, they need to have the solutions. >> Regina: Yes. They're not going to like get in the weed and say, "Oh, this is a better Hadoop cluster than this Kubernetes cluster. So it's not about sometimes the tech, it's about the solution. >> It is, and one of the things that was interesting for us in our session earlier this week was the fact that we had so many customers approach us after that session and say, "I just need help preparing my data. Running my models, training my models, and making sure that they run and can be deployed. And I don't want to move all this data all the time and have all this failure rate that I'm experiencing." And so it was very basic requirements and needs as people begin into their journey on AI/ML for their business. And so it was reaffirming that we're on the right track and driving the right tools for them. I want to get your perspective on what you're thinking about the show, but first, I want to ask this since you brought that up. Swami was on stage and he said, "You can spend your entire time and your career just trying to figure out what's going on, machine learning." >> Regina: Yup. >> "Which open source framework's going to be better than the other one." I mean, it's just a lot of work to even figure it out. We just had the Fiddler's AI CEO on who worked out all the hyperscalers, say Facebook tend to, you know, real, you know, super alpha geek, if you will. And he was saying, and we were talking about open source, free software, integrations are a big part of where cloud scale, and the value is being captured for companies and people who are doing projects. Integrating some managed services, so this is where I see you, guys, going right now with Teradata, having all these cloud services built on the install base. >> Right. Which is not, doesn't hurt that at all. It just only helps it as they would migrate to cloud, its integrations, so you take a little bit of Amazon here, a little bit of Teradata there. >> Regina: Absolutely. >> What's your perspective, what's your reaction to that? >> So, I agree. And we think that's part of our secret sauce. You know, what we want to have is a data analytics platform in the cloud that allows data scientists, and architects, etc., to bring their own tools. So whatever they're utilizing today, we want them to be able to utilize it in vantage, and make sure that, A, can drive some efficiencies, and also, some better, smarter economics, as it relates to their particular projects. And so I agree with you 100% , and would tell you that we view that as somewhat our competitive advantage. It's not about being all proprietary. We want those integrations, and we've got dozens of them with AWS, and- >> Can you give example, can you give a couple examples of some integrations that highlight that? >> Sure, so right now we've got an integration with SageMaker today that allows our customers or data scientists to come in, prepare the data, and actually leverage SageMaker to build and train the models, and then deploy very quickly and easily without having to do all the data movement within their architecture. >> It's just so fascinating. I can't wait to have more conversation with you guys about this because I just think the world's spinning in a direction where, with low code, no code, >> Regina: Yup. >> you can see code, companion whisperer, that they have CodeWhisperer they launched today, they're writing subroutines for machine learning. And so it's not autocomplete, it's subroutine. So you're seeing all these advances on the technology. So it comes back to the building blocks, the integration. It just seems like going to be like a plug and play. That's old, were all, are old words. Mix and match, plug and play, interoperability, were old words, like, in the old days. Now they're becoming more relevant. What's your take on all that? >> Yeah, I would agree. I don't think that we should be competing against the algorithms, and neither do we. We want to just actually build out the toolsets that drive the enablement based on what a customer's requirements and needs are, and based on what the investments that they've already made within their own enterprises. >> You know, what's interesting about this event, I love to get your reaction to what re:MARS means to you because it's machine learning, automation, robotics, and space. Not your typical tech conference. >> Regina: No. >> Okay, little bit of a mixed bag there, so to speak. I love it. I think it's like super alpha geek, very nerdy, super nerds are here. And the topics kind of reflect the future. For the people that are watching that aren't here, what's your vibe on the show? What's your takeaway? How would you explain what's going on here from a market perspective, from a vibe perspective, what's happening? >> This is my first re:MARS actually, and I would have to tell you that I feel like it just, general observation, a few things, one, the conversations are more meaningful and we're getting into the meat of what a data scientist truly needs in order to be successful in their role and help drive their enterprise. That's number one. So I think, to your point, we're all kind of geeking out together here. The other thing that I think is pretty exciting is the amount of use cases, and ways in which we are driving impact. AWS and Teradata driving impact for the business analysts in the enterprise environment, but also for the people, their customers. That's pretty exciting to see. >> You know, it's interesting. When I first, was kind of like thinking about the show and what I was going to expect, it kind of overexceeded my expectations in the sense of what I was thinking about IOT, industrial, and digital innovation. 'Cause that's going to scale. I think now we're at a tipping point with machine learning that the industrial, IOT markets is going to explode 'cause machine learning's ready. But there was a whole positive, save the earth angle >> Regina: Yes. >> that caught my attention. >> Regina: Yes. You know, the discoveries from space are going to potentially have impact for the good, not just a cliche some sustainability messaging. It was actually real. >> Right, I think that that's exciting in an area in which we're excited to explore. We're doing a lot of work behind the scenes around sustainability and ESG initiatives for our customers, but also for the greater good. It's about driving outcomes for the greater good and being responsible with how we approach that. You know, the other thing I noticed too from a robotics standpoint, given I live in California, is a huge robotics culture there, you know. It's like bigger than football and baseball, and some sports. They provide A and B team and people get cut from the B team. There's so much demand to be on the robotics team. It's not a club, it's a team. >> Regina: Right. And so, you look at what's going on robotics, it's so exciting in the sense that if you're young and you're into tech, this is like- >> Regina: This is the place to be. >> I mean, why wouldn't you be hanging out here? >> Yeah, well, and I visited the booth over at University of Michigan, and how they're driving robotics to help support the human body to go further distances, and to drive better performance and health for individuals, and was really impressed with the work that they're doing, and even saw a use case and a need where I thought, you know, I have a quadriplegic sister-in-law, who I thought, "Wow, someday, maybe she'll be upright and walking again." >> John: Yeah. >> And those were exciting conversations to have while I was here. >> The advances on the material management robots I think is fascinating to see that growth. Well, let's get back to Teradata real quick to kind of close out future of what's next. Obviously, a lot of migration to the cloud happening. What's the outlook on the landscape and where do you see it evolving? Because you're seeing what the hyperscalers are doing, the cloud service providers, they're providing the CapEx. In fact, we coined the term supercloud, last re:Invent, that's become a thing. And Charles Fitzgerald would think it's not a thing, he debates us online all the time on Twitter. But it's, you can build on top of a CapEx. >> Regina: Yup. >> They did all the heavy lifting. You know, Snowflake, Databricks, the list goes on and on. So building on top of that to build proprietary advantages or even just sustainable advantages is now easier to do. So superclouds are kind of in play. So that means whoever's got the playbook can win. So you guys seem to be executing that playbook of having the installed base, and then working with AWS >> Regina: Yes. >> to ride that wave. Tell us about the migration strategies you're seeing, and what are your customers doing specifically, and take us through a customer that's leaning into the cloud and driving. >> So when I think about specific customers that are leaning in, you know, the first and most important thing that we're hearing is, you've got to be able to scale. I've got 1,000 nodes or 100 nodes, or whatnot. And so we're addressing that because we think that there's a place for hybrid cloud. We think everyone's moving and rushing towards the cloud, but even one of our competitors last week announced that there's a place for on-prem, and we would agree. >> John: Yeah. >> So that is something that we're really focused on, and you take, for example, the automotive industry. We're seeing a lot of work being done together with our joint customers, AWS and Teradata, and some of these auto manufacturers who are experiencing supply chain issues and challenges today, and also need to drive better quality control measures within their own lines, in the manufacturing lines. And so we're working together with them to look at what type of machine learning and AI can we be leveraging together as part of the overall solution to drive those analytics, and make sure that they have better quality control >> You know, that's really good insight about the on-premise thing. And I think that supports what we're seeing around hybrid. We see hybrid as a steady state going forward, period. >> Regina: Yeah. >> And that will evolve into multi thing. Multi-cloud, you want to call it, or superclouds, and more things. Basically, distributed computing. So if you look at the edge here, the edge is just on-premise. What is the premise? It's an edge or big device, small device, data center is a large edge. >> Regina: Right. >> And so if you're using cloud hybrid, the distinction kind of goes away. And I think this is where we'll going to see the winners emerge in data. Because remember, you go back to 2010, Hadoop was the big thing, big data. And that kind of crashed and burned. And then now you're seeing Databricks picking up a lot of that. Snowflake, you guys are there. And so it's still going on, this transformation in data. >> Regina: It is. And I think hybrid's a huge deal. What are customers saying around that? Because I think they're just trying to figure out cloud scale. >> I think they're trying to figure out cloud scale, I think they're also trying to figure out security. And so, you know, when we're talking to our customers, that absolutely is critical. And I would also suggest that the customer base is really looking for, "Hey, don't just help me migrate, I really need to modernize." And so driving the right use cases for the customer is important. >> You know, another thing that you, guys, have a lot of core expertise in is governance. And we've seen how that has played in all the compliance, and all these conversations are kind of converging. Do you have closed, do you have open? Machine learning needs more data, dow do you protect it? So that set a hot area that I see as well. And that's something that's emerging, 'cause cyber's also involved too, like, you have cyber security threats on code, so I'm curious to see how that turns out. What's your perspective on, what's Teradata's perspective on the security, open, closed perspective? Any- >> It's a priority for, security is a priority for us. And I don't think that we've officially made that determination yet, right? We're still exploring, and we're going to do whatever our customers require of us. In terms of an open, closed perspective, I think we want to be flexible. Again, like I said before, it's about being open and supportive of whatever the customer requirement is especially across the different industries. >> Well, Regina, great to have you on theCUBE. Thanks for coming. I really appreciate it. Great insight, great to catch up on Teradata, cloud play. Very strong move. I think it's a good one. Final question I want to ask you though, is a little bit more about the personnel in the industry, like, obviously, if you're young, you're seeing all this space here, machine learning's not obvious. I know schools now are training it, but you start to see new personas come into the workforce. Where are the gaps? I mean, obviously, we have a lot of new opportunities, like, cybersecurity has a lot of job openings. Is there any observations that you have around or advice to younger folks coming in, from a career standpoint? Because a lot of job openings are skills that weren't even taught in school. >> Regina: Right, that's- >> You know. >> And then you got the women in check, and you have all kinds of opportunities now that aren't just engineering, right? >> Regina: Yes. >> It's not just engineering. It's computer science, so there's a whole in-migration of new talent coming in the industry. >> Yes, I think maintaining a curious mind is really critical, and taking time to invest in learning. You know, there are so many resources available to us at our disposal that that don't cost us a dime. And so my advice to anybody who is curious, remain curious, dig in, and get some experience, and don't be afraid to stick your neck out, and try it. >> Well, in this conference we have robots welcome, you know, in this out there. >> Yeah. (laughs) >> Regina, thanks for coming out here. Really appreciate it >> John, thank you, it's a pleasure. >> CUBE coverage here in Las Vegas for Amazon re:MARS. I'm John Furrier, your host. Stay with more live coverage after this short break. (upbeat bright music)

(upbeat music) >> Hey everyone, welcome back to theCUBE's three day coverage of Snowflake Summit 22. Lisa Martin here with Dave Vellante. We have been here as I said for three days. Dave, we have had an amazing three days. The energy, the momentum, the number of people still here speaks volumes for- >> Yeah, I was just saying, you look back, theCUBE, when it started, early days was a big part of the Hadoop ecosystem. You know Cloudera kind of got it started, the whole big data movement, it was awesome energy, and that whole ecosystem has been, I think, just hoovered into the Snowflake ecosystem. They've taken over as the data company, the data cloud, I mean, that was Cloudera, it could have been Cloudera, and now they didn't, they missed it, it was a variety of factors, but Snowflake has nailed it. And now it's theirs to lose. Benoit talked about that on our previous segment, how he knew that technically Hadoop was too complex, and was going to fail, and they didn't know it was going to do this. They were going to turn their company into what we see here. But the event itself, Lisa, is almost 10,000 people, the right people, people are doing business, we've had a number of people tell us that they're booking deals. That's why people come to face-to-face shows, right? That's the criticism of virtual. It takes too long to close business. Salespeople want to be belly-to-belly. And this is a belly-to belly-show. >> It absolutely is. When you and I were trying to get into the keynote on Tuesday, we finally got in standing room only, multiple overflow rooms, and we're even hearing that, so this is day four of the summit for them, there are still queues to get into breakout sessions. The momentum, but the appetite for this flywheel, and what they're creating, but also they're involving this massively growing ecosystem in its evolution. It's that synergy was really very much heard, and echoed throughout pretty much all of our segments the last couple days. >> Yeah, it was amazing actually. So we like to go, we want to be in the front row in the keynotes, we're taking notes, we always do that. Sometimes we listen remotely, but when you listen remotely, you miss some things. When you're there, you can see the executives, you can feel their energy, you can chit chat to them on the side, be seen, whatever. And it was crazy, we couldn't get in. So we had to do our thing, and sneak our way in, and "Hey, we're media." "Oh yeah, come on in." And then no, they were taking us to a breakout room. We had to sneak in a side door, got like the last two seats, and wow, I'm glad we were in there because it gave us a better sense. When you're in the remote watching rooms you just can't get a sense of the energy. That's why I like to be there, I know you do too. And then to your point about ecosystem. So we've said many times that what Snowflake is developing is what we call supercloud. It's not just a SaaS, it's not just a cloud database, it's a new layer that they're creating. And so what are the attributes of that layer? Well, it hides the underlying complexity of the underlying primitives of the cloud. We've said that ad nauseam, and it adds new value on top. Well, what's that value that they're adding? Well, they're adding value of being able to share data, collaborate, have data that's governed, and secure, globally. And now the other hallmark of a cloud company is ecosystem. And so they're building that ecosystem much more rapidly than we saw at ServiceNow, which is Slootman's previous company. And the key to me is they've launched an application development platform, essentially a super PaaS, so that you can develop applications on top of the data cloud. And we're hearing tons about monetization. Duh, you could actually make money with data. You can package data into data products, and data services, or feed data products and services, and actually sell that in a cloud, in a supercloud. That's exactly what's happening here. So that's critical. I think my one question mark if I had to lay one out, is the other hallmark of a cloud is startup, startups come into that cloud. And I think we're seeing that, maybe not at the pace that AWS did, it's a little different. Snowflake are, they're whale hunters. They're after big companies. But it looks to me like they're relying on the ecosystem to be the startup innovators. That's the important thing about cloud, cloud brings scale. It definitely brings lower cost 'cause you're eliminating all this undifferentiated labor, but it also brings innovation through startups. So unlike AWS, who sold the startups directly, and startups built businesses on AWS, and by paying AWS, it's a little bit indirect, but it's actually happening where startups in the ecosystem are building products on the data cloud, and that ultimately is going to drive value for customers, and money for Snowflake, and ultimately AWS, and Google, and Azure. The other thing I would say is the criticism or concern that the cost of goods sold for cloud are going to be so high that it's going to force people to come back on-prem. I think it's a step in the wrong direction. I think cloud, and the cloud operating model is here to stay. I think it's going to be very difficult to replicate that on-prem. I don't think you can do cloud without cloud, and we'll see what the edge brings. >> Curious what your thoughts are. We were just at Dell technologies world a month or so ago when the big announcement, the Snowflake partnership there, cloud native companies recognizing, ah, there's still a lot of data that lives on-prem. Given that, and everything that we've heard the last couple of days, what are your thoughts around that and their partnerships there? >> So Dell is, I think finally, now maybe they weren't publicly talking like this, but certainly their marketing was defensive. But in the last year or so, Dell has really embraced cloud, not just the cloud operating model, Dell has said, "Look, we can build value on top of all these hyperscalers." And we saw some examples at Dell Tech World of them stepping their toe into supercloud. Project Alpine is an example, and there are others. And then of course the Snowflake deal, where Snowflake and Dell got together, I asked Frank Slootman how that deal came about. And 'cause I said, "Did the customer get you into a headlock?" 'Cause I presume that was the case. Customer said, "You got to do this or we're not going to do business with you." He said, "Well, no, not really. Michael and I had a chat, and that's how it started." Which was my other scenario, and that's exactly what happened I guess. The point being that those worlds are coming together. And so what it means for Dell is as they embrace cloud, as they develop supercloud capabilities, they're going to do a lot of business. Dell for sure knows how to sell, they know how to execute. What I would be doing if I were Dell, is I would be trying to substantially replicate what's happening in the cloud on-prem with on-prem data. So what happens with that Snowflake deal is, it's read-only data, you read the data into the cloud, the compute is in the cloud. And I should've asked Terry this, I mean Benoit. Can there be an architecture on-prem? We've seen at Vertica has one, it's called Vertica Eon where you separate compute from storage. It doesn't have unlimited elasticity, but you can grow, compute, and storage independently, and have a lot more. With Dell doing APEX on demand, it's cloudlike, they could begin to develop a little mini data cloud, or a big data cloud within on-prem that connects to the public cloud. So what Snowflake is missing, a big part of their TAM that they're missing is the on-prem. The Dell and Pure deals are forays into that, but this on-prem is massive, and Dell is the on-prem poster child. So I think again what it means for them is they've got to continue to embrace it, they got to do more in software, more in data management, they got to push on APEX. And I'd say the same thing for HPE. I think they're both well behind this in terms of ecosystems. I mean they're not even close. But they have to start, and they got to start somewhere, and they've got resources to make it happen. >> You said in your breaking analysis that you published just a few days ago before the event that Snowflake plans to create a de facto standard in data platforms. What we heard from our guests on this program, your mainstage session with Frank Slootman. Still think that? >> I do. I think it more than I believed it coming in. And the reason I called it that is because I am a super fan of Zhamak Dehghani and her data mesh. And what her vision is, it's kind of the Immaculate Conception, where she wants everything to be open, open standards, and those don't exist today. And I think she perfectly realizes the practicality of de facto standards are going to get to market, and add value sooner than open standards. Now open standards over time, and I'll come back to that, may occur, but that's clear to me what Snowflake is creating, is the de facto standard for data platforms, the data cloud, the supercloud. And what's most impressive, or I think really important, is they're layering applications now on top of that. The metric to me, and I don't know if we can even count this, but VMware used to use it. For every dollar spent on VMware license, $15 was spent in the ecosystem. It started at 1 to 1.5, 1 to 2, 1 to 10, 1 to 15, I think it went up to 1 to 30 at the max. I don't know how they counted that, but it's countable. Reasonable people can make estimates like that. And I think as the ecosystem grows, what Snowflake's doing is it's in many respects modeling the cloud, what the cloud has. Cloud has ecosystems, we talked about startups, and the cloud also has optionality. And optionality means open source. So what you saw with Apache Iceberg is we're going to extend to open technologies. What you saw with Hybrid tables is we're going to extend a new workloads like transactions. The other thing about Snowflake that's really impressive is you're seeing the vertical focus. Financial services, healthcare, retail, media and entertainment. It's very rare for a company in this tenure, they're only 10 years old, to really start going vertical with their go-to-market, and building expertise around that. I think what's going to happen is the GSIs are going to come in, they love to eat at the trough, the trough here is maybe not big enough for them yet, but it will be. And they're going to start to align with the GSIs, and they're going to do really well within those industries, connecting people, collaborating with data. But I think it's a killer strategy, but they're executing on it. >> Right, and we heard a lot of great customer stories from all of those four verticals that you talked about, and then some, that that direction and that pivot from a customer perspective, from a sales and marketing perspective is all aligned. And that was kind of one of the themes as well that Frank talked about in his keynote is mission alignment, mission alignment with customers, but also with the ecosystem. And I feel that I heard that with every customer conversation, with every partner conversation, and Snowflake conversation that we had over the last I think 36 segments, Dave. >> Yeah, I mean, yeah, it's the power of many versus the resources of one. And even though Snowflake tell you they have $5 billion in cash, and assets on the balance sheet, and that's fine, that's nothing compared to what an ecosystem has. And Amazon's part of that ecosystem. Azure is part of that ecosystem. Google is part of that ecosystem. Those companies have huge resources, and Snowflake it seems has figured out how to tap those resources, and build value on top of it. To me they're doing a better job than a lot of the cloud databases out there. They don't necessarily have a better database, in fact, I could argue that their database is less functional. And I would argue that actually in many cases. Their database is less functional if you just want a database. But if you want a data cloud, and an ecosystem, and develop applications on top of that, and to be able to monetize, that's unique, and that is a moat that they're building that is highly differentiable, and being able to do that relatively easily. I mean, I think they overstate the simplicity with which that is being done. We talked to some customers who said, he didn't say same wine, new bottle. I did ask him that, about Hadoop complexity. And he said, "No, it's not that bad." But you still got to put this stuff together. And I think in the early parts of a market that are immature, people get really excited because it's so much easier than what was previous. So my other question is, okay, what's somebody working on now, that's looking at what Snowflake's doing and saying, I can improve on that. And what's going to be really interesting to see is, can they improve on it in a way, and can they raise enough capital such that they can disrupt, or is Snowflake going to keep staying paranoid, 'cause they got good leaders, and keep executing? And then I think the other wild card is edge. Snowflake doesn't really have an edge strategy right now. I think they will develop one. >> Through the ecosystem? >> And I don't think they're missing the boat, and they'll do it through the ecosystem, exactly. I don't think they're missing the boat, I think they're just like, "Well, we don't know what to do today." It's all distributed data, and it's ephemeral, and nobody's storing the data. You know anything that comes back to the cloud, we get. But new architectures are emerging on the edge that are going to bring new economics. There's new silicon, you see what's happening with Apple, and the M1, the M1 Ultra, and the new systems that they've just developed. What Tesla is doing with custom silicon, and amazing things, and programmability of the arm model. So it's early days, but semiconductors are the mainspring of innovation in this industry. Without chips, you got nothing. And when you get innovations in silicon, it drives innovations in software, because developers go, "Wow, I can do that now?" I can do things in parallel, I can do things faster, I can do things more simply, and programmable at scale. So that's happening. And that's going to bring a new set of economics that the premise is that will eventually bleed into the data center. It will, it always does. And I guess the other thing is every 15 years or so, the world gets disrupted, the tech world. We're about 15, 16 years in now to the cloud. So at this point, everybody's like, "Wow this is insurmountable, this is all we'll ever see. Everything that's ever been invented, this is the model of the future." We know that's not the case. I don't know how it's going to get disrupted, but I think edge is going to be part of that. It could be public policy. Governments could come in and take big tech on, seems like Sharekhan wants to do that. So that's what makes this industry so fun. >> Never a dull moment, Dave. This has been a great three days hosting this show with you. We've uncovered a lot. Your breaking analysis was great to get me prepared for the show. If you haven't seen it, check it out on siliconangle.com. Thanks, Dave, I appreciate all of your insights. >> Thank you, Lisa, It's been a pleasure working with you. >> Always good to work with you. >> Awesome, great job. >> Likewise. Great job to the team. >> Yes, thank you to our awesome production team. They've kept us going for three days. >> Yes, and the team back, Kristin, and Cheryl, and everybody back at the office. >> Exactly, it takes a village. For Dave Vellante, I am Lisa Martin. We are wrappin' up three days of wall-to-wall coverage at Snowflake Summit 22 from Vegas. Thanks for watching guys, we'll see you soon. (upbeat music)

(upbeat music) >> Welcome back everyone, theCUBE's three days of wall to wall coverage of Snowflake Summit '22 is coming to an end, but Dave Vellante and I, Lisa Martin are so pleased to have our final guest as none other than the co-founder and president of products at Snowflake, Benoit Dageville. Benoit, thank you so much for joining us on the program. Welcome. >> Thank you. Thank you, thank you. >> So this is day four, 'cause you guys started on Monday. This is Thursday. The amount of people that are still here speaks volumes. We've had close to 10,000 people here. >> Yeah. >> Could you ever have imagined back in the day, 10 years ago that it would come to something like this in such a short period of time? >> Absolutely not. And I always say if I had imagined that I might not have started Snowflake, right. This is somehow scary. I mean and yeah, it's huge. And you can feel the excitement of everyone. It is like mind boggling and the fact that so many people are still there after four days is great. >> Your keynote on Tuesday was fantastic. Your energy was off the charts. It was standing room only. There were overflow rooms. Like we just mentioned, a lot of people are still here. Talk about the evolution of Snowflake, this week's announcements and what it means for the future of the data cloud. >> Yeah, so evolution, I mean, I will start with the evolution. It's true that that's what we have announced. This week is not where we started necessarily. So we started really very quickly with big data combined with data warehouse as one thing. We saw that the world was moving into fragmented siloing data and we thought with Thierry, we are going to combine big data and data warehouse in one system for the cloud with this elasticity and this service simplicity. So simplicity, amazing elasticity, which is this multi workload architecture that I was explaining during the keynotes and really extreme simplicity with the service. Then we realized that there is one other attribute in the cloud, which is unique, which doesn't exist on-premise, which is collaboration. How you can connect different tenets of the platform together. And Google showed that with Google Docs. I always say to me, it was amazing that you could share document and have direct access to document that you didn't produce and you can collaborate on this document. So we wanted to do the same thing for data and this is where we created the data cloud and the marketplace where you can have all these data sets available and really the next evolution I would say is really about applications that are (indistinct) by that data, but are way simpler to use for all the tenets of the data cloud. And this is the way you can share expertise also, including, ML model, everyone talks about ML and the democratization of ML. How are you going to democratize ML? It's not by making necessary training super easy. Such that everyone can train their ML for themselves. It's by having very specialized application where data and ML is at the core, which are shared, through the marketplace and we shall leverage by many tenets of this marketplace that have no necessary knowledge about building this ML models. So that's where, yeah. >> When you and Thierry started the company, I go back to the improbable rise of Kubernetes and there were other more sophisticated container management systems back then, but they chose to focus on simplicity. And you've told me before, that was our main tenet. We are not going to worry about all the complex database stuff. You knew how to do that, but you chose not to. So my question is, did you envision solving those complex problems over time yourselves or through an ecosystem? Was this by design or did you... As you started to get into it, say let's not even try to go there let's partner to go there. >> Yeah, I mean, it's both. It's a combination of both. Snowflake, the simplicity of the platform is really important because if our partners are struggling to put their solution and build solution on top of Snowflake they will not build it. So it's very important that number one, our platform is really easy to use from day one. And that really has to be built inside the platform. You cannot build simplicity on top. You cannot have a complex solution and all of a sudden realize that, oh, this is complex. I need to build another layer on top of it to make it simpler, that will not work. So it had to be built from day one, but you're right. What is going to be Snowflake? I always say in 10 years from now, we just turn 10 years old or we are going to turn 10 years old in few months. Actually a few months, yes. >> Right. >> So for the next 10 years I really believe that most of Snowflake will not be built by Snowflake. And that's the power of the partners and these applications. When you are going to say I'm using Snowflake, actually, probably you are not going to use directly code developed by Snowflake. That code will leverage our platform, but you will use a solution that has been built on top of Snowflake. And this is the way we are going to decouple, the effort of Snowflake and multiply it. >> It's an interesting balance, isn't it? When I think of what you did with Apache Iceberg, if I use Iceberg and I'm not going to get as much functionality, but I may want that openness, but I'm going to get more functionality inside of the data cloud. And I don't know, but if you know the answer to what's going to happen. >> No, that's a super good question. So to explain what we did with Apache Iceberg, and the fact that now it's a native format for us. So everything that you can do with our internal formats, you can do it with Apache Iceberg, including security, defining masking, data masking all the governors that we have, fine grain security aspects, the replications you can define you can use (indistinct) on top of... >> But there's a but, right? But if I do that with native Snowflake tools, I'm going to get an even greater advantage, am I not? >> Yes. So that's what I'm saying. So that's why we embraced Iceberg, because I think we can bring all the benefit of Snowflake to people who have decided to use Iceberg, I mean open formats. Iceberg is a table format. So and why it was important because people had massive investments in open source in Hadoop. And we had a lot of companies saying, we love Snowflake. We want to be a Snowflake customer, but we cannot really migrate all our data. I mean, it will be really costly. And we have a lot of tools that need access, direct access. So this is why we created Iceberg because we can really... I mean, we really think that we can bring the benefit of Snowflake to this data. >> Gives customers optionality. Okay. I use this term super cloud. You don't use the term, but that's okay. And I get a lot of heat for it. But to me, what you're doing is quite a bit different than multicloud because you're creating that abstraction layer. You're bringing value above it. My question to you is, the most of the heat I get is, oh, that's just SaaS. Are you just SaaS? >> No. I mean, no, absolutely not. I mean, you're right we are a super cloud. I mean it's a much better word than saying we are multicloud. Multicloud is often viewed as oh, I have my system and now I can run this system in the different cloud providers. Snowflake is different. We have one single platform for the world, which happens to have some regions are AWS region, some regions are Azure, some regions are GCP, Google and we merge them together. We have this Snowgrid technology that connects all our regions together so that we have really one platform for the world. And that's very important because when you talk about connections of data and expertise applications you want to have global reach, right. It doesn't exist. We are not siloed by region of the world, right? You have a lot of companies which are multinational that have presence everywhere. And you want to have this global reach. The world is not a independent set of regions and countries, right. And that's the realization. So we had to create this global platform for our customers. >> And now you have people building clouds on top of your data cloud, well that to me is the next signal. In your keynote, you talked about seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace, governance, which ones are the most important? >> All of them. It's like when you have kids, you don't want to pick and say, this one is my preferred one, so they are really important. All of them, as I said without data, there is no Snowflake, right? So all data is so important that we can reach every data, wherever it is. And Iceberg is a part of that, but all workload is really important because you don't want to put your data in one platform, if you cannot run all your workloads and workloads are much broader than just data warehousing, there is data engineering, data science, ML engineering, (indistinct) all these workloads applications. So that's critical. Programmable is where we are moving, right. We want to be the place where data applications are built. And we think we have a lot of advantages because data application needs to use many workloads at once, right? It's not that that application will do only data warehousing, they need to store their states, they need to use this new workload that we define, which is Unistore. They need to do data engineering because they need to get data, right. They have to save this data. So they need to combine many workload and if they have to stitch this workload, because the platform was not designed as one single product where everything is consistent and works together, that you have to stitch, it's complicated for this application to make it work. So Snowflake is we believe an ideal platform to run these data applications. So all workloads, programmable, obviously, so that you can program. And programmable has two aspects, which is big part of our announcement. Is both data programmability, which is running Python against petabyte, terabytes of data at scale and doing it scale out. So that's what we call data programmability. So both Java, Python and (indistinct), but also running applications like UI. And we had this acquisition of Streamlit. Streamlit now has been fully integrated in Snowflake. We announced that such that not only you can have this data programmability, but you can expose your data through this nice UIs, interactive UI to business users potentially. So it goes all the way there. Global is super important. As we say, we want to be one platform for the world. And of course, as I said, the last pillar, which is somehow critical for us, because we are cloud, we need to have governance. We need to have security of our data. And why it took us so long to do Python is not because it's out to run Python, right? Everyone can run Python it's because we had to secure it. And I talk about it creating this amazing sandboxing technology, such that when you include third party libraries and third party codes, you are guaranteed that this third party code will not reach to infiltrate your data, right. We control the environment that Snowflake provides. >> Can you share us some of the feedback from the customer? You probably had many customer conversations over the last four days. >> Look at that smile. (interviewer laughing) (Lisa laughing) >> Actually not because I was so busy everywhere. Unfortunately, I didn't speak to many customers. Saying that, I had everyone stopping me and talking about what they heard and yeah, there is a huge excitement about all of this. >> What's been the feedback around the theme of the event? The world of data collaboration. Data collaboration is so critical as every company these days must be a data company to compete, to win. What's been from just some of the feedback that you've had customers really embracing data collaboration, what Snowflake is enabling. >> Yeah. I mean, almost every company which is using Snowflake, is collaborating with data. You have heard, the number of stable edges that we have, and there is a real need for that because your data alone... You cannot make sense of your data if it is just alone. It needs to be connected with other data. You haven't not generated. So all data, when you say the first pillar of Snowflake is all data is not only about your data, but is about all the data that's created around you. That puts perspective on your own data. And that's critical and it's so painful to get. I mean, even your data is difficult to have access to your data, but imagine data that you didn't produce. And so yes, so the data collaboration is critical, and then now we expanded it to application and expertise, sharing models, for example, That's going to have a huge impact. >> All data includes now transaction data, right? >> Yes. >> That's a big part of the announcements that you guys made. >> Yeah. So and that's the motivation for that was really, if we want to run application, full application, we announced native applications, which are fully executed and run inside the (indistinct) data cloud, right. They need all the services that application need and in particular managing their states. And so we created Unistore, which is a new workload, which allows you to combine transactional data, which are generated by this application. And at the same time being able to do analytics directly on this data. So we call it Hybrid Table because it has this hybrid aspect. You can do both transactional access to this data and at the same time analytic here without having data pipeline and moving data and transforming it from the transactional system to the analytical system, right. Snowflake is one system. Again, in the spirit of simplifying everything, this is the Snowflake (indistinct). >> I can ask the same question I ask at first, (indistinct) when was the aha moment that you and Thierry had that said, this is not just a better data warehouse, it's actually more than that. You probably didn't call it a data cloud until later on, but did you know that from the beginning or was that something you kind of stumbled into? >> No. So as I said, we founded Snowflake in 2012 and Thierry and I, we locked in my apartment and we were doing the blueprint of Snowflake and trying to find what is the revolution with the cloud for this data warehouse system and analytical system, both big data and data warehouse. And the aha moment was but of course cloud, okay. What is cloud? It's elasticity, it's service and later collaboration. So in the elasticity aspect, when you ask database people, what is elasticity, they will tell you, oh, you have a cluster of nodes. Like if it is Oracle, it would be a (indistinct) cluster. And the elasticities that you can add one node, two node to this cluster without having too much impact on the existing workload, because you need to shuffle data, right. It's hard and doing it online, right, that's elasticity. If you can do that, you are elastic. We thought that that was not very interesting to do that. What is interesting with elasticity is to plug new workloads. You can plug a workload like that and that workload is running without having any impact on other workloads, which are running on the platform. So elasticity for us was having dedicated computer resources to workloads. And these computer resources could start and be part as soon as the workload starts and will shut down when the workload finishes and they will be sized exactly for the demand of that workload. And we thought the aha moment was, okay if we can do that, now we can run a workload with, let's say 10X more computer resources than what you would have used or 100X more. Okay, let's say 100X more because we paralyzed things. Now this workload can run 100X faster, right? That's assuming we do a good job in the scale, which is our IP. And if we can do that, now the computer resources that you have used, you have used them for 100 times less. So you have used 100 times more resources because you have more nodes, but because you go fast, you use them for less time, right? So if you multiply the two it's constant. So you can run and accelerate workload dramatically 10X, 100X for the same price. Even if we are not better in efficiency than competition, just having that was the magic, right? >> You know how Google founders originally had trouble raising money because who needs another search engine? Did you get from original, like when you started going to raise money, Amazon's got a database, so who needs another cloud database? Did you get that early on or was it just obvious Speiser and companies as well. >> Speiser is a little bit on the crazy side and ambitious and so Speiser is Speiser. And of course he had no doubt, but even him was saying Benoit, Thierry, Hadoop, right. Everyone is saying Hadoop is going to be the revolution. And you guys are betting actually against Hadoop because we told Speiser, Hadoop is a bad system, it's going to fail, but at the time everyone was so bullish about Hadoop, everyone was implementing Hadoop that it didn't look like it was going to fail and we were probably wrong. So there was a lot of skepticism about not leveraging Hadoop and not being an Hadoop. Okay, something being on top of Hadoop. That was number one. There was no cloud warehouse at the time we started. Redshift was not started. It was the pioneer somewhere when Snowflake was founded. So creating a data warehouse in the cloud sounded crazy to people. How am I going to move my data over there? And security and what about security, the cloud is not secure. So that was another... >> So you guys predated that Parexel move by... >> Yes. >> Okay, so that's interesting. And I thought when Redshift... I mean, Amazon announced Redshift, I was sure that Mike Speiser will come and say, guys it's too sad, but they beat you guys and they build something and actually it was the reverse. Mike Speiser was super excited and so it was interesting to me. >> Wow, that's amazing. 'Cause John Furrier and I, we were early with theCUBE. when theCUBE started it was like the beginning of Hadoop. And so we brought theCUBE to, I think it was the second Hadoop World and we was rubbing nickels together at the time. And I was so excited bring compute to storage and it made so much sense. But I remember and I won't say who it was, but an early Hadoop committer told me this is going to fail. And I'm like, what? And he started going age basis crap and all this stuff. And I was sad because I was so excited, but it turned out that you had the same (indistinct). >> Because of complexity. Okay, Hadoop failed for two reasons. One is because they decided that, oh, a lot of this database thing, you don't need transaction, you don't need SQL, you don't necessarily, you don't need to go fast. It'll be batch, normal real time interaction with data, no one needs that. >> Cheap storage. >> So a lot of compromise on the very important technology. And at the same time, extreme complexity and complexity for me was, where I was I knew that it was going to fail big time and we bet Snowflake on the failure of Hadoop indeed. >> And there was no cloud early on in Hadoop. >> And there was no cloud too. >> And that was what killed it. That was like... >> You're right. And the model that Hadoop had for data didn't work on block storage. Block storage is not as efficient as HGFS. So that was also another figure. >> Do you ever sit back and think about... So you think about how much money has poured in to separating compute from storage and cloud databases and you started it all. (interviewer laughing) >> Yeah. No, this is... >> Pretty amazing. >> Yeah. >> Right, so that's good. That means that you're onto a good idea, but a lot of people get confused that again, they think that you're a cloud data warehouse and you're not, I mean, you're much more than that. >> Yeah, I hate that. I have to say, because from day one we were not a cloud data warehouse. As I said, it was all about combining the big data, massive amount of unstructured data, petabytes stored as files. Okay, that's very important, store as files where it's very easy to drop data in the system without... Very low cost to combine with data warehouse, full multi statement transaction when people will tell you today, oh, now we are a data warehouse. They don't have multi statement transaction, right. So we had from day one multi statement transaction really efficient SQL. You could run your dashboard. So combining these two worlds was I think the crazy thing, that's the crazy innovation that Snowflake did initially. >> Yeah. >> And I know it's really easy to build data warehouse somewhere, because if you don't think about big data, petabytes, extremely structured data, you remove a lot of complexity. >> This is why Lisa, when you get excited about technology, but you always have to have a, somebody who really deeply understands technology to stink test it, all right so awesome. Thank you for sharing that story. >> Yeah. >> Fantastic. So over 5,900 customers now. I saw over 500 in the Forbes G2K, over almost 10,000 people here this year. If we think back to 2019, there was about what? Less than 2000 people. >> Yeah. >> What do you think is going to happen next year? >> I don't know. I don't like to think about next year. I mean, I always say, Snowflake is so exciting to me because it is like a TV show, right. Where you wait the next season and we have one season every year. So I'm really excited to know what is going to happen next year. And I don't want to project what I think will happen, but all these movements to the Snowflake being the platform for data application. I want to see what people are going to build on our platform. I mean, that's the excitement. >> Season 11 coming up. >> Yes. Season 11. Yes. >> No binge watching here. Benoit, it's been a pleasure to have you on the program. >> Thank you. >> Congratulations on incredible success, the momentum, the energy is contagious. We love it. (Benoit laughing) >> Thank you so much. >> Thank you. >> Bye bye. >> For Benoit Dageville and Dave Vellante, I'm Lisa Martin. You're watching theCUBE's coverage of Snowflake Summit '22. Dave and I will be right back with a wrap. (upbeat music)

>>Hey everyone. Welcome back to the cube. Lisa Martin here with Dave ante, we're covering snowflake summit 22. This is Dave two of our wall to wall cube coverage of three days. We've been talking with a lot of customers partners, and we've got some more partners to talk with us. Next. Informatica two of our guests are back with us on the program. Rick TA Daniels joins us the G P global ecosystems and technology at Informatica and Peter COO vice president and chief strategist banking and financial services. Welcome guys. >>Thank you guys. Thanks for having us, Peter, >>Talk to us about what some of the trends are that you're seeing in the financial services space with respect to cloud and data and AI. >>Absolutely. You know, I'd say 10 years ago, the conversation around cloud was what is that? Right? How do we actually, or no way, because there was a lot of concerns about privacy and security and so forth. You know, now, as you see organizations modernizing their business capabilities, they're investing in cloud solutions for analytics applications, as well as data data being not only just a byproduct of transactions and interactions in financial services, it truly fuels business success. But we have a term here in Informatica where data really has no value unless it's fit for business. Use data has to be accessible in the systems and applications you use to run your business. It has to be clean. It has to be valid. It has to be transparent. People need to understand where it comes from, where it's going, how it's used and who's using it. It also has to be understood by the business. >>You can have all the data in the world and your business applications, but people don't know what they need it to use it for how they should use it. It has no value as well. And then lastly, it has to be protected when it matters most what we're seeing across financial services, that with the evolution of cloud now, really being the center of focus for many of the net new investments, data is scattered everywhere, not just in one cloud environment, but in multiple cloud environments, but they're still dealing with many of the on premise systems that have been running this industry for many, many years. So organizations need to have the ability to understand what they need to do with their data. More importantly, tie that to a measurable business outcome. So we're seeing the data conversation really at the board level, right? It's an asset of the business. It's no longer just owned by it. Data governance brings both business technology and data leaders together to really understand how do we use manage, govern and really leverage data for positive business outcomes. So we see that as an imperative that cuts across all sectors of financial services, both for large firms, as well as for the mid-market so >>Quick follow up. If I, may you say it's a board level. I totally agree. Is it also a line of business level? Are you seeing increasingly that line of businesses are leaning in owning the data, be building data products and the like >>Absolutely. Because at the end of the day business needs information in order to be successful. And data ownership now really belongs in the front office. Business executives understand that data again is not just a bunch of zeros and ones. These are critical elements for them make decisions and to run their business, whether it's to improve customer experience, whether it's to grow Wallace share, whether it's to comply with regulations, manage risks in today's environment. And of course being agile business knows that data's important. They have ownership of it and technology and data organizations help facilitate that solutions. And of course the investments to ensure that business can make the decisions and take the appropriate actions. >>A lot of asks and requirements on data. That's a big challenge for organizations. You mentioned. Well, one of the things that we've mentioned many times on this program recently is every company has to be a data company. There is no more, it's not an option anymore. If you wanna be successful, how does Informatica help customers navigate all of the requirements on data for them to be able to extract that business value and create new products and services in a timely fashion? >>So Informatica announced what we call the intelligent data management cloud platform. The platform has capabilities to help organizations access the data that they need, share it across to applications that run their business, be able to identify and deal with data, quality issues and requirements. Being able to provide that transparency, the lineage that people need across multiple environments. So we've been investing in this platform that really allows our customers to take advantage of these critical data management, data governance and data privacy requirements, all in one single solution. So we're no longer out there just selling piecemeal products. The platform is the offering that we provide across all industries. >>So how has that affected the way Informatica does business over the last several years? Snowflake is relatively new. You guys have been around a long time. How has your business evolved and specifically, how are you serving the snowflake yeah. Joint customers with >>Informatica? Yeah, I think then when I've been talking with folks here at the event, there are two big areas that keep coming up. So, so data governance, data governance, data governance, right? It's such a hot topic out there. And as Peter was mentioning, data governance is a critical enabler of access to data. In fact, there is an IDC study for last year that said that, you know, 80, 84% of executives, you know, no surprise, right? They wanna have data driven outcomes, data driven organizations, but only 30% of practitioners actually use data to make decisions. There's a huge gap there. And really that's where governance comes in and creating trust around data and not only creating trust, but delivering data to and users. So that's one big trend. The other one is departmental user adoption. We're seeing a, a huge push towards agility and rapid startup of new projects, new data driven transformations that are happening at the departmental level, you know, individual contributors, that sort of thing. So Informatica, we did a made announcement yesterday with snowflake of a whole host of innovations that are really targeting those two big trend areas. >>I wanna get into the announcements, but you know, the point about governance and, and users, business users being reluctant, it's kind of chicken and egg, isn't it. If, if I don't have the governance, I'm, I'm afraid to use it. But even if I do have it, there's the architecture of my, my, my company, my, my data organization, you know, may not facilitate that. And so I'm gonna change the architect, but then it's a wild west. So it has to be governed. Isn't that a challenge that company companies >>Absolutely, and, and governance is, is a lot more than just technology, right? It's of a people process problem. And there really is a community or an ecosystem inside every organization for governance. So it's really important that when you think about deploying governance and being successful, that every stakeholder have the ability to interact with this common framework, right. They get what they need out of it. It's tailored for how they wanna work. You've got your it folks, you got your chief data officer data stewards, you have your privacy folks and you have your business users. They're all different personas. So we really focus on creating a holistic, single pane of glass view with our cloud data governance and catalog offering that that really takes all the way from the raw technical data and actually delivers data in, in a shopping cart, like experience for actual enterprise users. Right? And, and so I think that's when data governance goes from historically data, governments was seen as an impediment. It was seen as a tax, I think, but now it's really an accelerator, an enabler and driving consumption of data, which in turn for our friends here at snowflake is exactly what they're looking for. >>Talk about the news. So data loader, what does that do? >>Well, it's all in the name. We say, no, the data loader it, it's a free utility that we announced here at, at snowflake summit that allows any user to sign up. It's completely free, no capacity limits. You just need an email address, three simple steps start rapidly loading data into snowflake. Right? So that first step is just get data in there. Start working with snowflake. Informatica is investing and making that easy for every single user out there. And especially those departmental users who wanna get started quickly. >>Yeah. So, I mean, that's a key part point of getting data into the snowflake data cloud, right? It's like any cloud, you gotta get data in. How does it work with, with customers? I mean, you guys are, are known, you have a long history of, you know, extract transform ETL. How does it work in the snowflake world? Is it, is it different? Is it, you remember the Hadoop days? It was, it was E LT, right? How are customers doing that today in this environment? >>Yeah, it's different. I mean, there, there are a lot of the, the same patterns are still in play. There's a lot more of a rapid data loading, right. Is a key theme. Just get it into snowflake and then work on the data, transform it inside of snowflake. So it's, it's a flavor of T right. But it's really pushing down to the snowflake data cloud as opposed to Hado with spark or something like that. Right. So that, that's definitely how customers are using it. And, you know, majority of our customers actually with snowflake are using our cloud technology, but we're also helping customers who are on premise customers, automate the migration from our on-premises technology to our cloud native platform as well. Yeah. >>And I'd say, you know, in addition to that, if you think about building a snowflake environment, Informatica helps with our data loader solution, but that's not enough. Then now you need to get value out of your data. So you can put raw data into the snowflake environment, but then you realize the data's not actually fit for business use, what do we need to do actually transform it to clean it, to govern it. And our customers that use Informatica with snowflake are managing the entire data management and data governance process so that they can allow the business to get value out of the snowflake investment. >>How quickly can you enable a business to get value from that data to be able to make business decisions that can transform right. Deliver competitive advantage? >>I think it really depends on an organization on a case by case basis. At the end of the day, you need to understand why are you doing this in the first place, right? What's the business outcome that you're trying to achieve next, identify what data elements do you actually need to capture, govern and manage in order to support the decisions and the actions that the business needs to take. If you don't have those things defined, that's where data governance comes into play. Then all you're doing is setting up a technical environment with a bunch of zeros in ones that no one knows what to do with. So we talk about data governance more holistically, say, you need to align it to your business outcomes, but ensure that you have people, processes, roles, and responsibilities, and the underlying technology to not just load data into snowflake, but to leverage it again for the business needs across the organization. >>Oh, good, please. >>I just wanted to add to that real quickly. Yeah. One of the things Informatica we're philosophically focused on is how do you accelerate the entire business of data management? So with our, our cloud platform, we have what's called our clear AI engine, right? So we use AI techniques, machine learning recommendations to accelerate with the, the knowledge of the metadata of what's gone on the organization. For example, that when we discover data assets figure out is this customer data, is it product data that dramatically shortens the time to find data assets deliver them? And so across our whole portfolio, we're taking things that were traditionally months to do. We're taking 'em down to weeks and days and even hours, right? So that's the whole goal is just accelerate that entire journey and life cycle through cloud native approaches and AI. Yeah, >>You kind of just answered my question. I think Rick, so you have this joint value statement together. We help customers. This is informatic and snowflake together. We help customers modernize their data. Architecture enable the most critical workloads, provide AI driven data governance and accelerate added value with advanced analytics. I mean, you definitely touched on some of those, but kind of unpack the rest of that. What do you mean by modernize? What is their data architecture? What is that? Let's start there. What does that look like? Modernizing a data. Yeah. >>So, so a lot with so many customers, right? They, they built data warehouses, core data and analytics systems on premises, right? They're using ETL technology using those, those either warehouse, appliances or databases. And what they're looking for is they wanna move to a cloud native model, right. And all the benefits of cloud in terms of TCO elasticity, instant scale up agility, all those benefits. So we're looking, we're looking to do with our, our modernization programs for our, for our current customer base that are on premises. We automate the process to get them to a fully cloud native, which means they can now do hybrid. They can do multi-cloud elastic processing. And it's all also in a consumption based model that we introduced about about a year and a half ago. So, so they're looking for all those elements of a cloud native platform and they're, but they're solving the same problems, right? We still have to connect data. We still have to transform data, prepare it, cleanse it, all those things exist, but in a, in a cloud native footprint, and that's what we're helping them get to. >>And the modern architecture these days, quite honestly, it's no longer about getting best breed tools and stitching them together and hoping that it will actually work. And Informatica is value proposition that our platform has all those capabilities as services. So our customers don't have to deal with the costs and the risks of trying to make everything work behind the scenes and what we've done with IDMC or intelligent data management cloud for financial services, retail, CPG, and healthcare and life sciences. In addition to our core capabilities and our clear AI machine learning engine, we also have industry accelerators, prebuilt data, quality rules for certain regulations in within banking. We've got master data management, customer models for healthcare insurance industry, all prebuilt. So these are accelerators that we've actually built over the years. And we're now making available to our customers who adopt informatic as intelligent data management cloud for their data management and governance needs. >>And then, and then the other part of this statement that that's interesting is provide AI driven data governance. You know, we are seeing a move toward, you know, decentralized data architectures and, and, and organizations. And we talk to snowflake about that. They go, yeah, we're globally distributed cloud. Okay, great. So that's decent place, but what we see a lot of customers doing to say, okay, we're gonna give lines of business responsibility for data. We're gonna argue about who owns what. And then once we settle that here's your own, here's your own data lake. Maybe they they'll try to cobble together a catalog or a super catalog. Right. And then they'll try to figure out, you know, some algorithms to, to determine data quality, you know, best, you know, okay. Don't use. Right, right. So that, so if I understand it, you automate all that. >>So what we're doing with AI machine learning is really helping the data professional, whether in the business, in technology or in between not only to get the job done faster, better, and cheaper, but actually do it intelligently. What do we mean by that? For example, our AI engine machine learning will look at data patterns and determine not only what's wrong with your data, but how should you fix it and recommend data quality rules to actually apply them and get those errors addressed. We also infer data relationships across a multi-cloud environment where those definitions were never there in the beginning. So we have the ability to scan the metadata and determine, Hey, this data set is actually related to that data set across multiple clouds. It makes the organization more productive, but more importantly, it increases the confidence level that these organizations have the right infrastructure in place in order to manage and govern their data for what they're trying to do from a business perspective. >>And I add that as well. I think you're talking a lot about data mesh architectures, right? That, that are really kind of popular right now. And I think those kind of, they live or die on, on data governance. Right? If you don't have data governance to share taxonomy, these things, it's very hard to, I think, scale those individual working groups. But if you have a platform where they, the data owners can publish out visibility to what their data means, how to use it, how to interpret it and get that insight, that context directly to the data consumers that's game changing. Right. And that's exactly what we're doing with our cloud data governance and catalog. >>Well, the data mesh, you talk about data mesh, there's four principles, right? It's like decentralized architecture data products. So if, once you figure out those two yep. You just created two more problems, which is the other two parts of the Princip four, two parts of the four principles, self service infrastructure, and computational governance. And that's like the hardest part of federated, federated, computational governance. That's the hardest part. That's the problem that you're solving. >>Yeah. Yeah, absolutely. I mean, think about the whole decentralization and self-service, well, I may be able to access my data in mesh architecture, but if I don't know what it means, how to use it for what purpose, when not to use it, you're creating more problems than what you originally expected to solve. So what we're doing is addressing the data management and the governance requirements, regardless of what the architecture is, whether it's a mesh architecture, a fabric architecture or a traditional data lake or a data store. >>Yeah. Mean, I say, I think data mesh is more of an organizational construct than it is. I, I'm not quite sure what data fabric is. I think Gartner confused the issue that data fabric was an old NetApp term. Yeah. You're probably working in NetApp at the time and it made sense in the NetApp context. And then I think Gartner didn't like the fact that Jamma Dani co-opted this cool term. So they created data fabric, but whatever. But my, my point being, I think when I talk to customers that are they're, they're trying to get more value outta data and they recognize that going through all these hyper specialized roles is time consuming and it's not working for them. And they're frustrated to your points and your joint statement. They want to accelerate that. And they're realizing, and the only way to do that is to distribute responsibility, get more people involved in the process. >>And, and that's, it kind of dovetails with some, the announcements we made on data governance for snowflake, right, is you're taking these, these operational controls of the snowflake layer that are typically managed by SQL and you, and that decentralized architecture data owner doesn't know how to set those patterns and things like that. Right. So we're saying, all right, we're, we're creating these deep integration so that again, we have a fit for persona type experience where they can publish data assets, they can set the rules and policies, and we're gonna push that down to snowflake. So when it actually comes to provisioning data and doing data sharing through snowflake, it's all a seamless experience for the end user and the data owner. Yeah. >>That's great. Beautiful, >>Seamless experience absolutely necessary these days for everybody above guys. Thanks so much for joining David me today, talking about Informatica what's new, what you're doing with snowflake and what you're enabling customers to do in terms of really extracting value from that data. We appreciate your insights. >>Thank you. Yep. >>Thank you for having us >>For our guests and Dave ante. I'm Lisa Martin. You're watching the cubes coverage of snowflake summit day two of the cubes coverage stick around Dave. And I will be right back with our next guest.

(upbeat music) >> Hi, everyone, welcome back to theCUBE's coverage of AWS re:Invent 2021. We're wrapping up four days of coverage, two sets. Two remote sets, one in Boston, one in Palo Alto. And really, it's a pleasure to introduce Benoit Dageville. He's the Press Co-founder of Snowflake and President of Products. Benoit, thanks for taking some time out and coming to theCUBE. >> Yeah, thank you for having me, Dave. >> You know, it's really a pleasure. We've been watching Snowflake since, maybe not 2012, but mid last decade you hit our radar. We said, "Wow, this company is going to go places." And yeah, we made that call correctly. But it's been a pleasure to sort of follow you. We've talked a little bit remotely. I kind of want to go back to some of the fundamentals. First of all, I wanted mention your earnings last night. If you guys didn't see it, again, triple digit growth, $1.8 billion RPO, cashflow actually looking pretty good. So, pretty amazing. Oh, and 173% NRR, you know, wow. And Mike Scarpelli is kind of bummed that you did so well. And I know why, right? Because it's going to be at some point, and he dials it down for the expectations and Wall Street says, "Oh, he's sandbagging." And then at some point you're actually going to meet expectations and people are going to go, "Oh, they met expectations." But anyway, he's a smart guy, he know what he's doing. (Benoit laughing) I loved it, it was so funny listening to him last night. But anyway, I want to go back to, when I talked to practitioners about data warehousing pre-cloud, they would say sound bites like, it's like a snake swallowing a basketball, they would tell me. And the other thing they said, "We just chased the chips. Every time a new Intel chip comes out, we have to bring in new servers, and we're struggling." The cloud changed all that. Your vision and Terry's vision changed all that. Maybe go back to the fundamentals of what you saw. >> Yeah, we really wanted to address what we call the data challenges. And if you remember at that time, data challenge was first of the volume of data, machine-generated data. So it was way more than just structured data, right? Machine-generated data is weblogs, and it's at petabyte scale. And there was no good solution for that type of data. Big data was not a great solution, Hadoop was really bad. And there was no good solution for that. So we thought we should do something for big data. The other aspect was concurrency, right? Everyone wants to use these data analytic platform in an enterprise, right? And you have more and more workload running against the same data, and the systems that were built were not scaling for these workloads. So you had to silo data, right? That's the only way big enterprise could deal with that, is to create many different silos, Oracle, Teradata, data mass, you would hear data mass. All of it was to afloat, right, this data? And then there was the, what do we call, data sharing. How to get access to data which is not born inside the enterprise, right? So with Terry, we wanted to solve all these challenges and we thought the only way to solve it was the cloud. And the cloud has really two free aspects. One is the elasticity, for all of a sudden, you can run every workload that you want concurrently, in parallel, on different computer resources, and you can run them against the same data. So this is kind of the data lake model, if you want. At the same time, you can, in the cloud, create a service. So you can remove complexity from users and make it really easy for new workloads to be added to the system, because you can manage, you can create a managed service, where all the sudden our customers, they don't need to manage infrastructure, they don't need to patch, they don't need to tune. Everything is done by Snowflake, the service, and they can just load in and run their query. And the third aspect is really collaboration. Is how to connect data sets together. And that's almost a new product for Snowflake, this data sharing. So we really at Snowflake was all about combining big data and data warehouse in one system in the cloud, and have only one single system where you can put all your data and all your workload. >> So you weren't necessarily trying to solve the data warehouse problem, you were trying to solve a data problem. And then it just so happened data warehouse was a logical entry point for you. >> It's really not that. Yes, we wanted to solve the data problem. And for us big data was a really important problem to solve. So from day one, Snowflake was all about machine generated data, petabyte scale, but we wanted to do it right. And for us, right was not compromising on data warehouse principle, which is a CDT of transaction, which is really fast response time, and which is also simplicity. So as I said, we wanted to solve kind of all the problems at the time of volume of data, concurrency, and these sharing aspects. >> This was 2012. You knew at that time that Hadoop wasn't going to be the answer. >> No, I mean, we were really, I mean, everyone knew that. Everyone knew Hadoop was really bad. You know, complex to manage, really slow. It had good aspects, right? This was the only system that could manage petabyte scale data sets. That's the only thing- >> Cheaply. >> Yeah, and cheaply which was good. And we wanted really to do that, plus have all the good attributes of data warehouse system. And at the same time, we wanted to build a system where if you are data warehouse customer, if you are coming from Teradata, you can migrate to Snowflake and you will get to a system which is faster than what you had on-premise, right. That's why it's pretty cool. So we wanted to do big data without compromising on data warehouse. >> So several years ago we looked at the hyperscalers and said, "Wow, last year they spent $100 billion in CapEx." And so, we started to think about this abstraction layer. And then we saw what you guys announced with the data cloud. We call it super clouds. And we see that as exactly what you're building. So that's clearly not just a data warehouse or database, it's technology that really hides the underlying complexity of all those clouds, and it allows you to have federated governance and data sharing, all those things. Can you talk about sort of how you think about that architecture? >> So for me, what I say is that really Snowflake is the worldwide web of data. And we are indeed a super cloud, or we are super-posed to the infrastructure cloud, which is our friends at Amazon, and of course, Azure, I mean, Microsoft and Google. And as any cloud, we have regions, Snowflake regions all over the world, and located on different cloud providers. At the same time, our platform is global in the sense that every region interconnects with all the other regions, this is our snow grid and data mesh, if you want. So that as an organization you can have your presence on several Snowflake region. It doesn't matter which cloud provider, so you can mix AWS with Azure. You can use our cloud like that. And indeed you can, this is a cloud where you can store your data, that's the thing that really matters, and data is structured, but it's machine structure, as I say, machine generated, petabyte scale, but there's also unstructured, right? We have added support for images, text, videos, where you can process this data in our system, and that's the workload spout. And workload, what is very important is that you can run this workload, any number of workloads. So the number of workloads is effectively unlimited with Snowflake because each workload can have its dedicated set of compute resources all operating on the same data set. And the type of workloads is also very important. It's not only about dashboards and data warehouse, it's data engineering, it's data science, it's building application. We have many of our customers who are building full-scale cloud applications on top of Snowflake. >> Yeah so the other thing, if you're not familiar with Snowflake, I don't know, maybe your head has been in the sand for a while, but separating compute and storage, I don't know if you were the first, but you were certainly the first to popularize it. And that allowed you to solve that chasing the chips problem and the swallowing the basketball, right? Because you have virtually infinite resources now at your disposal. >> Yeah, this is really the concurrency challenge that I was mentioning. Everyone wants to access the data. And of course, if everyone runs on the same set of compute resources, you have a bottleneck. So Snowflake was really about this multi-workload. We call it Multi-Cluster Shared Data Architecture. But it's not difficult to run multiple cluster if you don't have consistency of data. So how to do that while maintaining transactional property of data as CDT, right? You cannot modify data from different clusters. And when you commit, every other cluster will immediately see the change, right, as if everyone was running on the same cluster. So that was the challenge that we solve when we started Snowflake. >> Used the term data mesh. What is data mesh to Snowflake? Is it a concept, is it fabric? >> No, it's a very interesting point. As much as we like to centralize data, this becomes a bottleneck, right? When you are a large organization with different independent units, everyone wants to manage their own data and they have domain-specific expertise about that data. So having it centralized in IT is not practical. At the same time, you really want to be able to connect these different data sets together and join different data together, right? So that's the data mesh architecture. Each data set is managed independently by business owners, and then there is a contract which is exposed to others, and you can combine. And Snowflake architectures with data sharing, right. Data sharing that can happen within an organization, or across organization, allows you to connect any data with any other data on our platform. >> Yeah, so when I first heard that term, you guys using the term data mesh, I got very excited because it was kind of the data mesh is, my view, anyway, is going to be the fundamental architecture of this decade and beyond. And the principles, if I understand it correctly, you're applying the principles of Jim Octagon's data mesh within Snowflake. So decentralized data doesn't have to be physically in one place. Logically it's in the data cloud. >> It's logically decentralized, right? It's independently managed, and the reason, right, is the data that you need to use is not produced by your, even if in your company you want to centralize the data and having only one organization, let's say IT managing that, let's say, pretend. Yet you need to connect with other datasets, which is managed by other organizations. So by nature, the data that you use cannot be centralized, right? So now that you have this principle, if you have a platform where you can store all the data, wherever it is, and you can connect these data very seamlessly, then we can use that platform for your enterprise, right? To have different business units independently manage their data sets, connects these together so that as a company you have a 360 view of your customers, for example. But you can expand that outside of your enterprise and connect with data sets, which are from your vertical, for example, financial data set that you don't have in your company, or any public data set. >> And the other key principles, I think, that you've touched on really is the line of business now. Increasingly they're building data products that are creating value, and then also there's a self-service component. Assuming there's the fourth principle, governance. You got to have federated governance. And it seems like you've kind of ticked the boxes, more than tick the boxes, but engineered a solution to solve for those. >> No, it's very true. So Snowflake was really built to be really simple to use. And you're right. Our vision was, it would be more than IT, right? Who is going to use Snowflake is going now to be business unit, because you do not have to manage infrastructure. You do not have to patch. You do not have to do these things that business cannot do. You just have to load your data and run your queries, and run your applications. So now business can directly use Snowflake and create value from that. And yes, you're right, then connect that data with other data sets and to get maximum insights. >> Can you please talk about some of the things you do with AWS here at the event. I'm interested in what you're doing with your machine learning initiatives that you've recently announced, the AI piece. >> Yes, so one key aspects is data is not only about SQL, right? We started with SQL, but we expanded our platform to what we call data programmability, which is really about running program at scale across a large volume of data. And this was made popular with a programming model which was introduced by Pendal, DataFrames. Later taken by Spark, and now we have DataFrames in Snowflake, Where we are different than other systems, is that these DataFrame programs, which are in Python, or Java, or Scala, you program with data. These DataFrames are compiled to our single execution platforms. So we have one single execution platform, which is a data flow execution platform, which can run both SQL very efficiently, as I said, data warehouse speed, and also these very complex programs running Python and Java against this data. And this is a single platform. You don't need to use two different systems. >> Now so, you kind of really attack the traditional analytics base. People said, "Wow, Snowflake's really easy." Now you're injecting AI and machine intelligence. I see Databricks coming at it from the other angle. They started with machine learning, now they're sort of going after the analytics. Does there need to be a semantic layer to connect, 'cause it's the same raw data. Does there need to be a semantic layer to connect those two worlds? >> Yes, and that's what we are doing in our platform. And that's very novel to Snowflake. As I said, you interact with data in different program. You pick your program. You are a SQL programmer, use SQL. You are a Python programmer, use DataFrames with Python. It doesn't really matter. And then the semantic layer is our compiler and our processing engine, is going to translate both your program and my program in Python, your program in SQL, to the same execution platform and to the same programming language that Snowflake internally, we don't expose our programming language, but it's a data flow programming language that our execution platform executes. So at the end, we might execute exactly the same program, potentially. And that's very important because we spent all our IP and all our time, engineering time to optimize this platform, to make it the fastest platform. And we want to use that platform for any type of workloads, whether it's data programs or SQL. >> Now, you and Terry were at Oracle, so you know a lot about bench marketing. As Larry would stand up and say, "We killed the competition." You guys are probably behind it, right. So you know all about that. >> We are very behind it. >> So you know a lot about that. I've had some experience, I'm not a technologist, but I'm an observer and analyst. You have to take benchmarking with a very big grain of salt. So you guys have generally stayed away from that. Databricks came out and they came up with all these benchmarks. So you had to respond, because otherwise it's out there. Now you reran the benchmarks, you took out the materialized views and all the expensive stuff that they included in your cost, your price performance, but then you wrote, I thought, a very cogent blog. Maybe you could talk about sort of why you did that and your general philosophy around bench marketing. >> Yeah, from day one, with Terry we say never again we will participate in this really stupid benchmark war, because it's really not in the interest of customers. And we have been really at the frontline of that war with Terry, both of us, really doing special tricks, right? And optimizing this query to death, this query that no one runs apart from the synthetic benchmark. We optimize them to death to have the best number when we were at Oracle. And we decided that this is really not helping customers in the end. So we said, with Snowflake, we'll not do that. And actually, we are not the only one not to do that. If you look at who has published TPC-DS, you will see no one, none of the big vendors. It's not because they cannot run TPC-DS, Oracle can run it, I know that. And all the other big data warehouse vendor can, but it's something of a little bit of past. And TPC was really important at some point, and is not really relevant now. So we are not going to compete. And that's what we said is basically now our blog. We are not interesting in participating in this war. We want to invest our engineering effort and our IP in solving real world issues and performance issues that we have. And we want to improve our engine for these real world customers. And the nice thing with Snowflake, because it's a service, we see exactly all the queries that our customers are executing. So we know where we are struggling as a system, and that's where we want to invest and we want to improve. And if you look at many announcements that we made, it's all about under-the-cover improving Snowflake and getting the benefit of this improvement to our customer. So that was the message of that blog. And yes, the message was okay. Mr. Databricks, it's nice, and it's perfect that, I mean, everyone makes a decision, right? We made the decision not to participate. Databricks made another decision, which is very fine, and that's fine that they publish their number on their system. Where it is not fine is that they published number using Snowflake and misrepresenting our performance. And that's what we wanted also to correct. >> Yeah, well, thank you for going into that. I know it's, look, leaders don't necessarily have to get involved in that mudslide. (crosstalk) Enough said about that, so that's cool. I want to ask you, I interviewed Frank last spring, right after the lockdown, he was kind enough to come on virtually, and I asked him about on-prem. And he was, you know Frank, he doesn't mix words, He said, "We're not getting into a halfway house. That's not going to happen." And of course, you really can't do what you do on-prem. You can't separate compute, some have tried, but it's not the same. But at the same time that you see like Andreessen comes out with this blog that says a huge portion of your cost of goods sold is going to be the cloud, so you're going to have to repatriate. Help me square that circle. Is it cloud forever? Is it will you never say never? What can you share of that? >> I will never say never, it's not my style. I always say you can always change your mind, and maybe different factors can change your mind. What was true at some point might not be true at a later point. But as of now, I don't see any reason for us to go on-premise. As you mentioned at the beginning, right, Snowflake is growing like crazy. The world is moving to the cloud. I think maybe it goes both ways, but I would say 90% or 99% of the world is moving to the cloud. Maybe 1% is coming back for some very specific reasons. I don't think that the world is going to move back on-premise. So in the end we might miss a small percentage of the workload that will stay on-premise and that's okay. >> And as well, if you dig into some of the financial statements you'll see, read the notes where you've renegotiated, right? We're talking big numbers. Hundreds and hundreds of millions of dollars of cost reduction, actually more, over a 10 year period. Billions of your cloud bills. So the cloud suppliers, they don't want to lose you as a customer, right? You're one of their biggest customer. So it's awesome. Last question is kind of, your work now is to really drive the data cloud, get adoption up, build that supercloud, we call it. Maybe you could talk a little bit about how you see the future. >> The future is really broadened, the scope of Snowflake, and really, I would say the marketplace, and data sharing, and services, which are directly built natively on Snowflake and are shared through our platform, and can operate, it can mix data on provider-side with data on consumer-side, and creating this collaboration within the Snowflake data cloud, I think is really the future. And we are really only scratching the surface of that. And you can see the enthusiasm of Snowflake data cloud and vertical industry We have nuanced the final show data cloud. Industry, complete vertical industry, latching on that concept and collaborating via Snowflake, which was not possible before. And I think you talked about machine learning, for example. Machine learning, collaboration through machine learning, the ones who are building this advanced model might not be the same as the one who are consuming this model, right? It might be this collaboration between expertise and consumer of that expertise. So we are really at the beginning of this interconnected world. And to me the world wide web of data that we are creating is really going to be amazing. And it's all about connecting. >> And I'm glad you mentioned the ecosystem. I didn't give enough attention to that. Because as a cloud provider, which essentially you are, you've got to have a strong ecosystem. That's a hallmark of cloud. And then the other, vertical, that we didn't touch on, is media and entertainment. A lot of direct-to-consumer. I think healthcare is going to be a huge vertical for you guys. All right we got to go, Terry. Thanks so much for coming on "theCUBE." I really appreciate you. >> Thanks, Dave. >> And thank you for watching. This a wrap from AWS re:Invent 2021. "theCUBE," the leader in global tech coverage. We'll see you next time. (upbeat music)

>> Voiceover: From theCUBE's Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE Conversation. >> Welcome back to theCUBE's coverage of the AWS Startup Showcase. Next Big Thing in AI, Security and Life Sciences featuring Ahana for the AI Trek. I'm your host, John Furrier. Today, we're joined by two great guests, Steven Mih, Ahana CEO, and Sachin Nayyar, Securonix CEO. Gentlemen, thanks for coming on theCUBE. We're talking about the Next-Gen technologies on AI, Open Data Lakes, et cetera. Thanks for coming on. >> Thanks for having us, John. >> Thanks, John. >> What a great line up here. >> Sachin: Thanks, Steven. >> Great, great stuff. Sachin, let's get in and talk about your company, Securonix. What do you guys do? Take us through, I know you've got a slide to help us through this, I want to introduce your stuff first then jump in with Steven. >> Absolutely. Thanks again, Steven. Ahana team for having us on the show. So Securonix, we started the company in 2010. We are the leader in security analytics and response capability for the cybermarket. So basically, this is a category of solutions called SIEM, Security Incident and Event Management. We are the quadrant leaders in Gartner, we now have about 500 customers today and have been plugging away since 2010. Started the company just really focused on analytics using machine learning and an advanced analytics to really find the needle in the haystack, then moved from there to needle in the needle stack using more algorithms, analysis of analysis. And then kind of, I evolved the company to run on cloud and become sort of the biggest security data lake on cloud and provide all the analytics to help companies with their insider threat, cyber threat, cloud solutions, application threats, emerging internally and externally, and then response and have a great partnership with Ahana as well as with AWS. So looking forward to this session, thank you. >> Awesome. I can't wait to hear the news on that Next-Gen SIEM leadership. Steven, Ahana, talk about what's going on with you guys, give us the update, a lot of stuff happening. >> Yeah. Great to be here and thanks for that such, and we appreciate the partnership as well with both Securonix and AWS. Ahana is the open source company based on PrestoDB, which is a project that came out of Facebook and is widely used, one of the fastest growing projects in data analytics today. And we make a managed service for Presto easily on AWS, all cloud native. And we'll be talking about that more during the show. Really excited to be here. We believe in open source. We believe in all the challenges of having data in the cloud and making it easy to use. So thanks for having us again. >> And looking forward to digging into that managed service and why that's been so successful. Looking forward to that. Let's get into the Securonix Next-Gen SIEM leadership first. Let's share the journey towards what you guys are doing here. As the Open Data Lakes on AWS has been a hot topic, the success of data in the cloud, no doubt is on everyone's mind especially with the edge coming. It's just, I mean, just incredible growth. Take us through Sachin, what do you guys got going on? >> Absolutely. Thanks, John. We are hearing about cyber threats every day. No question about it. So in the past, what was happening is companies, what we have done as enterprise is put all of our eggs in the basket of solutions that were evaluating the network data. With cloud, obviously there is no more network data. Now we have moved into focusing on EDR, right thing to do on endpoint detection. But with that, we also need security analytics across on-premise and cloud. And your other solutions like your OT, IOT, your mobile, bringing it all together into a security data lake and then running purpose built analytics on top of that, and then having a response so we can prevent some of these things from happening or detect them in real time versus innovating for hours or weeks and months, which is is obviously too late. So with some of the recent events happening around colonial and others, we all know cybersecurity is on top of everybody's mind. First and foremost, I also want to. >> Steven: (indistinct) slide one and that's all based off on top of the data lake, right? >> Sachin: Yes, absolutely. Absolutely. So before we go into on Securonix, I also want to congratulate everything going on with the new cyber initiatives with our government and just really excited to see some of the things that the government is also doing in this space to bring, to have stronger regulation and bring together the government and the private sector. From a Securonix perspective, today, we have one third of the fortune 500 companies using our technology. In addition, there are hundreds of small and medium sized companies that rely on Securonix for their cyber protection. So what we do is, again, we are running the solution on cloud, and that is very important. It is not just important for hosting, but in the space of cybersecurity, you need to have a solution, which is not, so where we can update the threat models and we can use the intelligence or the Intel that we gather from our customers, partners, and industry experts and roll it out to our customers within seconds and minutes, because the game is real time in cybersecurity. And that you can only do in cloud where you have the complete telemetry and access to these environments. When we go on-premise traditionally, what you will see is customers are even thinking about pushing the threat models through their standard Dev test life cycle management, and which is just completely defeating the purpose. So in any event, Securonix on the cloud brings together all the data, then runs purpose-built analytics on it. Helps you find very few, we are today pulling in several million events per second from our customers, and we provide just a very small handful of events and reduce the false positives so that people can focus on them. Their security command center can focus on that and then configure response actions on top of that. So we can take action for known issues and have intelligence in all the layers. So that's kind of what the Securonix is focused on. >> Steven, he just brought up, probably the most important story in technology right now. That's ransomware more than, first of all, cybersecurity in general, but ransomware, he mentioned some of the government efforts. Some are saying that the ransomware marketplace is bigger than some governments, nation state governments. There's a business model behind it. It's highly active. It's dominating the scene and it's a real threat. This is the new world we're living in, cloud creates the refactoring capabilities. We're hearing that story here with Securonix. How does Presto and Securonix work together? Because I'm connecting the dots here in real time. I think you're going to go there. So take us through because this is like the most important topic happening. >> Yeah. So as Sachin said, there's all this data that needs to go into the cloud and it's all moving to the cloud. And there's a massive amounts of data and hundreds of terabytes, petabytes of data that's moving into the data lakes and that's the S3-based data lakes, which are the easiest, cheapest, commodified place to put all this data. But in order to deliver the results that Sachin's company is driving, which is intelligence on when there's a ransomware or possibility, you need to have analytics on them. And so Presto is the open source project that is a open source SQL query engine for data lakes and other data sources. It was created by Facebook as part of the Linux foundation, something called Presto foundation. And it was built to replace the complicated Hadoop stack in order to then drive analytics at very lightning fast queries on large, large sets of data. And so Presto fits in with this Open Data Lake analytics movement, which has made Presto one of the fastest growing projects out there. >> What is an Open Data Lake? Real quick for the audience who wants to learn on what it means. Does is it means it's open source in the Linux foundation or open meaning it's open to multiple applications? What does that even mean? >> Yeah. Open Data Lake analytics means that you're, first of all, your data lake has open formats. So it is made up of say something called the ORC or Parquet. And these are formats that any engine can be used against. That's really great, instead of having locked in data types. Data lakes can have all different types of data. It can have unstructured, semi-structured data. It's not just the structured data, which is typically in your data warehouses. There's a lot more data going into the Open Data Lake. And then you can, based on what workload you're looking to get benefit from, the insights come from that, and actually slide two covers this pictorially. If you look on the left here on slide two, the Open Data Lake is where all the data is pulling. And Presto is the layer in between that and the insights which are driven by the visualization, reporting, dashboarding, BI tools or applications like in Securonix case. And so analytics are now being driven by every company for not just industries of security, but it's also for every industry out there, retail, e-commerce, you name it. There's a healthcare, financials, all are looking at driving more analytics for their SaaSified applications as well as for their own internal analysts, data scientists, and folks that are trying to be more data-driven. >> All right. Let's talk about the relationship now with where Presto fits in with Securonix because I get the open data layer. I see value in that. I get also what we're talking about the cloud and being faster with the datasets. So how does, Sachin' Securonix and Ahana fit in together? >> Yeah. Great question. So I'll tell you, we have two customers. I'll give you an example. We have two fortune 10 customers. One has moved most of their operations to the cloud and another customer which is in the process, early stage. The data, the amount of data that we are getting from the customer who's moved fully to the cloud is 20 times, 20 times more than the customer who's in the early stages of moving to the cloud. That is because the ability to add this level of telemetry in the cloud, in this case, it happens to be AWS, Office 365, Salesforce and several other rescalers across several other cloud technologies. But the level of logging that we are able to get the telemetry is unbelievable. So what it does is it allows us to analyze more, protect the customers better, protect them in real time, but there is a cost and scale factor to that. So like I said, when you are trying to pull in billions of events per day from a customer billions of events per day, what the customers are looking for is all of that data goes in, all of data gets enriched so that it makes sense to a normal analyst and all of that data is available for search, sometimes 90 days, sometimes 12 months. And then all of that data is available to be brought back into a searchable format for up to seven years. So think about the amount of data we are dealing with here and we have to provide a solution for this problem at a price that is affordable to the customer and that a medium-sized company as well as a large organization can afford. So after a lot of our analysis on this and again, Securonix is focused on cyber, bringing in the data, analyzing it, so after a lot of our analysis, we zeroed in on S3 as the core bucket where this data needs to be stored because the price point, the reliability, and all the other functions available on top of that. And with that, with S3, we've created a great partnership with AWS as well as with Snowflake that is providing this, from a data lake perspective, a bigger data lake, enterprise data lake perspective. So now for us to be able to provide customers the ability to search that data. So data comes in, we are enriching it. We are putting it in S3 in real time. Now, this is where Presto comes in. In our research, Presto came out as the best search engine to sit on top of S3. The engine is supported by companies like Facebook and Uber, and it is open source. So open source, like you asked the question. So for companies like us, we cannot depend on a very small technology company to offer mission critical capabilities because what if that company gets acquired, et cetera. In the case of open source, we are able to adopt it. We know there is a community behind it and it will be kind of available for us to use and we will be able to contribute in it for the longterm. Number two, from an open source perspective, we have a strong belief that customers own their own data. Traditionally, like Steven used the word locked in, it's a key term, customers have been locked in into proprietary formats in the past and those days are over. You should be, you own the data and you should be able to use it with us and with other systems of choice. So now you get into a data search engine like Presto, which scales independently of the storage. And then when we start looking at Presto, we came across Ahana. So for every open source system, you definitely need a sort of a for-profit company that invests in the community and then that takes the community forward. Because without a company like this, the community will die. So we are very excited about the partnership with Presto and Ahana. And Ahana provides us the ability to take Presto and cloudify it, or make the cloud operations work plus be our conduit to the Ahana community. Help us speed up certain items on the roadmap, help our team contribute to the community as well. And then you have to take a solution like Presto, you have to put it in the cloud, you have to make it scale, you have to put it on Kubernetes. Standard thing that you need to do in today's world to offer it as sort of a micro service into our architecture. So in all of those areas, that's where our partnership is with Ahana and Presto and S3 and we think, this is the search solution for the future. And with something like this, very soon, we will be able to offer our customers 12 months of data, searchable at extremely fast speeds at very reasonable price points and you will own your own data. So it has very significant business benefits for our customers with the technology partnership that we have set up here. So very excited about this. >> Sachin, it's very inspiring, a couple things there. One, decentralize on your own data, having a democratized, that piece is killer. Open source, great point. >> Absolutely. >> Company goes out of business, you don't want to lose the source code or get acquired or whatever. That's a key enabler. And then three, a fast managed service that has a commercial backing behind it. So, a great, and by the way, Snowflake wasn't around a couple of years ago. So like, so this is what we're talking about. This is the cloud scale. Steven, take us home with this point because this is what innovation looks like. Could you share why it's working? What's some of the things that people could walk away with and learn from as the new architecture for the new NextGen cloud is here, so this is a big part of and share how this works? >> That's right. As you heard from Sachin, every company is becoming data-driven and analytics are central to their business. There's more data and it needs to be analyzed at lower cost without the locked in and people want that flexibility. And so a slide three talks about what Ahana cloud for Presto does. It's the best Presto out of the box. It gives you very easy to use for your operations team. So it can be one or two people just managing this and they can get up to speed very quickly in 30 minutes, be up and running. And that jump starts their movement into an Open Data Lake analytics architecture. That architecture is going to be, it is the one that is at Facebook, Uber, Twitter, other large web scale, internet scale companies. And with the amount of data that's occurring, that's now becoming the standard architecture for everyone else in the future. And so just to wrap, we're really excited about making that easy, giving an open source solution because the open source data stack based off of data lake analytics is really happening. >> I got to ask you, you've seen many waves on the industry. Certainly, you've been through the big data waves, Steven. Sachin, you're on the cutting edge and just the cutting edge billions of signals from one client alone is pretty amazing scale and refactoring that value proposition is super important. What's different from 10 years ago when the Hadoop, you mentioned Hadoop earlier, which is RIP, obviously the cloud killed it. We all know that. Everyone kind of knows that. But like, what's different now? I mean, skeptics might say, I don't believe you, but it's just crazy. There's no way it works. S3 costs way too much. Why is this now so much more of an attractive proposition? What do you say the naysayers out there? With Steve, we'll start with you and then Sachin, I want you to like weigh in too. >> Yeah. Well, if you think about the Hadoop era and if you look at slide three, it was a very complicated system that was done mainly on-prem. And you'd have to go and set up a big data team and a rack and stack a bunch of servers and then try to put all this stuff together and candidly, the results and the outcomes of that were very hard to get unless you had the best possible teams and invested a lot of money in this. What you saw in this slide was that, that right hand side which shows the stack. Now you have a separate compute, which is based off of Intel based instances in the cloud. We run the best in that and they're part of the Presto foundation. And that's now data lakes. Now the distributed compute engines are the ones that have become very much easier. So the big difference in what I see is no longer called big data. It's just called data analytics because it's now become commodified as being easy and the bar is much, much lower, so everyone can get the benefit of this across industries, across organizations. I mean, that's good for the world, reduces the security threats, the ransomware, in the case of Securonix and Sachin here. But every company can benefit from this. >> Sachin, this is really as an example in my mind and you can comment too on if you'd believe or not, but replatform with the cloud, that's a no brainer. People do that. They did it. But the value is refactoring in the cloud. It's thinking differently with the assets you have and making sure you're using the right pieces. I mean, there's no brainer, you know it's good. If it costs more money to stand up something than to like get value out of something that's operating at scale, much easier equation. What's your thoughts on this? Go back 10 years and where we are now, what's different? I mean, replatforming, refactoring, all kinds of happening. What's your take on all this? >> Agreed, John. So we have been in business now for about 10 to 11 years. And when we started my hair was all black. Okay. >> John: You're so silly. >> Okay. So this, everything has happened here is the transition from Hadoop to cloud. Okay. This is what the result has been. So people can see it for themselves. So when we started off with deep partnerships with the Hadoop providers and again, Hadoop is the foundation, which has now become EMR and everything else that AWS and other companies have picked up. But when you start with some basic premise, first, the racking and stacking of hardware, companies having to project their entire data volume upfront, bringing the servers and have 50, 100, 500 servers sitting in their data centers. And then when there are spikes in data, or like I said, as you move to the cloud, your data volume will increase between five to 20x and projecting for that. And then think about the agility that it will take you three to six months to bring in new servers and then bring them into the architecture. So big issue. Number two big issue is that the backend of that was built for HDFS. So Hadoop in my mind was built to ingest large amounts of data in batches and then perform some spark jobs on it, some analytics. But we are talking in security about real time, high velocity, high variety data, which has to be available in real time. It wasn't built for that, to be honest. So what was happening is, again, even if you look at the Hadoop companies today as they have kind of figured, kind of define their next generation, they have moved from HDFS to now kind of a cloud based platform capability and have discarded the traditional HDFS architecture because it just wasn't scaling, wasn't searching fast enough, wasn't searching fast enough for hundreds of analysts at the same time. And then obviously, the servers, et cetera wasn't working. Then when we worked with the Hadoop companies, they were always two to three versions behind for the individual services that they had brought together. And again, when you're talking about this kind of a volume, you need to be on the cutting edge always of the technologies underneath that. So even while we were working with them, we had to support our own versions of Kafka, Solr, Zookeeper, et cetera to really bring it together and provide our customers this capability. So now when we have moved to the cloud with solutions like EMR behind us, AWS has invested in in solutions like EMR to make them scalable, to have scale and then scale out, which traditional Hadoop did not provide because they missed the cloud wave. And then on top of that, again, rather than throwing data in that traditional older HDFS format, we are now taking the same format, the parquet format that it supports, putting it in S3 and now making it available and using all the capabilities like you said, the refactoring of that is critical. That rather than on-prem having servers and redundancies with S3, we get built in redundancy. We get built in life cycle management, high degree of confidence data reliability. And then we get all this innovation from companies like, from groups like Presto, companies like Ahana sitting on double that S3. And the last item I would say is in the cloud we are now able to offer multiple, have multiple resilient options on our side. So for example, with us, we still have some premium searching going on with solutions like Solr and Elasticsearch, then you have Presto and Ahana providing majority of our searching, but we still have Athena as a backup in case something goes down in the architecture. Our queries will spin back up to Athena, AWS service on Presto and customers will still get served. So all of these options, but what it doesn't cost us anything, Athena, if we don't use it, but all of these options are not available on-prem. So in my mind, I mean, it's a whole new world we are living in. It is a world where now we have made it possible for companies to even enterprises to even think about having true security data lakes, which are useful and having real-time analytics. From my perspective, I don't even sign up today for a large enterprise that wants to build a data lake on-prem because I know that is not, that is going to be a very difficult project to make it successful. So we've come a long way and there are several details around this that we've kind of endured through the process, but very excited where we are today. >> Well, we certainly follow up with theCUBE on all your your endeavors. Quickly on Ahana, why them, why their solution? In your words, what would be the advice you'd give me if I'm like, okay, I'm looking at this, why do I want to use it, and what's your experience? >> Right. So the standard SQL query engine for data lake analytics, more and more people have more data, want to have something that's based on open source, based on open formats, gives you that flexibility, pay as you go. You only pay for what you use. And so it proved to be the best option for Securonix to create a self-service system that has all the speed and performance and scalability that they need, which is based off of the innovation from the large companies like Facebook, Uber, Twitter. They've all invested heavily. We contribute to the open source project. It's a vibrant community. We encourage people to join the community and even Securonix, we'll be having engineers that are contributing to the project as well. I think, is that right Sachin? Maybe you could share a little bit about your thoughts on being part of the community. >> Yeah. So also why we chose Ahana, like John said. The first reason is you see Steven is always smiling. Okay. >> That's for sure. >> That is very important. I mean, jokes apart, you need a great partner. You need a great partner. You need a partner with a great attitude because this is not a sprint, this is a marathon. So the Ahana founders, Steven, the whole team, they're world-class, they're world-class. The depth that the CTO has, his experience, the depth that Dipti has, who's running the cloud solution. These guys are world-class. They are very involved in the community. We evaluated them from a community perspective. They are very involved. They have the depth of really commercializing an open source solution without making it too commercial. The right balance, where the founding companies like Facebook and Uber, and hopefully Securonix in the future as we contribute more and more will have our say and they act like the right stewards in this journey and then contribute as well. So and then they have chosen the right niche rather than taking portions of the product and making it proprietary. They have put in the effort towards the cloud infrastructure of making that product available easily on the cloud. So I think it's sort of a no-brainer from our side. Once we chose Presto, Ahana was the no-brainer and just the partnership so far has been very exciting and I'm looking forward to great things together. >> Likewise Sachin, thanks so much for that. And we've only found your team, you're world-class as well, and working together and we look forward to working in the community also in the Presto foundation. So thanks for that. >> Guys, great partnership. Great insight and really, this is a great example of cloud scale, cloud value proposition as it unlocks new benefits. Open source, managed services, refactoring the opportunities to create more value. Stephen, Sachin, thank you so much for sharing your story here on open data lakes. Can open always wins in my mind. This is theCUBE we're always open and we're showcasing all the hot startups coming out of the AWS ecosystem for the AWS Startup Showcase. I'm John Furrier, your host. Thanks for watching. (bright music)

(upbeat music) >> Hey everyone, I'm John Furrier with The Cube, we're here in Palo Alto, California for a remote interview and session for The Cube presents AWS startup showcase, the next big thing in AI security in life sciences. I'm John Furrier. We're here with a great segment on cloud. Next big thing in Cloud with Chaos Search, Thomas Hazel, Chief Technology and Science Officer of Chaos Search joined by Jeremy Foran, the head of data analytics, the bad boy of data analyst as they say, but BAI communications, Jeremy Thomas, great to have you on. >> Great to be here. >> Pleasure to be here. >> So we're going to be talking about applying large scale log analytics to building the future of the transit industry. Obviously Telco's a big part of that, smart cities, you name the use case self-driving trucks, cars, you name it, everything's now edge. That the edge is super valuable, it's a new kind of last mile if you will, it's moving fast, it's mobile. This is a huge deal. Let's get into it, Thomas. What's this big story around this, this session? >> Well, we provide unique ability to take all that edge data and drive it into a data lake offering that we provide data analytics, both in logs, BI and coming out with ML there this year into next. So our unique play is transforming customers' cloud outer storage into an analytical platform. And really, I think with BIA is a log analytics specifically where, you know there's a lot of data streams from all those devices going into a lake that we transform their lake into analytics for driving, I guess, operational analysis. >> You know, Jeremy, I remember back in the day, I'm old enough to remember when the edge was the remote switch or campus hub or something. And then even on the Telco side, there was no wifi back in 2000 and you know, someone was driving in a car and you got any signal, you're lucky. Now you got, you know, no perimeter you have unlimited connectivity everywhere. This has opened up more of an Omni channel data problem. How do you see that world? Because you still got more devices pushing out at this edge and it's getting super local, right? Even on the body, even on people in the car. So certainly a lot of change on the infrastructure side. What does that pose for data challenge? >> Yeah, I, I would say that, you know users always want more, more bandwidth, more performance and that requires us to create more systems that require more complexity to deliver that user experience that we're, we're very proud of. And with that complexity means, you know exponentially more data. And so one of the wifi networks we offer in the Toronto subway system, T-connect, you know we see a 100-200,000 unique users a day and you can imagine just the amount of infrastructure to support that so that everyone has a seamless experience and can get their news and emails and even stream media while they're waiting for the subway. >> So you guys provide state of the art infrastructure for cell, wifi, broadcast, radio, IP networks, basically I mean, I call it the smart city kind of go-to. But that's basically anything involving kind of that edge piece. This is a huge thing. So as smart cities are on the table, which and you seeing 5G being called more of an enterprise app where there's feeding large dense areas of people this is now a new modern version of what I would call the, the smart city blueprint. What's changed in your mind on this whole modernization of this smart city infrastructure concept? What's new? What's cutting edge? >> Yeah. I would say that, you know there was an explosion of data and a lot of our insights aren't coming from one system anymore. It's coming from collecting data from all of the different pieces, the different infrastructure whether that's your fiber infrastructure or your wireless infrastructure, and then to solve problems you need to correlate data across those systems. So we're seeing more and more technologies that allow you to do that correlation. And that's really where we're finding tons of value, right? >> Thomas, take us through what you guys do as a, as a, as a product, a value proposition, the secret sauce, and and why I'm here with Jeremy? Why is this conversation important for the folks watching? What's the connection between Chaos Search and BAI communication? >> Well, it's data, right? And lots of it. So our unique platform allows people like Jeremy to stream all this data, right? In you know, today's world terabytes go to petabytes really easily, billions go to trillion really easily, and so providing the analysis of that data for their operations is challenging particularly based on technology and architectures that have been around for a long time. So what we do here at Chaos Search is the ability for BIA to stream all these devices, all these services into one centralized data lake on their cloud outer storage, where we connect to that cloud outer storage and transform it into an analytical database to do, in this case log analytics and do it seamlessly, easily where a new workload a new stream just streams into that lake. And we, as a service take over, we discover we index it and publish well-known open API and visualization so that they can focus on their business, not all the operational data pipeline, database and data engineering type work that again, at these types of scales is is frankly a nightmare. >> You know, one of the things that we've always observed on The Cube when you see new things come out that are really cool groundbreaking products like you guys are doing it's always a challenge to manage the cost and complexity of bringing in the new. So Jeremy, take us through this tech stack here because you know, it's, sometimes it might be unwieldy just in from a tech stack perspective, nevermind the business logic or the business processes that got to be either unwound or changed. Can you take us through the IT stack that's critical to support your, your area? >> Yeah, absolutely. So with all the various different equipment you know, to provide our public wifi and and our desks, carrier agnostic, LT and 5G networks, you know, we need to be able to adhere to PCI compliance and ISO 27,000, so that, you know, requires us to keep a tremendous amount of our data. And the challenge we were facing is how do we do that cost effectively, and not have to make any sort of compromises on how we do that? A lot of times you'll find you don't know the value of your data today until tomorrow. An example would be COVID. You know, we, when we were storing data two years ago we weren't planning for a pandemic, but now that we were able to retain that data and look back we can see a tremendous amount of value with trying to forecast how our systems will recover when things get back to normal. And so when I met Thomas and we were sort of talking about how we were going to solve some of these data retention problems, he started explaining to me their compression in some of the performance metrics of their profession. And, you know, I said, oh, middle out compression. And it was a bit, it's been a bit of a running joke between me and him and I'm sure others, but it's incredibly impressive the amount of data we're able to store at the kind of cost, right? >> What, what problem does, did he solve for you? Because I mean, these guys, honestly, you know the startups have a lot and the Cloud's enabling more value now, we're seeing this, but when you look at this what was your, what was your core problem that you had? >> Yeah, so we, when you we want to be able to, I mean, primarily this is for our CIS log server. And CIS long servers today aren't what they were 10, 15 years ago where you just sort of had a machine and if something broke you went and looked, right? Now, they're very complex, that data is feeding to various systems and third-party software. So, you know, we're actively looking for changes in patterns and we have our, you know security teams auditing these from, for penetration testing and such. And then the getting that data to S3 so that we could have it in case, you know, for two, three years of storage. Well, the problem we were facing is all of that all of these different systems we needed to feed and retain data, we couldn't do that on site. We wanted to do use S3 but when we were doing some projections, it's like, we, we don't really have the budget for all of these places. Meeting Thomas and, and working with Chaos Search, you know, using their compression brought those costs down drastically. And then as we've been working with them the really exciting thing is they we're bringing more and more features to that surface or offering. So, you know, first it was just storing that data away. And now we're starting to build solutions off of that sitting in storage. So that's where it gets really exciting because you know, there, it's nothing to start getting anomaly detection off those logs, which, you know originally it was just, we need to store them in case somebody needs them two, three years from now. >> So Thomas Thomas, if I get this right then what I'm hearing is obviously I've put aside the complexity and the governing side the regulations for a minute just generally. Data retention as, as a key value proposition and having data available when you need it and then to do that and doing it in a very cost-effective simple way. It sounds like what you guys are offering. Is that right? >> Yeah, I mean, one key aspect of our solution is retention, right? Those are a lot of the challenges, but at the same time we provide real time notification like a classic log analytic type platform, alerting, monitoring. The key thing is to bringing both those worlds together and solving that problem. And so this, you know, middle in middle out, well, to be frank, we created a new technology called what we call Chaos Index that is a database index that is wonderfully small as as we're indicating, but also provides all the features that makes Cloud object storage, high performance. And so the idea is that use this lake offering to store all your data in a cost effective way but our service allows you to analyze it both in a long retention perspective as well as real-time perspective and bringing those two worlds together is so key because typically you have Silo Solutions and whether it's real-time at scale or retention scale the cost complexity and time to build out those solutions I know Jeremy knows also, well, a lot of folks come to us to solve those problems because you know when you're dealing with, you know terabytes and up, you know these things get complicated and to be frank, fall over quite often. >> Yeah. Let me, let me just ask you the question that's probably on everyone's mind who's watching and you guys probably have both heard this many times, because a lot of people just throw the data lake solution around like it's, you know why they whitewash their kind of old legacy solutions with data lake, store it on data lake. It's been called a data swamp. So people are fearful that, okay. I love this idea of a data lake, who doesn't like throwing data into a repository, having it available at will with notifications, all this secret magic beans that just magically create value. But I doubt that, I don't want to turn into a data swamp. So Thomas and Jeremy, talk about that, that concern. How do you mitigate that? How do you talk to that? Because if done properly, there's huge value in having a control plane or some sort of data system that is going to be tied in with signals and just storage retention. So I see the value. How do you manage the concern that people might say, Hey, I don't want to date a swamp? >> Yeah, I'll jump into that. So, you know, let's just be frank, Hadoop was a great tool for a very narrow scenario. I think that data swamp came out because people were using the tooling in an incorrect way. I've always had the belief that data lakes are the future. You just have the right to have the right service the right philosophy to leverage it. So what we do here at Chaos Search is we allow you to organize it, discover it, automatically index that data so that swamp doesn't get swampy. You know, when you stream data into your lake how do you organize it, such that it's has a nice stream? How do you transform that data into a value? So with our service we actually start where the storage begins, not a end point, not an archive. So we have tooling and services that keep your lake from being swampy to be, to be clear. And, but the key value is the benefits of the lake, the cost effectiveness, the reliability, security, the scale, those are all the benefits. The problem was that no one really made cloud offer storage a first-class citizen and we've done that. We've dressed the swamp nature but provided all the value of analysis. And that cost metrics, that scale. No one can touch cloud outer storage, it just, you can't. But what we've done is cracked the code of how you make it analytical. >> Jeremy, I want to get your thoughts on this too, on your side I mean, as a practitioner and customer of, of of these solutions, you know, the concern is am I missing anything? And I've been a big proponent of data retention for many, many years. You know, Dave Alondra in our Cube knows all know that I bang on the table all the time, store your data, be a data hoarder, because it's going to come back and be valuable. Costs are going down so I'm a big fan of data retention. But the fear might be on, what am I missing? Because machine learning starts to come in down the road you got AI, the more data you have that's accessible in real time, the more machine learning is effective. Do you, do you worry about missing anything or do you just store everything? >> We, we store everything. Sometimes it's, it's interesting where the value and insights come from your data. Something that see, might seem trivial today down the road offers tremendous, tremendous value. So one of the things we do is provide because we have wifi in the subway infrastructure, you know taking that wifi data, we can start to understand the flow of people in and out of the subway network. And we can take that and provide insights to the rail operators, which get them from A to B quicker. You know, when we built the wifi it wasn't with the intention of getting Torontonians across the city faster. But that was one of the values that we were able to get from the data in terms of, you know, Thomas's solution, I think one of the reasons we we engaged him in the first place is because I didn't believe his compression. It sounded a little too good to be true. And so when it was time to try them out, you know all we had to do was ship data to an S3 bucket. You know, there's tons of, of solutions to do that. And, and data shippers right out of the box. It took a few, you know, a few minutes and then to start exploring the data was in Cabana, which is or their dashboard, which is, you know, an interface that's easy to use. So we were, you know, within a two days getting the value out of that data that we were looking for which is, you know, phenomenal. We've been very happy. >> Thomas, sounds like you've got a great, great testimonial here and it's not like an easy problem that he's living in there. I mean, I think, you know, I was mentioning this earlier and we're going to get into it now. There's regulations and there's certain compliance issues. First of all, everyone has this now problem now, it's not just within that space. But just the technical complexities of packets moving around I got on my wifi and the stop here, I'm jumping over here, and there's a ton of data it's all over the place, it's totally unstructured. So it's a tough, tough test for you guys, Chaos Search. So yeah, it's almost like the Mount Everest of customer testimonials. You've got to, it's a big, it's a big use case here. How does this translate to other clients? And talk about this governance and security controls because I know this highly regulated and you got there's penalties involved on his side of the world and Telco, the providers that have these edge devices there's actually penalties and, and whatnot so, not just commercial, it's maybe a, you know risk management, but here there's actually penalties. >> Absolutely. So, you know centralizing your data has a real benefit of of not getting in trouble, right? So you have one place, you store one place that's a good thing, but what we've done and this was a key aspect to our offering is we as Chaos, Chaos Search folks, we don't own the customer's data. We don't own BIA's data. They own the data. They give us access rights, very standard way with Cloud App storage roll on policies from Amazon, read only access rights to their data. And so not owning a customer's data is a big selling point not only for them, but for us for compliance regulatory perspective. So, you know, unlike a lot of solutions where you move the data into them and now they are responsible, actually BIA owns everything. We, they provide access so that we could provide an analysis that they could turn off at any point in time. We're also SOC 2 type 1 and type 2 compliant you got to do it, you know, in this, this world, you know when we were young we ran at this because of all of these compliance scenarios that we will be in, but, you know, the long as short of it is, we're transient service. The storage, cloud storage is the source of truth where all data resides and, you know, think about it, it's architecturally smart, it's cost effective, it's secure, it's reliable, it's durable. But from a security perspective, having the customer own their own data is a big differentiation in the market, a big differentiation. >> Jeremy, talk about on your end the security controls surrounding the log management environments that span across countries with different regulations. Now you've got all kinds of policy dimensions and technical dimensions and topology dimensions. >> Yeah, absolutely. So how we approach it is we look at where we have offerings across the globe and we figure out what the sort of highest watermark level of adherence we need to hit. And then we standardize across that. And by shipping to S3, it allows us to enforce that governance really easily and right to Tom's point you know, we manage the data, which is very important to us and we don't have to be worried about a third party or if we want to change providers years down the road. Although I don't think anyone's coming out with 81% compression anytime soon (laughs). But yeah, so that's, for us, it's about meeting those high standards and having the technologies that enable us to do it. And Chaos Search is a very big part of that right now. >> All right let me ask you a question, for the folks watching that are like really interested in this topic, what would you say to them when evaluating Chaos Search obviously, your use case is complex, but so are others as enterprises start to have an edge, obviously the security posture shifts, everything shifts. There's no more perimeter and the data problem becomes acute to them. So the enterprises are going to start seeing what you've been living for in your world. What's your advice to people watching? >> My advice would be to give them a try. You know, it's it's has been really quite impressive. The customer service has been hands-on and we've been getting, you know, they've been under-promising and over-delivering, which when you have the kind of requirements to manage solutions in these very complex environment, cloud local, you know various data centers and such, you know that kind of customer service is very important, right? It enables us to continue to deliver those high quality solutions. >> So Thomas give us the, the overview of the secret sauce. You've got a great testimonial here. You got people watching, what's different now in the world that you're going after, what wave are you on? Talk to the people who are watching this and saying, okay why Chaos Search? Why are you relevant? Obviously there's some cool things you're doing. I love that. What's cool, and what's relevant and why what's in it for them if they work with you? >> Yeah. So you know, that that whole Silicon Valley reference actually got that from my patent attorney when we were talking. But yeah, no, we, we, you know, focus on if we can crack this code of making data, one a face small, store small, moves small, process small. But then make it multimodal access make it virtual transformation. If we could do that, and we could transform cloud outer storage into a high-performance medical database all these heavy, heavy problems, all that complexity that scaffolding that you build to do these type of scales would be solved. Now what we had to focus on and this has been my, I guess you say life passion is working on a new data representation. And that's our secret sauce that enables a new architecture a new service that where the customer folks on their tooling, their APIs, their visualizations that they know and love, what we focus is on taking that data lake, and again, to transform it into an analytical database, both for log analytics think of like elastic search replacement, as well as a BI replacement for your SQL warehousing database. And coming out later this year into 2022, ML support on one representation. You don't have the silo your information you don't have to re index your data, both. So elastic search CQL and actually ML TensorFlow actions on the exact same representation. So think about the data retention, doing some post analysis on all those logs of data, months, years, and then maybe set up some triggers if you see some anomaly that's happening within your service. So you think about it, the hunt with BI reporting, with predictive analysis on one platform. Again, it sounds a little unicorn, I agree with Jeremy, maybe it didn't sound true but it's been a life's work. So it didn't happen overnight. And you know, it's eight years, at least in the in the making, but I guess the life journey in the end. >> Well, you know, the timing is great. You know, all the database geeks out there who have been following the data industry know that, you know there's a good point for structured data but when you start getting into mechanisms and they become a bottleneck or a blocker to innovation, you know you starting to see this idea of a data lake being let the data kind of form, let it be. You know, I hate the word control plane but more of a, a connective tissue between systems is become an interesting thing. So now you can store everything so you know, no worries there, no blind spots and then let the magic of machine learning in the future, come around. So Jeremy, with that, I got to ask you since you're the bad boy of data analytics at BAI communications head of data analytics, what does that, what do you look for in the future as you start to set this up because I can almost imagine and connecting the dots here in the interview, you got the data lake you're storing everything, which is good. Now you have to create more insights and get ahead of the curve and provide some prescriptive and automated ways to do things better. What's your vision? >> First I would just like to say that, you know when astrophysicists talk about, you know, dark dark energy, dark matter, I'm convinced that's where Thomas is hiding the ones and zeros to get that compression, right? I don't don't know that to be fact but I know it to be true. And then in terms of machine learning and these sort of future technologies, which are becoming available you know, starting from scratch and trying to build out you know, models that have value, you know that takes a fair amount of work. And that landscape keeps changing, right? Being able to push our data into an S3 bucket and then you know, retain that data and then get anomaly detection on top of it. That's, I mean, that's something special and that unlocks a lot of ability for you know, our teams to very easily deliver anomaly detection, machine learning to our customers, without having to take on a lot of work to understand the latest and greatest in machine learning. So, I mean, it's really empowering to our team, right? And, and a tool that we're going to. >> Yeah, I love and I love the name, Chaos Search, Thomas. I got to say, you know it brings up the inside baseball around chaos monkey which everyone knows was a DevOps tool to create kind of day two simulate day two operations and disruptions in DevOps. But what you're really getting at is your whole new architecture that's beyond DevOps movement, it's like next gen architecture. Talk about that to the people watching who have a lot of legacy and want to transform over to a more enabling platform that's going to give them some headroom for their data. What, what do you say to them? How do they get started? What, how should they, how what's their mindset? What they, what are some first principles you can share? >> Well, you know, I always start with first principles but you know, I like to say we're the next next gen. The key thing with the Chaos Search offering is you can start today with B, without even Chaos Search. Stream your data to S3. We're going to make hip and cool data lakes again. And actually it's a, Google it now, data lakes are hip and cool. So start streaming now, start managing your data in a well-formed centralized viewpoint with security governance and cost effectiveness. Then call Chaos Search shop, and we'll make access to it easily, simply to ultimately solve your problems. The bug whether your security issue, the bug, whether it's more performance issues at scale, right? And so when workloads can be added instantaneously in your data lake it's, it's game changing it's mind changing. So from the DevOps folks where, you know, you're up all night trying to say, how am I going to scale from terabyte, you know one today to 50 terabytes, don't. Stream it to S3. We'll take over, we'll worry about that scale pain. You worry about your job of security, performance, operations, integrity. >> That really highlights the cloud scale the value proposition as, as apps start to be using data as an input, not just as a a part of a repo repo, so great stuff. Thomas, thanks for sharing your life's work and your technology magic. Jeremy, thanks for coming on and sharing your use cases with us and how you are making it all work. Appreciate it. >> Thank you. >> My pleasure. >> Okay. This is The Cubes, coverage and presenting AWS this time showcase the next big thing here with Chaos Search. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat music) >> It'll be five, four, three and then silent two, one, and then you guys just follow my lead. We're just making some last minute adjustments. Like I said, we're down two hands today. So, you good Alex? Okay, are you guys ready? >> I'm ready. >> Ready. >> I got to get get one note here. >> So I noticed Maria you stopped anyway, so I have time. >> Just so they know Dave and the Boston Studio, are they both kind of concurrently be on film even when they're not speaking or will only the speaker be on film for like if Gerald's drawing while Maria is talking about-- >> Sorry but then I missed one part of my onboarding spiel. There should be, if you go into gallery there should be a label. There should be something labeled Boston live switch feed. If you pin that gallery view you'll see what our program currently being recorded is. So any time you don't see yourself on that feed is an excellent time to take a drink of water, scratch your nose, check your notes. Do whatever you got to do off screen. >> Can you give us a three shot, Alex? >> Yes, there it is. >> And then go to me, just give me a one-shot to Dave. So when I'm here you guys can take a drink or whatever >> That makes sense? >> Yeah. >> Excellent, I will get my recordings restarted and we'll open up when Dave's ready. >> All right, you guys ready? >> Ready. >> All right Steve, you go on mute. >> Okay, on me in 5, 4, 3. Developers have become the new king makers in the world of digital and cloud. The rise of containers and microservices has accelerated the transition to cloud native applications. A lot of people will talk about application architecture and the related paradigms and the benefits they bring for the process of writing and delivering new apps. But a major challenge continues to be, the how and the what when it comes to accessing, processing and getting insights from the massive amounts of data that we have to deal with in today's world. And with me are two experts from the data management world who will share with us how they think about the best techniques and practices based on what they see at large organizations who are working with data and developing so-called data-driven apps. Please welcome Maria Colgan and Gerald Venzl, two distinguish product managers from Oracle. Folks, welcome, thanks so much for coming on. >> Thanks for having us Dave. >> Thank you very much for having us. >> Okay, Maria let's start with you. So, we throw around this term data-driven, data-driven applications. What are we really talking about there? >> So data-driven applications are applications that work on a diverse set of data. So anything from spatial to sensor data, document data as well as your usual transaction processing data. And what they're going to do is they'll generate value from that data in very different ways to a traditional application. So for example, they may use machine learning, they are able to do product recommendations in the middle of a transaction. Or we could use graph to be able to identify an influencer within the community so we can target them with a specific promotion. It could also use spatial data to be able to help find the nearest stores to a particular customer. And because these apps are deployed on multiple platforms, everything from mobile devices as well as standard browsers, they need a data platform that's going to be both secure, reliable and scalable. >> Well, so when you think about how the workloads are shifting I mean, we're not talking about, you know it's not anymore a world of just your ERP or your HCM or your CRM, you know kind of the traditional operational systems. You really are seeing an explosion of these new data oriented apps. You're seeing, you know, modeling in the cloud, you are going to see more and more inferencing, inferencing at the edge. But Maria maybe you could talk a little bit about sort of the benefits that customers are seeing from developing these types of applications. I mean, why should people care about data-driven apps? >> Oh, for sure, there's massive benefits to them. I mean, probably the most obvious one for any business regardless of the industry, is that they not only allow you to understand what your customers are up to, but they allow you to be able to anticipate those customer's needs. So that helps businesses maintain that competitive edge and retain their customers. But it also helps them make data-driven decisions in real time based on actual data rather than on somebody's gut feeling or basing those decisions on historical data. So for example, you can do real-time price adjustments on products based on demand and so forth, that kind of thing. So it really changes the way people do business today. >> So Gerald, you think about the narrative in the industry everybody wants to be a platform player all your customers they are becoming software companies, they are becoming platform players. Everybody wants to be like, you know name a company that is huge trillion dollar market cap or whatever, and those are data-driven companies. And so it would seem to me that data-driven applications, there's nobody, no company really shouldn't be data-driven. Do you buy that? >> Yeah, absolutely. I mean, data-driven, and that naturally the whole industry is data-driven, right? It's like we all have information technologies about processing data and deriving information out of it. But when it comes to app development I think there is a big push to kind of like we have to do machine learning in our applications, we have to get insights from data. And when you actually look back a bit and take a step back, you see that there's of course many different kinds of applications out there as well that's not to be forgotten, right? So there is a usual front end user interfaces where really the application all it does is just entering some piece of information that's stored somewhere or perhaps a microservice that's not attached to a data to you at all but just receives or asks calls (indistinct). So I think it's not necessarily so important for every developer to kind of go on a bandwagon that they have to be data-driven. But I think it's equally important for those applications and those developers that build applications, that drive the business, that make business critical decisions as Maria mentioned before. Those guys should take really a close look into what data-driven apps means and what the data to you can actually give to them. Because what we see also happening a lot is that a lot of the things that are well known and out there just ready to use are being reimplemented in the applications. And for those applications, they essentially just ended up spending more time writing codes that will be already there and then have to maintain and debug the code as well rather than just going to market faster. >> Gerald can you talk to the prevailing approaches that developers take to build data-driven applications? What are the ones that you see? Let's dig into that a little bit more and maybe differentiate the different approaches and talk about that? >> Yeah, absolutely. I think right now the industry is like in two camps, it's like sort of a religious war going on that you'll see often happening with different architectures and so forth going on. So we have single purpose databases or data management technologies. Which are technologies that are as the name suggests build around a single purpose. So it's like, you know a typical example would be your ordinary key-value store. And a key-value store all it does is it allows you to store and retrieve a piece of data whatever that may be really, really fast but it doesn't really go beyond that. And then the other side of the house or the other camp would be multimodal databases, multimodal data management technologies. Those are technologies that allow you to store different types of data, different formats of data in the same technology in the same system alongside. And, you know, when you look at the geographics out there of what we have from technology, is pretty much any relational database or any database really has evolved into such a multimodal database. Whether that's MySQL that allows you to store or chase them alongside relational or even a MongoDB that allows you to do or gives you native graph support since (mumbles) and as well alongside the adjacent support. >> Well, it's clearly a trend in the industry. We've talked about this a lot in The Cube. We know where Oracle stands on this. I mean, you just mentioned MySQL but I mean, Oracle Databases you've been extending, you've mentioned JSON, we've got blockchain now in there you're infusing, you know ML and AI into the database, graph database capabilities, you know on and on and on. We talked a lot about we compared that to Amazon which is kind of the right tool, the right job approach. So maybe you could talk about, you know, your point of view, the benefits for developers of using that converged database if I can use that word approach being able to store multiple data formats? Why do you feel like that's a better approach? >> Yeah, I think on a high level it comes down to complexity. You are actually avoiding additional complexity, right? So not every use case that you have necessarily warrants to have yet another data management technology or yet the special build technology for managing that data, right? It's like many use cases that we see out there happily want to just store a piece of a chase and document, a piece of chase in a database and then perhaps retrieve it again afterwards so write some simple queries over it. And you really don't have to get a new database technology or a NoSQL database into the mix if you already have some to just fulfill that exact use case. You could just happily store that information as well in the database you already have. And what it really comes down to is the learning curve for developers, right? So it's like, as you use the same technology to store other types of data, you don't have to learn a new technology, you don't have to associate yourself with new and learn new drivers. You don't have to find new frameworks and you don't have to know how to necessarily operate or best model your data for that database. You can essentially just reuse your knowledge of the technology as well as the libraries and code you have already built in house perhaps in another application, perhaps, you know framework that you used against the same technology because it is still the same technology. So, kind of all comes down again to avoiding complexity rather than not fragmenting you know, the many different technologies we have. If you were to look at the different data formats that are out there today it's like, you know, you would end up with many different databases just to store them if you were to fully religiously follow the single purpose best built technology for every use case paradigm, right? And then you would just end up having to manage many different databases more than actually focusing on your app and getting value to your business or to your user. >> Okay, so I get that and I buy that by the way. I mean, especially if you're a larger organization and you've got all these projects going on but before we go back to Maria, Gerald, I want to just, I want to push on that a little bit. Because the counter to that argument would be in the analogy. And I wonder if you, I'd love for you to, you know knock this analogy off the blocks. The counter would be okay, Oracle is the Swiss Army knife and it's got, you know, all in one. But sometimes I need that specialized long screwdriver and I go into my toolbox and I grab that. It's better than the screwdriver in my Swiss Army knife. Why, are you the Swiss Army knife of databases? Or are you the all-in-one have that best of breed screwdriver for me? How do you think about that? >> Yeah, that's a fantastic question, right? And I think it's first of all, you have to separate between Oracle the company that has actually multiple data management technologies and databases out there as you said before, right? And Oracle Database. And I think Oracle Database is definitely a Swiss Army knife has many capabilities of since the last 40 years, you know that we've seen object support coming that's still in the Oracle Database today. We have seen XML coming, it's still in the Oracle Database, graph, spatial, et cetera. And so you have many different ways of managing your data and then on top of that going into the converge, not only do we allow you to store the different data model in there but we actually allow you also to, you apply all the security policies and so forth on top of it something Maria can talk more about the mission around converged database. I would also argue though that for some aspects, we do actually have to or add a screwdriver that you talked about as well. So especially in the relational world people get very quickly hung up on this idea that, oh, if you only do rows and columns, well, that's kind of what you put down on disk. And that was never true, it's the relational model is actually a logical model. What's probably being put down on disk is blocks that align themselves nice with block storage and always has been. So that allows you to actually model and process the data sort of differently. And one common example or one good example that we have that we introduced a couple of years ago was when, column and databases were very strong and you know, the competition came it's like, yeah, we have In-Memory column that stores now they're so much better. And we were like, well, orienting the data role-based or column-based really doesn't matter in the sense that we store them as blocks on disks. And so we introduced the in memory technology which gives you an In-Memory column, a representation of your data as well alongside your relational. So there is an example where you go like, well, actually you know, if you have this use case of the column or analytics all In-Memory, I would argue Oracle Database is also that screwdriver you want to go down to and gives you that capability. Because not only gives you representation in columnar, but also which many people then forget all the analytic power on top of SQL. It's one thing to store your data columnar, it's a completely different story to actually be able to run analytics on top of that and having all the built-in functionalities and stuff that you want to do with the data on top of it as you analyze it. >> You know, that's a great example, the kilometer 'cause I remember there was like a lot of hype around it. Oh, it's the Oracle killer, you know, at Vertica. Vertica is still around but, you know it never really hit escape velocity. But you know, good product, good company, whatever. Natezza, it kind of got buried inside of IBM. ParXL kind of became, you know, red shift with that deal so that kind of went away. Teradata bought a company, I forget which company it bought but. So that hype kind of disapated and now it's like, oh yeah, columnar. It's kind of like In-Memory, we've had a In-Memory databases ever since we've had databases you know, it's a kind of a feature not a sector. But anyway, Maria, let's come back to you. You've got a lot of customer experience. And you speak with a lot of companies, you know during your time at Oracle. What else are you seeing in terms of the benefits to this approach that might not be so intuitive and obvious right away? >> I think one of the biggest benefits to having a multimodel multiworkload or as we call it a converged database, is the fact that you can get greater data synergy from it. In other words, you can utilize all these different techniques and data models to get better value out of that data. So things like being able to do real-time machine learning, fraud detection inside a transaction or being able to do a product recommendation by accessing three different data models. So for example, if I'm trying to recommend a product for you Dave, I might use graph analytics to be able to figure out your community. Not just your friends, but other people on our system who look and behave just like you. Once I know that community then I can go over and see what products they bought by looking up our product catalog which may be stored as JSON. And then on top of that I can then see using the key-value what products inside that catalog those community members gave a five star rating to. So that way I can really pinpoint the right product for you. And I can do all of that in one transaction inside the database without having to transform that data into different models or God forbid, access different systems to be able to get all of that information. So it really simplifies how we can generate that value from the data. And of course, the other thing our customers love is when it comes to deploying data-driven apps, when you do it on a converged database it's much simpler because it is that standard data platform. So you're not having to manage multiple independent single purpose databases. You're not having to implement the security and the high availability policies, you know across a bunch of different diverse platforms. All of that can be done much simpler with a converged database 'cause the DBA team of course, is going to just use that standard set of tools to manage, monitor and secure those systems. >> Thank you for that. And you know, it's interesting, you talk about simplification and you are in Juan's organization so you've big focus on mission critical. And so one of the things that I think is often overlooked well, we talk about all the time is recovery. And if things are simpler, recovery is faster and easier. And so it's kind of the hallmark of Oracle is like the gold standard of the toughest apps, the most mission critical apps. But I wanted to get to the cloud Maria. So because everything is going to the cloud, right? Not all workloads are going to the cloud but everybody is talking about the cloud. Everybody has cloud first mentality and so yes, it's a hybrid world. But the natural next question is how do you think the cloud fits into this world of data-driven apps? >> I think just like any app that you're developing, the cloud helps to accelerate that development. And of course the deployment of these data-driven applications. 'Cause if you think about it, the developer is instantly able to provision a converged database that Oracle will automatically manage and look after for them. But what's great about doing something like that if you use like our autonomous database service is that it comes in different flavors. So you can get autonomous transaction processing, data warehousing or autonomous JSON so that the developer is going to get a database that's been optimized for their specific use case, whatever they are trying to solve. And it's also going to contain all of that great functionality and capabilities that we've been talking about. So what that really means to the developer though is as the project evolves and inevitably the business needs change a little, there's no need to panic when one of those changes comes in because your converged database or your autonomous database has all of those additional capabilities. So you can simply utilize those to able to address those evolving changes in the project. 'Cause let's face it, none of us normally know exactly what we need to build right at the very beginning. And on top of that they also kind of get a built-in buddy in the cloud, especially in the autonomous database. And that buddy comes in the form of built-in workload optimizations. So with the autonomous database we do things like automatic indexing where we're using machine learning to be that buddy for the developer. So what it'll do is it'll monitor the workload and see what kind of queries are being run on that system. And then it will actually determine if there are indexes that should be built to help improve the performance of that application. And not only does it bill those indexes but it verifies that they help improve the performance before publishing it to the application. So by the time the developer is finished with that app and it's ready to be deployed, it's actually also been optimized by the developers buddy, the Oracle autonomous database. So, you know, it's a really nice helping hand for developers when they're building any app especially data-driven apps. >> I like how you sort of gave us, you know the truth here is you don't always know where you're going when you're building an app. It's like it goes from you are trying to build it and they will come to start building it and we'll figure out where it's going to go. With Agile that's kind of how it works. But so I wonder, can you give some examples of maybe customers or maybe genericize them if you need to. Data-driven apps in the cloud where customers were able to drive more efficiency, where the cloud buddy allowed the customers to do more with less? >> No, we have tons of these but I'll try and keep it to just a couple. One that comes to mind straight away is retrace. These folks built a blockchain app in the Oracle Cloud that allows manufacturers to actually share the supply chain with the consumer. So the consumer can see exactly, who made their product? Using what raw materials? Where they were sourced from? How it was done? All of that is visible to the consumer. And in order to be able to share that they had to work on a very diverse set of data. So they had everything from JSON documents to images as well as your traditional transactions in there. And they store all of that information inside the Oracle autonomous database, they were able to build their app and deploy it on the cloud. And they were able to do all of that very, very quickly. So, you know, that ability to work on multiple different data types in a single database really helped them build that product and get it to market in a very short amount of time. Another customer that's doing something really, really interesting is MindSense. So these guys operate the largest mines in Canada, Chile, and Peru. But what they do is they put these x-ray devices on the massive mechanical shovels that are at the cove or at the mine face. And what that does is it senses the contents of the buckets inside these mining machines. And it's looking to see at that content, to see how it can optimize the processing of the ore inside in that bucket. So they're looking to minimize the amount of power and water that it's going to take to process that. And also of course, minimize the amount of waste that's going to come out of that project. So all of that sensor data is sent into an autonomous database where it's going to be processed by a whole host of different users. So everything from the mine engineers to the geo scientists, to even their own data scientists utilize that data to drive their business forward. And what I love about these guys is they're not happy with building just one app. MindSense actually use our built-in low core development environment, APEX that comes as part of the autonomous database and they actually produce applications constantly for different aspects of their business using that technology. And it's actually able to accelerate those new apps to the business. It takes them now just a couple of days or weeks to produce an app instead of months or years to build those new apps. >> Great, thank you for that Maria. Gerald, I'm going to push you again. So, I said upfront and talked about microservices and the cloud and containers and you know, anybody in the developer space follows that very closely. But some of the things that we've been talking about here people might look at that and say, well, they're kind of antithetical to microservices. This is our Oracles monolithic approach. But when you think about the benefits of microservices, people want freedom of choice, technology choice, seen as a big advantage of microservices and containers. How do you address such an argument? >> Yeah, that's an excellent question and I get that quite often. The microservices architecture in general as I said before had architectures, Linux distributions, et cetera. It's kind of always a bit of like there's an academic approach and there's a pragmatic approach. And when you look at the microservices the original definitions that came out at the early 2010s. They actually never said that each microservice has to have a database. And they also never said that if a microservice has a database, you have to use a different technology for each microservice. Just like they never said, you have to write a microservice in a different programming language, right? So where I'm going with this is like, yes you know, sometimes when you look at some vendors out there, some niche players, they push this message or they jump on this academic approach of like each microservice has the best tool at hand or I'd use a different database for your purpose, et cetera. Which almost often comes across like us. You know, we want to stay part of the conversation. Nothing stops a developer from, you know using a multimodal database for the microservice and just using that as a document store, right? Or just using that as a relational database. And, you know, sometimes I mean, it was actually something that happened that was really interesting yesterday I don't know whether you follow Dave or not. But Facebook had an outage yesterday, right? And Facebook is one of those companies that are seen as the Silicon Valley, you know know how to do microservices companies. And when you add through the outage, well, what happened, right? Some unfortunate logical error with configuration as a force that took a database cluster down. So, you know, there you have it where you go like, well, maybe not every microservice is actually in fact talking to its own database or its own special purpose database. I think there, you know, well, what we should, the industry should be focusing much more on this argument of which technology to use? What's the right tool for a job? Is more to ask themselves, what business problem actually are we trying to solve? And therefore what's the right approach and the right technology for this. And so therefore, just as I said before, you know multimodal databases they do have strong benefits. They have many built-in functionalities that are already there and they allow you to reduce this complexity of having to know many different technologies, right? And so it's not only to store different data models either you know, treat a multimodal database as a chasing documents store or a relational database but most databases are multimodal since 20 plus years. But it's also actually being able to perhaps if you store that data together, you can perhaps actually derive additional value for somebody else but perhaps not for your application. But like for example, if you were to use Oracle Database you can actually write queries on top of all of that data. It doesn't really matter for our query engine whether it's the data is format that then chase or the data is formatted in rows and columns you can just rather than query over it. And that's actually very powerful for those guys that have to, you know get the reporting done the end of the day, the end of the week. And for those guys that are the data scientists that they want to figure out, you know which product performed really well or can we tweak something here and there. When you look into that space you still see a huge divergence between the guys to put data in kind of the altarpiece style and guys that try to derive new insights. And there's still a lot of ETL going around and, you know we have big data technologies that some of them come and went and some of them came in that are still around like Apache Spark which is still like a SQL engine on top of any of your data kind of going back to the same concept. And so I will say that, you know, for developers when we look at microservices it's like, first of all, is the argument you were making because the vendor or the technology you want to use tells you this argument or, you know, you kind of want to have an argument to use a specific technology? Or is it really more because it is the best technology, to best use for this given use case for this given application that you have? And if so there's of course, also nothing wrong to use a single purpose technology either, right? >> Yeah, I mean, whenever I talk about Oracle I always come back to the most important applications, the mission critical. It's very difficult to architect databases with microservices and containers. You have to be really, really careful. And so and again, it comes back to what we were talking before about with Maria that the complexity and the recovery. But Gerald I want to stay with you for a minute. So there's other data management technologies popping out there. I mean, I've seen some people saying, okay just leave the data in an S3 bucket. We can query that, then we've got some magic sauce to do that. And so why are you optimistic about you know, traditional database technology going forward? >> I would say because of the history of databases. So one thing that once struck me when I came to Oracle and then got to meet great people like Juan Luis and Andy Mendelsohn who had been here for a long, long time. I come to realization that relational databases are around for about 45 years now. And, you know, I was like, I'm too young to have been around then, right? So I was like, what else was around 45 years? It's like just the tech stack that we have today. It's like, how does this look like? Well, Linux only came out in 93. Well, databases pre-date Linux a lot rather than as I started digging I saw a lot of technologies come and go, right? And you mentioned before like the technologies that data management systems that we had that came and went like the columnar databases or XML databases, object databases. And even before relational databases before Cot gave us the relational model there were apparently these networks stores network databases which to some extent look very similar to adjacent documents. There wasn't a harder storing data and a hierarchy to format. And, you know when you then start actually reading the Cot paper and diving a little bit more into the relation model, that's I think one important crux in there that most of the industry keeps forgetting or it hasn't been around to even know. And that is that when Cot created the relational model, he actually focused not so much on the application putting the data in, but on future users and applications still being able to making sense out of the data, right? And that's kind of like I said before we had those network models, we had XML databases you have adjacent documents stores. And the one thing that they all have along with it is like the application that puts the data in decides the structure of the data. And that's all well and good if you had an application of the developer writing an application. It can become really tricky when 10 years later you still want to look at that data and the application that the developer is no longer around then you go like, what does this all mean? Where is the structure defined? What is this attribute? What does it mean? How does it correlate to others? And the one thing that people tend to forget is that it's actually the data that's here to stay not someone who does the applications where it is. Ideally, every company wants to store every single byte of data that they have because there might be future value in it. Economically may not make sense that's now much more feasible than just years ago. But if you could, why wouldn't you want to store all your data, right? And sometimes you actually have to store the data for seven years or whatever because the laws require you to. And so coming back then and you know, like 10 years from now and looking at the data and going like making sense of that data can actually become a lot more difficult and a lot more challenging than having to first figure out and how we store this data for general use. And that kind of was what the relational model was all about. We decompose the data structures into tables and columns with relationships amongst each other so therefore between each other. So that therefore if somebody wants to, you know typical example would be well you store some purchases from your web store, right? There's a customer attribute in it. There's some credit card payment information in it, just some product information on what the customer bought. Well, in the relational model if you just want to figure out which products were sold on a given day or week, you just would query the payment and products table to get the sense out of it. You don't need to touch the customer and so forth. And with the hierarchical model you have to first sit down and understand how is the structure, what is the customer? Where is the payment? You know, does the document start with the payment or does it start with the customer? Where do I find this information? And then in the very early days those databases even struggled to then not having to scan all the documents to get the data out. So coming back to your question a bit, I apologize for going on here. But you know, it's like relational databases have been around for 45 years. I actually argue it's one of the most successful software technologies that we have out there when you look in the overall industry, right? 45 years is like, in IT terms it's like from a star being the ones who are going supernova. You have said it before that many technologies coming and went, right? And just want to add a more really interesting example by the way is Hadoop and HDFS, right? They kind of gave us this additional promise of like, you know, the 2010s like 2012, 2013 the hype of Hadoop and so forth and (mumbles) and HDFS. And people are just like, just put everything into HDFS and worry about the data later, right? And we can query it and map reduce it and whatever. And we had customers actually coming to us they were like, great we have half a petabyte of data on an HDFS cluster and we have no clue what's stored in there. How do we figure this out? What are we going to do now? Now you had a big data cleansing problem. And so I think that is why databases and also data modeling is something that will not go away anytime soon. And I think databases and database technologies are here for quite a while to stay. Because many of those are people they don't think about what's happening to the data five years from now. And many of the niche players also and also frankly even Amazon you know, following with this single purpose thing is like, just use the right tool for the job for your application, right? Just pull in the data there the way you wanted. And it's like, okay, so you use technologies all over the place and then five years from now you have your data fragmented everywhere in different formats and, you know inconsistencies, and, and, and. And those are usually when you come back to this data-driven business critical business decision applications the worst case scenario you can have, right? Because now you need an army of people to actually do data cleansing. And there's not a coincidence that data science has become very, very popular the last recent years as we kind of went on with this proliferation of different database or data management technologies some of those are not even database. But I think I leave it at that. >> It's an interesting talk track because you're right. I mean, no schema on right was alluring, but it definitely created some problems. It also created an entire, you know you referenced the hyper specialized roles and did the data cleansing component. I mean, maybe technology will eventually solve that problem but it hasn't up at least up tonight. Okay, last question, Maria maybe you could start off and Gerald if you want to chime in as well it'd be great. I mean, it's interesting to watch this industry when Oracle sort of won the top database mantle. I mean, I watched it, I saw it. It was, remember it was Informix and it was (indistinct) too and of course, Microsoft you got to give them credit with SQL server, but Oracle won the database wars. And then everything got kind of quiet for awhile database was sort of boring. And then it exploded, you know, all the, you know not only SQL and the key-value stores and the cloud databases and this is really a hot area now. And when we looked at Oracle we said, okay, Oracle it's all about Oracle Database, but we've seen the kind of resurgence in MySQL which everybody thought, you know once Oracle bought Sun they were going to kill MySQL. But now we see you investing in HeatWave, TimesTen, we talked about In-Memory databases before. So where do those fit in Maria in the grand scheme? How should we think about Oracle's database portfolio? >> So there's lots of places where you'd use those different things. 'Cause just like any other industry there are going to be new and boutique use cases that are going to benefit from a more specialized product or single purpose product. So good examples off the top of my head of the kind of systems that would benefit from that would be things like a stock exchange system or a telephone exchange system. Both of those are latency critical transaction processing applications where they need microsecond response times. And that's going to exceed perhaps what you might normally get or deploy with a converged database. And so Oracle's TimesTen database our In-Memory database is perfect for those kinds of applications. But there's also a host of MySQL applications out there today and you said it yourself there Dave, HeatWave is a great place to provision and deploy those kinds of applications because it's going to run 100 times faster than AWS (mumbles). So, you know, there really is a place in the market and in our customer's systems and the needs they have for all of these different members of our database family here at Oracle. >> Yeah, well, the internet is basically running in the lamp stack so I see MySQL going away. All right Gerald, will give you the final word, bring us home. >> Oh, thank you very much. Yeah, I mean, as Maria said, I think it comes back to what we discussed before. There is obviously still needs for special technologies or different technologies than a relational database or multimodal database. Oracle has actually many more databases that people may first think of. Not only the three that we have already mentioned but there's even SP so the Oracle's NoSQL database. And, you know, on a high level Oracle is a data management company, right? And we want to give our customers the best tools and the best technology to manage all of their data. Rather than therefore there has to be a need or there should be a part of the business that also focuses on this highly specialized systems and this highly specialized technologies that address those use cases. And I think it makes perfect sense. It's like, you know, when the customer comes to Oracle they're not only getting this, take this one product you know, and if you don't like it your problem but actually you have choice, right? And choice allows you to make a decision based on what's best for you and not necessarily best for the vendor you're talking to. >> Well guys, really appreciate your time today and your insights. Maria, Gerald, thanks so much for coming on The Cube. >> Thank you very much for having us. >> And thanks for watching this Cube conversation this is Dave Vellante and we'll see you next time. (upbeat music)

>>Really excited about this year's data, citizens with so many of you together. Uh, I'm going to talk today about accessible data, because what good is the data. If you can get it into your hands and shop for it, but you can't understand it. Uh, and I'm here today with, uh, bald, really thrilled to be here with Paul. Paul is an award-winning author on all topics data. I think 20 books with 21st on the way over 300 articles, he's been a frequent speaker. He's an expert in future trends. Uh, he's a VP at cognitive systems, uh, over at IBM teachers' data also, um, at the business school and as a champion of diversity initiatives. Paul, thank you for being here, really the conformance, uh, to the session with you. >>Oh, thanks for having me. It's a privilege. >>So let's get started with, uh, our origins and data poll. Um, and I'll start with a little story of my own. So, uh, I trained as an engineer way back when, uh, and, um, in one of the courses we got as an engineer, it was about databases. So we got the stick thick book of CQL and me being in it for the programming. I was like, well, who needs this stuff? And, uh, I wanted to do my part in terms of making data accessible. So essentially I, I was the only book that I sold on. Uh, obviously I learned some hard lessons, uh, later on, as I did a master's in AI after that, and then joined the database research lab at the university that Libra spun off from. Uh, but Hey, we all learned along the way. And, uh, Paula, I'm really curious. Um, when did you awaken first to data? If you will? >>You know, it's really interesting Stan, because I come from the opposite side, an undergrad in economics, uh, with some, uh, information systems research at the higher level. And so I think I was always attuned to what data could do, but I didn't understand how to get at it and the kinds of nuances around it. So then I started this job, a database company, like 27 years ago, and it started there, but I would say the awakening has never stopped because the data game is always changing. Like I look at these epochs that I've been through data. I was a real relational databases thinking third normal form, and then no SQL databases. And then I watch no SQL be about no don't use SQL, then wait a minute. Not only sequel. And today it's really for the data citizens about wait, no, I need SQL. So, um, I think I'm always waking up in data, so I'll call it a continuum if you will. But that was it. It was trying to figure out the technology behind driving analytics in which I took in school. >>Excellent. And I fully agree with you there. Uh, every couple of years they seem to reinvent new stuff and they want to be able to know SQL models. Let me see. I saw those come and go. Uh, obviously, and I think that's, that's a challenge for most people because in a way, data is a very abstract concepts, um, until you get down in the weeds and then it starts to become really, really messy, uh, until you, you know, from that end button extract a certain insights. Um, and as the next thing I want to talk about with you is that challenging organizations, we're hearing a lot about data, being valuable data, being the new oil data, being the new soil, the new gold, uh, data as an asset is being used as a slogan all over. Uh, people are investing a lot in data over multiple decades. Now there's a lot of new data technologies, always, but still, it seems that organizations fundamentally struggle with getting people access to data. What do you think are some of the key challenges that are underlying the struggles that mud, that organizations seem to face when it comes to data? >>Yeah. Listen, Stan, I'll tell you a lot of people I think are stuck on what I call their data, acumen curves, and you know, data is like a gym membership. If you don't use it, you're not going to get any value on it. And that's what I mean by accurate. And so I like to think that you use the analogy of some mud. There's like three layers that are holding a lot of organizations back at first is just the amount of data. Now, I'm not going to give you some stat about how many times I can go to the moon and back with the data regenerate, but I will give you one. I found interesting stat. The average human being in their lifetime will generate a petabyte of data. How much data is that? If that was my apple music playlist, it would be about 2000 years of nonstop music. >>So that's some kind of playlist. And I think what's happening for the first layer of mud is when I first started writing about data warehousing and analytics, I would be like, go find a needle in the haystack. But now it's really finding a needle in a stack of needles. So much data. So little time that's level one of mine. I think the second thing is people are looking for some kind of magic solution, like Cinderella's glass slipper, and you put it on her. She turns into a princess that's for Disney movies, right? And there's nothing magical about it. It is about skill and acumen and up-skilling. And I think if you're familiar with the duper, you recall the Hadoop craze, that's exactly what happened, right? Like people brought all their data together and everyone was going to be able to access it and give insights. >>And it teams said it was pretty successful, but every line of business I ever talked to said it was a complete failure. And the third layer is governance. That's actually where you're going to find some magic. And the problem in governance is every client I talked to is all about least effort to comply. They don't want to violate GDPR or California consumer protection act or whatever governance overlooks, where they do business and governance. When you don't lead me separate to comply and try not to get fine, but as an accelerant to your analytics, and that gets you out of that third layer of mud. So you start to invoke what I call the wisdom of the crowd. Now imagine taking all these different people with intelligence about the business and giving them access and acumen to hypothesize on thousands of ideas that turn into hundreds, we test and maybe dozens that go to production. So those are three layers that I think every organization is facing. >>Well. Um, I definitely follow on all the days, especially the one where people see governance as a, oh, I have to comply to this, which always hurts me a little bit, honestly, because all good governance is about making things easier while also making sure that they're less riskier. Um, but I do want to touch on that Hadoop thing a little bit, uh, because for me in my a decade or more over at Libra, we saw it come as well as go, let's say around 2015 to 2020 issue. So, and it's still around. Obviously once you put your data in something, it's very hard to make it go away, but I've always felt that had do, you know, it seemed like, oh, now we have a bunch of clusters and a bunch of network engineers. So what, >>Yeah. You know, Stan, I fell for, I wrote the book to do for dummies and it had such great promise. I think the problem is there wasn't enough education on how to extract value out of it. And that's why I say it thinks it's great. They liked clusters and engineers that you just said, but it didn't drive lineup >>Business. Got it. So do you think that the whole paradigm with the clouds that we're now on is going to fundamentally change that or is just an architectural change? >>Yeah. You know, it's, it's a great comment. What you're seeing today now is the movement for the data lake. Maybe a way from repositories, like Hadoop into cloud object stores, right? And then you look at CQL or other interfaces over that not allows me to really scale compute and storage separately, but that's all the technical stuff at the end of the day, whether you're on premise hybrid cloud, into cloud software, as a service, if you don't have the acumen for your entire organization to know how to work with data, get value from data, this whole data citizen thing. Um, you're not going to get the kind of value that goes into your investment, right? And I think that's the key thing that business leaders need to understand is it's not about analytics for kind of science project sakes. It's about analytics to drive. >>Absolutely. We fully agree with that. And I want to touch on that point. You mentioned about the wisdom of the crowds, the concept that I love about, right, and your organization is a big grout full of what we call data citizens. Now, if I remember correctly from the book of the wisdom of the crowds, there's, there's two points that really, you have to take Canada. What is, uh, for the wisdom of the grounds to work, you have to have all the individuals enabled, uh, for them to have access to the right information and to be able to share that information safely kept from the bias from others. Otherwise you're just biasing the outcome. And second, you need to be able to somehow aggregate that wisdom up to a certain decision. Uh, so as Felix mentioned earlier, we all are United by data and it's a data citizen topic. >>I want to touch on with you a little bit, because at Collibra we look at it as anyone who uses data to do their job, right. And 2020 has sort of accelerated digitization. Uh, but apart from that, I've always believed that, uh, you don't have to have data in your title, like a data analyst or a data scientist to be a data citizen. If I take a look at the example inside of Libra, we have product managers and they're trying to figure out which features are most important and how are they used and what patterns of behavior is there. You have a gal managers, and they're always trying to know the most they can about their specific accounts, uh, to be able to serve as them best. So for me, the data citizen is really in its broadest sense. Uh, anyone who uses data to do their job, does that, does that resonate with you? >>Yeah, absolutely. It reminds me of myself. And to be honest in my eyes where I got started from, and I agree, you don't need the word data in your title. What you need to have is curiosity, and that is in your culture and in your being. And, and I think as we look at organizations to transform and take full advantage of their, their data investments, they're going to need great governance. I guarantee you that, but then you're going to have to invest in this data citizen concept. And the first thing I'll tell you is, you know, that kind of acumen, if you will, as a team sport, it's not a departmental sport. So you need to think about what are the upskilling programs of where we can reach across to the technical and the non-technical, you know, lots and lots of businesses rely on Microsoft Excel. >>You have data citizens right there, but then there's other folks who are just flat out curious about stuff. And so now you have to open this up and invest in those people. Like, why are you paying people to think about your business without giving the data? It would be like hiring Tom Brady as a quarterback and telling him not to throw a pass. Right. And I see it all the time. So we kind of limit what we define as data citizen. And that's why I love what you said. You don't need the word data in your title and more so if you don't build the acumen, you don't know how to bring the data together, maybe how to wrangle it, but where did it come from? And where can you fixings? One company I worked with had 17 definitions for a sales individual, 17 definitions, and the talent team and HR couldn't drive to a single definition because they didn't have the data accurate. So when you start thinking of the data citizen, concept it about enabling everybody to shop for data much. Like I would look for a USB cable on Amazon, but also to attach to a business glossary for definition. So we have a common version of what a word means, the lineage of the data who owns it, who did it come from? What did it do? So bring that all together. And, uh, I will tell you companies that invest in the data, citizen concept, outperform companies that don't >>For all of that, I definitely fully agree that there's enough research out there that shows that the ones who are data-driven are capturing the most markets, but also capturing the most growth. So they're capturing the market even faster. And I love what you said, Paul, about, um, uh, the brains, right? You've already paid for the brains you've already invested in. So you may as well leverage them. Um, you may as well recognize and, and enable the data citizens, uh, to get access to the assets that they need to really do their job properly. That's what I want to touch on just a little bit, if, if you're capable, because for me, okay. Getting access to data is one thing, right? And I think you already touched on a few items there, but I'm shopping for data. Now I have it. I have a cul results set in my hands. Let's say, but I'm unable to read and write data. Right? I don't know how to analyze it. I don't know maybe about bias. Uh, maybe I, I, I don't know how to best visualize it. And maybe if I do, maybe I don't know how to craft a compelling persuasion narrative around it to change my bosses decisions. So from your viewpoint, do you think that it's wise for companies to continuously invest in data literacy to continuously upgrade that data citizens? If you will. >>Yeah, absolutely. Forest. I'm going to tell you right now, data literacy years are like dog years stage. So fast, new data types, new sources of data, new ways to get data like API APIs and microservices. But let me take it away from the technical concept for a bit. I want to talk to you about the movie. A star is born. I'm sure most of you have seen it or heard it Bradley Cooper, lady Gaga. So everyone knows the movie. What most people probably don't know is when lady Gaga teamed up with Bradley Cooper to do this movie, she demanded that he sing everything like nothing could be auto-tuned everything line. This is one of the leading actors of Hollywood. They filmed this remake in 42 days and Bradley Cooper spent 18 months on singing lessons. 18 months on a guitar lessons had a voice coach and it's so much and so forth. >>And so I think here's the point. If one of the best actors in the world has to invest three and a half years for 42 days to hit a movie out of the park. Why do we think we don't need a continuous investment in data literacy? Even once you've done your initial training, if you will, over the data, citizen, things are going to change. I don't, you don't. If I, you Stan, if you go to the gym and workout every day for three months, you'll never have to work out for the rest of your life. You would tell me I was ridiculous. So your data literacy is no different. And I will tell you, I have managed thousands of individuals, some of the most technical people around distinguished engineers, fellows, and data literacy comes from curiosity and a culture of never ending learning. That is the number one thing to success. >>And that curiosity, I hire people who are curious, I'll give you one more story. It's about Mozart. And this 21 year old comes to Mozart and he says, Mozart, can you teach me how to compose a symphony? And Mozart looks at this person that says, no, no, you're too young, too young. You compose your fourth symphony when you were 12 and Mozart looks at him and says, yeah, but I didn't go around asking people how to compose a symphony. Right? And so the notion of that story is curiosity. And those people who show up in always want to learn, they're your home run individuals. And they will bring data literacy across the organization. >>I love it. And I'm not going to try and be Mozart, but you know, three and a half years, I think you said two times, 18 months, uh, maybe there's hope for me yet in a singing, you'll be a good singer. Um, Duchy on the, on the, some of the sports references you've made, uh, Paul McGuire, we first connected, uh, I'm not gonna like disclose where you're from, but, uh, I saw he did come up and I know it all sorts of sports that drive to measure everything they can right on the field of the field. So let's imagine that you've done the best analysis, right? You're the most advanced data scientists schooled in the classics, as well as the modernist methods, the best tools you've made a beautiful analysis, beautiful dashboards. And now your coach just wants to put their favorite player on the game, despite what you're building to them. How do you deal with that kind of coaches? >>Yeah. Listen, this is a great question. I think for your data analytics strategy, but also for anyone listening and watching, who wants to just figure out how to drive a career forward? I would give the same advice. So the story you're talking about, indeed hockey, you can figure out where I'm from, but it's around the Ottawa senators, general manager. And he made a quote in an interview and he said, sometimes I want to punch my analytics, people in the head. Now I'm going to tell you, that's not a good culture for analytics. And he goes on to say, they tell me not to play this one player. This one player is very tough. You know, throws four or five hits a game. And he goes, I'd love my analytics people to get hit by bore a wacky and tell me how it feels. That's the player. >>Sure. I'm sure he hits hard, but here's the deal. When he's on the ice, the opposing team gets more shots on goal than the senators do on the opposing team. They score more goals, they lose. And so I think whenever you're trying to convince a movement forward, be it management, be it a project you're trying to fund. I always try to teach something that someone didn't previously know before and make them think, well, I never thought of it that way before. And I think the great opportunity right now, if you're trying to get moving in a data analytics strategy is around this post COVID era. You know, we've seen post COVID now really accelerate, or at least post COVID in certain parts of the world, but accelerate the appetite for digital transformation by about half a decade. Okay. And getting the data within your systems, as you digitize will give you all kinds of types of projects to make people think differently than the way they thought before. >>About data. I call this data exhaust. I'll give you a great example, Uber. I think we're all familiar with Uber. If we all remember back in the days when Uber would offer you search pricing. Okay? So basically you put Uber on your phone, they know everything about you, right? Who are your friends, where you going, uh, even how much batteries on your phone? Well, in a data science paper, I read a long time ago. They recognize that there was a 70% chance that you would accept a surge price. If you had less than 10% of your battery. So 10% of battery on your phone is an example of data exhaust all the lawns that you generate on your digital front end properties. Those are logs. You can take those together and maybe show executive management with data. We can understand why people abandoned their cart at the shipping phase, or what is the amount of shipping, which they abandoned it. When is the signal when our systems are about to go to go down. So, uh, I think that's a tremendous way. And if you look back to the sports, I mean the Atlanta Falcons NFL team, and they monitor their athletes, sleep performance, the Toronto Raptors basketball, they're running AI analytics on people's personalities and everything they tweet and every interview to see if the personality fits. So in sports, I think athletes are the most important commodity, if you will, or asset a yet all these teams are investing in analytics. So I think that's pretty telling, >>Okay, Paul, it looks like we're almost out of time. So in 30 seconds or less, what would you recommend to the data citizens out there? >>Okay. I'm going to give you a four tips in 30 seconds. Number one, remember learning never ends be curious forever. You'll drive your career. Number two, remember companies that invest in analytics and data, citizens outperform those that don't McKinsey says it's about 1.4 times across many KPIs. Number three, stop just collecting the dots and start connecting them with that. You need a strong governance strategy and that's going to help you for the future because the biggest thing in the future is not going to be about analytics, accuracy. It's going to be about analytics, explainability. So accuracy is no longer going to be enough. You're going to have to explain your decisions and finally stay positive and forever test negative. >>Love it. Thank you very much fall. Um, and for all the data seasons is out there. Um, when it comes down to access to data, it's more than just getting your hands on the data. It's also knowing what you can do with it, how you can do that and what you definitely shouldn't be doing with it. Uh, thank you everyone out there and enjoy your learning and interaction with the community. Stay healthy. Bye-bye.

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

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

>> From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE Conversation. >> Hey, everybody, this is Dave Vellante, and welcome to this CXO Series. As you know, I've been running this series discussing major trends and CXOs, how they've navigated through the pandemic. And we've got some good news and some bad news today. And Ed Walsh is here to talk about that. Ed, how you doing? Great to see you. >> Great seeing you, thank you for having me on. I really appreciate it. So the bad news is Ed Walsh is leaving IBM as the head of the storage division (indistinct). But the good news is, he's joining a new startup as CEO, and we're going to talk about that, but Ed, always a pleasure to have you. You're quite a run at at IBM. You really have done a great job there. So, let's start there if we can before we get into the other part of the news. So, you give us the update. You're coming off another strong quarter for the storage business. >> I would say listen, they're sweet, heartily, but to be honest, we're leaving them in a really good position where they have sustainable growth. So they're actually IBM storage in a very good position. I think you're seeing it in the numbers as well. So, yeah, listen, I think the team... I'm very proud of what they were able to pull off. Four years ago, they kind of brought me in, hey, can we get IBM storage back to leadership? They were kind of on their heels, not quite growing, or not growing but falling back in market share. You know, kind of a distant third place finisher, and basically through real innovation that mattered to clients which that's a big deal. It's the right innovation that matters to the clients. We really were able to dramatically grow, grow all different four segments of the portfolio. But also get things like profitability growing, but also NPS growing. It really allowed us to go into a sustainable model. And it's really about the team. You heard I've talked about team all the time, which is you get a good team and they really nailed great client experiences. And they take the right offerings and go to market and merge it. And I'll tell you, I'm very proud of what the IBM team put together. And I'm still the number one fan and inside or outside IBM. So it might be bittersweet, but I actually think they're ready for quite some growth. >> You know Ed, when you came in theCUBE, right after you had joined IBM, a lot of people are saying, Ed Walsh joined an IBM storage division to sell the division. And I asked you on theCUBE, are you there to sell division? And you said, no, absolutely not. So it's always it seemed to me, well, hey, it's good. It's a good business, good cash flow business, got a big customer base, so why would IBM sell it? Never really made sense to me. >> I think it's integral to what IBM does, I think it places their client base in a big way. And under my leadership, really, we got more aligned with what IBM is doing from the big IBM right. What we're doing around Red Hat hybrid multi cloud and what we're doing with AI. And those are big focuses of the storage portfolio. So listen, I think IBM as a company is in a position where they're really innovating and thriving, and really customer centric. And I think IBM storage is benefiting from that. And vice versa. I think it's a good match. >> So one of the thing I want to bring up before we move on. So you had said you were seeing a number. So I want to bring up a chart here. As you know, we've been using a lot of data and sharing data reporting from our partner. ETR, Enterprise Technology Research, they do quarterly surveys. They have a very tight methodology, it's similar to NPS. But it's a net score, we call it methodology. And every quarter they go out and what we're showing here is the results from the last three quarter, specific to IBM storage and IBM net score in storage. And net scores is essentially, we ask people are you spending more, are you spending less, we subtract the less from the more and that's the net score. And you can see when you go back to the October 19, survey, you know, low single digits and then it dipped in the April survey, which was the height of the pandemic. So this was this is forward looking. So in the height of the pa, the lockdown people were saying, maybe I'm going to hold off on budgets. But then now look at the July survey. Huge, huge up check. And I think this is testament to a couple of things. One is, as you mentioned, the team. But the other is, you guys have done a good job of taking R&D, building a product pipeline and getting it into the field. And I think that shows up in the numbers. That was really a one of the hallmarks of your leadership. >> Yeah, I mean, they're the innovation. IBM is there's almost an embarrassment of riches inside. It's how do you get in the pipeline? We went from a typically about for four years, four and a half year cycles, not a two year cycle product cycle. So we're able to innovate and bring it to market much quicker. And I think that's what clients are looking for. >> Yeah, so I mean, you brought a startup mentality to the division and of course now, cause your startup guy, let's face it. Now you're going back to the startup world. So the other part of the news is Ed Walsh is joining ChaosSearch as the CEO. ChaosSearches is a local Boston company, they're focused on log analytics but more on we're going to talk about that. So first of all, congratulations. And tell us about your decision. Why ChaosSearch? And you know where you're out there? >> Yeah, listen, if you can tell from the way I describe IBM, I mean, it was a hard decision to leave IBM, but it was a very, very easy decision to go to Chaos, right. So I knew the founder, I knew what he was working on for the last seven years, right. Last five years as a company, and I was just blown away at their fundamental innovation, and how they're really driving like how to get insights at scale from your data lake in the cloud. But also and also instead, and statements slash cost dramatically. And they make it so simple. Simply put your data in your S3 or really Cloud object storage. But right now, it's, Amazon, they'll go the rest of clouds, but just put your data in S3. And what we'll do is we'll index it, give you API so you can search it and query it. And it literally brings a way to do at scale data analysts. And also login analytics on everything you just put into S3 basically bucket. It makes it very simple. And because they're really fundamental, we can go through it. Fundamental on hard technology that data layer, but they kept all the API. So you're using your normal tools that we did for Elastic Search API's. You want to do Glyfada, you want to do Cabana, or you want to do SQL or you want to do use Looker, Tableau, all those work. Which is that's a part of it. It's really revolutionary what they're doing as far as the value prop and we can explain it. But also they made it evolution, it's very easy for clients to go. Just run in parallel, and then they basically turn off what they currently have running. >> So data lakes, really the term became popular during the sort of early big data, Hadoop era. And, Hadoop obviously brought a lot of innovation, you know, leave the data where it is. Bring the compute to the data, really launched the Big Data initiative, but it was very complicated. You had, MapReduce and and elastic MapReduce in the cloud. And, it really was a big batch job, where storage was really kind of a second class citizen, if you will. There wasn't a lot of real time stuff going on. And then, Spark comes in. And still there's this very complicated situation. So it's sounds like, ChaosSearch is really attacking that problem. And the first use case, it's really going after is log analytics. Explain that a little bit more, please. >> Yeah, so listen, they finally went after it with this, it's called a data lake engine for scalable and we'll say log analytics firstly. It was the first use case to go after it. But basically, they allows for log analytics people, everyone does it, and everyone's kind of getting to scale with it, right. But if you asked your IT department, are you even challenged with scale, or cost, or retention levels, but also management overlay of what they're doing on log analytics or security log analytics, or all this machine data they're collecting? The answer be absolutely no, it's a nightmare. It starts easy and becomes a big, very costly application for our environments. And what Chaos does is because they deal with a real issue, which is the data layer, but keep the API's on top. And so people easily use the data insights at scale, what they're able to do is very simply run in parallel and we'll save 80% of your cost, but also get better data retention. Cause there's typically a trade off. Clients basically have this trade off, or it gets really expensive. It gets to scale. So I should just retain less. We have clients that went from nine day retention and security logs to literally four and five days. If they didn't catch it in that time, it was too late. Now what they're able to do is, they're able to go to our solution. Not change what they're doing applications, because you're using the same API's, but literally save 80% and this is millions and 10s of millions of dollars of savings, but also basically get 90 day retention. There's really limitless, whatever you put into your S3 bucket, we're going to give you access to. So that alone shows you that it's literally revolutions that CFO wins because they save money. The IT department wins because they don't that wrestle with this data technology that wasn't really built. It is really built 30 years ago, wasn't built for this volume and velocity of data coming in. And then the data analytics guys, hey, I keep my tool set but I get all the retention I want. No one's limiting me anymore. So it's kind of an easy win win. And it makes it really easy for clients to have this really big benefit for them. And dramatic cost savings. But also you get the scale, which really means a lot in security login or anything else. >> So let's dig into that a little bit. So Cloud Object Storage has kind of become the de facto bucket, if you will. Everybody wants it, because it's simple. It's a get put kind of paradigm. And it's cheap, but it's also got performance issues. So people will throw cash at the problem, they'll have to move data around. So is that the problem that you're solving? Is it a performance? You know, problem is it a cause problem or both? And explain that a little bit. >> Yeah, so it's all over. So basically, if you were building a data lake, they would like to just put all their data in one very cost effective, scalable, resilient environment. And that is Cloud Object Storage, or S3, or every cloud has around, right? You can do also on prem, everyone would love to do that. And then literally get their insights out of it. But they want to go after it with our tools. Is it Search or is it SQL, they want to go after their own tools. That's the vision everyone wants. But what everyone does now is because this is where the core special sauce what ChaosSearch provides, is we built from the ground up. The database, the indexing technology, the database technology, how to actually make your Cloud object storage a database. We don't move it somewhere, we don't cash it. You put it in the inside the bucket, we literally make the Cloud object storage, the database. And then around it, we basically built a Chaos fabric that allows you to spin up compute nodes to go at the data in different ways. We truly have separated that the data from the compute, but also if a worker nodes, beautiful, beauty of like containerization technology, a worker nodes goes away, nothing happens. It's not like what you do on Prem. And all sudden you have to rebuild clusters. So by fundamentally solving that data layer, but really what was interesting is they just published API's, you mentioned put and get. So the API's you're using cloud obvious sources of put and get. Imagine we just added to that API, your Search API from elastic, or your SQL interface. It's just all we're doing is extending. You put it in the bucket will extend your ability to get after it. Really is an API company, but it's a hard tech, putting that data layer together. So you have cost effectiveness, and scale simultaneously. But we can ask for instance, log analytics. We don't cash, nothing's on the SSD, nothing's on local storage. And we're as fast as you're running Elastic Search on SSDs. So we've solved the performance and scale issues simultaneously. And that's really the core fundamental technology. >> And you do that with math, with algorithms, with machine learning, what's the secret sauce? Yeah, we should really have I'll tell you, my founder, just has the right interesting way of looking at problems. And he really looked at this differently and went after how do you make a both, going after data. He really did it in a different way, and really a modern way. And the reason it differentiates itself is he built from the ground up to do this on object storage. Where basically everyone else is using 30 year old technology, right? So even really new up and coming companies, they're using Tableau, Looker, or Snowflake could be another example. They're not changing how the data stored, they always have to move it ETL at somewhere to go after it. We avoid all that. In fact, we're probably a pretty good ecosystem players for all those partners as we go forward. >> So your talking about Tom Hazel, you're founder and CTO and he's brought in the team and they've been working on this for a while. What's his background? >> Launched Telkom, building out God boxes. So he's always been in the database space. I can't do his in my first day of the job, I can't do justice to his deep technology. There's a really good white paper on our website that does that pretty well. But literally the patent technology is a Chaos index, which is a database that it makes your object storage, the database. And then it's really the chaos fabric that puts around in the chaos refinery that gives you virtual views. But that's one solution. And if you look for log analytics, you come in log in and you get all the tools you're used to. But underneath the covers, were just saving about 80% of overall cost, but also almost limitless retention. We see people going from literally have been reduced the number of logs are keeping because of cost, and complexity, and scale, down to literally a very small amount and going right back at nine days. You could do longer, but that's what we see most people go into when they go to our service. >> Let's talk about the market. I mean, as a startup person, you always look for large markets. Obviously, you got to have good tech, a great team. And you want large markets. So the, space that you're in, I mean, I would think it started, early days and kind of the decision support. Sort of morphed into the data warehouse, you mentioned ETL, that's kind of part of it. Business Intelligence, it's sort of all in there. If you look at the EDW market, it's probably around 18 to 20 billion. Small slice of that is data lakes, maybe a billion or a billion plus. And then you got this sort of BI layer on top, you mentioned a lot of those. You got ETL, you probably get up into the 30,35 billion just sort of off the top of my head and from my historical experience and looking at these markets. But I have to say these markets have traditionally failed to live up to the expectations. Things like 360 degree views of the customer, real time analytics, delivering insights and self service to the business. Those are promises that these industries made. And they ended up being cumbersome, slow, maybe requiring real experts, requiring a lot of infrastructure, the cloud is changing that. Is that right? Is that the way to look at the market that you're going after? You're a player inside of that very large team. >> Yeah, I think we're a key fundamental component underneath that whole ecosystem. And yes, you're seeing us build a full stack solution for log analytics, because there's really good way to prove just how game changing the technology is. But also how we publishing API's, and it's seamless for how you're using log analytics. Same thing can be applied as we go across the SQL and different BI and analytic type of platforms. So it's exactly how we're looking at the market. And it's those players that are all struggling with the same thing. How they add more value to clients? It's a big cost game, right? So if I can literally make your underlying how you store your data and mix it literally 80% more cost effective. that's a big deal or simultaneously saving 80% and give you much longer retention. Those two things are typically, Lily a trade off, you have to go through, and we don't have to do that. That's what really makes this kind of the underlying core technology. And really I look at log analytics is really the first application set. But or if you have any log analytics issues, if you talk to your teams and find out, scale, cost, management issues, it's a pretty we make it very easy. Just run in parallel, we'll do a PLC, and you'll see how easy it is you can just save 80% which is, 80% and better retention is really the value proposition you see at scale, right. >> So this is day zero for you. Give us the hundred day plan, what do you want to accomplish? Where are you going to focus your priorities? I mean, obviously, the company's been started, it's well funded, but where are you going to focus in the next 100 days? >> No, I think it's building out where are we taking the next? There's a lot of things we could do, there's degrees of freedom as far as where we'd go with this technology is pretty wide. You're going to see us be the best log analytic company there. We're getting, really a (mumbling) we, you saw the announcement, best quarter ever last quarter. And you're seeing this nice as a service ramp, you're going to see us go to VPC. So you can do as a service with us, but now we can put this same thing in your own virtual private data center. You're going to see us go to Google, Azure, and also IBM cloud. And the really, clients are driving this. It's not us driving it, but you're going to see actually the client. So we'll go into Google because we had a couple financial institutions that are saying they're driving us to go do exactly that. So it's more really working with our client sets and making sure we got the right roadmap to support what they're trying to do. And then the ecosystem is another play. How to, you know, my core technology is not necessarily competitive with anyone else. No one else is doing this. They're just kind of, hey, move it here, I'll put it on this, you know, a foundational DV or they'll put it on on a presto environment. They're not really worried about the bottom line economics, which is really that's the value prop and that's the hard tech and patented technology that we bring to this ecosystem. >> Well, people are definitely worried about their cloud bills. The the CFO saying, whoa, cause it's so easy to spin up, instances in the cloud. And so, Ed it really looks like you're going after a real problem. You got some great tech behind you. And of course, we love the fact that it's another Boston based company that you're joining, cause it's more Boston based startups. Better for us here at the East Coast Cube, so give us a give us your final thoughts. What should we look for? I'm sure we're going to be being touched and congratulations. >> No, hey, thank you for the time. I'm really excited about this. I really just think it's fundamental technology that allows us to get the most out of everything you're doing around analytics in the cloud. And if you look at a data lake model, I think that's our philosophy. And we're going to drive it pretty aggressively. And I think it's a good fundamental innovation for the space and that's the type of tech that I like. And I think we can also, do a lot of partnering across ecosystems to make it work for a lot of different people. So anyway, so I guess thank you very much for the time appreciate. >> Yeah, well, thanks for coming on theCUBE and best of luck. I'm sure we're going to be learning a lot more and hearing a lot more about ChaosSearch, Ed Walsh. This is Dave Vellante. Thank you for watching everybody, and we'll see you next time on theCUBE. (upbeat music)