>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data-driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting cost could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data marts, data hubs, and yes, even data lakes were broken and left us wanting from more welcome to the data doesn't lie, or doesn't a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have featured parody with the data lake or vice versa is the so-called modern data stack, simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for hit that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience show? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know, right. You actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like star Oxley, for things like security for certainly very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited Jamma, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come. I think that's the story at Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenants of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about SAR, brain Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but what does that mean? Does that mean the E D w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems, maybe either those that either source systems for the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got to, you know, domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue are two, you know, challenges, self-serve infrastructure let's park that for a second. And then in your industry, the one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at emus is we have a single security layer that sits on top of our data match, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin, I mean, Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, do an analytic queries and with data that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah. I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively a almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself, >>Okay. G guys grab a sip of water. After this short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence, keep it right there. >>Your company has more data than ever, and more people trying to understand it, but there's a problem. Your data is stored across multiple systems. It's hard to access and that delays analytics and ultimately decisions. The old method of moving all of your data into a single source of truth is slow and definitely not built for the volume of data we have today or where we are headed while your data engineers spent over half their time, moving data, your analysts and data scientists are left, waiting, feeling frustrated, unproductive, and unable to move the needle for your business. But what if you could spend less time moving or copying data? What if your data consumers could analyze all your data quickly? >>Starburst helps your teams run fast queries on any data source. We help you create a single point of access to your data, no matter where it's stored. And we support high concurrency, we solve for speed and scale, whether it's fast, SQL queries on your data lake or faster queries across multiple data sets, Starburst helps your teams run analytics anywhere you can't afford to wait for data to be available. Your team has questions that need answers. Now with Starburst, the wait is over. You'll have faster access to data with enterprise level security, easy connectivity, and 24 7 support from experts, organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact our Trino experts to get started. >>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay, we're gonna get to lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you you'll never get performance because you need to be column there. You need to store data in a column format. And then, you know, column formats we're introduced to, to data apes, you have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and Hodi that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, know it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a line and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, look closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen a technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to Shaak about this and you know, obviously her vision is there's an open source that, that the data meshes open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but to come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to Haddo and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in Haddo back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, that's interesting reminded when I, you know, I see the, the gas price, the tees or gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. Th that that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you want to use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you in and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit, yeah, you know, they're jamming us on price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast in ROI? >>I think the answer to that is it can depend a bit. It depends on your businesses skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is, oh, is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you commander 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years. And in world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse fit in this, in this world? >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a deal lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle? When it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage. >>Your data is capable of producing incredible results, but data consumers are often left in the dark without fast access to the data they need. Starers makes your data visible from wherever it lives. Your company is acquiring more data in more places, more rapidly than ever to rely solely on a data centralization strategy. Whether it's in a lake or a warehouse is unrealistic. A single source of truth approach is no longer viable, but disconnected data silos are often left untapped. We need a new approach. One that embraces distributed data. One that enables fast and secure access to any of your data from anywhere with Starburst, you'll have the fastest query engine for the data lake that allows you to connect and analyze your disparate data sources no matter where they live Starburst provides the foundational technology required for you to build towards the vision of a decentralized data mesh Starburst enterprise and Starburst galaxy offer enterprise ready, connectivity, interoperability, and security features for multiple regions, multiple clouds and everchanging global regulatory requirements. The data is yours. And with Starburst, you can perform analytics anywhere in light of your world. >>Okay. We're back with Justin Boardman. CEO of Starbust Richard Jarvis is the CTO of EMI health and Theresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie it's it is it's is that it's not modern. Justin, what do you say? >>Yeah. I mean, I think new isn't modern, right? I think it's the, it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exist just yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess, the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, a microservices layer on top of leg legacy apps. How do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more, more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. De-risked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that. I'm a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >>Of, of how it should look like or, or how >>It's yeah. What it should be. >>Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of it was starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go buy a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, my takeaway there is it's inclusive, whether it's a data Mar data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include there on Preem data? O obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on Preem? I mean, most implementations I've seen in data mesh, frankly really aren't, you know, adhering to the philosophy. They're maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data. Me, the fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both worlds. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, talking about data as product, wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight. And in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured, managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned you, you appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have, and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use case? >>Yeah, it makes sense. It's got the context. If the, if the domains own the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's send Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Theresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave Valante for the cube, and we'll see you next time. >>The explosion of data sources has forced organizations to modernize their systems and architecture and come to terms with one size does not fit all for data management today. Your teams are constantly moving and copying data, which requires time management. And in some cases, double paying for compute resources. Instead, what if you could access all your data anywhere using the BI tools and SQL skills your users already have. 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>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data-driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting cost could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data marts, data hubs, and yes, even data lakes were broken and left us wanting from more welcome to the data doesn't lie, or doesn't a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have featured parody with the data lake or vice versa is the so-called modern data stack, simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for hit that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience show? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know, right. You actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like star Oxley, for things like security for certainly very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited Jamma, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come. I think that's the story at Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenants of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about SAR, brain Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but what does that mean? Does that mean the E D w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems, maybe either those that either source systems for the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got to, you know, domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue are two, you know, challenges, self-serve infrastructure let's park that for a second. And then in your industry, the one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at emus is we have a single security layer that sits on top of our data match, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin, I mean, Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, do an analytic queries and with data that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah. I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively a almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself, >>Okay. G guys grab a sip of water. After this short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence, keep it right there. >>Your company has more data than ever, and more people trying to understand it, but there's a problem. Your data is stored across multiple systems. It's hard to access and that delays analytics and ultimately decisions. The old method of moving all of your data into a single source of truth is slow and definitely not built for the volume of data we have today or where we are headed while your data engineers spent over half their time, moving data, your analysts and data scientists are left, waiting, feeling frustrated, unproductive, and unable to move the needle for your business. But what if you could spend less time moving or copying data? What if your data consumers could analyze all your data quickly? >>Starburst helps your teams run fast queries on any data source. We help you create a single point of access to your data, no matter where it's stored. And we support high concurrency, we solve for speed and scale, whether it's fast, SQL queries on your data lake or faster queries across multiple data sets, Starburst helps your teams run analytics anywhere you can't afford to wait for data to be available. Your team has questions that need answers. Now with Starburst, the wait is over. You'll have faster access to data with enterprise level security, easy connectivity, and 24 7 support from experts, organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact our Trino experts to get started. >>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay, we're gonna get to lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you you'll never get performance because you need to be column there. You need to store data in a column format. And then, you know, column formats we're introduced to, to data apes, you have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and Hodi that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, know it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a line and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, look closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen a technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to Shaak about this and you know, obviously her vision is there's an open source that, that the data meshes open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but to come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to Haddo and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in Haddo back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, that's interesting reminded when I, you know, I see the, the gas price, the tees or gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. Th that that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you want to use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you in and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit, yeah, you know, they're jamming us on price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast in ROI? >>I think the answer to that is it can depend a bit. It depends on your businesses skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is, oh, is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you commander 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years. And in world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse fit in this, in this world? >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a deal lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle? When it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage. >>Your data is capable of producing incredible results, but data consumers are often left in the dark without fast access to the data they need. Starers makes your data visible from wherever it lives. Your company is acquiring more data in more places, more rapidly than ever to rely solely on a data centralization strategy. Whether it's in a lake or a warehouse is unrealistic. A single source of truth approach is no longer viable, but disconnected data silos are often left untapped. We need a new approach. One that embraces distributed data. One that enables fast and secure access to any of your data from anywhere with Starburst, you'll have the fastest query engine for the data lake that allows you to connect and analyze your disparate data sources no matter where they live Starburst provides the foundational technology required for you to build towards the vision of a decentralized data mesh Starburst enterprise and Starburst galaxy offer enterprise ready, connectivity, interoperability, and security features for multiple regions, multiple clouds and everchanging global regulatory requirements. The data is yours. And with Starburst, you can perform analytics anywhere in light of your world. >>Okay. We're back with Justin Boardman. CEO of Starbust Richard Jarvis is the CTO of EMI health and Theresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie it's it is it's is that it's not modern. Justin, what do you say? >>Yeah. I mean, I think new isn't modern, right? I think it's the, it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exist just yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess, the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, a microservices layer on top of leg legacy apps. How do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more, more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. De-risked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that. I'm a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >>Of, of how it should look like or, or how >>It's yeah. What it should be. >>Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of it was starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go buy a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, my takeaway there is it's inclusive, whether it's a data Mar data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include there on Preem data? O obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on Preem? I mean, most implementations I've seen in data mesh, frankly really aren't, you know, adhering to the philosophy. They're maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data. Me, the fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both worlds. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, talking about data as product, wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight. And in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured, managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned you, you appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have, and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use case? >>Yeah, it makes sense. It's got the context. If the, if the domains own the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's send Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Theresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave Valante for the cube, and we'll see you next time. >>The explosion of data sources has forced organizations to modernize their systems and architecture and come to terms with one size does not fit all for data management today. Your teams are constantly moving and copying data, which requires time management. And in some cases, double paying for compute resources. Instead, what if you could access all your data anywhere using the BI tools and SQL skills your users already have. 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(gentle upbeat music) >> Welcome back everyone, to "theCUBE"'s live stage performance here in Palo Alto, California at "theCUBE" Studios. I'm John Furrier with Dave Vellante, kicking off our first inaugural Supercloud event. It's an editorial event, we wanted to bring together the best in the business, the smartest, the biggest, the up-and-coming startups, venture capitalists, everybody, to weigh in on this new Supercloud trend, this structural change in the cloud computing business. We're about to run the Ecosystem Speaks, which is a bunch of pre-recorded companies that wanted to get their voices on the record, so stay tuned for the rest of the day. We'll be replaying all that content and they're going to be having some really good commentary and hear what they have to say. I had a chance to interview and so did Dave. Dave, this is our closing segment where we kind of unpack everything or kind of digest and report. So much to kind of digest from the conversations today, a wide range of commentary from Supercloud operating system to developers who are in charge to maybe it's an ops problem or maybe Oracle's a Supercloud. I mean, that was debated. So so much discussion, lot to unpack. What was your favorite moments? >> Well, before I get to that, I think, I go back to something that happened at re:Invent last year. Nick Sturiale came up, Steve Mullaney from Aviatrix; we're going to hear from him shortly in the Ecosystem Speaks. Nick Sturiale's VC said "it's happening"! And what he was talking about is this ecosystem is exploding. They're building infrastructure or capabilities on top of the CapEx infrastructure. So, I think it is happening. I think we confirmed today that Supercloud is a thing. It's a very immature thing. And I think the other thing, John is that, it seems to me that the further you go up the stack, the weaker the business case gets for doing Supercloud. We heard from Marianna Tessel, it's like, "Eh, you know, we can- it was easier to just do it all on one cloud." This is a point that, Adrian Cockcroft just made on the panel and so I think that when you break out the pieces of the stack, I think very clearly the infrastructure layer, what we heard from Confluent and HashiCorp, and certainly VMware, there's a real problem there. There's a real need at the infrastructure layer and then even at the data layer, I think Benoit Dageville did a great job of- You know, I was peppering him with all my questions, which I basically was going through, the Supercloud definition and they ticked the box on pretty much every one of 'em as did, by the way Ali Ghodsi you know, the big difference there is the philosophy of Republicans and Democrats- got open versus closed, not to apply that to either one side, but you know what I mean! >> And the similarities are probably greater than differences. >> Berkely, I would probably put them on the- >> Yeah, we'll put them on the Democrat side we'll make Snowflake the Republicans. But so- but as we say there's a lot of similarities as well in terms of what their objectives are. So, I mean, I thought it was a great program and a really good start to, you know, an industry- You brought up the point about the industry consortium, asked Kit Colbert- >> Yep. >> If he thought that was something that was viable and what'd they say? That hyperscale should lead it? >> Yeah, they said hyperscale should lead it and there also should be an industry consortium to get the voices out there. And I think VMware is very humble in how they're putting out their white paper because I think they know that they can't do it all and that they do not have a great track record relative to cloud. And I think, but they have a great track record of loyal installed base ops people using VMware vSphere all the time. >> Yeah. >> So I think they need a catapult moment where they can catapult to the cloud native which they've been working on for years under Raghu and the team. So the question on VMware is in the light of Broadcom, okay, acquisition of VMware, this is an opportunity or it might not be an opportunity or it might be a spin-out or something, I just think VMware's got way too much engineering culture to be ignored, Dave. And I think- well, I'm going to watch this very closely because they can pull off some sort of rallying moment. I think they could. And then you hear the upstarts like Platform9, Rafay Systems and others they're all like, "Yes, we need to unify behind something. There needs to be some sort of standard". You know, we heard the argument of you know, more standards bodies type thing. So, it's interesting, maybe "theCUBE" could be that but we're going to certainly keep the conversation going. >> I thought one of the most memorable statements was Vittorio who said we- for VMware, we want our cake, we want to eat it too and we want to lose weight. So they have a lot of that aspirations there! (John laughs) >> And then I thought, Adrian Cockcroft said you know, the devs, they want to get married. They were marrying everybody, and then the ops team, they have to deal with the divorce. >> Yeah. >> And I thought that was poignant. It's like, they want consistency, they want standards, they got to be able to scale And Lori MacVittie, I'm not sure you agree with this, I'd have to think about it, but she was basically saying, all we've talked about is devs devs devs for the last 10 years, going forward we're going to be talking about ops. >> Yeah, and I think one of the things I learned from this day and looking back, and some kind of- I've been sauteing through all the interviews. If you zoom out, for me it was the epiphany of developers are still in charge. And I've said, you know, the developers are doing great, it's an ops security thing. Not sure I see that the way I was seeing before. I think what I learned was the refactoring pattern that's emerging, In Sik Rhee brought this up from Vertex Ventures with Marianna Tessel, it's a nuanced point but I think he's right on which is the pattern that's emerging is developers want ease-of-use tooling, they're driving the change and I think the developers in the devs ops ethos- it's never going to be separate. It's going to be DevOps. That means developers are driving operations and then security. So what I learned was it's not ops teams leveling up, it's devs redefining what ops is. >> Mm. And I think that to me is where Supercloud's going to be interesting- >> Forcing that. >> Yeah. >> Forcing the change because the structural change is open sources thriving, devs are still in charge and they still want more developers, Vittorio "we need more developers", right? So the developers are in charge and that's clear. Now, if that happens- if you believe that to be true the domino effect of that is going to be amazing because then everyone who gets on the wrong side of history, on the ops and security side, is going to be fighting a trend that may not be fight-able, you know, it might be inevitable. And so the winners are the ones that are refactoring their business like Snowflake. Snowflake is a data warehouse that had nothing to do with Amazon at first. It was the developers who said "I'm going to refactor data warehouse on AWS". That is a developer-driven refactorization and a business model. So I think that's the pattern I'm seeing is that this concept refactoring, patterns and the developer trajectory is critical. >> I thought there was another great comment. Maribel Lopez, her Lord of the Rings comment: "there will be no one ring to rule them all". Now at the same time, Kit Colbert, you know what we asked him straight out, "are you the- do you want to be the, the Supercloud OS?" and he basically said, "yeah, we do". Now, of course they're confined to their world, which is a pretty substantial world. I think, John, the reason why Maribel is so correct is security. I think security's a really hard problem to solve. You've got cloud as the first layer of defense and now you've got multiple clouds, multiple layers of defense, multiple shared responsibility models. You've got different tools for XDR, for identity, for governance, for privacy all within those different clouds. I mean, that really is a confusing picture. And I think the hardest- one of the hardest parts of Supercloud to solve. >> Yeah, and I thought the security founder Gee Rittenhouse, Piyush Sharrma from Accurics, which sold to Tenable, and Tony Kueh, former head of product at VMware. >> Right. >> Who's now an investor kind of looking for his next gig or what he is going to do next. He's obviously been extremely successful. They brought up the, the OS factor. Another point that they made I thought was interesting is that a lot of the things to do to solve the complexity is not doable. >> Yeah. >> It's too much work. So managed services might field the bit. So, and Chris Hoff mentioned on the Clouderati segment that the higher level services being a managed service and differentiating around the service could be the key competitive advantage for whoever does it. >> I think the other thing is Chris Hoff said "yeah, well, Web 3, metaverse, you know, DAO, Superclouds" you know, "Stupercloud" he called it and this bring up- It resonates because one of the criticisms that Charles Fitzgerald laid on us was, well, it doesn't help to throw out another term. I actually think it does help. And I think the reason it does help is because it's getting people to think. When you ask people about Supercloud, they automatically- it resonates with them. They play back what they think is the future of cloud. So Supercloud really talks to the future of cloud. There's a lot of aspects to it that need to be further defined, further thought out and we're getting to the point now where we- we can start- begin to say, okay that is Supercloud or that isn't Supercloud. >> I think that's really right on. I think Supercloud at the end of the day, for me from the simplest way to describe it is making sure that the developer experience is so good that the operations just happen. And Marianna Tessel said, she's investing in making their developer experience high velocity, very easy. So if you do that, you have to run on premise and on the cloud. So hybrid really is where Supercloud is going right now. It's not multi-cloud. Multi-cloud was- that was debunked on this session today. I thought that was clear. >> Yeah. Yeah, I mean I think- >> It's not about multi-cloud. It's about operationally seamless operations across environments, public cloud to on-premise, basically. >> I think we got consensus across the board that multi-cloud, you know, is a symptom Chuck Whitten's thing of multi-cloud by default versus multi- multi-cloud has not been a strategy, Kit Colbert said, up until the last couple of years. Yeah, because people said, "oh we got all these multiple clouds, what do we do with it?" and we got this mess that we have to solve. Whereas, I think Supercloud is something that is a strategy and then the other nuance that I keep bringing up is it's industries that are- as part of their digital transformation, are building clouds. Now, whether or not they become superclouds, I'm not convinced. I mean, what Goldman Sachs is doing, you know, with AWS, what Walmart's doing with Azure connecting their on-prem tools to those public clouds, you know, is that a supercloud? I mean, we're going to have to go back and really look at that definition. Or is it just kind of a SAS that spans on-prem and cloud. So, as I said, the further you go up the stack, the business case seems to wane a little bit but there's no question in my mind that from an infrastructure standpoint, to your point about operations, there's a real requirement for super- what we call Supercloud. >> Well, we're going to keep the conversation going, Dave. I want to put a shout out to our founding supporters of this initiative. Again, we put this together really fast kind of like a pilot series, an inaugural event. We want to have a face-to-face event as an industry event. Want to thank the founding supporters. These are the people who donated their time, their resource to contribute content, ideas and some cash, not everyone has committed some financial contribution but we want to recognize the names here. VMware, Intuit, Red Hat, Snowflake, Aisera, Alteryx, Confluent, Couchbase, Nutanix, Rafay Systems, Skyhigh Security, Aviatrix, Zscaler, Platform9, HashiCorp, F5 and all the media partners. Without their support, this wouldn't have happened. And there are more people that wanted to weigh in. There was more demand than we could pull off. We'll certainly continue the Supercloud conversation series here on "theCUBE" and we'll add more people in. And now, after this session, the Ecosystem Speaks session, we're going to run all the videos of the big name companies. We have the Nutanix CEOs weighing in, Aviatrix to name a few. >> Yeah. Let me, let me chime in, I mean you got Couchbase talking about Edge, Platform 9's going to be on, you know, everybody, you know Insig was poopoo-ing Oracle, but you know, Oracle and Azure, what they did, two technical guys, developers are coming on, we dig into what they did. Howie Xu from Zscaler, Paula Hansen is going to talk about going to market in the multi-cloud world. You mentioned Rajiv, the CEO of Nutanix, Ramesh is going to talk about multi-cloud infrastructure. So that's going to run now for, you know, quite some time here and some of the pre-record so super excited about that and I just want to thank the crew. I hope guys, I hope you have a list of credits there's too many of you to mention, but you know, awesome jobs really appreciate the work that you did in a very short amount of time. >> Well, I'm excited. I learned a lot and my takeaway was that Supercloud's a thing, there's a kind of sense that people want to talk about it and have real conversations, not BS or FUD. They want to have real substantive conversations and we're going to enable that on "theCUBE". Dave, final thoughts for you. >> Well, I mean, as I say, we put this together very quickly. It was really a phenomenal, you know, enlightening experience. I think it confirmed a lot of the concepts and the premises that we've put forth, that David Floyer helped evolve, that a lot of these analysts have helped evolve, that even Charles Fitzgerald with his antagonism helped to really sharpen our knives. So, you know, thank you Charles. And- >> I like his blog, by the I'm a reader- >> Yeah, absolutely. And it was great to be back in Palo Alto. It was my first time back since pre-COVID, so, you know, great job. >> All right. I want to thank all the crew and everyone. Thanks for watching this first, inaugural Supercloud event. We are definitely going to be doing more of these. So stay tuned, maybe face-to-face in person. I'm John Furrier with Dave Vellante now for the Ecosystem chiming in, and they're going to speak and share their thoughts here with "theCUBE" our first live stage performance event in our studio. Thanks for watching. (gentle upbeat music)

>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting costs could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data, Mars, data hubs, and yes, even data lakes were broken and left us wanting for more welcome to the data doesn't lie, or does it a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have feature parody with the data lake or vice versa is the so-called modern data stack simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for IDU that was acquired by Teradata. And when I got to Teradata, of course, Terada is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on-prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience, Joe? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know? Right. But you actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like swans Oxley, for things like security, for certain very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited JAK, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come, I think that's the story of Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenets of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about, so Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and con contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but the, what does that mean? Does that mean the ed w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's gonna be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems. Maybe either those that either source systems, the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to lose all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got, you know, the domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue or two, you know, challenges self-serve infrastructure let's park that for a second. And then in your industry, one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And, and I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at EMI is we have a single security layer that sits on top of our data mesh, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. >>And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin mean Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, doing analytic queries and with data, that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah, I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively, almost eCommerce, like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself. >>Okay. G guys grab a sip of water. After the short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence. Keep it right there.

>>Okay, welcome back. Everyone. Live cube coverage here in Las Vegas for Amazon re Mars hot event, machine learning, automation, robotics, and space. Two days of live coverage. We're talking to all the hot technologists. We got all the action startups and segment on sustainability and F pan hero for vet global lead, Amazon sustainability data initiative. Thanks for coming on the cube. Can I get that right? Can >>You, you, you did. >>Absolutely. Okay, great. <laugh> thank >>You. >>Great to see you. We met at the analyst, um, mixer and, um, blown away by the story going on at Amazon around sustainability data initiative, because we were joking. Everything's a data problem now, cuz that's cliche. But in this case you're using data in your program and it's really kind of got a bigger picture. Take a minute to explain what your project is, scope of it on the sustainability. >>Yeah, absolutely. And thank you for the opportunity to be here. Yeah. Um, okay. So, um, I, I lead this program that we launched several years back in 2018 more specifically, and it's a tech for good program. And when I say the tech for good, what that means is that we're trying to bring our technology and our infrastructure and lend that to the world specifically to solve the problems related to sustainability. And as you said, sustainability, uh, inherently needs data. You need, we need data to understand the baseline of where we are and also to understand the progress that we are making towards our goals. Right? But one of the big challenges that the data that we need is spread everywhere. Some of it is too large for most people to be able to, um, access and analyze. And so, uh, what we're trying to tackle is really the data problem in the sustainability space. >>Um, what we do more specifically is focus on Democrat democratizing access to data. So we work with a broader community and we try to understand what are those foundational data sets that most people need to use in the space to solve problems like climate change or food security or think about sustainable development goals, right? Yeah. Yeah. Like all the broad space. Um, and, and we basically then work with the data providers, bring the data to the cloud, make it free and open to everybody in the world. Um, I don't know how deep you want me to go into it. There's many other layers into that. So >>The perspective is zooming out. You're, you're, you're looking at creating a system where the democratizing data means making it freely available so that practitioners or citizens, data, Wrangler, people interested in helping the world could get access to it and then maybe collaborate with people around the world. Is that right? >>Absolutely. So one of the advantages of using the cloud for this kind of, uh, effort is that, you know, cloud is virtually accessible from anywhere where you have, you know, internet or bandwidth, right? So, uh, when, when you put data in the cloud in a centralized place next to compute, it really, uh, removes the, the need for everybody to have their own copy. Right. And to bring it into that, the traditional way is that you bring the data next to your compute. And so we have this multiple copies of data. Some of them are on the petabyte scale. There's obviously the, the carbon footprint associated with the storage, but there's also the complexity that not everybody's able to actually analyze and have that kind of storage. So by putting it in the cloud, now anyone in the world independent of where of their computer capabilities can have access to the same type of data to solve >>The problems. You don't remember doing a report on this in 2018 or 2017. I forget what year it was, but it was around public sector where it was a movement with universities and academia, where they were doing some really deep compute where Amazon had big customers. And there was a movement towards a open commons of data, almost like a national data set like a national park kind of vibe that seems to be getting momentum. In fact, this kind of sounds like what you're doing some similar where it's open to everybody. It's kinda like open source meets data. >>Uh, exactly. And, and the truth is that these data, the majority of it's and we primarily work with what we call authoritative data providers. So think of like NASA Noah, you came me office organizations whose mission is to create the data. So they, their mandate is actually to make the data public. Right. But in practice, that's not really the case. Right. A lot of the data is stored like in servers or tapes or not accessible. Um, so yes, you bring the data to the cloud. And in this model that we use, Amazon never actually touches the data and that's very intentional so that we preserve the integrity of the data. The data provider owns the data in the cloud. We cover all the costs, but they commit to making it public in free to anybody. Um, and obviously the computer is next to it. So that's, uh, evaluated. >>Okay. Anna. So give me some examples of, um, some successes. You've had some of the challenges and opportunities you've overcome, take me through some of the activities because, um, this is really needed, right? And we gotta, sustainability is top line conversation, even here at the conference, re Mars, they're talking about saving climate change with space mm-hmm <affirmative>, which is legitimate. And they're talking about all these new things. So it's only gonna get bigger. Yeah. This data, what are some of the things you're working on right now that you can share? >>Yeah. So what, for me, honestly, the most exciting part of all of this is, is when I see the impact that's creating on customers and the community in general, uh, and those are the stories that really bring it home, the value of opening access to data. And, and I would just say, um, the program actually offers in addition to the data, um, access to free compute, which is very important as well. Right? You put the data in the cloud. It's great. But then if you wanna analyze that, there's the cost and we want to offset that. So we have a, basically an open call for proposals. Anybody can apply and we subsidize that. But so what we see by putting the data in the cloud, making it free and putting the compute accessible is that like we see a lot, for instance, startups, startups jump on it very easily because they're very nimble. They, we basically remove all the cost of investing in the acquisition and storage of the data. The data is connected directly to the source and they don't have to do anything. So they easily build their applications on top of it and workloads and turn it on and off if you know, >>So they don't have to pay for it. >>They have to pay, they basically just pay for the computes whenever they need it. Right. So all the data is covered. So that makes it very visible for, for a lot of startups. And then we see anything like from academia and nonprofits and governments working extensively on the data, what >>Are some of the coolest things you've seen come out of the woodwork in terms of, you know, things that built on top of the, the data, the builders out there are creative, all that heavy, lifting's gone, they're being creative. I'm sure there's been some surprises, um, or obvious verticals that jump healthcare jumps out at me. I'm not sure if FinTech has a lot of data in there, but it's healthcare. I can see, uh, a big air vertical, obviously, you know, um, oil and gas, probably concern. Um, >>So we see it all over the space, honestly. But for instance, one of the things that is very, uh, common for people to use this, uh, Noah data like weather data, because no, basically weather impacts almost anything we do, right? So you have this forecast of data coming into the cloud directly streamed from Noah. And, um, a lot of applications are built on top of that. Like, um, forecasting radiation, for instance, for the solar industry or helping with navigation. But I would say some of the stories I love to mention because are very impactful are when we take data to remote places that traditionally did not have access to any data. Yeah. And for instance, we collaborate with a, with a program, a nonprofit called digital earth Africa where they, this is a basically philanthropically supported program to bring earth observations to the African continents in making it available to communities and governments and things like illegal mining fighting, illegal mining are the forestation, you know, for mangroves to deep forest. Um, it's really amazing what they are doing. And, uh, they are managing >>The low cost nature of it makes it a great use case there >>Yes. Cloud. So it makes it feasible for them to actually do this work. >>Yeah. You mentioned the Noah data making me think of the sale drone. Mm-hmm <affirmative> my favorite, um, use case. Yes. Those sales drones go around many them twice on the queue at reinvent over the years. Yeah. Um, really good innovation. That vibe is here too at the show at Remar this week at the robotics showcases you have startups and growing companies in the ML AI areas. And you have that convergence of not obvious to many, but here, this culture is like, Hey, we have, it's all coming together. Mm-hmm <affirmative>, you know, physical, industrial space is a function of the new O T landscape. Mm-hmm <affirmative>. I mean, there's no edge in space as they say, right. So the it's unlimited edge. So this kind of points to the major trend. It's not stopping this innovation, but sustainability has limits on earth. We have issues. >>We do have issues. And, uh, and I, I think that's one of my hopes is that when we come to the table with the resources and the skills we have and others do as well, we try to remove some of these big barriers, um, that make it things harder for us to move forward as fast as we need to. Right. We don't have time to spend that. Uh, you know, I've been accounted that 80% of the effort to generate new knowledge is spent on finding the data you need and cleaning it. Uh, we, we don't have time for that. Right. So can we remove that UN differentiated, heavy lifting and allow people to start at a different place and generate knowledge and insights faster. >>So that's key, that's the key point having them innovate on top of it, right. What are some things that you wanna see happen over the next year or two, as you look out, um, hopes, dreams, KPIs, performance metrics, what are you, what are you driving to? What's your north star? What are some of those milestones? >>Yeah, so some, we are investing heavily in some areas. Uh, we support, um, you know, we support broadly sustainability, which as, you know, it's like, it's all over, <laugh> the space, but, uh, there's an area that is, uh, becoming more and more critical, which is climate risk. Um, climate risk, you know, for obvious reasons we are experienced, but also there's more regulatory pressures on, uh, business and companies in general to disclose their risks, not only the physical, but also to transition risks. And that's a very, uh, data heavy and compute heavy space. Right. And so we are very focusing in trying to bring the right data and the right services to support that kind of, of activity. >>What kind of break was you looking for? >>Um, so I think, again, it goes back to this concept that there's all that effort that needs to be done equally by so many people that we are all repeating the effort. So I'll put a plug here actually for a project we are supporting, which is called OS climates. Um, I don't know if you're familiar with it, but it's the Linux foundation effort to create an open source platform for climate risk. And so they, they bought the SMP global Airbus, you know, Alliance all these big companies together. And we are one of the funding partners to basically do that basic line work. What are the data that is needed? What are the basic tools let's put it there and do the pre-competitive work. So then you can do the build the, the, the competitive part on top of it. So >>It's kinda like a data clean room. >>It kind of is right. But we need to do those things, right. So >>Are they worried about comp competitive data or is it more anonymized out? How do you, >>It has both actually. So we are primarily contributing, contributing with the open data part, but there's a lot of proprietary data that needs to be behind the whole, the walls. So, yeah, >>You're on the cutting edge of data engineering because, you know, web and ad tech technologies used to be where all that data sharing was done. Mm-hmm <affirmative> for the commercial reasons, you know, the best minds in our industry quoted by a cube alumni are working on how to place ads better. Yeah. Jeff Acker, founder of Cloudera said that on the cube. Okay. And he was like embarrassed, but the best minds are working on how to make ads get more efficient. Right. But that tech is coming to problem solving and you're dealing with data exchange data analysis from different sources, third parties. This is a hard problem. >>Well, it is a hard problem. And I'll, I'll my perspective is that the hardest problem with sustainability is that it goes across all kinds of domains. Right. We traditionally been very comfortable working in our little, you know, swimming lanes yeah. Where we don't need to deal with interoperability and, uh, extracting knowledge. But sustainability, you, you know, you touch the economic side, it touches this social or the environmental, it's all connected. Right. And you cannot just work in the little space and then go sets the impact in the other one. So it's going to force us to work in a different way. Right. It's, uh, big data complex data yeah. From different domains. And we need to somehow make sense of all of it. And there's the potential of AI and ML and things like that that can really help us right. To go beyond the, the modeling approaches we've been done so >>Far. And trust is a huge factor in all this trust. >>Absolutely. And, and just going back to what I said before, that's one of the main reasons why, when we bring data to the cloud, we don't touch it. We wanna make sure that anybody can trust that the data is nowhere data or NASA data, but not Amazon data. >>Yes. Like we always say in the cube, you should own your data plane. Don't give it up. <laugh> well, that's cool. Great. Great. To hear the update. Is there any other projects that you're working on you think might be cool for people that are watching that you wanna plug or point out because this is an area people are, are leaning into yeah. And learning more young, younger talents coming in. Um, I, whether it's university students to people on side hustles want to play with data, >>So we have plenty of data. So we have, uh, we have over a hundred data sets, uh, petabytes and petabytes of data all free. You don't even need an AWS account to access the data and take it out if you want to. Uh, but I, I would say a few things that are exciting that are happening at Mars. One is that we are actually got integrated into ADX. So the AWS that exchange and what that means is that now you can find the open data, free data from a STI in the same searching capability and service as the paid data, right. License data. So hopefully we'll make it easier if I, if you wanna play with data, we have actually something great. We just announced a hackathon this week, uh, in partnership with UNESCO, uh, focus on sustainable development goals, uh, a hundred K in prices and, uh, so much data <laugh> you >>Too years, they get the world is your oyster to go check that out at URL at website, I'll see it's on Amazon. It use our website or a project that can join, or how do people get in touch with you? >>Yeah. So, uh, Amazon SDI, like for Amazon sustainability, that initiative, so Amazon sdi.com and you'll find, um, all the data, a lot of examples of customer stories that are using the data for impactful solutions, um, and much more >>So, and these are, there's a, there's a, a new kind of hustle going out there, seeing entrepreneurs do this. And very successfully, they pick a narrow domain and they, they own it. Something really obscure that could be off the big player's reservation. Mm-hmm <affirmative> and they just become fluent in the data. And it's a big white space for them, right. This market opportunities. And at the minimum you're playing with data. So this is becoming kind of like a long tail domain expertise, data opportunity. Yeah, absolutely. This really hot. So yes. Yeah. Go play around with the data, check it outs for good cause too. And it's free. >>It's all free. >>Almost free. It's not always free. Is it >>Always free? Well, if you, a friend of mine said is only free if your time is worth nothing. <laugh>. Yeah, >>Exactly. Well, Anna, great to have you on the cube. Thanks for sharing the stories. Sustainability is super important. Thanks for coming on. Thank you for the opportunity. Okay. Cube coverage here in Las Vegas. I'm Sean. Furier, we've be back with more day one. After this short break.

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome back to downtown Miami. Everybody. We're here at the Intercontinental hotel covering the IBM data AI form hashtag data AI forum. My name is Dave Volante and you're watching the cube, the leader in live tech coverage. Ritika gunner is here. She's the vice president of data and AI expert labs and learning at IBM. Ritika, great to have you on. Again, always a pleasure to be here. Dave. I love interviewing you because you're a woman executive that said a lot of different roles at IBM. Um, you know, you've, we've talked about the AI ladder. You're climbing the IBM ladder and so it's, it's, it's, it's awesome to see and I love this topic. It's a topic that's near and dear to the cubes heart, not only women in tech, but women in AI. So great to have you. Thank you. So what's going on with the women in AI program? We're going to, we're going to cover that, but let me start with women in tech. It's an age old problem that we've talked about depending on, you know, what statistic you look at. 15% 17% of, uh, of, of, of the industry comprises women. We do a lot of events. You can see it. Um, let's start there. >>Well, obviously the diversity is not yet there, right? So we talk about women in technology, um, and we just don't have the representation that we need to be able to have. Now when it comes to like artificial intelligence, I think the statistic is 10 to 15% of the workforce today in AI is female. When you think about things like bias and ethicacy, having the diversity in terms of having male and female representation be equal is absolutely essential so that you're creating fair AI, unbiased AI, you're creating trust and transparency, set of capabilities that really have the diversity in backgrounds. >>Well, you work for a company that is as chairman and CEO, that's, that's a, that's a woman. I mean IBM generally, you know, we could see this stuff on the cube because IBM puts women on a, we get a lot of women customers that, that come on >>and not just because we're female, because we're capable. >>Yeah. Well of course. Right. It's just because you're in roles where you're spokespeople and it's natural for spokespeople to come on a forum like this. But, but I have to ask you, with somebody inside of IBM, a company that I could say the test to relative to most, that's pretty well. Do you feel that way or do you feel like even a company like IBM has a long way to go? >>Oh, um, I personally don't feel that way and I've never felt that to be an issue. And if you look at my peers, um, my um, lead for artificial intelligence, Beth Smith, who, you know, a female, a lot of my peers under Rob Thomas, all female. So I have not felt that way in terms of the leadership team that I have. Um, but there is a gap that exists, not necessarily within IBM, but in the community as a whole. And I think it goes back to you want to, you know, when you think about data science and artificial intelligence, you want to be able to see yourself in the community. And while there's only 10 to 15% of females in AI today, that's why IBM has created programs such as women AI that we started in June because we want strong female leaders to be able to see that there are, is great representation of very technical capable females in artificial intelligence that are doing amazing things to be able to transform their organizations and their business model. >>So tell me more about this program. I understand why you started it started in June. What does it entail and what's the evolution of this? >>So we started it in June and the idea was to be able to get some strong female leaders and multiple different organizations that are using AI to be able to change their companies and their business models and really highlight not just the journey that they took, but the types of transformations that they're doing and their organizations. We're going to have one of those events tonight as well, where we have leaders from Harley Davidson in Miami Dade County coming to really talk about not only what was their journey, but what actually brought them to artificial intelligence and what they're doing. And I think Dave, the reason that's so important is you want to be able to understand that those journeys are absolutely approachable. They're doable by any females that are out there. >>Talk about inherent bias. The humans are biased and if you're developing models that are using AI, there's going to be inherent bias in those models. So talk about how to address that and why is it important for more diversity to be injected into those models? >>Well, I think a great example is if you took the data sets that existed even a decade ago, um, for the past 50 years and you created a model that was to be able to predict whether to give loans to certain candidates or not, all things being equal, what would you find more males get these loans than females? The inherent data that exists has bias in it. Even from the history based on what we've had yet, that's not the way we want to be able to do things today. You want to be able to identify that bias and say all things being equal, it is absolutely important that regardless of whether you are a male or a female, you want to be able to give that loan to that person if they have all the other qualities that are there. And that's why being able to not only detect these things but have the diversity and the kinds of backgrounds of people who are building AI who are deploying this AI is absolutely critical. >>So for the past decade, and certainly in the past few years, there's been a light shined on this topic. I think, you know, we were at the Grace Hopper conference when Satya Nadella stuck his foot in his mouth and it said, Hey, it's bad karma for you know, if you feel like you're underpaid to go complain. And the women in the audience like, dude, no way. And he, he did the right thing. He goes, you know what, you're right. You know, any, any backtrack on that? And that was sort of another inflection point. But you talk about the women in, in AI program. I was at a CDO event one time. It was I and I, an IBM or had started the data divas breakfast and I asked, can I go? They go, yeah, you can be the day to dude. Um, which was, so you're seeing a lot of initiatives like this. My question is, are they having the impact that you would expect and that you want to have? >>I think they absolutely are. Again, I mean, I'll go back to, um, I'll give you a little bit of a story. Um, you know, people want to be able to relate and see that they can see themselves in these females leaders. And so we've seen cases now through our events, like at IBM we have a program called grow, which is really about helping our female lead female. Um, technical leaders really understand that they can grow, they can be nurtured, and they have development programs to help them accelerate where they need to be on their technical programs. We've absolutely seen a huge impact from that from a technology perspective. In terms of more females staying in technology wanting to go in the, in those career paths as another story. I'll, I'll give you kind of another kind of point of view. Um, Dave and that is like when you look at where it starts, it starts a lot earlier. >>So I have a young daughter who a year, year and a half ago when I was doing a lot of stuff with Watson, she would ask me, you know, not only what Watson's doing, but she would say, what does that mean for me mom? Like what's my job going to be? And if you think about the changes in technology and cultural shifts, technology and artificial intelligence is going to impact every job, every industry, every role that there is out there. So much so that I believe her job hasn't been invented yet. And so when you think about what's absolutely critical, not only today's youth, but every person out there needs to have a foundational understanding, not only in the three RS that you and I know from when we grew up have reading, writing and arithmetic, we need to have a foundational understanding of what it means to code. And you know, having people feel confident, having young females feel confident that they can not only do that, that they can be technical, that they can understand how artificial intelligence is really gonna impact society. And the world is absolutely critical. And so these types of programs that shed light on that, that help bridge that confidence is game changing. >>Well, you got kids, I >>got kids, I have daughters, you have daughter. Are they receptive to that? So, um, you know, I think they are, but they need to be able to see themselves. So the first time I sent my daughter to a coding camp, she came back and said, not for me mom. I said, why? Because she's like, all the boys, they're coding in their Minecraft area. Not something I can relate to. You need to be able to relate and see something, develop that passion, and then mix yourself in that diverse background where you can see the diversity of backgrounds. When you don't have that diversity and when you can't really see how to progress yourself, it becomes a blocker. So as she started going to grow star programs, which was something in Austin where young girls coded together, it became something that she's really passionate about and now she's Python programming. So that's just an example of yes, you need to be able to have these types of skills. It needs to start early and you need to have types of programs that help enhance that journey. >>Yeah, and I think you're right. I think that that is having an impact. My girls who code obviously as a some does some amazing work. My daughters aren't into it. I try to send them to coder camp too and they don't do it. But here's my theory on that is that coding is changing and, and especially with artificial intelligence and cognitive, we're a software replacing human skills. Creativity is going to become much, much more important. My daughters are way more creative than my sons. I shouldn't say that, but >>I think you just admitted that >>they, but, but in a way they are. I mean they've got amazing creativity, certainly more than I am. And so I see that as a key component of how coding gets done in the future, taking different perspectives and then actually codifying them. Your, your thoughts on that. >>Well there is an element of understanding like the outcomes that you want to generate and the outcomes really is all about technology. How can you imagine the art of the possible with technology? Because technology alone, we all know not useful enough. So understanding what you do with it, just as important. And this is why a lot of people who are really good in artificial intelligence actually come from backgrounds that are philosophy, sociology, economy. Because if you have the culture of curiosity and the ability to be able to learn, you can take the technology aspects, you can take those other aspects and blend them together. So understanding the problem to be solved and really marrying that with the technological aspects of what AI can do. That's how you get outcomes. >>And so we've, we've obviously talking in detail about women in AI and women in tech, but it's, there's data that shows that diversity drives value in so many different ways. And it's not just women, it's people of color, it's people of different economic backgrounds, >>underrepresented minorities. Absolutely. And I think the biggest thing that you can do in an organization is have teams that have that diverse background, whether it be from where they see the underrepresented, where they come from, because those differences in thought are the things that create new ideas that really innovate, that drive, those business transformations that drive the changes in the way that we do things. And so having that difference of opinion, having healthy ways to bring change and to have conflict, absolutely essential for progress to happen. >>So how did you get into the tech business? What was your background? >>So my background was actually, um, a lot in math and science. And both of my parents were engineers. And I have always had this unwavering, um, need to be able to marry business and the technology side and really figure out how you can create the art of the possible. So for me it was actually the creativity piece of it where you could create something from nothing that really drove me to computer science. >>Okay. So, so you're your math, uh, engineer and you ended up in CS, is that right? >>Science. Yeah. >>Okay. So you were coded. Did you ever work as a programmer? >>Absolutely. My, my first years at IBM were all about coding. Um, and so I've always had a career where I've coded and then I've gone to the field and done field work. I've come back and done development and development management, gone back to the field and kind of seen how that was actually working. So personally for me, being able to create and work with clients to understand how they drive value and having that back and forth has been a really delightful part. And the thing that drives me, >>you know, that's actually not an uncommon path for IBM. Ours, predominantly male IBM, or is in the 50 sixties and seventies and even eighties. Who took that path? They started out programming. Um, I just think, trying to think of some examples. I know Omar para, who was the CIO of Aetna international, he started out coding at IBM. Joe Tucci was a programmer at IBM. He became CEO of EMC. It was a very common path for people and you took the same path. That's kind of interesting. Why do you think, um, so many women who maybe maybe start in computer science and coding don't continue on that path? And what was it that sort of allowed you to break through that barrier? >>No, I'm not sure why most women don't stay with it. But for me, I think, um, you know, I, I think that every organization today is going to have to be technical in nature. I mean, just think about it for a moment. Technology impacts every part of every type of organization and the kinds of transformation that happens. So being more technical as leaders and really understanding the technology that allows the kinds of innovations and business for informations is absolutely essential to be able to see progress in a lot of what we're doing. So I think that even general CXOs that you see today have to be more technically acute to be able to do their jobs really well and marry those business outcomes with what it fundamentally means to have the right technology backbone. >>Do you think a woman in the white house would make a difference for young people? I mean, part of me says, yeah, of course it would. Then I say, okay, well some examples you can think about Margaret Thatcher in the UK, Angela Merkel, and in Germany it's still largely male dominated cultures, but I dunno, what do you think? Maybe maybe that in the United States would be sort of the, >>I'm not a political expert, so I wouldn't claim to answer that, but I do think more women in technology, leadership role, CXO leadership roles is absolutely what we need. So, you know, politics aside more women in leadership roles. Absolutely. >>Well, it's not politics is gender. I mean, I'm independent, Republican, Democrat, conservative, liberal, right? Absolutely. Oh yeah. Well, companies, politics. I mean you certainly see women leaders in a, in Congress and, and the like. Um, okay. Uh, last question. So you've got a program going on here. You have a, you have a panel that you're running. Tell us more about. >>Well this afternoon we'll be continuing that from women leaders in AI and we're going to do a panel with a few of our clients that really have transformed their organizations using data and artificial intelligence and they'll talk about like their backgrounds in history. So what does it actually mean to come from? One of, one of the panelists actually from Miami Dade has always come from a technical background and the other panelists really etched in from a non technical background because she had a passion for data and she had a passion for the technology systems. So we're going to go through, um, how these females actually came through to the journey, where they are right now, what they're actually doing with artificial intelligence in their organizations and what the future holds for them. >>I lied. I said, last question. What is, what is success for you? Cause I, I would love to help you achieve that. That objective isn't, is it some metric? Is it awareness? How do you know it when you see it? >>Well, I think it's a journey. Success is not an endpoint. And so for me, I think the biggest thing I've been able to do at IBM is really help organizations help businesses and people progress what they do with technology. There's nothing more gratifying than like when you can see other organizations and then what they can do, not just with your technology, but what you can bring in terms of expertise to make them successful, what you can do to help shape their culture and really transform. To me, that's probably the most gratifying thing. And as long as I can continue to do that and be able to get more acknowledgement of what it means to have the right diversity ingredients to do that, that success >>well Retika congratulations on your success. I mean, you've been an inspiration to a number of people. I remember when I first saw you, you were working in group and you're up on stage and say, wow, this person really knows her stuff. And then you've had a variety of different roles and I'm sure that success is going to continue. So thanks very much for coming on the cube. You're welcome. All right, keep it right there, buddy. We'll be back with our next guest right after this short break, we're here covering the IBM data in a AI form from Miami right back.

>> Live from San Diego, California It's the queue covering Sisqo Live US 2019 Tio by Cisco and its ecosystem. Barker's >> back to San Diego. Everybody watching the Cube, the leader and live tech coverage. My name is Dave Volante, and I'm with my co host, Steuben. Amanda, this is Day two for Sisqo. Live 2019. We're in the definite. So still. I was walking around earlier in the last interview, and I think I saw Ron Burgundy out there. Stay classy Sleeping Gow is here. He's the founder and CEO of Met Net Brain Technology's just outside of Boston. Thanks very much for coming on the Q. Thank you there. So you're very welcome. So I want to ask you, I always ask Founders passion for starting companies. Why did you start? >> Well, maybe tired of doing things, Emmanuel. Well, that's alongside the other side of Yes, I used Teo took exam called a C C. I a lot of folks doing here. I failed on my first try. There was a big blow to my eagle, so I decided that we're gonna create a softer help them the past. This is actually the genesis of nettle. I met a friend help people three better doing their network management. >> That's a great story. So tell us more about that brain. What do you guys all about? >> Sure, we're the industry. First chasing time. Little confirmations after our mission is to Democrat ties. Merrick Automation. Every engineer, every task. They should've started with automation before human being touched. This task, >> you know, way go back. Let's say, 10 years ago people were afraid of automation. You know, they thought I was going to take away their jobs. They steal and they still are. We'll talk about that. You get this and I want to ask you about the blockers. They were fearful they wanted the touch thing. But the reality is people talk about digital transformation. And it's really all about how you use data, how your leverage data. And you can't be spending your time doing all this stuff that doesn't add value to your business. You have to automate that and move up to more valuable test. But so people are still afraid of automation. Why, what's the blocker there? >> They have the right reason to be afraid. Because so many automation was created a once used exactly wass right. And then you have the cost ofthe tradition automation. You have the complexity to create in their dark automation. You guys realize that middle confirmation You cannot have little gotta measure only work on a portion of your little way. You have to walk on maturity if not all of your narrow right. So that's became very complex. Just like a You wanna a self driving car? 10 You can't go buy a Tesla a new car. You can drive on a song. But if you want to your Yoder Puta striving always song Richard feared it. That's a very complex Well, let's today, Netto. Condemnation had to deal with you. Had a deal with Marty Venna Technology Marty, years of technology. So people spent a lot of money return are very small. There's so they have a right to a fair afraid of them. But the challenges there is what's alternative >> way before you're there. So there, if I understand it, just playing back there, solving a very narrow problem, they do it once, maybe twice. Maybe a rudimentary example would be a script. Yeah, right, right. And then it breaks or it doesn't afford something else in the network changes, and it really doesn't affect that, right? >> Yeah. I mean, you know, I think back to money network engineers. It's like, Well, I'm sitting there, I've got all my keep knobs and I get everything done and they say, No, don't breathe on it because it's just the way I want it less. It can't be that doesn't scale. It doesn't respond to the business. I need to be able to, you know, respond fast what is needed. And things are changing in every environment. So it's something that I couldn't, as you know, a person or a team keep up with myself, and therefore I need to have more standardized components, and I need to have intelligence that can help me. >> Let's sit and let's >> s so we've laid out the generalized way that we've laid out the problem. What's what's the better approach? >> Well, give you looking out of the challenge today is you have to have Dave ups, which a lot of here they have not engineer know howto script and the mid off the engineer who know how little cooperates walk together. So there's a date, a part of it. There's a knowledge. A part of this too has to meet to create a narrow coordination and that Ned Ogata may have to be a scale. So the challenge traditional thoracotomy here, why is for short lie on if you're going down? Technical level is wise A terra, too many data and structure and the otherwise Our knowledge knowledge cannot be codified. So you have the knowledge sitting people's head, right, Eh Programa had to walk in with a narrow canyon near together. You make it a cost hire. You make it a very unskilled apple. So those are the challenge. So how fast Motor way have to do so neither brand for last 15 years You decide to look differently that we created some saying called operating system off total network and actually use this to manage over 1,000 of mental models technology. And he threw problem. You can't continually adding new savings into this problem. So the benefit of it is narrow. Canyon near anybody can create automation. They don't have to know how to writing a code. Right? And Deborah, who knows the code can also use this problem. All the people who are familiar with technology like and people they can integrate that never >> pray. Okay, so you have all this data I wish I could say is unstructured So he doesn't have any meaning. Data's plentiful insights aren't, uh And then you have this what I call tribal knowledge. Joe knows how to do it, but nobody else knows how to do it. So you're marrying those two. How are you doing that? Using machine intelligence and and iterating building models, can you get that's amore colors? Tow How you go about that? What's the secret sauce >> way? Took a hybrid approach. First call on you have to more than the entire network. With this we'll kind of operating system called on their own way have about 20 12,000 valuables modeling a device and that 12,000 valuable adults across your let's say 1,000 known there or there will be 12,000,000 valuables describing your medal. That's that's first. Zang on top of 12,000,000 valuables will be continually monitored. A slow aye aye, and the machine learning give something called a baseline data. But on top of it, the user, the human being will have the knowledge young what is considered normal what is considered abnormal. They can add their intelligence through something called excludable rumble on couple of this system, and their system now can be wrong at any time. Which talking about where somebody attacking you when that OK is un afford all you through a human being, all our task Now the automation can be wrong guessing time. So >> this the expert, the subject matter expert, the main expert that the person with the knowledge he or she can inject that neck knowledge into your system, and then it generates and improves overtime. That's right, >> and it always improve, and other people can open the hood. I can't continue improving. Tell it so the whole automation in the past, it was. Why is the writer wants only used once? Because it's a colossal? It's a script. You I you input and output just text. So it wasn't a designer with a company, has a motive behind it. So you do it, You beauty your model. You're writing a logical whizzing a same periods off, we decided. We think that's you. Cannot a scale that way. >> OK, so obviously you can stop Dave from inputting his lack of knowledge into the system with, you know, security control and access control. Yeah, but there must be a bell curve in terms of the quality of the knowledge that goes into the system. You know, Joe might be a you know, a superstar. And, you know, stew maybe doesn't know as much about it. No offense, too. Student. So good. So how do you sort of, you know, balance that out? Do you tryto reach an equilibrium or can you wait? Jos Knowledge more than Stu's knowledge. How does that work? >> So the idea that this automation platform has something called excludable Rambo like pseudo Rambo can sure and implacably improved by Sri source One is any near themselves, right? The otherwise by underlying engine. So way talk about a I and the machine learning we have is that we also have a loo engine way. Basically, adjusting that ourselves certainly is through Claverie Partner, for example, Sisko, who run many years of Qatar where they have a lot of no house. Let's attack that knowledge can be pushed to the user. We actually have a in our system that a partnership with Cisco attack South and those script can be wrong. slow. Never prayer without a using woman getting the benefit of without talking with attack. Getting the answer? >> Yes, I think you actually partially answered. The question I have is how do you make sure we don't automata bad process? Yeah. So And maybe talk a little bit about kind of the training process to your original. Why of the company is to make things easier. You know, What's the ramp up period for someone that gets in giving me a bit of a how many engineers you guys have >> worked with? The automatic Allied mission. Our mission statement of neda prayer is to Democrat ties. Network automation, you know, used to be network automation on ly the guru's guru to it. Right, Dave off. Send a satchel. And a young generation. My generation who used come, Ally, this is not us, right? This is the same, you know. But we believe nowadays, with the complicity of middle with a cloud, computing with a cybersecurity demand the alternative Genetic automation is just no longer viable. So way really put a lot of starting to it and say how we can put a network automation into everyone's hand. So the things we tell as three angle of it, while his other missions can be created by anyone, the second meaning they've ofthe net off. Anyone who know have knowledge on metal can create automation. Second piece of automation can lunched at any time. Somebody attacking you middle of the night. They don't tell you Automation can lunch to protect Theo, and they're always out. You don't have people the time of the charter. Automation can lunch the tax losses, so it's called a lunch. Any time certain want is can adapt to any work follow. You have trouble shooting. You have nettle changes. You have compliance, right? You have documentation workflow. The automation should be able to attack to any of this will clothe topping digression tomorrow. We have when service now. So there's a ticket. Human being shouldn't touches a ticket before automation has dies, she'll write. Is a human should come in and then use continually use automation. So >> So you talk about democratizing automation network automation. So it's so anybody who sees a manual process that's wasting time. I can sort of solve that problem is essentially what you're >> doing. That's what I did exactly what we >> know So is there, uh, is there a pattern emerging in terms of best practice in terms of how customers are adopting your technology? >> Yes. Now we see more animal customer creating This thing's almost like a club, the power user, and we haven't caught it. Normal user. They have knowledge in their heads. Pattern immunity is emergent. We saw. Is there now work proactively say, How can I put that knowledge into a set of excludable format so that I don't get escalate all the time, right? So that I can do the same and more meaningful to me that I be repeating the same scene 10 times a month? Right? And I should want it my way. Caught a shift to the left a little while doing level to the machine doing the Level one task level two. Level three are doing more meaningful sex. >> How different is what you're doing it net brain from what others are doing in the marketplace. What's the differentiation? How do you compete? >> Yeah, Little got 1,000,000 so far has being a piecemeal, I think, a fragment. It's things that has done typical in a sweeping cracker. Why is wholesale Hardaway approach you replace the hardware was esti N S P. Where's d? Let there's automation Capitol Building Fifth, I caught a Tesla approached by a Tesla, and you can drive and a self driving. The second approaches softer approach is as well. We are leading build a model of your partner or apply machine learning and statistics and was behind but also more importantly, open architecture. Allow a human being to put their intelligence into this. Let's second approach and insert approaches. Actually service little outsourcer take you, help you We're moving way or walk alone in the cloud because there's a paid automation there, right so way are focusing on the middle portion of it. And the landscaper is really where we have over 2,000 identifies customer and they're automating. This is not a just wall twice a week, but 1,000 times a day. We really excited that the automation in that escape scale is transforming how metal and is being managed and enable things like collaboration. But I used to be people from here. People from offshore couldn't walk together because knowledge, data and knowledge is hard to communicate with automation. We see collaboration is happening more collaboration happening. So we've >> been talking about automation in the network for my entire career. Feels like the promise has been there for decades. That site feels like over the last couple of years, we've really seen automation. Not just a networking, but we've been covering a lot like the robotic process automation. All the different pieces of it are seeing automation. Bring in, gives a little bit look forward. What? What do you predict is gonna happen with automation in I t over the next couple of years? A >> future that's great Way have a cloud computing. We have cyber security. We have the share of scale middle driving the network automation to the front and center as a solution. And my prediction in the next five years probably surrounded one izing automation gonna be ubiquitous. Gonna be everywhere. No human being should touch a ticket without automation through the first task. First right second way. Believe things called a collaborative nature of automation will be happy. The other was a local. Automation is following the packet from one narrow kennedy to the other entity. Example would be your manager service provider and the price they collaborated. Manager Nettle common little But when there's something wrong we don't know each part Which part? I have issues so automation define it by one entity Could it be wrong Across multiple So is provider like cloud provider also come Automation can be initiated by the Enterprise Client way also see the hado A vendor like Cisco and their customer has collaborated Automation happening So next five years will be very interesting The Manu away to manage and operate near Oca will be finally go away >> Last question Give us the business update You mentioned 2,000 customers You're hundreds of employees Any other business metrics you Khun, you can share with us Where do you want to take this company >> way really wanted behind every enterprise. Well, Misha is a Democrat. Eyes network automation way Looking at it in the next five years our business in a girl 10 times. >> Well, good luck. Thank you. Thanks very much for coming on the queue of a great story. Thank you. Thank you for the congratulations For all your success. Think Keep right! Everybody stew and I will be back. Lisa Martin as well as here with an X guest Live from Cisco Live 2019 in San Diego. You watching the cube right back

>> From Sand Hill Road in the heart of Silicon Valley, it's theCUBE. Presenting, the People First Network; insights from entrepreneurs and tech leaders. >> Hello everyone, we are here for CUBE conversation in San Francisco. I'm John Furrier with siliconANGLE media theCUBE. We are in San Francisco with John Zimmer, who is the co-founder of president of Lyft, the famous ride sharing company that's dominating the world and changing the game in transportation. We all use Lyft, we love it. John, great to see you here for this People First Network special conversation. Thanks for spending the time. >> Thanks for having me. >> I know you're super busy, you guys are growing, billions of dollars in raised capital. You guys are growing like a weed on a rocket ship. A lot of things happening. But, you know, it's interesting, you guys are not that old of a company and the growth has just been fantastic. So, as you continue to ride the wave here, there's a lot of lessons that you've learned. So, tell the story about how you guys got started. You and your co-founder have a great relationship, and this has been a part of the culture at Lyft. How did it all get started? >> Yeah, so I'll start with Logan, my co-founder. He grew up in L.A. surrounded by traffic and he hated that. And he wanted to find a better way to get around. So when he went to college, he went to UC Santa Barbara, he did not take his car. He rode the bus, he car pooled, he had friends with cars. And then he went to start a car sharing program before Zipcar was around on college campuses. He got the attention of the local transit board, he got elected as the youngest member ever on the transit board. And he fell in love with the promise of public transportation. Affortable, accessible transportation for everyone. But frustrated by the reality that it was dependent on tax money. So, he wanted to create a better solution and he started coding his own website, named Zimride, named after a trip he took to Zimbabwe, for long distance car pooling. My own journey was I was on the east coast. I did not know Logan, was in love with hospitality, making people happy through great service. So I went to Cornell Hotel School, I took a city planning course, and I saw that the most important hospitality experience we have in society today is the city itself, and yet unfortunately we've designed cities for cars, and not people. What I mean by that is most of our cities are paved over. There's roads, there's parking lots, and if you design a city instead for people, pedestrians, safe places to bike, and don't need people to own cars in order to get around, then you could have a much more durable place to live. So we came together in 2007 to work on Zimride. And then a few years later, in 2012, we launched Lyft. >> So this is a transportation problem, ultimately, to solve. But the itch you guys were scratching was just the need for transportation. You saw it as more of a convenience thing as well. The hospitality thing kind of comes together, boom, Lyft is born. Then you guys enter the market, and the transportation problems are still there, and then you have the growth of mobile, so sort of a perfect storm coming together. What is the biggest challenge and exciting things that you guys see in this transportation scheme? Is it it's antiquated and inadequate? Is it a technical thing? What are some of the challenges that you guys are exited about? >> Well I think the biggest thing is this fact that the American dream has almost become, or been, historically, synonymous with a car in every garage. And that everyone should own a car. And that was your sense of freedom. But the reality is not quite that. American families spend more on their car than they do on food. It's the second highest household expense. A new car costs, on average, an American family $9,000 per year to own and operate. And so, there's a lot of ingrained behaviors, and designs of cities so that it does cater to needing to own a car. So we're trying to break that down piece by piece and making progress. But we're about 1% of the way there. >> Yeah, it's a cultural change too. But I also want to get to that in a second about culture, both with Lyft and and into your audience, which is the cities and the environments you guys deploy in, but also the users. But the founding and the story of you guys growing is interesting, because startups are all about execution and culture. You've had an interesting relationship with your co-founder. And this is the secret sauce of startups. It's documented somewhat, but it's a people first mindset, where you get a good team early on, you kind of feel your way through those first couple of years. Talk about that relationship with the founders, because this is something that's important. It's not just a number on a cap table, it's a little more than that. Talk about the relationship. >> I mean Logan has become my best friend. We actually carpool to work, still. Almost every day. And we weren't friends prior. So, a lot of times you have friends that start a company together. We were two people that were incredibly passionate about our mission, which is to improve people's lives with the best transportation. So we shared this passion, we share this vision, and we're two completely different people. So our approaches were different. His approach is often product-oriented and my approach is often hospitality-oriented. And the fact is, for transportation, you need to combine those two pieces. So it worked out really well for us. So I think having a co-founder is a massive advantage, because you can have two different people and then you want to find the thing in common, which is the thing you're fighting for, within our case the mission. >> How did you guys work together to play off each other, to get that innovation spark. Because when you get into the ride sharing, certainly it's a brand new category, huge demand, and there's a lot of build up, a lot of things you've got to stand up for the business. At the same time, you also want to differentiate and be innovative. You're kind of a first mover, with Uber, these guys are out there too. You guys are building a business, and growing really fast. So, how do you guys nurture that innovation? How do you put a twist on it? How do you keep it alive, versus the blocking and tackling and standing up the basic business activities? >> Well I think because we, you know at the beginning, we created a new category. We're the first to do peer-to-peer ride sharing. Uber existed, but they were doing cabs and limos. And we said, that may work for 1% of the population, but we wanted to use this under-utilized asset, which is the car that's sitting in everyone's parking spot or garage. And so that DNA of innovation, that DNA of being the underdog, the challenger, has always been true to us, but also the people that we we've brought on and hired. People and the hiring is something that, over the last ten years, is probably the one activity we've spent the most time on. Because that's the best way to keep those values, keep that focus on vision. >> And certainly these days, people want to work for a company that has a purpose. And that has a mission. When you hear the word people first, what pops into your head? >> Obvious. It just feels, in everything I've tried to do as a person, whether that was studying- like hospitality is the business of people first. How do you give people a great service and a great experience. And so I think often times, when people think about technology, they think about the what, which is I made this phone, I made this device, or I made this app, when way more important to that, is the why. Why did you do that? Who are you doing that for? And so we try to start everything we do with the person we're trying to- you know our mission is to improve people's lives with the world's best transportation. It's not to build the worlds best transportation. >> So that's your why. I was talking about how you guys scaled to a world-class organization. You guys have build a world-class team, certainly got great investors, Floodgate, Mayfield and then the rest is all on the web. You guys raised a lot of money, but you can't just throw money at the problem, you have to have that foundation and culture. How do you scale up a world-class organization? What's the learnings, can you share your perspective? >> Yeah, so first having clarity on the mission, which we've talked about, but also having clarity on core values. So we have three core values that have been true for a very long time. So, one is to be yourself. It also sounds very simple, like people first, but a lot of corporate environments have made spaces where people aren't comfortable being themselves, where there's group think, where people don't feel comfortable bringing their full self, and therefore their most productive self, to work. So be yourself, respecting the diversity of our team, has been critical from the beginning. The second is uplift others. So we use that both internally and externally. Life's short, we spend a lot of our time working. We might as well enjoy what we're doing. Again, all these values are both the right thing to do, make for a better place to work, and lead to better productivity and business success. And the last is make it happen. That's pretty self explanatory. Be an owner, go out and take action and get stuff done. And so with those three simple core values, looking for amazing, talented people, who also care about our mision. People are mission oriented, people want to care about what they're working on. And if you're fortunate to have a choice where you work, what we've seen is that people will follow a mission. >> Yeah, it's totally true. I can see that in culture here. And I've also seen you guys got kind of a cool factor too in the way I've seen some of your activations out in the marketplace. You kind of got a cool factor going on as well. But I think what's interesting, and I want to get your reaction to this, I think this points to some of the cultural discussions, just recently during the elections I saw you guys really wanted to make an effort to help people to get to the polls. Here in California, the disasters of wildfires are really tragic. You guys are doing some work there. This speaks to the culture. You say, hey, Lyft's available, and you're helping people out. Talk about what that means to you and the team here, and the culture at Lyft. >> Yeah, at the end of the day, when we look back on the work we've done, we want to make sure it has improved people's lives. And when we see opportunities to take our ability to provide transportation that will benefit people in a meaningful way, whether it was, you know, in the last- not this most recent election, but in the last election, in the last presidential election, I believe it was about 15 million people listed transportation as a reason why they couldn't vote. >> They've got a way, hey! >> Yeah, let's solve that. We can. When you think about unfortunate natural disasters, if we can help people get to safety, or help a horrible situation, then we should do that. I think that's just a moral and civic responsibility. It allows us to be aware and proud of the solution we've created, and I think it keeps our team extremely motivated. >> And I think it's one of those intangibles in terms of the mission, changing the transportation industry sounds academic and corporate. But here, you're changing lives by one, the voting, and two, saving lives potentially, with the disasters. So, great job. Okay, so what I thought, let's talk about the growth okay. I had a great conversation with the CEO of Amazon Web Services, Andy Jassy, a few years ago, talking about the early days of AWS. You have to be misunderstood for a while, and get through that early on, if you're going to be successful, because most big things are misunderstood. He also made a point about the key learnings during the early days. When you're trying to do stuff, things going so fast, that there's learnings that come out of it. And if you can persevere through it, that sets the culture. Share a story around something that you guys have been through at Lyft, where you persevered through it. It might have been some scar tissue. It might have been you got a little bloody, a little dirty. But you got through it and you learned from it. You applied it, and changed the culture. >> Well I think there's two main ones that come to mind. So, you know, people may think Lyft, in the last five years, has really come out of nowhere, but Logan and I have been working together for eleven years. And the first idea was Zimride, was long distance car pooling. And we built a team of 20, 25 people, we got this to break even. That's actually the company that Mayfield invested in, or the product. But it didn't have product-market fit in a massive way. It wasn't a massive success. And then so we tried to reinvent ourselves five years later, and that was Lyft. And at this point, that was a crazy idea. To have people riding in what everyone thought of as a stranger's other vehicle. And so that was a reinvention, an acknowledgement that the first solution we created did not fully work in the way that we wanted it to. The second was about four to five years ago, we wake up and Uber raises three billion dollars. And we have a hundred million dollars in the bank and about five months left. And everyone said Lyft is done. There is no way that they can survive this, it's a winner take all market, Uber is way more aggressive. And we proved that wrong. By focusing and staying true to our values and to our mission. By having an incredible team. An amazing community of drivers providing great service to our customers, we've gone from the early days of single digit market share to nearly 40% market share, amidst that pressure and belief that we couldn't survive. >> Game's on. Either rally or fold, right? It's a cultural test really. What's your mindset around the capital market. I know, I've done a lot of startups myself, I know a lot of fellow entrepreneurs, and when you raise that money, and you guys had that product-market fit, post the first venture, where you got through that. Then you get lightning in a bottle, whoa, let's double down on this. I want to go back to the early stages when you were thinking about investment. Was there any cautions around VC, cause a lot of startups have that conversation. What was the narrative for you guys at that time? Hey, let's go to Mayfield, should we raise money, should we bootstrap and make it cashflow positive. What was your mindset as founders, at that time when you were doing the venture round? >> Well, I think we knew that we needed a certain amount of capital to get to a scale that was interesting to us. So, not every business needs as much capital. But for they type of transportation infrastructure that we wanted to change, the type of scale we wanted to get to, we knew that it was important to raise VC money. So, money that was substantial and also understood the level of risk we were taking. So, at that point, we were fortunate to have a firm like Mayfield believe in us. And what we were looking for was people that care about who we were, cared about our mission, and understood what it was like to be an entrepreneur and an operator, not just an investor. >> What's the rallying call now for the team as you guys look out a6nd continue to have this growth? Obviously you guys cleared the runway in a big way. And there's still a lot more work to do, the market's still early. You know, you think about transportation and the regulatory environment and how technology and policy are coming together. A lot of forces out there, you got some tailwinds and some headwinds. How do you guys look at the future? What's the next mountain you're going to climb? >> Yeah, so, we've now done a billion rides. Since inception. And we're focused on providing a full alternative to car ownership. So I don't think people grasp that. The idea is not to provide an alternative to a taxi, or a late ride home. It's to completely replace car ownership. And so, we are 1% of the way there. Those that are joining our team and our mission get to be there for the 99% rest of that. And at the same time, as we go towards the next billion rides, we want to stay focused and rally around the individual stories behind each ride. So, every single week, we have over ten million rides happening, where two people are coming together. They could be two people that helped each other have a better day. They could be a Democrat and a Republican sitting next to each other and finding common ground. And so to us, yes we have big milestones and big opportunities ahead, but also care about each ride that's happening on the platform. >> And the other thing I love about your background in hospitality is you're bringing an experience as well. Not just math, in terms of the bottom line numbers. There's a lot of people doing the math and saying hmm, should I have a car? But I got to ask you a question. So what you learned at school, Cornell great school, great Lacrosse team, great Ivy League school, they teach you the textbook, the old hospitality. This is a new era we're living in. What is happening in your world that they don't teach you in the textbook from a hospitality standpoint? As you look at the experience of ride sharing and transportation for users, what is different, what's the twist in hospitality that has not yet been written in the textbooks, that you're exploring or thinking about? >> I actually think the old basics are more important than ever. There's all this flashy technology and opportunity to do it at larger scale, and to use data, that's new. To use data in ways that help inform providing great service. But, the basics of human interaction, communication, and treating people with respect, can get you pretty far. >> And happy customers, right? Final question, I know you got to go, I appreciate your time. Share a story or something about Lyft that people might not know about. First of all, everyone knows about your brass, you guys are doing a great job out there with the market share. But tell a story about Lyft, or something a datapoint, anecdotal piece of information, that they might not know about, that they should know about. Share an inside story or factoid about Lyft, that people should know about that they might not know about. >> I think it's really deep, deep in the mission. That people may not understand what gets us out of bed in the morning. You know, every time I have a new hire orientation, I try to talk to every new hire that comes to the company and really emphasize the importance of every driver, every passenger. And I read a story about a driver and passenger that really helped each other. And don't really want to provide the details because they're private to those individuals, but it's incredibly powerful to hear about. And so, I would just, we may look like a big company or brand at this point, but we care deeply about each individual that's on the platform. >> The fabric of society is being changed by you guys, really appreciate the work you've done, and congratulations, and a lot more work to do. Thanks for the conversation. >> Yeah, thanks. >> I'm John Furrier, here in San Francisco at Lyft's headquarters, talking with John Zimmer, who's the co-founder and President of Lyft, sharing his stories and successes, and a lot more work to do here at the People First conversations. With theCUBE, and Mayfield, I'm John Furrier, thanks for watching. (outro music)

Silicon Valley, it's theCUBE covering People First Network. Brought to you by Mayfield. >> Hello, I'm John Furrier here in Palo Alto for an exclusive conversation, CUBE conversation, part of the People First Network with theCUBE and Mayfield fund. I'm here with John Chambers at his house in Palo Alto. John Chambers is the former CEO/Chairman of Cisco Systems, now running J2C, JC2 Ventures. Great to see you, thanks for spending time! >> It's a pleasure to be together again. >> I'm here for two reasons. One, I wanted a conversation about People First and technology waves, but also, I want to talk about your new book, which is exciting, called Connecting the Dots. And it's not your standard business book, where, you know, hey, rah-rah, you know, like a media post these days on the internet; it's some personal stories weaved in with the lessons you've learned through the interactions you've had with many people over the years, so exciting book and I'm looking forward to talking about that. >> Thank you! >> Again, John Chambers, legend, Cisco, 1991 when you joined the company from Wang before that. 400 employees, one product, 70 million in revenue. And when you retired in 2015, not so much retired, 'cos you've got some--. >> I'm working on my next chapter! >> You've got your next chapter (laughs)! 180 acquisitions, 447 billion in revenue, you made 10,000 people millionaires, you created a lot of value, probably one of the biggest inflection points in computer history, the evolution of inter-networking and tying systems together, it was probably one of the biggest waves somewhat before the wave we're on now. So an amazing journey, now you're running JC2 Ventures and investing in game-changing start-ups. So you're not retired? >> No. It was only my next chapter. I made my decision almost 10 years before I left Cisco first, to make for a very smooth transition because it's my family, and out of the 75,000 people, I hired all but 23 of them! And in terms of what I wanted to do next, I really wanted to both give back, create more jobs, get our start-up engine going again in this country, and it's currently broken, and I want to do that on a global basis, in places like France and India as well. So I'm on to my next chapter, but the fun part in this chapter is that I do the things that I love. >> And you've got a great team behind you, but also, you have a great personal network. And I want to get into that, of your personal stories as well as your social network in business and in the community; but one of the things I want to get up front, because I think this is important for this conversation is, you've been very strong. I've seen you present many times over the years, going way back into the 90's. You're eloquent, you're people-oriented, but you have a knack for finding the waves, seeing transitions, you've been through many waves. >> Yes I have, good and bad. >> Good and bad. But one of the big ones, how do you spot those transitions? And what wave are we in now? I mean, talk about the wave that's happening now, it's unprecedented on many levels, but, different, but it's still a wave. >> It is, and outgoing market transitions and often combined with either economic changes or business model changes with technology. And part of the reason that I've been fortunate to be able to identify many of them is I listen to customers very carefully, but also, you're often a product of your prior experiences. Having experienced West Virginia, one of the top states in the US in terms of the chemical industry, uh, during the 40's and 50's and 60's when I was growing up there, and literally more millionaires in West Virginia than there were in the entire Great Britain. We were on top of the world in the chemical industry, and the coal industry, and yet, because we missed transitions, and we should've seen them coming, the state fell a long way, so now we're trying to correct that with some of the start-up activity we'll talk about later. As you see this, and then I went to Boston, 128, we were talking earlier, Wang Laboratories, the mini-computer era, but I was in IBM first out of the central part of the nation, so I watched IBM and Mainframes, and then I watched them miss on going to the mini-computer, and then miss in terms of the internet. So I was able to see the transitions that occurred in Boston, Route 128, where we were the Silicon Valley of the world, and we knew it, and this unusual area out in California called Silicon Valley, we paid almost no attention to, and we didn't realize we failed to make a transition from the mini-computer era to the pc and the internet era. Then I joined Cisco, and saw the internet era. So part of it is, you're a product of your experiences, and know the tremendous pain that occurs, because Boston 128 is nowhere near what it used to be, so there's no entitlement in this new world out of the thousand high-tech companies that I was associated with, including four or five giants in mini-computers, none of them are really in existence today, so it shows you, if you don't identify the transitions, number one, you're going to have an opportunity to benefit by them, but number two, you sure have an opportunity to get hurt by them. >> And you know, these waves also create a lot of wealth and value; not just personal wealth, but community wealth, and Cisco in particular had a good thing going for them, you know, TCP-IP was a defact-- not even a standard, it was a defacto standard at that time, IBM and these kinds of digital equipment corporations dominated the network protocol. Even today, people are still trying to take out Cisco competitively, and they can't because they connected the world. Now the world's connected with digital, it's connected with mobile, so we're kind of seeing this connected wave globally. How do you think about that, now that you've seen the movie at the plumbing levels at Cisco, you now have been traveling the world, we're all connected. >> We are. And it's important to understand that I'm completely arms-length with Cisco, it's their company to run now, and I'm excited about their future. But I'm focused on the next chapter in my life, and while I think about the people at Cisco everyday, I'm into the start-up world now, so how do I think about it now? I think most of the innovation over the next decade will come from start-ups. The majority of the top engineering students, for example, at a Stanford or an MIT or a Polytechnique in France, which is the top engineering school, I think, in Europe, or at the ITs in India, they are all thinking about going to start-ups, which means this is where innovations going to come from. And if you think about a digital world going from the time you and I, we almost recruited you to Cisco, and then we finally did; there's only a thousand devices connected then when Cisco was founded. Today there are about 20 billion devices connected to the internet; in the future, it's going to be 500 billion in a decade, and so this concept of digitalization combined with artificial intelligence, all of a sudden we'll get the right information at the right time to the right person or machine to make the right decision, sounds complex, and it is. And it's ability to do that, I think start-ups are well-positioned to play a key role in, especially in innovation. So while the first stage of the internet, and before that were all dominated by the very large companies, I think you're going to see, in this next phase of digitalization, you're going to see a number of start-ups really emerge, in terms of the innovation leaders, and that's what I'm trying to do with my 16 investments I've made, but also coaching probably another 50 uh, start-ups around the world on a regular basis. >> And the impact of outside Silicon Valley, globally, how do you see that ecosystem developing with the entrepreneurship models that are now globally connected in with these connection points like Silicon Valley? >> It will partially in parallel, partially, it's a new phenomenon. I sold the movie of Boston 128, as I said earlier, and on top of the world, and there is no entitlement. The same thing's true with Cisco, um, sorry, of Silicon Valley today; there's no entitlement for the future, and just because we've led up until this point in time, doesn't mean we will in 10 years, so you can't take anything for granted. What you are seeing, since almost all job creation will be from start-ups, and small companies getting bigger, the large companies in total will probably not add any head count over this next decade because of artificial intelligence and digitization, and so you're now going to see job growth coming from those smaller companies, if these small companies don't get a forum to all 50 states, if they don't get a chance to grow their head count there, and the economic benefits of that, then we're going to leave whole states behind. So I think it's very important that we look at the next wave of innovation, I think there's a very good probability that it will be more inclusive, both by geography, by gender, and all diversity measures, and I'm optimistic about the future, but there are no guarantees, and we'll see how it plays out. >> Let's talk about your next chapter. I was going to wait, but I want to jump while we're on the topic. JC2 is a global start-up, game-changing start-up focus that you have. What is the thesis? What are you looking for, and talk about your mission? >> Well, our mission is very simple. I had a chance to change the world one time with Cisco, and many people, when I said Cisco's going to change the way the world works, lives, learns, and plays by enabling the internet, everybody said nice marketing, but you're a router company. And yet, I think most people would agree, probably more than any other company, we had the leadership role in changing the internet and the direction going on, and now, a chance to do it again, because I think the next wave of innovation will come from the start-ups, and it doesn't come easy. They need coaches, they need strategic partners, they need mentors as much as they need the venture capitalists, so I would think of as this focusing on disruptive start-ups that get very excited in these new areas of technology, ranging from physical and virtual worlds coming together, to artificial intelligence and automation everywhere, to the major capabilities on cyber security across that to the internet of things, so we're trying to say, how do we help these companies grow in skill? But if I was just after financial returns, I'd stay right here in the Valley. I can channel anybody, VC's here that I trust and they trust me, and it would be a better financial return. But I'm after, how do you do this across a number of states, already in seven states, and how do you do it in France and India as role models? >> It's got a lot of purpose. It's not just a financial purpose. I mean, entrepreneurs want to make money, too, but you've made some good money over the years, but this is a mission for you, this is a purpose. >> It is, but you referred to it in your opening comments. When we were at Cisco, I've always believed that the most successful owe an obligation to give back, and we did. We won almost every corporate social responsibility award there was. We won it from the Democrats and the Republicans, from Condie Rice and George Bush and from Hillary Clinton and President Obama. We also, as you said, made 10,000 Cisco employees millionaires just in the first decade. And we tried to give back to society with training programs like Network Academies and trained seven million students. And I think it's very important for the next generation of leaders here in the Valley to be good at giving back. And it's something that I think they owe an obligation to do, and I think we're in danger now of not doing it as well as we should, and for my start-ups, I try to pick young CEOs that understand, they want to make a financial return, and they want to get a great product out of this, but they also want to be fair and giving back to society and make it a win-win, if you will. >> And I think that's key. Mission-driven companies are attracting the best talent, too, these days, because people are more cognizant of that. I want to get into some of your personal stories. You mentioned giving back. And reading your book, your parents have had a big role in your life--. >> Yes, they have. >> And being in West Virginia has had a big role in your life. You mentioned it having a prosperity environment, and then missing that transition. Talk about the story of West Virginia and the role your parents played, because, they were doctors, so they were in the medical field. The combination of those two things, the culture where you were brought up, and your family impacted your career. >> I'm very proud of being from West Virginia, and very proud of the people in West Virginia, and you see it as you travel around the world. All of us who, whether we're in West Virginia, or came out of it, care about the state a great deal. The people are just plain good people, and I think they care about treating people with respect. If I were ever run off a road at night in the middle of the night, I'd want to be in West Virginia, (both laugh) when I go up to knock on that door. And I think it carries through. And also, the image of our state is one that people tend to identify in terms of a area that you like the people. Now what I'm trying to do in West Virginia, and what we just announced since last week, was to take the same model we did on doing acquisitions, 180 of them, and say here's the playbook, the innovation playbook for doing acquisitions better than anyone else, and take the model that we did on country digitization, which we did in Israel and France and India with the very top leaders, with Netanyahu and Shimon Peres in Israel, with Macron in France and with Modi in India, and drove it through, and then do the same thing in terms of how we take the tremendous prosperity and growth that you see in Silicon Valley, and make it more uniform across the country, especially as traditional business won't be adding head count. And while I'd like to tell you the chemical industry will come back to West Virginia and mining industry will come back in terms of job creation, they probably won't, a lot of that will be automated in the future. And so it is the ability to get a generation of start-ups, and do it in a unique way! And the hub of this has to be the university. They have to set the pace. Gordon Gee, the President there, gets this. He's created a start-up mentality across the university. The Dean of the business school, Javier Reyes is going across all of the university, in terms of how you do start-ups together with business school, with engineering, with computer science, with med school, et cetera. And then how do you attract students who will want to really be a part of this, how do you bring in venture capital, how do you get the Governor and the President and the Senate and the Speaker of the House on board? How do you get our two national senators, Shelly Moore Capito and also Joe Manchin, a Democrat and a Republican working together on common goals? And then how do you say here's what's possible, write the press release, be the model for how a country, or a state, comes from behind and that at one time, then a slow faller, how do we leap frog? And before you say it can't be done, that was exactly what people said first about India, when I said India would be the strongest growing economy in the world, and it is today, probably going to grow another seven to 10%. That means you double the per capita of everyone in India, done right, every seven to 10 years. And France being the innovation engine in Europe to place your new business, you and I would have said John, no way, just five years ago, yet it has become the start-up engine for Europe. >> It's interesting, you mentioned playbook, and I always see people try to replicate Silicon Valley. I moved out here from the East Coast in 1999, and it's almost magical here, it's hard to replicate, but you can reproduce some things. One of the common threads, though, is education. The role of education in the ecosystem of these new environments seems to be a key ingredient. Your thoughts about how education's going to play a role in these ecosystems, because education and grit, and entrepreneurial zeal, are kind of the magic formula. >> Well they are in many ways. It's about leadership, it's about the education foundation, it's about getting the best and brightest into your companies, and then having the ability to dream, and role models you can learn from. We were talking about Hewlett-Packard earlier, a great role model of a company that did the original start-up and Lou Platt, who was the President of HP when I came out here, I called him up and said, you don't know me, Lou, I'm with a company you've probably never heard of, and we have 400 people, but I don't know the Valley, can you teach me? And he did, and he met with me every quarter for three years, and then when I said what can I do to repay you back, because at that time, Cisco was on a roll, he said John, do it for the next generation. And so, that's what I'm trying to do, in terms of, you've got to have role models that you can learn from and can help you through this. The education's a huge part. At the core of almost all great start-up engines is a really world-class university. Not just with really smart students, but also with an entrepreneur skill and the ability to really create start-ups. John Hennessey, Stanford did an amazing thing over the last 17 years on how to create that here at Stanford, the best in the world, probably 40% of the companies, when I was with Cisco, we bought were direct or indirect outgrowth of Stanford. Draw a parallel. Mercury just across the way, and this isn't a Stanford/CAL issue, (both laugh) equally great students, very good focus on interdisciplinary activities, but I didn't buy a single company out of there. You did not see the start-ups grow with anywhere near the speed, and that was four times the number of students. This goes back to the educational institution, it has to have a focus on start-ups, it has to say how they drive it through, this is what MIT did in Boston, and then lost it when 128 lost it's opportunity, and this is what we're trying to do at West Virginia. Make a start-up engine where you've got a President, Gordon Gee, who really wants to drive this through, bring the political leaders in the state, and bring the Mountaineers, the global Mountaineers to bare, and then bring financial resources, and then do it differently. So to your point, people try to mimic Silicon Valley, but they do it in silos. What made Silicon Valley go was an ecosystem, an education system, a environment for risk-taking, role models that you could steal people from--. >> And unwritten rules, too. They had these unwritten rules like pay it forward, your experience with Lou Platt, Steve Jobs talks about his relationship with David Packard, and this goes on and on and on. This is an important part. Because I want to just--. >> Debt for good is a big, big issue. Last comment on education, it's important for this country to know, our K through 12 system is broken. We're non-competitive. People talk about STEM, and that's important, but if I were only educating people in three things, entrepreneurship, how to use technology, and artificial intelligence; I would build that into the curriculum where we lose a lot of our diversity, especially among females in the third, fourth, fifth grade, so you haveta really, I think, get people excited about this at a much earlier age. If we can become an innovation engine again, in this country, we are not today. We're not number one in innovation, we're number 11! Imagine that for America? >> I totally agree with ya! And I don't want to rant and waste a lot of time, but my rants are all on Facebook and Twitter. (both laugh) Education's a problem. It's like linear, it's like a slow linear train wreck, in my opinion, but now you have that skills gaps, you mentioned AI. So AI and community are two hot trends right now. I'm going to stay with community for a minute. You mentioned paying it forward. Open source software, these new forms of operational scale, cloud computing, open source software, that all have this ethos of pay it forward; community. And now, community is more important than ever. Not just from the tech world, but you're talking about in West Virginia, now on a global scale. How does the tech industry, how can the tech industry, in your opinion, nurture community at local, regional, global scale? >> This is a tough one John, and I'd probably answer it more carefully if I was still involved directly with Cisco. But the fun thing is, now I represent myself. >> In your own opinion, not Cisco. There's a cultural thing. This is, Silicon Valley has magic here, and community is part of it. >> Yes, well it's more basic than that. I think, basically, we were known for two decades, not just Cisco, but all of the Valley as tech for good, and we gave back to the communities, and we paid it forward all the time, and I use the example of Cisco winning the awards, but so do many of our peers. We're going to Palestine and helping to rebuild Palestine in terms of creating jobs, et cetera. We went in with the Intels of the world, and the Oracles and the other players and HP together, even though at times we might compete. I think today, it's not a given. I think there is a tug of war going on here, in terms of what is the underlying purpose of the Valley. Is it primarily to have major economic benefits, and a little bit of arm's length from the average citizen from government, or is it do well financially, but also do very well in giving back and making it inclusive. That tug of war is not a given. When you travel throughout the US, today, or around the world, there are almost as many people that view tech for bad as they do tech for good, so I think it's going to be interesting to watch how this plays out. And I do think there are almost competing forces here in the Valley about which way should that go and why. The good news is, I think we'll eventually get it right. The bad news is, it's 50/50 right now. >> Let's talk about the skill gap. A lot of leaders in companies right now are looking at a work force that needs to be leveled up, and as new jobs are coming online that haven't been trained for, these openings they don't have skills for because they haven't been taught. AI is one example, IOT you mentioned a few of those. How do great leaders, proactively and reactively, too, get the skills gaps closed? What strategies can you do, what's the playbook there? >> Well two separate issues. How do they get it closed, in terms of their employees, and second issue, how do we train dramatically better than we've done before? Let's go to the first one. In terms of the companies, I think that your ability to track the millennials, the young people, is based upon your vision of doing more than quote just making a profit, and you want to be an exciting place to work with a great culture, and part of that culture should be giving back. Having said that, however, the majority of the young people today, and I'm talking about the tops out of the key engineering schools, et cetera, they want to go to start-ups. So what you're going to see is, how well established companies work with start-ups, in a unique partnership, is going to be one of the textbook opportunities for the future, because most companies, just like they didn't know how to acquire tech companies and most of all tech acquisitions failed, even through today. We wrote the textbook on how to do it differently. I think how these companies work with start-ups and how they create a strategic relationship with a company they know has at least a 50/50 probability of going out of business. And how do you create that working relationship so that you can tap into these young innovative ideas and partnerships, and so, what you see with the Spark Cognition, 200 people out of Texas, brilliant, brilliant CEO there in terms of what he is focused on, partnering with Boeing in that 50/50 joint venture, 50/50 joint venture to do the next FAA architecture for unmanned aircraft in this country. So you're going to see these companies relate to these start-ups in ways they haven't done before. >> Partnership and collaboration and acquisitions are still rampant on the horizon, certainly as a success for you. Recently in the tech industry we're seeing big acquisitions, Dell, EMC, IBM bought Red Hat, and there's some software ones out there. One was just going public and got bought, just recently, by SAP, how do you do the acqui-- you've done 180 of them? How do you do them successfully without losing the innovation and losing the people before they invest and leave; and this is a key dynamic, how do companies maintain innovation in an era of collaboration, partnerships, and enmity? >> I had that discussion this morning at Techonomy with David Kirkpatrick, and David said how do you do this. And then as I walked out of the room, I had a chance to talk with other people and one of them from one of the very largest technology companies said, John, we've watched you do this again and again; we assumed that when we acquired a company, we'd get them to adjust to our culture and it almost never worked, and we lost the people at a tremendously fast pace, especially after their lock-in of 18 to 24 months came up. We did the reverse. What we did was develop a replicatible innovation playbook, and I talk about it in that book, but we did this for almost everything we did at Cisco, and I would've originally called that, bureaucracy, John. (both laugh) I would've said that's what slow companies do. And actually, if done right, allows you to move with tremendous speed and agility, and so we'd outline what we'd look for in terms of strategy and vision; if our cultures weren't the same, we didn't acquire them. And if we couldn't keep the people, to generate the next generation of product, that was a bad financial decision for us, as well. So our attrition rate averaged probably about 5% or over while I was at Cisco for 20 years. Our voluntary attrition rate of our acquired companies, which normally runs 20% in these companies, we had about four. So we kept the people, we got the next generation product out, and we went in with that attitude in terms of you're acquiring to be able to keep the people and make them a part of your family and culture. And I realize that that might sound corny today, but I disagree. I think to attract people, to get them to stay at your company, it is like a family, it is like how you succeed and occasionally lose together, and how you build that family attitude under every employee, spouse, or their children that was life-threatening, and we were there for them in the ways that others were not. So you're there when your employees have a crisis, or your customer does, and that's how you form trust in relationships. >> And here's the question, what does People First mean to you? >> Well people first is our customer first. It means your action and everything you do puts your customers and your people first, that's what we did at Cisco. Any customer you would talk to, almost every customer I've ever met in my life would do business with us again, or with me again, because your currency in today's world is trust, track record, and relationships, and we built that very deep. Same thing with the employees. I still get many, many notes from people we helped 10 or 15 years ago; here's the picture of my child that you all helped make a difference in, Cisco and John, and you were there for us when we needed you most. And then in customers. It surprises you, when you help them through a crisis, they remember that more than when you helped them be successful, and they're there for you. >> Talk about failure and successes. You talk about this in the book. This is part of entrepreneurship, you can't succeed without failures. Handling failures is just as important as handling successes, your thoughts on people should think about that from a mindset standpoint? >> Well, you know, what's fun is those of you who are parents, or who will be parents in the future, when your child scores a goal in soccer or makes a good grade on a test, you're proud for them, but that isn't what worries you. What worries you is when they have their inevitable setbacks, everybody has that in life. How do you learn to deal with them? How do you understand how much were self-inflicted and how much of it was done by other causes, and how they navigate through that determines who they are. Point back to the West Virginia roots, I'm dyslexic, which means that I read backwards. Some people in early grade school thought I might not even graduate from high school much less go to college. My parents were doctors, they got it, but how I handled that was key. And while I write in the book about our successes, I spend as much time on when disaster strikes, how you handle that determines who you are in the future. Jack Welch told me in the 90's, he said John, you have a very good company, and I said Jack, you're good at teaching me something there, we're about to become the most valuable company in the world, we've won all of the leadership awards and everything else, what does it take to have a great company? He said a near-death experience. At the time I didn't understand it. At the end of 2001 after the dot com bubble, he called me up, he said, you now have a great company, I said Jack, it doesn't feel like it. Our stock price is down dramatically, people are questioning can I even run the company now, many of the people who were so positive turned very tough and--. >> How did you handle that? How did you personally handle that, 'cos--. >> It's a part of leadership. It's easy to be a leader when everything goes well, it's how you handle when things are tough, and leadership is lonely, you're by yourself. No matter how many friends you have around you, it's about leadership, and so you'd lead it through it. So 2001, took a real hard look, we made the mistake of focusing, me, on the numbers, and my numbers in the first week of December were growing at 70% year over year. We'd never had anything negative to speak of, much less below even 30% growth, and by the middle of January, we were -30%. And so you have to be realistic, how much was self-inflicted, how much the market, I felt the majority of it was market-inflicted, I said at the time it's a hundred year flood. I said to the employees, here's how we're going to go forward, we need to bring our head count back in line to a new reality, and we did it in 51 days. And then you paint the picture from the very beginning of what you look like as you recover and in the future and why your employees want to stay here, your customers stay with you and your shareholders. It wiped out most of our competitors. Jack Welch said, John, this is probably your best leadership year ever, and I said Jack, you're the only one that's going to say that. He said probably, and he has been. >> And you've got the scar tissue to prove it. And I love this story. >> But you're a product of your scars. And do you learn how to deal with them? >> Yeah, and how you-- and be proud of them, it's what, who you are. >> I don't know if proud's the right word. >> Well, badge of honor. (both laugh) >> Red badge of honor, they're painful! >> Just don't do it again twice, right? >> We still make the same mistake twice, but at the same time when I teach all these start-ups, I expect you to make mistakes. If you don't make mistakes, you're not taking enough risk. And while people might've, might say John, one of your criticisms is that you spread yourself a little bit too thin in the company at times, and you were too aggressive. After thinking about it, I respectfully disagree. If I had to do it over, I'd be even bolder, and more aggressive, and take more risks, and I would dream bigger dreams. With these start-ups, that's what I'm teaching them, that's what I'm doing myself. >> And you know, this is such a big point, because the risk is key. Managing risk is actually, you want to be as risky as possible, just don't cut an artery, you know, do the right things. But in your book, you mention this about how you identify transitions, but also you made the reference to your parents again. This is, I think, important to bring up, because we have an expression in our company: let's put the patient on the table and let's look at the problem. Solving the problems and not going out of business at that time, but your competitors did, you had to look at this holistically, and in the book, you mentioned that experience your parents taught you, being from West Virginia, that it changed how you do problem solving. Can you share what that, with that in conscience? >> Well, both parents were doctors, and the good news is, you got a lot of help, the bad news is, you didn't get a lot of self 'cos they'd fix you. But they always taught me to focus on the real, underlying issue, to your point. What is the real issue, not what the symptom is, the temperature, or something else. And then you want to determine how much of that was self-inflicted, and how much of it was market, and if your strategy's working before, continue, if your strategy was starting to get long in the tooth, how do you change it, and then you got to have the courage to reinvent yourself again and again. And so they taught me how to deal with that. I start off the book by talking about how I almost drowned at six years of age, and as I got pulled down through the rapids, I could still see my dad in my mind today running down the side of the river yelling hold on to the fishing pole. It was an ugly fishing pole. Might've cost $5. But he was concerned about the fishing pole, so therefore I obviously couldn't be drowning so I focused both hands on the fishing pole and as I poked my head above water, I could still see him running down. He got way down river, swam out, pulled me in, set me on the side, and taught me about how you deal when you find yourself with major setbacks. How do you not panic, how do you not try to swim against the tide or the current, how you be realistic of the situation that you're in, work your way to the side, and then you know what he did? He put me right back in the rapids and let me do it myself. And taught me how to deal with it. Dad taught me the business picture and how you deal with challenges, Mom, uh, who was internal medicine, psychiatry, taught me the emotional IQ side of the house, in terms of how you connect with people, and I believe, this whole chapter, I build relationships for life. And I really mean it. I think your currency is trust, relationships, and track record. >> And having that holistic picture to pull back and understand what to focus on, and this is a challenge for entrepreneurs. You're now dealing with a lot of entrepreneurs and coaching them; a lot of times they get caught in the forest and miss the trees, right? Or have board meetings or have, worry about the wrong metrics, or hey, I got to get financing. How should an entrepreneur, or even a business leader, let's talk about entrepreneur first and then business leader, handle their advisors, their investors, how do they manage that, how do they tap into that? A lot of people say, ah, they don't add much value, I just need money. This is important, because this could save them, this could be the pole for them. >> It could, or it could also be the pole that causes the tent to collapse (both laugh). So I think the first thing when you advise young entrepreneurs, is realize you're an advisor, not a part of management. And I only take young entrepreneurs who want to be coached. And as I advise them, I say all I'm asking is that you listen to my thoughts and then you make the decision, and I'll support you either way you go, once you've listened to the trade-offs. And I think you want to very quickly realize where they are in vision and strategy, and where they are on building the right team and evolving the team and changing the team, where they are in culture, and where they are on their communication skills because communication skills were important to me, they might not have been to Jack Welch, the generation in front of me, but they were extremely important to ours. And today, your communication mismatch on social media could cost your company a billion dollars. If you're not good at listening, if you're not good at communicating with people and painting the picture, you've got a problem. So how do you teach that to the young players? Then most importantly, regardless of whether you're in a big company or a small company, public or private sector, you know what you know and know what you don't. Many people who, especially if they're really good in one area, assume that carries over to others, and assume they'll be equally as good in the others, that's huge mistake; it's like an engineer hiring a good sales lead, very rarely does it happen. They recruit business development people who appeals to an engineer, not the customer. (both laugh) So, know what you know, know what you don't. For those things you don't know, surround yourself with those people in your leadership team and with your advisors to help you navigate through that. And I had, during my career, through three companies, I always had a number of advisors, formal and informal, that I went to and still go to today. Some of them were very notable players, like our President Clinton or President Bush, Shimon Peres, Henry Kissinger, or names that were just really technical leads within companies, or people that really understood PR like Thomas Freedman out of the New York Times, or things of that. >> You always love being in the trenches. I noticed that in Cisco as an observer. But now that you're in start-ups, it's even more trenches deeper (laughs) and you've got to be seeing the playing field, so I got to ask ya a personal question. How do you look back at the tech trends that's happening right now, globally, both political, regulatory technology, what advice would you give your 23-year-old self if you were breaking into the business, you were at Wang and you were going to make your move; in this world today, what's going on, what would you be doing? >> Well the first thing on the tech trend is, don't get too short-term focused. Picture the ones that are longer term, what we refer to as digitization, artificial intelligence, et cetera. If I were 23 years old, or better yet, 19 years old, and were two years through college and thinking what did I want to do in college and then on to MBA school and perhaps beyond that, legal degree if I'd followed the prior path. I would focus on entrepreneurship and really understand it in a lot more detail. I learned it over 40 years in the business. And I learned it from my dad and my mom, but also from the companies I went into before. I would focus on entrepreneurship, I'd focus on technology that enables entrepreneurship, I would probably focus on what artificial intelligence can do for that and that's what we're doing at West Virginia, to your point earlier. And then I would think about security across that. If you want really uh, job security and creativity for the future, if you're a really good entrepreneur, with artificial intelligence capability, and security capability, you're going to be a very desired resource. >> So, we saw you, obviously networking is a big part of it. You got to be networking with other people and in the industry, would you be hosting meet ups? Young John Chambers right now, tech meet ups, would you be at conferences, would you be writing code, would you be doing a start-up? >> Well, if we were talking about me advising them? >> No, you're 23-years-old right now. >> No, I'd just be fooling around. No, I'd be in MBA school and I'd be forming my own company. (both laugh) And I would be listening to customers. I think it's important to meet with your peers, but while I developed strong relationships in the high-tech industry, I spent the majority of time with my customers and with our employees. And so, I think at that age, my advice to people is there was only one Steve Jobs. He just somehow knew what to build and how to build it. And when you think about where they were, it still took him seven years (laughs). I would say, really get close to your customers, don't get too far away; if there's one golden rule that a start-up ought to think about, it's learning and staying close to your customers. There too, understand your differentiation and your strategy. Well John, thanks so much. And the book, Connecting the Dots, great read, it's again, not a business book in the sense of boring, a lot of personal stories, a lot of great lessons and thanks so much for giving the time for our conversation. >> John, it was my pleasure. Great to see you again. >> I'm John Furrier here with the People First interview on theCUBE, co-created content with Mayfield. Thanks for watching! (upbeat electronic music)

>> Announcer: Live from New York City, it's theCube covering Cyber Connect 2017. Brought to you by Centrify and The Institute for Critical Infrastructure Technology. >> Okay, welcome back everyone, this is a live Cube coverage here in New York City at the Grand Hyatt Ballroom. I'm John Furrier with my co-host Dave Vellante. This is Cyber Connect 2017, the inaugural conference of a new kind of conference bringing industry and government and practitioners together to solve the crisis of this generation, according to Keith Alexander, who was on stage earlier. Our next guest is the CEO of the company that's under running this event, Tom Kemp, co-founder and CEO of Centrify. Congratulations, Tom, we met, we saw you last week, came in the studio in Palo Alto. Day one was coming to a close. Great day. >> Yeah, it's been amazing, we've had over 500 people here. We've been webcasting this, we have 1,000 people. And, of course, we've got your audience as well. So, clearly, over 2,000 people participating in this event, so we're really pleased with the first day turn-out. >> So, I would say this is, like, a new kind of event, a little bit different than most events in the business. Response has been very well received, sold out, packed house, I couldn't get a chair, strolled in, not late, but, I mean, you know, towards the end of your Keynote. This is the dynamic, there's demand for this. Why is this so popular? You guys had a good hunch here, what's been the feedback? >> Well, the feedback's been great, first of all. But, the reality is, is that, organizations are spending 10% more per year on security but the reality is the breaches are growing 40 to 70% per year. So, no matter how much money they're throwing at it, the problem's getting worse, and so people are, for the most part, kind of throwing up their hands and saying, how can we re-think security as well? So, I think there's just a complete hunger to hear best practices from some of the top CSO's. You know we had US bank CSO, we had Etna, Blue Cross Blue Shield, etcetera. What are these guys doing to keep their data secure and make sure that they don't make headlines? >> So, I want to ask you a question on the business front, obviously we saw last week, Alphabet, AKA Google, Twitter and Facebook in front of the Setna committee, around this influence thing going on with the media, still an exploit, but a little bit different than pay load based stuff we're normally seeing with security hacks, still relevant, causes some problems, you guys have been very successful in Washington. I'm not saying you're lobbying, but as a start up, you ingratiated yourself into the community there, took a different approach. A lot of people are saying that the tech companies could do a better job in D.C., and a lot of the times Google and these treasure troves of data, they're trying to figure it out. You took a different approach and the feedback we heard on theCube is working. You guys are well received in there, obviously the product, good timing to have an identity solution, and zero trust philosophy you have. Well, you did something different. What was the strategy? Why so much success in D.C. for Centrify? >> Well, we actually partnered with the IT folks and the security people. I mean, we actually spent a lot of time on site, talking with them, and actually, we built a lot of capabilities for what the government was looking to address from an identity access security perspective. That's just the reality of the situation. And so, we took a long haul view, we've done very great in the, two of our largest customers are intelligence agencies, but we actually have over 20% of our sales that goes to the federal government, state and local as well. So, you really can't just go in there, spend a lot of money, do a lot of hype. You actually have to roll up your sleeves and help them solve the mission. They call it the mission, right, they have mission, and you got to be focused on how you can address them and work with the technologist out there to make sure, so it was just, really just blocking and tackling the ground game, >> So common sense sounds like, just do the work. >> Yeah, do the work, really listen. And think about it as a multi-year investment, right? I mean, in a lot of start ups, they just, like, oh, can't get the sale, move on, right. But you actually have to realize, especially in security, that most tech companies that have a big security presence, they should get 15-20% of their business from the US government. >> That's a big bet for you guys, were you nervous at first? I mean, obviously, you have confidence now looking back, I mean, it must've been pretty nerve wracking because it's a big bet. >> It's a big bet because you also have to meet certain government standards and requirements. You got to get FIP certification, you got to get common criteria, in the cloud, you got to get FedRAMP, and that means you also have to have customers in the federal government approve you and bring you in and then you have to go through the lengthy audit process. And we're actually about to get our FedRAMP certification, just passed the audit and that's going to be coming up pretty soon as well. So, yeah, to go get common criteria, to get FedRAMP, you have to spend a million dollars for those types of certifications. At the same time, working with the large federal agencies. >> So Tom, you gave us the numbers, 10% more spending every year on security but breaches are up 40 to 70%, you said in your talk that's two trillion dollars in lost dollars, productivity, IP, etcetera, so obviously it's not working, you've mentioned a number of folks in here talking today. What's their mindset? Is their mindset this is a do-over? Or, is it, just we got to do a better job? >> I think we're getting to the point where its' going to be a do-over. And I think, first of all, people realize that the legacy technology that they have have historically focused on premises. But, the world's rapidly moving to the cloud, right? And so, you need to have cloud-based scale, a cloud-based architecture, to deliver security nowadays because the perimeter is completely going away. That's the first thing. And, I think there's also realization that there needs to be Big Data machine learning applied to this. And you guys talk about this all the time, the whole rise of Big Data. But, security is probably the best vertical. >> Data application. >> Exactly, it's probably the best vertical, because you need real-time instantaneous should I let this person come into the system or not, right? Or, over time, is this, does this represent malicious activity as well? So, I think people are realizing that what they've been doing's not working, they realize they're moving to the cloud, they need to adopt cloud, to, not only secure cloud, but have their technology be based in the cloud and they need to apply machine learning to the problem as well. >> So, in your talk, you talked about a paradigm shift, which I inferred as a mindset shift in how security practices in technologies should be applied, you got to lot of content in there. But could you summarize for our audience sort of the fundamentals? >> Well, the first fundamental is, is that the attack vector is completely changed, right? Before, it was all about vulnerabilities that someone hadn't patched this latest version of Windows, etcetera. Those problems are really solved, for the most part. I mean, occasionally it kind of pops in now and then, but for the most part, enterprises and governments are good about patching systems etcetera. You don't hear about sequel injections anymore. So, a lot of those problems have been resolved. But, where the attackers are going, they're going after the actual users, and so, I know you had the Verizon folks here on theCube, and if you look at the latest Verizon data breacher port, eight out of 10 breaches involve stolen and compromised credentials, right? And that has grown over the last few years from 50% to 60% now to over 80%. Look at the election, right? You talk about all this Twitter stuff and Facebook and all that stuff, it's John Podesta's emails getting stolen, it's the democrat's emails getting stolen, and you know, now that people have the Equifax data, they've got even more information to help figure out-- >> Social engineering is a big theme here. >> Absolutely. >> They have this data out on the dark web, this methodologies and there's also, you know, we talked with the critical interset guys that you're partnering with about all the terrorism activity, so, there's influence campaigns going on that are influencing through social engineering, but that data's being cross connected for, you know, radicalizing people to kill people in the United States. >> Well, there's that. And then there's nation states, there's insiders. So, the reality is, is that, it turns out from a security perspective, that we, the humans, we're the weakest link in this. And so, yes, there needs to be process, there needs to be technology, there needs to be education here as well. But the reality is that the vast majority of spin on security is for the old stuff, it's like we're trying to fight a land war in Asia, and that's how we're investing, we're still investing in M1 tanks in security, but the reality is that 80% of the breaches are occurring because they're attacking the individuals. They're either fooling them, or stealing it by some means or mechanisms, and so the attack vector is now the user. And that's this, and people are probably spending less than 10% securing the users, but it represents 80% of the actual attack vector. >> Talk about the general, you've had some one-on-one times with him, he's giving a keynote here, gave a keynote this morning, very inspiring. I mean, I basically heard him pounding on the table, "we don't fix this mess, You know, we're going to be in trouble, it's going to be worse than it is!" Think differently, almost re-imagining, his vibe was almost about let's re-imagine, let's partner, let's be a community. What else can you share with you interaction with him? I know he's a very rare to get to speak, but you know, running the cyber command for the NSA, great on offense, we need work on defense. What have you learned from him that industry could take away? >> Yeah, I think you hit it, which is, and I didn't realize that there's a bigger opportunity here, which is, is that in real time, there needs to be more sharing among like constituents. For example, in the energy industry, these organizations, they need to come together and they need to share, not only in terms of round tables, but they actually need to share data. And it probably needs to happen in the cloud, where there's the threats, the attacks that are happening in real time, need to be shared with their peers in the industry as well. And so, and I think government needs to also play a part in that as well. Because each of us, we're trying to fight the Russians, right? And the Chinese and the North Koreans, etcetera and a enterprise just can't deal with that alone and so they need to band together, share information, not only from an educational, like we have today, but actually real time information. And then again, leverage that machine learning. That artificial intelligence to say, "wait a minute, we've detected this of our peers and so we should apply some preventative controls to stop it." >> And tech is at the center of the government transformation more than ever. And again, Twitter, Facebook, and Alphabet in front of the senate, watching them, watching the senators kind of fumbling with the marbles. You know, hey, what's Facebook again? I mean, the magnitude of the data and the impact of these new technologies and with Centrify, the collision between government and industry is happening very rapidly. So, the question is that, you know, how will you guys, seeing this going forward, is it going to be, you know, the partnership as they come together fast or will more mandates come and regulations, which could stifle innovations, so, there's this dimension going on now where I see the formation of either faster partnership with industry and government, or, hey industry, if you don't move fast enough poof, more regulations. >> And that's also what the general brought up as well, is that if you guys don't do something on your own, if you don't fix your own problems, right, then the government's going to step in. Actually, that's what's already starting to happen right now, that if Facebook, Twitter, all these other social networks are not going to do something about foreign governments advertising on their platform, they're going to get regulated. So, if they don't start doing something. So, it's better to be in front of these things right here, the reality is that, yes, from a cyber security in terms of protecting users, protecting data, enterprise needs to do more. But, you know what, regulations are starting to already occur, so, there's a major regulation that came out of New York with the financial services that a lot of these financial firms are talking about. And then in Europe, you got GDPR, right? And that goes into effect I think in May of next year. And there's some serious finds. It could be up to four percent of your revenue as well, while, in the past, the kind of, the hand slaps that have happened here, so if you do business in Europe, if you're a financial services firm doing business in New York. >> People are going to run from there, Europe. I mean, regulation, I'm not a big fan of more regulation, I like regulation at the right balance, cause innovation's key. What have you heard here from talks? Share, cause we haven't had a chance 'cause we've been broadcasting all day, share some highlights from today's sessions after, you know, Jim from Etna was on there, which, I'm sure you got a kick out of his history comment, you're a history buff. Weren't you a history major and computer science? >> I was a history major and computer science, you got that right. >> You'd be a great dean of the sciences by today's standards. But I mean, he had a good point. Civilization crumbles when there's no trust. That comment, he made that interesting comment. >> So, it's interesting what Etna's done, from his presentation, was they've invested heavily in models, they've modeled this. And I think that kind of goes back to the whole Big Data, so I think Etna is ahead of the game, and it's very impressive what he's put forth as well. And just think about the information that Etna has about their customers etcetera. That is not something that you want. >> He was also saying that he modeled, you don't model for model's sake because stuff's going on in real time, you know what I'm saying? So, the data lake wasn't the answer. >> Well, he said his mistake was, so they were operationalizing the real time, you know, security Big Data activity, and he didn't realize it, he said that was the real answer, not just, sort of, analyzing the data swamp, so. >> Yeah, absolutely. >> So, that was the epiphany that he realized. You know, that is where the opportunity was. >> John: It was unconventional tactics, too. >> What can businesses expect, Tom? What's the business outcome they can expect if they, sort of, follow the prescription that you talked about and, sort of, understand that humans are the weakest link and take actions to remediate that. What kind of business impact can that have? >> Yeah, so, we actually, we spent a lot of time on this and we partnered with Forrester, a well known analyst group, and we did this study with them, and they went out and they interviewed 120 large enterprises. And it was really interesting that one group, group A, was getting breached left and right and group B, about half the number of breaches, right? And we were like, what is group B doing versus group A? And it had to do with implementing a maturity model as it relates to identity which is, first and foremost, implementing identity assurance, getting, reducing the number of logins, delivering single sign-in, multi factor authentication. Which we should all do as consumers as well, turn on that MFA button for Twitter, and your Gmail etcetera. Then, from there, the organizations that were able to limit lateral movement and break down, make sure that people don't have too much access to too many things as well. There was an incident, it was Saudi Generale that there was a backend IT guy, he became a traitor, he started making some losses, and so he tried to, he doubled down, he leveraged the credentials that he had as a former IT person to continue trading even though he kind of turned off all the the guardrails right there, and he should have been shut down. When he made that move into that new position, so, there's just too much lateral movement aloud. And then, from there, you got to implement the concept of least privilege and then finally you got to audit, and so if you can follow this maturity model, we have seen that organizations have seen significant reduction in the number of breaches out there as well. So, that was another thing that I talked about at my keynote, that I presented this study that Forrester did by talking to customers and there turned out to be a significant difference between group A and group B in terms of the number of breaches as well. And that actually tied very well with what Jim was talking about as well, which was, you know, I call it a maturity model, he called it just models, right, as well. But there is a path forward that you can better be smarter about security. >> But there's a playbook. >> There is a playbook, absolutely. >> And it revolves around not having a lot of moving parts where human error, and this is where passwords and these directories of stuff out there, are silos, is that right? Did I get that right? So you want to go level? >> That's the first step, I mean the first step is that we're drowning in a sea of passwords, right, and we need what's known as identity assurance, we need to reduce the number of passwords. With the fewer passwords we have, we need to better protect it by adding stronger authentication. Multi-factor authentication. The new face ID technology, which I've been hearing good reviews about, coming from Apple as well, I mean, stuff like that, and say, look, before I log into that, yes, I need to do my thumbprint and do the old face ID. >> And multi factor authentication I think is a good point, also known as MFA, that's not two factor, it's more than one, but two seems to be popular cause you get your phone, multi factor could be device, IOT device, card readers, it starts getting down into other mechanisms, is that right? >> Absolutely, it's something you have, and something you know, right? >> Answer five questions. >> Yeah, but at the same time you don't want to make it too, >> Too restrictive. >> Too restrictive, etcetera. But then here's where the machine learning comes in, then you add the word adaptive in front of multi factor authentication. If the access is coming from the corporate network, odds are that means that person was badged, got through. So, maybe you don't ask as much, for much information to actually allow the person on right there. But, what if that person was, five minutes ago, was in New York, and now he's trying to access from China? Well wait a minute, right? Or what if it's a device that he or she's never accessed from before as well? So, you need to start using that machine learning and look at what is normal behavior and what deviates from that behavior? And then, factor it into the multi factor authentication. >> Well, we've seen major advancements in the last couple years, even, in fraud detection, you know, real time. And is that seeping into the enterprise? >> Well, it should, that's the ironic thing is, is that with our credit card, I mean, we get blocked all the time, right? >> It is annoying sometimes, but you know at the end of the day you say, good. >> Yeah, thank you for doing that, you know. And so that's, in effect, the multi factor authentication is you calling up the credit card company, ironically my credit card, maybe I shouldn't reveal this, too much information, someone will hack me, but I use US bank, right there, and we had Jason the CSO of US bank right there, but, you know, calling in and actually saying, yes, I'm trying to do this transaction represents another form of authentication. Why aren't we doing similar things for people logging onto mission critical servers or applications? It's just shocking. >> I'm going to ask you a personal question, so, you mentioned history and computer science, a lot of security folks that I talk to, when they were little kids, they used to sort of dream about saving the world. Did you do that? (laughter) >> Well, I definitely want to do something that adds value to society, so, you know, this is not like the Steve Jobs telling Scully, do you want to make sugared water and all that stuff? >> Dave: No, but like, superhero stuff, were you into that as a kid, or? >> D.C. or Marvel? >> Good versus evil? >> Don't answer that question, you like 'em both. >> But the nice thing about security is, when you're a security vendor, you're actually, the value that you have is real. It's not like, you know, some app or whatever where you get a bunch of teenagers to waste time and all that stuff. >> John: Serious business. >> Yeah, you're in serious business. You're protecting people, you're protecting individuals, their personal information, you're protecting corporations, their brand, look what happened to Equifax when their, when it was announced, the breach, their stock went down 13, 14%, Chipotle went down by 400 million, their market cap went. I mean, so, nowadays, if you have a, if there's a breach, you got to short that stock. >> Yeah, and security's now part of the product, cause the brand image, not just whatever the value is in the brand, I mean the product, the brand itself is the security. If you're a bank, security is the product. >> Absolutely, if you're known for being breached, who the heck's going to bank with you? >> Whole 'nother strategy there. Okay, final question from me is, this event, what are some of the hallway conversations, what's notable, what can you share for the folks watching? Some of the conversations, the interests, the kind of people here, what was the conversations? >> Yeah, I mean, the conference, we really did a great job working with our partner ICIT of attracting sea level folks, right? So, this was more of a business focus, this was not, you know, people gathered around a laptop and try to hack into the guy sitting right next to them as well. And, so, I think there, what has come out of the conversations is a better awareness of, as I said before, it's like, you know what, we got to completely, we got to like step back, completely rethink what we're trying to do here as well, cause what we're doing now is not working, right? And so I think it's, in effect, we're kind of forcing some soul searching here as well. And having others present what's been working for them, what technologies, cloud, machine learning, the zero trust concept, etcetera, where you only, you have to assume that your internal network is just as polluted as the outside. >> I know this might be early, but what's the current takeaway for you as you ruminate here on theCube that you're going to take back to the ranch in Palo Alto and Silicon Valley, what's the takeaway, personally, that you're now going to walk away with? Was there an epiphany, was there a moment of validation, what can you share about what you'll walk away with? >> There's just a hunger. I mean there's just a hunger to know more about the business of security etcetera. I mean, we're just, we were amazed with the turn out here, we're pleased with working with you guys and the level of interest with your viewership, our webcast, I mean, this is, you know, for the first time event to have both in-person and online, well over 2,000 people participating, that says a lot. That there's just this big hunger. So, we're going to work with you guys, we're going to work with ICIT and we're going to figure out how we're going to make this bigger and even better because there is an untapped need for a conference such as this. >> And a whole new generation's coming up though the ranks, our kids and the younger, new millennials , whatever they're called, Z or letters they're called, they're going to end up running the cyber. >> Yeah absolutely, absolutely. So there just needs to be a new way of going about it. >> Tom, congratulations. >> Thank you. >> Great event, you guys got a lot of credibility in D.C., you've earned it, it shows. The event, again, good timing lighting the bottle, The CyberConnect inaugural event, Cube exclusive coverage in Manhattan here, live in New York City at the Grand Hyatt Ballroom for the CyberConnect 2017 presented by Centrify, I'm here with the CEO and co-founder of Centrify, Tom Kemp, I'm John Furrier, Dave Vellante, more live coverage after this short break. (modern electronic music)