>>Okay. Okay. We're back at Caesar's Forum. The Snowflake summit 2022. The cubes. Continuous coverage this day to wall to wall coverage. We're so excited to have the analyst panel here, some of my colleagues that we've done a number. You've probably seen some power panels that we've done. David McGregor is here. He's the senior vice president and research director at Ventana Research. To his left is Tony Blair, principal at DB Inside and my in the co host seat. Sanjeev Mohan Sanremo. Guys, thanks so much for coming on. I'm glad we can. Thank you. You're very welcome. I wasn't able to attend the analyst action because I've been doing this all all day, every day. But let me start with you, Dave. What have you seen? That's kind of interested you. Pluses, minuses. Concerns. >>Well, how about if I focus on what I think valuable to the customers of snowflakes and our research shows that the majority of organisations, the majority of people, do not have access to analytics. And so a couple of things they've announced I think address those are helped to address those issues very directly. So Snow Park and support for Python and other languages is a way for organisations to embed analytics into different business processes. And so I think that will be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most most people in the organisation or not, analysts, they're doing some line of business function. Their HR managers, their marketing people, their salespeople, their finance people right there, not sitting there mucking around in the data. They're doing a job and they need analytics in that job. So, >>Tony, I thank you. I've heard a lot of data mesh talk this week. It's kind of funny. Can't >>seem to get away from it. You >>can't see. It seems to be gathering momentum, but But what have you seen? That's been interesting. >>What I have noticed. Unfortunately, you know, because the rooms are too small, you just can't get into the data mesh sessions, so there's a lot of interest in it. Um, it's still very I don't think there's very much understanding of it, but I think the idea that you can put all the data in one place which, you know, to me, stuff like it seems to be kind of sort of in a way, it sounds like almost like the Enterprise Data warehouse, you know, Clouded Cloud Native Edition, you know, bring it all in one place again. Um, I think it's providing, sort of, You know, it's I think, for these folks that think this might be kind of like a a linchpin for that. I think there are several other things that actually that really have made a bigger impression on me. Actually, at this event, one is is basically is, um we watch their move with Eunice store. Um, and it's kind of interesting coming, you know, coming from mongo db last week. And I see it's like these two companies seem to be going converging towards the same place at different speeds. I think it's not like it's going to get there faster than Mongo for a number of different reasons, but I see like a number of common threads here. I mean, one is that Mongo was was was a company. It's always been towards developers. They need you know, start cultivating data, people, >>these guys going the other way. >>Exactly. Bingo. And the thing is that but they I think where they're converging is the idea of operational analytics and trying to serve all constituencies. The other thing, which which also in terms of serving, you know, multiple constituencies is how snowflake is laid out Snow Park and what I'm finding like. There's an interesting I economy. On one hand, you have this very ingrained integration of Anaconda, which I think is pretty ingenious. On the other hand, you speak, let's say, like, let's say the data robot folks and say, You know something our folks wanna work data signs us. We want to work in our environment and use snowflake in the background. So I see those kind of some interesting sort of cross cutting trends. >>So, Sandy, I mean, Frank Sullivan, we'll talk about there's definitely benefits into going into the walled garden. Yeah, I don't think we dispute that, but we see them making moves and adding more and more open source capabilities like Apache iceberg. Is that a Is that a move to sort of counteract the narrative that the data breaks is put out there. Is that customer driven? What's your take on that? >>Uh, primarily I think it is to contract this whole notion that once you move data into snowflake, it's a proprietary format. So I think that's how it started. But it's hugely beneficial to the customers to the users, because now, if you have large amounts of data in parquet files, you can leave it on s three. But then you using the the Apache iceberg table format. In a snowflake, you get all the benefits of snowflakes. Optimizer. So, for example, you get the, you know, the micro partitioning. You get the meta data. So, uh, in a single query, you can join. You can do select from a snowflake table union and select from iceberg table, and you can do store procedures, user defined functions. So I think they what they've done is extremely interesting. Uh, iceberg by itself still does not have multi table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache iceberg in a raw format, they don't have it. But snowflake does, >>right? There's hence the delta. And maybe that maybe that closes over time. I want to ask you as you look around this I mean the ecosystems pretty vibrant. I mean, it reminds me of, like reinvent in 2013, you know? But then I'm struck by the complexity of the last big data era and a dupe and all the different tools. And is this different, or is it the sort of same wine new new bottle? You guys have any thoughts on that? >>I think it's different and I'll tell you why. I think it's different because it's based around sequel. So if back to Tony's point, these vendors are coming at this from different angles, right? You've got data warehouse vendors and you've got data lake vendors and they're all going to meet in the middle. So in your case, you're taught operational analytical. But the same thing is true with Data Lake and Data Warehouse and Snowflake no longer wants to be known as the Data Warehouse. There a data cloud and our research again. I like to base everything off of that. >>I love what our >>research shows that organisation Two thirds of organisations have sequel skills and one third have big data skills, so >>you >>know they're going to meet in the middle. But it sure is a lot easier to bring along those people who know sequel already to that midpoint than it is to bring big data people to remember. >>Mrr Odula, one of the founders of Cloudera, said to me one time, John Kerry and the Cube, that, uh, sequel is the killer app for a Yeah, >>the difference at this, you know, with with snowflake, is that you don't have to worry about taming the zoo. Animals really have thought out the ease of use, you know? I mean, they thought about I mean, from the get go, they thought of too thin to polls. One is ease of use, and the other is scale. And they've had. And that's basically, you know, I think very much differentiates it. I mean, who do have the scale, but it didn't have the ease of use. But don't I >>still need? Like, if I have, you know, governance from this vendor or, you know, data prep from, you know, don't I still have to have expertise? That's sort of distributed in those those worlds, right? I mean, go ahead. Yeah. >>So the way I see it is snowflake is adding more and more capabilities right into the database. So, for example, they've they've gone ahead and added security and privacy so you can now create policies and do even set level masking, dynamic masking. But most organisations have more than snowflake. So what we are starting to see all around here is that there's a whole series of data catalogue companies, a bunch of companies that are doing dynamic data masking security and governance data observe ability, which is not a space snowflake has gone into. So there's a whole ecosystem of companies that that is mushrooming, although, you know so they're using the native capabilities of snowflake, but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other, like relational databases, you can run these cross platform capabilities in that layer. So so that way, you know, snowflakes done a great job of enabling that ecosystem about >>the stream lit acquisition. Did you see anything here that indicated there making strong progress there? Are you excited about that? You're sceptical. Go ahead. >>And I think it's like the last mile. Essentially. In other words, it's like, Okay, you have folks that are basically that are very, very comfortable with tableau. But you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency, um, to San James Point. I think part of it, this kind of plays into it is what makes this different from the ado Pere is the fact that this all these capabilities, you know, a lot of vendors are taking it very seriously to make put this native obviously snowflake acquired stream. Let's so we can expect that's extremely capabilities are going to be native. >>And the other thing, too, about the Hadoop ecosystem is Claudia had to help fund all those different projects and got really, really spread thin. I want to ask you guys about this super cloud we use. Super Cloud is this sort of metaphor for the next wave of cloud. You've got infrastructure aws, azure, Google. It's not multi cloud, but you've got that infrastructure you're building a layer on top of it that hides the underlying complexities of the primitives and the a p I s. And you're adding new value in this case, the data cloud or super data cloud. And now we're seeing now is that snowflake putting forth the notion that they're adding a super path layer. You can now build applications that you can monetise, which to me is kind of exciting. It makes makes this platform even less discretionary. We had a lot of talk on Wall Street about discretionary spending, and that's not discretionary. If you're monetising it, um, what do you guys think about that? Is this something that's that's real? Is it just a figment of my imagination, or do you see a different way of coming any thoughts on that? >>So, in effect, they're trying to become a data operating system, right? And I think that's wonderful. It's ambitious. I think they'll experience some success with that. As I said, applications are important. That's a great way to deliver information. You can monetise them, so you know there's there's a good economic model around it. I think they will still struggle, however, with bringing everything together onto one platform. That's always the challenge. Can you become the platform that's hard, hard to predict? You know, I think this is This is pretty exciting, right? A lot of energy, a lot of large ecosystem. There is a network effect already. Can they succeed in being the only place where data exists? You know, I think that's going to be a challenge. >>I mean, the fact is, I mean, this is a classic best of breed versus the umbrella play. The thing is, this is nothing new. I mean, this is like the you know, the old days with enterprise applications were basically oracle and ASAP vacuumed up all these. You know, all these applications in their in their ecosystem, whereas with snowflake is. And if you look at the cloud, folks, the hyper scale is still building out their own portfolios as well. Some are, You know, some hyper skills are more partner friendly than others. What? What Snowflake is saying is that we're going to give all of you folks who basically are competing against the hyper skills in various areas like data catalogue and pipelines and all that sort of wonderful stuff will make you basically, you know, all equal citizens. You know the burden is on you to basically we will leave. We will lay out the A P. I s Well, we'll allow you to basically, you know, integrate natively to us so you can provide as good experience. But the but the onus is on your back. >>Should the ecosystem be concerned, as they were back to reinvent 2014 that Amazon was going to nibble away at them or or is it different? >>I find what they're doing is different. Uh, for example, data sharing. They were the first ones out the door were data sharing at a large scale. And then everybody has jumped in and said, Oh, we also do data sharing. All the hyper scholars came in. But now what snowflake has done is they've taken it to the next level. Now they're saying it's not just data sharing. It's up sharing and not only up sharing. You can stream the thing you can build, test deploy, and then monetise it. Make it discoverable through, you know, through your marketplace >>you can monetise it. >>Yes. Yeah, so So I I think what they're doing is they are taking it a step further than what hyper scale as they are doing. And because it's like what they said is becoming like the data operating system You log in and you have all of these different functionalities you can do in machine learning. Now you can do data quality. You can do data preparation and you can do Monetisation. Who do you >>think is snowflakes? Biggest competitor? What do you guys think? It's a hard question, isn't it? Because you're like because we all get the we separate computer from storage. We have a cloud data and you go, Okay, that's nice, >>but there's, like, a crack. I think >>there's uniqueness. I >>mean, put it this way. In the old days, it would have been you know, how you know the prime household names. I think today is the hyper scholars and the idea what I mean again, this comes down to the best of breed versus by, you know, get it all from one source. So where is your comfort level? Um, so I think they're kind. They're their co op a Titian the hyper scale. >>Okay, so it's not data bricks, because why they're smaller. >>Well, there is some okay now within the best of breed area. Yes, there is competition. The obvious is data bricks coming in from the data engineering angle. You know, basically the snowflake coming from, you know, from the from the data analyst angle. I think what? Another potential competitor. And I think Snowflake, basically, you know, admitted as such potentially is mongo >>DB. Yeah, >>Exactly. So I mean, yes, there are two different levels of sort >>of a on a longer term collision course. >>Exactly. Exactly. >>Sort of service now and in salesforce >>thing that was that we actually get when I say that a lot of people just laughed. I was like, No, you're kidding. There's no way. I said Excuse me, >>But then you see Mongo last week. We're adding some analytics capabilities and always been developers, as you say, and >>they trashed sequel. But yet they finally have started to write their first real sequel. >>We have M c M Q. Well, now we have a sequel. So what >>were those numbers, >>Dave? Two thirds. One third. >>So the hyper scale is but the hyper scale urz are you going to trust your hyper scale is to do your cross cloud. I mean, maybe Google may be I mean, Microsoft, perhaps aws not there yet. Right? I mean, how important is cross cloud, multi cloud Super cloud Whatever you want to call it What is your data? >>Shows? Cloud is important if I remember correctly. Our research shows that three quarters of organisations are operating in the cloud and 52% are operating across more than one cloud. So, uh, two thirds of the organisations are in the cloud are doing multi cloud, so that's pretty significant. And now they may be operating across clouds for different reasons. Maybe one application runs in one cloud provider. Another application runs another cloud provider. But I do think organisations want that leverage over the hyper scholars right they want they want to be able to tell the hyper scale. I'm gonna move my workloads over here if you don't give us a better rate. Uh, >>I mean, I I think you know, from a database standpoint, I think you're right. I mean, they are competing against some really well funded and you look at big Query barely, you know, solid platform Red shift, for all its faults, has really done an amazing job of moving forward. But to David's point, you know those to me in any way. Those hyper skills aren't going to solve that cross cloud cloud problem, right? >>Right. No, I'm certainly >>not as quickly. No. >>Or with as much zeal, >>right? Yeah, right across cloud. But we're gonna operate better on our >>Exactly. Yes. >>Yes. Even when we talk about multi cloud, the many, many definitions, like, you know, you can mean anything. So the way snowflake does multi cloud and the way mongo db two are very different. So a snowflake says we run on all the hyper scalar, but you have to replicate your data. What Mongo DB is claiming is that one cluster can have notes in multiple different clouds. That is right, you know, quite something. >>Yeah, right. I mean, again, you hit that. We got to go. But, uh, last question, um, snowflake undervalued, overvalued or just about right >>in the stock market or in customers. Yeah. Yeah, well, but, you know, I'm not sure that's the right question. >>That's the question I'm asking. You know, >>I'll say the question is undervalued or overvalued for customers, right? That's really what matters. Um, there's a different audience. Who cares about the investor side? Some of those are watching, but But I believe I believe that the from the customer's perspective, it's probably valued about right, because >>the reason I I ask it, is because it has so hyped. You had $100 billion value. It's the past service now is value, which is crazy for this student Now. It's obviously come back quite a bit below its IPO price. So But you guys are at the financial analyst meeting. Scarpelli laid out 2029 projections signed up for $10 billion.25 percent free time for 20% operating profit. I mean, they better be worth more than they are today. If they do >>that. If I If I see the momentum here this week, I think they are undervalued. But before this week, I probably would have thought there at the right evaluation, >>I would say they're probably more at the right valuation employed because the IPO valuation is just such a false valuation. So hyped >>guys, I could go on for another 45 minutes. Thanks so much. David. Tony Sanjeev. Always great to have you on. We'll have you back for sure. Having us. All right. Thank you. Keep it right there. Were wrapping up Day two and the Cube. Snowflake. Summit 2022. Right back. Mm. Mhm.

(bright music) >> In the 2010s, organizations became keenly aware that data would become the key ingredient to driving competitive advantage, differentiation, and growth. But to this day, putting data to work remains a difficult challenge for many, if not most organizations. Now, as the cloud matures, it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible. We've also seen better tooling in the form of data workflows, streaming, machine intelligence, AI, developer tools, security, observability, automation, new databases and the like. These innovations they accelerate data proficiency, but at the same time, they add complexity for practitioners. Data lakes, data hubs, data warehouses, data marts, data fabrics, data meshes, data catalogs, data oceans are forming, they're evolving and exploding onto the scene. So in an effort to bring perspective to the sea of optionality, we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond. Hello everyone, my name is Dave Velannte with theCUBE, and I'd like to welcome you to a special Cube presentation, analysts predictions 2022: the future of data management. We've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade. Let me introduce our six power panelists. Sanjeev Mohan is former Gartner Analyst and Principal at SanjMo. Tony Baer, principal at dbInsight, Carl Olofson is well-known Research Vice President with IDC, Dave Menninger is Senior Vice President and Research Director at Ventana Research, Brad Shimmin, Chief Analyst, AI Platforms, Analytics and Data Management at Omdia and Doug Henschen, Vice President and Principal Analyst at Constellation Research. Gentlemen, welcome to the program and thanks for coming on theCUBE today. >> Great to be here. >> Thank you. >> All right, here's the format we're going to use. I as moderator, I'm going to call on each analyst separately who then will deliver their prediction or mega trend, and then in the interest of time management and pace, two analysts will have the opportunity to comment. If we have more time, we'll elongate it, but let's get started right away. Sanjeev Mohan, please kick it off. You want to talk about governance, go ahead sir. >> Thank you Dave. I believe that data governance which we've been talking about for many years is now not only going to be mainstream, it's going to be table stakes. And all the things that you mentioned, you know, the data, ocean data lake, lake houses, data fabric, meshes, the common glue is metadata. If we don't understand what data we have and we are governing it, there is no way we can manage it. So we saw Informatica went public last year after a hiatus of six. I'm predicting that this year we see some more companies go public. My bet is on Culebra, most likely and maybe Alation we'll see go public this year. I'm also predicting that the scope of data governance is going to expand beyond just data. It's not just data and reports. We are going to see more transformations like spark jawsxxxxx, Python even Air Flow. We're going to see more of a streaming data. So from Kafka Schema Registry, for example. We will see AI models become part of this whole governance suite. So the governance suite is going to be very comprehensive, very detailed lineage, impact analysis, and then even expand into data quality. We already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management, data catalogs, also data access governance. So what we are going to see is that once the data governance platforms become the key entry point into these modern architectures, I'm predicting that the usage, the number of users of a data catalog is going to exceed that of a BI tool. That will take time and we already seen that trajectory. Right now if you look at BI tools, I would say there a hundred users to BI tool to one data catalog. And I see that evening out over a period of time and at some point data catalogs will really become the main way for us to access data. Data catalog will help us visualize data, but if we want to do more in-depth analysis, it'll be the jumping off point into the BI tool, the data science tool and that is the journey I see for the data governance products. >> Excellent, thank you. Some comments. Maybe Doug, a lot of things to weigh in on there, maybe you can comment. >> Yeah, Sanjeev I think you're spot on, a lot of the trends the one disagreement, I think it's really still far from mainstream. As you say, we've been talking about this for years, it's like God, motherhood, apple pie, everyone agrees it's important, but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking. I think one thing that deserves mention in this context is ESG mandates and guidelines, these are environmental, social and governance, regs and guidelines. We've seen the environmental regs and guidelines and posts in industries, particularly the carbon-intensive industries. We've seen the social mandates, particularly diversity imposed on suppliers by companies that are leading on this topic. We've seen governance guidelines now being imposed by banks on investors. So these ESGs are presenting new carrots and sticks, and it's going to demand more solid data. It's going to demand more detailed reporting and solid reporting, tighter governance. But we're still far from mainstream adoption. We have a lot of, you know, best of breed niche players in the space. I think the signs that it's going to be more mainstream are starting with things like Azure Purview, Google Dataplex, the big cloud platform players seem to be upping the ante and starting to address governance. >> Excellent, thank you Doug. Brad, I wonder if you could chime in as well. >> Yeah, I would love to be a believer in data catalogs. But to Doug's point, I think that it's going to take some more pressure for that to happen. I recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the nineties and that didn't happen quite the way we anticipated. And so to Sanjeev's point it's because it is really complex and really difficult to do. My hope is that, you know, we won't sort of, how do I put this? Fade out into this nebula of domain catalogs that are specific to individual use cases like Purview for getting data quality right or like data governance and cybersecurity. And instead we have some tooling that can actually be adaptive to gather metadata to create something. And I know its important to you, Sanjeev and that is this idea of observability. If you can get enough metadata without moving your data around, but understanding the entirety of a system that's running on this data, you can do a lot. So to help with the governance that Doug is talking about. >> So I just want to add that, data governance, like any other initiatives did not succeed even AI went into an AI window, but that's a different topic. But a lot of these things did not succeed because to your point, the incentives were not there. I remember when Sarbanes Oxley had come into the scene, if a bank did not do Sarbanes Oxley, they were very happy to a million dollar fine. That was like, you know, pocket change for them instead of doing the right thing. But I think the stakes are much higher now. With GDPR, the flood gates opened. Now, you know, California, you know, has CCPA but even CCPA is being outdated with CPRA, which is much more GDPR like. So we are very rapidly entering a space where pretty much every major country in the world is coming up with its own compliance regulatory requirements, data residents is becoming really important. And I think we are going to reach a stage where it won't be optional anymore. So whether we like it or not, and I think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption. We were focused on features and these features were disconnected, very hard for business to adopt. These are built by IT people for IT departments to take a look at technical metadata, not business metadata. Today the tables have turned. CDOs are driving this initiative, regulatory compliances are beating down hard, so I think the time might be right. >> Yeah so guys, we have to move on here. But there's some real meat on the bone here, Sanjeev. I like the fact that you called out Culebra and Alation, so we can look back a year from now and say, okay, he made the call, he stuck it. And then the ratio of BI tools to data catalogs that's another sort of measurement that we can take even though with some skepticism there, that's something that we can watch. And I wonder if someday, if we'll have more metadata than data. But I want to move to Tony Baer, you want to talk about data mesh and speaking, you know, coming off of governance. I mean, wow, you know the whole concept of data mesh is, decentralized data, and then governance becomes, you know, a nightmare there, but take it away, Tony. >> We'll put this way, data mesh, you know, the idea at least as proposed by ThoughtWorks. You know, basically it was at least a couple of years ago and the press has been almost uniformly almost uncritical. A good reason for that is for all the problems that basically Sanjeev and Doug and Brad we're just speaking about, which is that we have all this data out there and we don't know what to do about it. Now, that's not a new problem. That was a problem we had in enterprise data warehouses, it was a problem when we had over DoOP data clusters, it's even more of a problem now that data is out in the cloud where the data is not only your data lake, is not only us three, it's all over the place. And it's also including streaming, which I know we'll be talking about later. So the data mesh was a response to that, the idea of that we need to bait, you know, who are the folks that really know best about governance? It's the domain experts. So it was basically data mesh was an architectural pattern and a process. My prediction for this year is that data mesh is going to hit cold heart reality. Because if you do a Google search, basically the published work, the articles on data mesh have been largely, you know, pretty uncritical so far. Basically loading and is basically being a very revolutionary new idea. I don't think it's that revolutionary because we've talked about ideas like this. Brad now you and I met years ago when we were talking about so and decentralizing all of us, but it was at the application level. Now we're talking about it at the data level. And now we have microservices. So there's this thought of have we managed if we're deconstructing apps in cloud native to microservices, why don't we think of data in the same way? My sense this year is that, you know, this has been a very active search if you look at Google search trends, is that now companies, like enterprise are going to look at this seriously. And as they look at it seriously, it's going to attract its first real hard scrutiny, it's going to attract its first backlash. That's not necessarily a bad thing. It means that it's being taken seriously. The reason why I think that you'll start to see basically the cold hearted light of day shine on data mesh is that it's still a work in progress. You know, this idea is basically a couple of years old and there's still some pretty major gaps. The biggest gap is in the area of federated governance. Now federated governance itself is not a new issue. Federated governance decision, we started figuring out like, how can we basically strike the balance between getting let's say between basically consistent enterprise policy, consistent enterprise governance, but yet the groups that understand the data and know how to basically, you know, that, you know, how do we basically sort of balance the two? There's a huge gap there in practice and knowledge. Also to a lesser extent, there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data. You know, basically through the full life cycle, from develop, from selecting the data from, you know, building the pipelines from, you know, determining your access control, looking at quality, looking at basically whether the data is fresh or whether it's trending off course. So my prediction is that it will receive the first harsh scrutiny this year. You are going to see some organization and enterprises declare premature victory when they build some federated query implementations. You going to see vendors start with data mesh wash their products anybody in the data management space that they are going to say that where this basically a pipelining tool, whether it's basically ELT, whether it's a catalog or federated query tool, they will all going to get like, you know, basically promoting the fact of how they support this. Hopefully nobody's going to call themselves a data mesh tool because data mesh is not a technology. We're going to see one other thing come out of this. And this harks back to the metadata that Sanjeev was talking about and of the catalog just as he was talking about. Which is that there's going to be a new focus, every renewed focus on metadata. And I think that's going to spur interest in data fabrics. Now data fabrics are pretty vaguely defined, but if we just take the most elemental definition, which is a common metadata back plane, I think that if anybody is going to get serious about data mesh, they need to look at the data fabric because we all at the end of the day, need to speak, you know, need to read from the same sheet of music. >> So thank you Tony. Dave Menninger, I mean, one of the things that people like about data mesh is it pretty crisply articulate some of the flaws in today's organizational approaches to data. What are your thoughts on this? >> Well, I think we have to start by defining data mesh, right? The term is already getting corrupted, right? Tony said it's going to see the cold hard light of day. And there's a problem right now that there are a number of overlapping terms that are similar but not identical. So we've got data virtualization, data fabric, excuse me for a second. (clears throat) Sorry about that. Data virtualization, data fabric, data federation, right? So I think that it's not really clear what each vendor means by these terms. I see data mesh and data fabric becoming quite popular. I've interpreted data mesh as referring primarily to the governance aspects as originally intended and specified. But that's not the way I see vendors using it. I see vendors using it much more to mean data fabric and data virtualization. So I'm going to comment on the group of those things. I think the group of those things is going to happen. They're going to happen, they're going to become more robust. Our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half, so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access. Again, whether you define it as mesh or fabric or virtualization isn't really the point here. But this notion that there are different elements of data, metadata and governance within an organization that all need to be managed collectively. The interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not, it's almost double, 68% of organizations, I'm sorry, 79% of organizations that were using virtualized access express satisfaction with their access to the data lake. Only 39% express satisfaction if they weren't using virtualized access. >> Oh thank you Dave. Sanjeev we just got about a couple of minutes on this topic, but I know you're speaking or maybe you've always spoken already on a panel with (indistinct) who sort of invented the concept. Governance obviously is a big sticking point, but what are your thoughts on this? You're on mute. (panelist chuckling) >> So my message to (indistinct) and to the community is as opposed to what they said, let's not define it. We spent a whole year defining it, there are four principles, domain, product, data infrastructure, and governance. Let's take it to the next level. I get a lot of questions on what is the difference between data fabric and data mesh? And I'm like I can't compare the two because data mesh is a business concept, data fabric is a data integration pattern. How do you compare the two? You have to bring data mesh a level down. So to Tony's point, I'm on a warpath in 2022 to take it down to what does a data product look like? How do we handle shared data across domains and governance? And I think we are going to see more of that in 2022, or is "operationalization" of data mesh. >> I think we could have a whole hour on this topic, couldn't we? Maybe we should do that. But let's corner. Let's move to Carl. So Carl, you're a database guy, you've been around that block for a while now, you want to talk about graph databases, bring it on. >> Oh yeah. Okay thanks. So I regard graph database as basically the next truly revolutionary database management technology. I'm looking forward for the graph database market, which of course we haven't defined yet. So obviously I have a little wiggle room in what I'm about to say. But this market will grow by about 600% over the next 10 years. Now, 10 years is a long time. But over the next five years, we expect to see gradual growth as people start to learn how to use it. The problem is not that it's not useful, its that people don't know how to use it. So let me explain before I go any further what a graph database is because some of the folks on the call may not know what it is. A graph database organizes data according to a mathematical structure called a graph. The graph has elements called nodes and edges. So a data element drops into a node, the nodes are connected by edges, the edges connect one node to another node. Combinations of edges create structures that you can analyze to determine how things are related. In some cases, the nodes and edges can have properties attached to them which add additional informative material that makes it richer, that's called a property graph. There are two principle use cases for graph databases. There's semantic property graphs, which are use to break down human language texts into the semantic structures. Then you can search it, organize it and answer complicated questions. A lot of AI is aimed at semantic graphs. Another kind is the property graph that I just mentioned, which has a dazzling number of use cases. I want to just point out as I talk about this, people are probably wondering, well, we have relation databases, isn't that good enough? So a relational database defines... It supports what I call definitional relationships. That means you define the relationships in a fixed structure. The database drops into that structure, there's a value, foreign key value, that relates one table to another and that value is fixed. You don't change it. If you change it, the database becomes unstable, it's not clear what you're looking at. In a graph database, the system is designed to handle change so that it can reflect the true state of the things that it's being used to track. So let me just give you some examples of use cases for this. They include entity resolution, data lineage, social media analysis, Customer 360, fraud prevention. There's cybersecurity, there's strong supply chain is a big one actually. There is explainable AI and this is going to become important too because a lot of people are adopting AI. But they want a system after the fact to say, how do the AI system come to that conclusion? How did it make that recommendation? Right now we don't have really good ways of tracking that. Machine learning in general, social network, I already mentioned that. And then we've got, oh gosh, we've got data governance, data compliance, risk management. We've got recommendation, we've got personalization, anti money laundering, that's another big one, identity and access management, network and IT operations is already becoming a key one where you actually have mapped out your operation, you know, whatever it is, your data center and you can track what's going on as things happen there, root cause analysis, fraud detection is a huge one. A number of major credit card companies use graph databases for fraud detection, risk analysis, tracking and tracing turn analysis, next best action, what if analysis, impact analysis, entity resolution and I would add one other thing or just a few other things to this list, metadata management. So Sanjeev, here you go, this is your engine. Because I was in metadata management for quite a while in my past life. And one of the things I found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it, but graphs can, okay? Graphs can do things like say, this term in this context means this, but in that context, it means that, okay? Things like that. And in fact, logistics management, supply chain. And also because it handles recursive relationships, by recursive relationships I mean objects that own other objects that are of the same type. You can do things like build materials, you know, so like parts explosion. Or you can do an HR analysis, who reports to whom, how many levels up the chain and that kind of thing. You can do that with relational databases, but yet it takes a lot of programming. In fact, you can do almost any of these things with relational databases, but the problem is, you have to program it. It's not supported in the database. And whenever you have to program something, that means you can't trace it, you can't define it. You can't publish it in terms of its functionality and it's really, really hard to maintain over time. >> Carl, thank you. I wonder if we could bring Brad in, I mean. Brad, I'm sitting here wondering, okay, is this incremental to the market? Is it disruptive and replacement? What are your thoughts on this phase? >> It's already disrupted the market. I mean, like Carl said, go to any bank and ask them are you using graph databases to get fraud detection under control? And they'll say, absolutely, that's the only way to solve this problem. And it is frankly. And it's the only way to solve a lot of the problems that Carl mentioned. And that is, I think it's Achilles heel in some ways. Because, you know, it's like finding the best way to cross the seven bridges of Koenigsberg. You know, it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique, it's still unfortunately kind of stands apart from the rest of the community that's building, let's say AI outcomes, as a great example here. Graph databases and AI, as Carl mentioned, are like chocolate and peanut butter. But technologically, you think don't know how to talk to one another, they're completely different. And you know, you can't just stand up SQL and query them. You've got to learn, know what is the Carl? Specter special. Yeah, thank you to, to actually get to the data in there. And if you're going to scale that data, that graph database, especially a property graph, if you're going to do something really complex, like try to understand you know, all of the metadata in your organization, you might just end up with, you know, a graph database winter like we had the AI winter simply because you run out of performance to make the thing happen. So, I think it's already disrupted, but we need to like treat it like a first-class citizen in the data analytics and AI community. We need to bring it into the fold. We need to equip it with the tools it needs to do the magic it does and to do it not just for specialized use cases, but for everything. 'Cause I'm with Carl. I think it's absolutely revolutionary. >> Brad identified the principal, Achilles' heel of the technology which is scaling. When these things get large and complex enough that they spill over what a single server can handle, you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down. So that's still a problem to be solved. >> Sanjeev, any quick thoughts on this? I mean, I think metadata on the word cloud is going to be the largest font, but what are your thoughts here? >> I want to (indistinct) So people don't associate me with only metadata, so I want to talk about something slightly different. dbengines.com has done an amazing job. I think almost everyone knows that they chronicle all the major databases that are in use today. In January of 2022, there are 381 databases on a ranked list of databases. The largest category is RDBMS. The second largest category is actually divided into two property graphs and IDF graphs. These two together make up the second largest number databases. So talking about Achilles heel, this is a problem. The problem is that there's so many graph databases to choose from. They come in different shapes and forms. To Brad's point, there's so many query languages in RDBMS, in SQL. I know the story, but here We've got cipher, we've got gremlin, we've got GQL and then we're proprietary languages. So I think there's a lot of disparity in this space. >> Well, excellent. All excellent points, Sanjeev, if I must say. And that is a problem that the languages need to be sorted and standardized. People need to have a roadmap as to what they can do with it. Because as you say, you can do so many things. And so many of those things are unrelated that you sort of say, well, what do we use this for? And I'm reminded of the saying I learned a bunch of years ago. And somebody said that the digital computer is the only tool man has ever device that has no particular purpose. (panelists chuckle) >> All right guys, we got to move on to Dave Menninger. We've heard about streaming. Your prediction is in that realm, so please take it away. >> Sure. So I like to say that historical databases are going to become a thing of the past. By that I don't mean that they're going to go away, that's not my point. I mean, we need historical databases, but streaming data is going to become the default way in which we operate with data. So in the next say three to five years, I would expect that data platforms and we're using the term data platforms to represent the evolution of databases and data lakes, that the data platforms will incorporate these streaming capabilities. We're going to process data as it streams into an organization and then it's going to roll off into historical database. So historical databases don't go away, but they become a thing of the past. They store the data that occurred previously. And as data is occurring, we're going to be processing it, we're going to be analyzing it, we're going to be acting on it. I mean we only ever ended up with historical databases because we were limited by the technology that was available to us. Data doesn't occur in patches. But we processed it in patches because that was the best we could do. And it wasn't bad and we've continued to improve and we've improved and we've improved. But streaming data today is still the exception. It's not the rule, right? There are projects within organizations that deal with streaming data. But it's not the default way in which we deal with data yet. And so that's my prediction is that this is going to change, we're going to have streaming data be the default way in which we deal with data and how you label it and what you call it. You know, maybe these databases and data platforms just evolved to be able to handle it. But we're going to deal with data in a different way. And our research shows that already, about half of the participants in our analytics and data benchmark research, are using streaming data. You know, another third are planning to use streaming technologies. So that gets us to about eight out of 10 organizations need to use this technology. And that doesn't mean they have to use it throughout the whole organization, but it's pretty widespread in its use today and has continued to grow. If you think about the consumerization of IT, we've all been conditioned to expect immediate access to information, immediate responsiveness. You know, we want to know if an item is on the shelf at our local retail store and we can go in and pick it up right now. You know, that's the world we live in and that's spilling over into the enterprise IT world We have to provide those same types of capabilities. So that's my prediction, historical databases become a thing of the past, streaming data becomes the default way in which we operate with data. >> All right thank you David. Well, so what say you, Carl, the guy who has followed historical databases for a long time? >> Well, one thing actually, every database is historical because as soon as you put data in it, it's now history. They'll no longer reflect the present state of things. But even if that history is only a millisecond old, it's still history. But I would say, I mean, I know you're trying to be a little bit provocative in saying this Dave 'cause you know, as well as I do that people still need to do their taxes, they still need to do accounting, they still need to run general ledger programs and things like that. That all involves historical data. That's not going to go away unless you want to go to jail. So you're going to have to deal with that. But as far as the leading edge functionality, I'm totally with you on that. And I'm just, you know, I'm just kind of wondering if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way applications work. Saying that an application should respond instantly, as soon as the state of things changes. What do you say about that? >> I think that's true. I think we do have to think about things differently. It's not the way we designed systems in the past. We're seeing more and more systems designed that way. But again, it's not the default. And I agree 100% with you that we do need historical databases you know, that's clear. And even some of those historical databases will be used in conjunction with the streaming data, right? >> Absolutely. I mean, you know, let's take the data warehouse example where you're using the data warehouse as its context and the streaming data as the present and you're saying, here's the sequence of things that's happening right now. Have we seen that sequence before? And where? What does that pattern look like in past situations? And can we learn from that? >> So Tony Baer, I wonder if you could comment? I mean, when you think about, you know, real time inferencing at the edge, for instance, which is something that a lot of people talk about, a lot of what we're discussing here in this segment, it looks like it's got a great potential. What are your thoughts? >> Yeah, I mean, I think you nailed it right. You know, you hit it right on the head there. Which is that, what I'm seeing is that essentially. Then based on I'm going to split this one down the middle is that I don't see that basically streaming is the default. What I see is streaming and basically and transaction databases and analytics data, you know, data warehouses, data lakes whatever are converging. And what allows us technically to converge is cloud native architecture, where you can basically distribute things. So you can have a node here that's doing the real-time processing, that's also doing... And this is where it leads in or maybe doing some of that real time predictive analytics to take a look at, well look, we're looking at this customer journey what's happening with what the customer is doing right now and this is correlated with what other customers are doing. So the thing is that in the cloud, you can basically partition this and because of basically the speed of the infrastructure then you can basically bring these together and kind of orchestrate them sort of a loosely coupled manner. The other parts that the use cases are demanding, and this is part of it goes back to what Dave is saying. Is that, you know, when you look at Customer 360, when you look at let's say Smart Utility products, when you look at any type of operational problem, it has a real time component and it has an historical component. And having predictive and so like, you know, my sense here is that technically we can bring this together through the cloud. And I think the use case is that we can apply some real time sort of predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction, we have this real-time input. >> Sanjeev, did you have a comment? >> Yeah, I was just going to say that to Dave's point, you know, we have to think of streaming very different because in the historical databases, we used to bring the data and store the data and then we used to run rules on top, aggregations and all. But in case of streaming, the mindset changes because the rules are normally the inference, all of that is fixed, but the data is constantly changing. So it's a completely reversed way of thinking and building applications on top of that. >> So Dave Menninger, there seem to be some disagreement about the default. What kind of timeframe are you thinking about? Is this end of decade it becomes the default? What would you pin? >> I think around, you know, between five to 10 years, I think this becomes the reality. >> I think its... >> It'll be more and more common between now and then, but it becomes the default. And I also want Sanjeev at some point, maybe in one of our subsequent conversations, we need to talk about governing streaming data. 'Cause that's a whole nother set of challenges. >> We've also talked about it rather in two dimensions, historical and streaming, and there's lots of low latency, micro batch, sub-second, that's not quite streaming, but in many cases its fast enough and we're seeing a lot of adoption of near real time, not quite real-time as good enough for many applications. (indistinct cross talk from panelists) >> Because nobody's really taking the hardware dimension (mumbles). >> That'll just happened, Carl. (panelists laughing) >> So near real time. But maybe before you lose the customer, however we define that, right? Okay, let's move on to Brad. Brad, you want to talk about automation, AI, the pipeline people feel like, hey, we can just automate everything. What's your prediction? >> Yeah I'm an AI aficionados so apologies in advance for that. But, you know, I think that we've been seeing automation play within AI for some time now. And it's helped us do a lot of things especially for practitioners that are building AI outcomes in the enterprise. It's helped them to fill skills gaps, it's helped them to speed development and it's helped them to actually make AI better. 'Cause it, you know, in some ways provide some swim lanes and for example, with technologies like AutoML can auto document and create that sort of transparency that we talked about a little bit earlier. But I think there's an interesting kind of conversion happening with this idea of automation. And that is that we've had the automation that started happening for practitioners, it's trying to move out side of the traditional bounds of things like I'm just trying to get my features, I'm just trying to pick the right algorithm, I'm just trying to build the right model and it's expanding across that full life cycle, building an AI outcome, to start at the very beginning of data and to then continue on to the end, which is this continuous delivery and continuous automation of that outcome to make sure it's right and it hasn't drifted and stuff like that. And because of that, because it's become kind of powerful, we're starting to actually see this weird thing happen where the practitioners are starting to converge with the users. And that is to say that, okay, if I'm in Tableau right now, I can stand up Salesforce Einstein Discovery, and it will automatically create a nice predictive algorithm for me given the data that I pull in. But what's starting to happen and we're seeing this from the companies that create business software, so Salesforce, Oracle, SAP, and others is that they're starting to actually use these same ideals and a lot of deep learning (chuckles) to basically stand up these out of the box flip-a-switch, and you've got an AI outcome at the ready for business users. And I am very much, you know, I think that's the way that it's going to go and what it means is that AI is slowly disappearing. And I don't think that's a bad thing. I think if anything, what we're going to see in 2022 and maybe into 2023 is this sort of rush to put this idea of disappearing AI into practice and have as many of these solutions in the enterprise as possible. You can see, like for example, SAP is going to roll out this quarter, this thing called adaptive recommendation services, which basically is a cold start AI outcome that can work across a whole bunch of different vertical markets and use cases. It's just a recommendation engine for whatever you needed to do in the line of business. So basically, you're an SAP user, you look up to turn on your software one day, you're a sales professional let's say, and suddenly you have a recommendation for customer churn. Boom! It's going, that's great. Well, I don't know, I think that's terrifying. In some ways I think it is the future that AI is going to disappear like that, but I'm absolutely terrified of it because I think that what it really does is it calls attention to a lot of the issues that we already see around AI, specific to this idea of what we like to call at Omdia, responsible AI. Which is, you know, how do you build an AI outcome that is free of bias, that is inclusive, that is fair, that is safe, that is secure, that its audible, et cetera, et cetera, et cetera, et cetera. I'd take a lot of work to do. And so if you imagine a customer that's just a Salesforce customer let's say, and they're turning on Einstein Discovery within their sales software, you need some guidance to make sure that when you flip that switch, that the outcome you're going to get is correct. And that's going to take some work. And so, I think we're going to see this move, let's roll this out and suddenly there's going to be a lot of problems, a lot of pushback that we're going to see. And some of that's going to come from GDPR and others that Sanjeev was mentioning earlier. A lot of it is going to come from internal CSR requirements within companies that are saying, "Hey, hey, whoa, hold up, we can't do this all at once. "Let's take the slow route, "let's make AI automated in a smart way." And that's going to take time. >> Yeah, so a couple of predictions there that I heard. AI simply disappear, it becomes invisible. Maybe if I can restate that. And then if I understand it correctly, Brad you're saying there's a backlash in the near term. You'd be able to say, oh, slow down. Let's automate what we can. Those attributes that you talked about are non trivial to achieve, is that why you're a bit of a skeptic? >> Yeah. I think that we don't have any sort of standards that companies can look to and understand. And we certainly, within these companies, especially those that haven't already stood up an internal data science team, they don't have the knowledge to understand when they flip that switch for an automated AI outcome that it's going to do what they think it's going to do. And so we need some sort of standard methodology and practice, best practices that every company that's going to consume this invisible AI can make use of them. And one of the things that you know, is sort of started that Google kicked off a few years back that's picking up some momentum and the companies I just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing. You know, so like for the SAP example, we know, for example, if it's convolutional neural network with a long, short term memory model that it's using, we know that it only works on Roman English and therefore me as a consumer can say, "Oh, well I know that I need to do this internationally. "So I should not just turn this on today." >> Thank you. Carl could you add anything, any context here? >> Yeah, we've talked about some of the things Brad mentioned here at IDC and our future of intelligence group regarding in particular, the moral and legal implications of having a fully automated, you know, AI driven system. Because we already know, and we've seen that AI systems are biased by the data that they get, right? So if they get data that pushes them in a certain direction, I think there was a story last week about an HR system that was recommending promotions for White people over Black people, because in the past, you know, White people were promoted and more productive than Black people, but it had no context as to why which is, you know, because they were being historically discriminated, Black people were being historically discriminated against, but the system doesn't know that. So, you know, you have to be aware of that. And I think that at the very least, there should be controls when a decision has either a moral or legal implication. When you really need a human judgment, it could lay out the options for you. But a person actually needs to authorize that action. And I also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases. In some extent, they always will. So we'll always be chasing after them. But that's (indistinct). >> Absolutely Carl, yeah. I think that what you have to bear in mind as a consumer of AI is that it is a reflection of us and we are a very flawed species. And so if you look at all of the really fantastic, magical looking supermodels we see like GPT-3 and four, that's coming out, they're xenophobic and hateful because the people that the data that's built upon them and the algorithms and the people that build them are us. So AI is a reflection of us. We need to keep that in mind. >> Yeah, where the AI is biased 'cause humans are biased. All right, great. All right let's move on. Doug you mentioned mentioned, you know, lot of people that said that data lake, that term is not going to live on but here's to be, have some lakes here. You want to talk about lake house, bring it on. >> Yes, I do. My prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering. I say offering that doesn't mean it's going to be the dominant thing that organizations have out there, but it's going to be the pro dominant vendor offering in 2022. Now heading into 2021, we already had Cloudera, Databricks, Microsoft, Snowflake as proponents, in 2021, SAP, Oracle, and several of all of these fabric virtualization/mesh vendors joined the bandwagon. The promise is that you have one platform that manages your structured, unstructured and semi-structured information. And it addresses both the BI analytics needs and the data science needs. The real promise there is simplicity and lower cost. But I think end users have to answer a few questions. The first is, does your organization really have a center of data gravity or is the data highly distributed? Multiple data warehouses, multiple data lakes, on premises, cloud. If it's very distributed and you'd have difficulty consolidating and that's not really a goal for you, then maybe that single platform is unrealistic and not likely to add value to you. You know, also the fabric and virtualization vendors, the mesh idea, that's where if you have this highly distributed situation, that might be a better path forward. The second question, if you are looking at one of these lake house offerings, you are looking at consolidating, simplifying, bringing together to a single platform. You have to make sure that it meets both the warehouse need and the data lake need. So you have vendors like Databricks, Microsoft with Azure Synapse. New really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements, can meet the user and query concurrency requirements. Meet those tight SLS. And then on the other hand, you have the Oracle, SAP, Snowflake, the data warehouse folks coming into the data science world, and they have to prove that they can manage the unstructured information and meet the needs of the data scientists. I'm seeing a lot of the lake house offerings from the warehouse crowd, managing that unstructured information in columns and rows. And some of these vendors, Snowflake a particular is really relying on partners for the data science needs. So you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement. >> Thank you Doug. Well Tony, if those two worlds are going to come together, as Doug was saying, the analytics and the data science world, does it need to be some kind of semantic layer in between? I don't know. Where are you in on this topic? >> (chuckles) Oh, didn't we talk about data fabrics before? Common metadata layer (chuckles). Actually, I'm almost tempted to say let's declare victory and go home. And that this has actually been going on for a while. I actually agree with, you know, much of what Doug is saying there. Which is that, I mean I remember as far back as I think it was like 2014, I was doing a study. I was still at Ovum, (indistinct) Omdia, looking at all these specialized databases that were coming up and seeing that, you know, there's overlap at the edges. But yet, there was still going to be a reason at the time that you would have, let's say a document database for JSON, you'd have a relational database for transactions and for data warehouse and you had basically something at that time that resembles a dupe for what we consider your data life. Fast forward and the thing is what I was seeing at the time is that you were saying they sort of blending at the edges. That was saying like about five to six years ago. And the lake house is essentially on the current manifestation of that idea. There is a dichotomy in terms of, you know, it's the old argument, do we centralize this all you know in a single place or do we virtualize? And I think it's always going to be a union yeah and there's never going to be a single silver bullet. I do see that there are also going to be questions and these are points that Doug raised. That you know, what do you need for your performance there, or for your free performance characteristics? Do you need for instance high concurrency? You need the ability to do some very sophisticated joins, or is your requirement more to be able to distribute and distribute our processing is, you know, as far as possible to get, you know, to essentially do a kind of a brute force approach. All these approaches are valid based on the use case. I just see that essentially that the lake house is the culmination of it's nothing. It's a relatively new term introduced by Databricks a couple of years ago. This is the culmination of basically what's been a long time trend. And what we see in the cloud is that as we start seeing data warehouses as a check box items say, "Hey, we can basically source data in cloud storage, in S3, "Azure Blob Store, you know, whatever, "as long as it's in certain formats, "like, you know parquet or CSP or something like that." I see that as becoming kind of a checkbox item. So to that extent, I think that the lake house, depending on how you define is already reality. And in some cases, maybe new terminology, but not a whole heck of a lot new under the sun. >> Yeah. And Dave Menninger, I mean a lot of these, thank you Tony, but a lot of this is going to come down to, you know, vendor marketing, right? Some people just kind of co-op the term, we talked about you know, data mesh washing, what are your thoughts on this? (laughing) >> Yeah, so I used the term data platform earlier. And part of the reason I use that term is that it's more vendor neutral. We've tried to sort of stay out of the vendor terminology patenting world, right? Whether the term lake houses, what sticks or not, the concept is certainly going to stick. And we have some data to back it up. About a quarter of organizations that are using data lakes today, already incorporate data warehouse functionality into it. So they consider their data lake house and data warehouse one in the same, about a quarter of organizations, a little less, but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake. So it's pretty obvious that three quarters of organizations need to bring this stuff together, right? The need is there, the need is apparent. The technology is going to continue to converge. I like to talk about it, you know, you've got data lakes over here at one end, and I'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a server and you ignore it, right? That's not what a data lake is. So you've got data lake people over here and you've got database people over here, data warehouse people over here, database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities. So it's obvious that they're going to meet in the middle. I mean, I think it's like Tony says, I think we should declare victory and go home. >> As hell. So just a follow-up on that, so are you saying the specialized lake and the specialized warehouse, do they go away? I mean, Tony data mesh practitioners would say or advocates would say, well, they could all live. It's just a node on the mesh. But based on what Dave just said, are we gona see those all morphed together? >> Well, number one, as I was saying before, there's always going to be this sort of, you know, centrifugal force or this tug of war between do we centralize the data, do we virtualize? And the fact is I don't think that there's ever going to be any single answer. I think in terms of data mesh, data mesh has nothing to do with how you're physically implement the data. You could have a data mesh basically on a data warehouse. It's just that, you know, the difference being is that if we use the same physical data store, but everybody's logically you know, basically governing it differently, you know? Data mesh in space, it's not a technology, it's processes, it's governance process. So essentially, you know, I basically see that, you know, as I was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring, but there are going to be cases where, for instance, if I need, let's say like, Upserve, I need like high concurrency or something like that. There are certain things that I'm not going to be able to get efficiently get out of a data lake. And, you know, I'm doing a system where I'm just doing really brute forcing very fast file scanning and that type of thing. So I think there always will be some delineations, but I would agree with Dave and with Doug, that we are seeing basically a confluence of requirements that we need to essentially have basically either the element, you know, the ability of a data lake and the data warehouse, these need to come together, so I think. >> I think what we're likely to see is organizations look for a converge platform that can handle both sides for their center of data gravity, the mesh and the fabric virtualization vendors, they're all on board with the idea of this converged platform and they're saying, "Hey, we'll handle all the edge cases "of the stuff that isn't in that center of data gravity "but that is off distributed in a cloud "or at a remote location." So you can have that single platform for the center of your data and then bring in virtualization, mesh, what have you, for reaching out to the distributed data. >> As Dave basically said, people are happy when they virtualized data. >> I think we have at this point, but to Dave Menninger's point, they are converging, Snowflake has introduced support for unstructured data. So obviously literally splitting here. Now what Databricks is saying is that "aha, but it's easy to go from data lake to data warehouse "than it is from databases to data lake." So I think we're getting into semantics, but we're already seeing these two converge. >> So take somebody like AWS has got what? 15 data stores. Are they're going to 15 converge data stores? This is going to be interesting to watch. All right, guys, I'm going to go down and list do like a one, I'm going to one word each and you guys, each of the analyst, if you would just add a very brief sort of course correction for me. So Sanjeev, I mean, governance is going to to be... Maybe it's the dog that wags the tail now. I mean, it's coming to the fore, all this ransomware stuff, which you really didn't talk much about security, but what's the one word in your prediction that you would leave us with on governance? >> It's going to be mainstream. >> Mainstream. Okay. Tony Baer, mesh washing is what I wrote down. That's what we're going to see in 2022, a little reality check, you want to add to that? >> Reality check, 'cause I hope that no vendor jumps the shark and close they're offering a data niche product. >> Yeah, let's hope that doesn't happen. If they do, we're going to call them out. Carl, I mean, graph databases, thank you for sharing some high growth metrics. I know it's early days, but magic is what I took away from that, so magic database. >> Yeah, I would actually, I've said this to people too. I kind of look at it as a Swiss Army knife of data because you can pretty much do anything you want with it. That doesn't mean you should. I mean, there's definitely the case that if you're managing things that are in fixed schematic relationship, probably a relation database is a better choice. There are times when the document database is a better choice. It can handle those things, but maybe not. It may not be the best choice for that use case. But for a great many, especially with the new emerging use cases I listed, it's the best choice. >> Thank you. And Dave Menninger, thank you by the way, for bringing the data in, I like how you supported all your comments with some data points. But streaming data becomes the sort of default paradigm, if you will, what would you add? >> Yeah, I would say think fast, right? That's the world we live in, you got to think fast. >> Think fast, love it. And Brad Shimmin, love it. I mean, on the one hand I was saying, okay, great. I'm afraid I might get disrupted by one of these internet giants who are AI experts. I'm going to be able to buy instead of build AI. But then again, you know, I've got some real issues. There's a potential backlash there. So give us your bumper sticker. >> I'm would say, going with Dave, think fast and also think slow to talk about the book that everyone talks about. I would say really that this is all about trust, trust in the idea of automation and a transparent and visible AI across the enterprise. And verify, verify before you do anything. >> And then Doug Henschen, I mean, I think the trend is your friend here on this prediction with lake house is really becoming dominant. I liked the way you set up that notion of, you know, the data warehouse folks coming at it from the analytics perspective and then you get the data science worlds coming together. I still feel as though there's this piece in the middle that we're missing, but your, your final thoughts will give you the (indistinct). >> I think the idea of consolidation and simplification always prevails. That's why the appeal of a single platform is going to be there. We've already seen that with, you know, DoOP platforms and moving toward cloud, moving toward object storage and object storage, becoming really the common storage point for whether it's a lake or a warehouse. And that second point, I think ESG mandates are going to come in alongside GDPR and things like that to up the ante for good governance. >> Yeah, thank you for calling that out. Okay folks, hey that's all the time that we have here, your experience and depth of understanding on these key issues on data and data management really on point and they were on display today. I want to thank you for your contributions. Really appreciate your time. >> Enjoyed it. >> Thank you. >> Thanks for having me. >> In addition to this video, we're going to be making available transcripts of the discussion. We're going to do clips of this as well we're going to put them out on social media. I'll write this up and publish the discussion on wikibon.com and siliconangle.com. No doubt, several of the analysts on the panel will take the opportunity to publish written content, social commentary or both. I want to thank the power panelists and thanks for watching this special CUBE presentation. This is Dave Vellante, be well and we'll see you next time. (bright music)

(upbeat electronic music) >> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Okay, hello everyone. I'm John Furrier with theCUBE. We're here with Meet the Analysts segment Sunday morning. We've got everyone around the world here to discuss a bit of the news around the EU killing the privacy deal, striking it down, among other topics around, you know, data privacy and global commerce. We got great guests here, Ray Wang, CEO of Constellation Research. Bill Mew, founder and CEO of Cyber Crisis Management from the Firm Crisis Team. And JD, CEO of Spearhead Management. JD, I can let you say your name because I really can't pronounce it. How do I (laughs) pronounce it, doctor? >> I wouldn't even try it unless you are Dutch, otherwise it will seriously hurt your throat. (Ray laughing) So, JD works perfect for me. >> Doctor Drooghaag. >> And Sarbjeet Johal, who's obviously an influencer, a cloud awesome native expert. Great, guys. Great to have you on, appreciate it, thanks for comin' on. And Bill, thank you for initiating this, I appreciate all your tweets. >> Happy Sunday. (Bill laughing) >> You guys have been really tweeting up a storm, I want to get everyone together, kind of as an analyst, Meet the Analyst segment. Let's go through with it. The news is the EU and U.S. Privacy Shield for data struck down by the court, that's the BBC headline. Variety of news, different perspectives, you've got an American perspective and you've got an international perspective. Bill, we'll start with you. What does this news mean? I mean, basically half the people in the world probably don't know what the Privacy Shield means, so why is this ruling so important, and why should it be discussed? >> Well, thanks to sharing between Europe and America, it's based on a two-way promise that when data goes from Europe to America, the Americans promise to respect our privacy, and when data goes form America to Europe, the Europeans promise to respect the American privacy. Unfortunately, there are big cultural differences between the two blocks. The Europeans have a massive orientation around privacy as a human right. And in the U.S., there's somewhat more of a prioritization on national security, and therefore for some time there's been a mismatch here, and it could be argued that the Americans haven't been living up to their promise because they've had various different laws, and look how much talk about FISA and the Cloud Act that actually contravene European privacy and are incompatible with the promise Americans have given. That promise, first of all, was in the form of a treaty called Safe Harbor. This went to court and was struck down. It was replaced by Privacy Shield, which was pretty much the same thing really, and that has recently been to the court as well, and that has been struck down. There now is no other means of legally sharing data between Europe and America other than what are being called standard contractual clauses. This isn't a broad treaty between two nations, these are drawn by each individual country. But also in the ruling, they said that standard contractual clauses could not be used by any companies that were subject to mass surveillance. And actually in the U.S., the FISA courts enforce a level of mass surveillance through all of the major IT firms, of all major U.S. telcos, cloud firms, or indeed, social media firms. So, this means that for all of the companies out there and their clients, business should be carrying on as usual apart from if you're one of those major U.S. IT firms, or one of their clients. >> So, why did this come about? Was there like a major incident? Why now, was it in the court, stuck in the courts? Were people bitchin' and moanin' about it? Why did this go down, what's the real issue? >> For those of us who have been following this attentively, things have been getting more and more precarious for a number of years now. We've had a situation where there are different measures being taken in the U.S., that have continued to erode the different protections that there were for Europeans. FISA is an example that I've given, and that is the sort of secret courts and secret warrants that are issued to seize data without anyone's knowledge. There's the Cloud Act, which is a sort of extrajudicial law that means that warrants can be served in America to U.S. organizations, and they have to hand over data wherever that data resides, anywhere in the world. So, data could exist on a European server, if it was under the control of an American company, they'd have to hand that over. So, whilst FISA is in direct conflict with the promises that the Americans made, things like the Cloud Act are not only in controversion with the promise they've made, there's conflicting law here, because if you're a U.S. subsidiary of a big U.S. firm, and you're based in Europe, who do you obey, the European law that says you can't hand it over because of GDPR, or the American laws that says they've got extrajudicial control, and that you've got to hand it over. So, it's made things a complete mess. And to say has this stuff, hasn't really happened? No, there's been a gradual erosion, and this has been going through the courts for a number of years. And many of us have seen it coming, and now it just hit us. >> So, if I get you right in what you're saying, it's basically all this mishmash of different laws, and there's no coherency, and consistency, is that the core issue? >> On the European side you could argue there's quite a lot of consistency, because we uphold people's privacy, in theory. But there have been incidents which we could talk about with that, but in theory, we hold your rights dear, and also the rights of Europeans, so everyone's data should be safe here from the sort of mass surveillance we're seeing. In the U.S., there's more of a direct conflict between everything, including there's been a, in his first week in the White House, Donald Trump signed an executive order saying that the Privacy Act in the U.S., which had been the main protection for people in the U.S., no longer applied to non-U.S. citizens. Which was, if you wanted try and cause a storm, and if you wanted to try and undermine the treaty, there's no better way of doing it than that. >> A lot of ways, Ray, I mean simplify this for me, because I'm a startup, I'm hustlin', or I'm a big company, I don't even know who runs the servers anymore, and I've got data stored in multiple clouds, I got in regions, and Oracle just announced more regions, you got Amazon, a gazillion regions, I could be on-premise. I mean bottom line, what is this about? I mean, and -- >> Bill's right, I mean when Max Schrems, the Austrian. Bill's right, when Max Schrems the Austrian activist actually filed his case against Facebook for where data was being stored, data residency wasn't as popular. And you know, what it means for companies that are in the cloud is that you have to make sure your data's being stored in the region, and following those specific region rules, you can't skirt those rules anymore. And I think the cloud companies know that this has been coming for some time, and that's why there's been announced in a lot of regions, a lot of areas that are actually happening, so I think that's the important part. But going back to Bill's earlier point, which is important, is America is basically the Canary Islands of privacy, right? Privacy is there, but it isn't there in a very, very explicit sense, and I think we've been skirting the rules for quite some time, because a lot of our economy depends on that data, and the marketing of the data. And so we often confuse privacy with consent, and also with value exchange, and I think that's part of the problem of what's going on here. Companies that have been building their business models on free data, free private data, free personally identifiable data information are the ones that are at risk! And I think that's what's going on here. >> It's the classic Facebook issue, you're the product, and the data is your product. Well, I want to get into what this means, 'cause my personal take away, not knowing the specifics, and just following say, cyber security for instance, one of the tenets there is that data sharing is an invaluable, important ethos in the community. Now, everyone has their own privacy, or security data, they don't want to let everyone know about their exploits but, but it's well known in the security world that sharing data with each other, different companies and countries is actually a good thing. So, the question that comes in my mind, is this really about data sharing or data privacy, or both? >> I think it's about both. And actually what the ruling is saying here is, all we're asking from the European side is please stop spying on us and please give us a level of equal protection that you give to your own citizens. Because data comes from America to Europe, whatever that data belongs to, a U.S. citizen or a European citizen, it's given equal protection. It is only if data goes in the other direction, where you have secret courts, secret warrants, seizure of data on this massive scale, and also a level of lack of equivalence that has been imposed. And we're just asking that once you've sorted out a few of those things, we'd say everything's back on the table, away we go again! >> Why don't we merge the EU with the United States? Wouldn't that solve the problem? (Bill laughing) >> We just left Europe! (laughs heartily) >> Actually I always -- >> A hostile takeover of the UK maybe, the 52nd state. (Bill laughing loudly) >> I always pick on Bill, like Bill, you got all screaming loud and clear about all these concerns, but UKs trying to get out of that economic union. It is a union at the end of the day, and I think the problem is the institutional mismatch between the EU and U.S., U.S. is old democracy, bigger country, population wise, bigger economy. Whereas Europe is several countries trying to put together, band together as one entity, and the institutions are new, like you know, they're 15 years old, right? They're maturing. I think that's where the big mismatch is and -- >> Well, Ray, I want to get your thoughts on this, Ray wrote a book, I forget what year it was, this digital disruption, basically it was digital transformation before it was actually a trend. I mean to me it's like, do you do the process first and then figure out where the value extraction is, and this may be a Silicon Valley or an American thing, but go create value, then figure out how to create process or understand regulations. So, if data and entrepreneurship is going to be a new modern era of value, why wouldn't we want to create a rule based system that's open and enabling, and not restrictive? >> So, that's a great point, right? And the innovation culture means you go do it first, and you figure out the rules later, and that's been a very American way of getting things done, and very Silicon Valley in our perspective, not everyone, but I think in general that's kind of the trend. I think the challenge here is that we are trading privacy for security, privacy for convenience, privacy for personalization, right? And on the security level, it's a very different conversation than what it is on the consumer end, you know, personalization side. On the security side I think most Americans are okay with a little bit of "spying," at least on your own side, you know, to keep the country safe. We're not okay with a China level type of spying, which we're not sure exactly what that means or what's enforceable in the courts. We look like China to the Europeans in the way we treat privacy, and I think that's the perspective we need to understand because Europeans are very explicit about how privacy is being protected. And so this really comes back to a point where we actually have to get to a consent model on privacy, as to knowing what data is being shared, you have the right to say no, and when you have the right to say no. And then if you have a value exchange on that data, then it's really like sometimes it's monetary, sometimes it's non-monetary, sometimes there's other areas around consensus where you can actually put that into place. And I think that's what's missing at this point, saying, you know, "Do we pay for your data? Do we explicitly get your consent first before we use it?" And we haven't had that in place, and I think that's where we're headed towards. And you know sometimes we actually say privacy should be a human right, it is in the UN Charter, but we haven't figured out how to enforce it or talk about it in the digital age. And so I think that's the challenge. >> Okay, people, until they lose it, they don't really understand what it means. I mean, look at Americans. I have to say that we're idiots on this front, (Bill chuckling) but you know, the thing is most people don't even understand how much value's getting sucked out of their digital exhaust. Like, our kids, TikTok and whatnot. So I mean, I get that, I think there's some, there's going to be blow back for America for sure. I just worry it's going to increase the cost of doing business, and take away from the innovation for citizen value, the people, because at the end of the day, it's for the people right? I mean, at the end of the day it's like, what's my privacy mean if I lose value? >> Even before we start talking about the value of the data and the innovation that we can do through data use, you have to understand the European perspective here. For the European there's a level of double standards and an erosion of trust. There's double standards in the fact that in California you have new privacy regulations that are slightly different to GDPR, but they're very much GDPR like. And if the boot was on the other foot, to say if we were spying on Californians and looking at their personal data, and contravening CCPA, the Californians would be up in arms! Likewise if we having promised to have a level of equality, had enacted a local rule in Europe that said that when data from America's over here, actually the privacy of Americans counts for nothing, we're only going to prioritize the privacy of Europeans. Again, the Americans would be up in arms! And therefore you can see that there are real double standards here that are a massive issue, and until those addressed, we're not going to trust the Americans. And likewise, the very fact that on a number of occasions Americans have signed up to treaties and promised to protect our data as they did with Safe Harbor, as they did with Privacy Shield, and then have blatantly, blatantly failed to do so means that actually to get back to even a level playing field, where we were, you have a great deal of trust to overcome! And the thing from the perspective of the big IT firms, they've seen this coming for a long time, as Ray was saying, and they sought to try and have a presence in Europe and other things. But the way this ruling has gone is that, I'm sorry, that isn't going to be sufficient! These big IT firms based in the U.S. that have been happy to hand over data, well some of them maybe more happy than others, but they all need to hand over data to the NSA or the CIA. They've been doing this for some time now without actually respecting this data privacy agreement that has existed between the two trading blocks. And now they've been called out, and the position now is that the U.S. is no longer trusted, and neither are any of these large American technology firms. And until the snooping stops and equality is introduced, they can now no longer, even from their European operations, they can no longer use standard contractual clauses to transfer data, which is going to be a massive restriction on their business. And if they had any sense, they'd be lobbying very, very hard right now to the Senate, to the House, to try and persuade U.S. lawmakers actually to stick to some these treaties! To stop introducing really mad laws that ride roughshod over other people's privacy, and have a certain amount of respect. >> Let's let JD weigh in, 'cause he just got in, sorry on the video, I made him back on a host 'cause he dropped off. Just, Bill, real quick, I mean I think it's like when, you know, I go to Europe there's the line for Americans, there's the line for EU. Or EU and everybody else. I mean we might be there, but ultimately this has to be solved. So, JD, I want to let you weigh in, Germany has been at the beginning forefront of privacy, and they've been hardcore, and how's this all playing out in your perspective? >> Well, the first thing that we have to understand is that in Germany, there is a very strong law for regulation. Germans panic as soon as they know regulation, so they need to understand what am I allowed to do, and what am I not allowed to do. And they expect the same from the others. For the record I'm not German, but I live in Germany for some 20 years, so I got a bit of a feeling for them. And that sense of need for regulation has spread very fast throughout the European Union, because most of the European member states of the European Union consider this, that it makes sense, and then we found that Britain had already a very good framework for privacy, so GDPR itself is very largely based on what the United Kingdom already had in place with their privacy act. Moving forward, we try to find agreement and consensus with other countries, especially the United States because that's where most of the tech providers are, only to find out, and that is where it started to go really, really bad, 2014, when the mass production by Edward Snowden came out, to find out it's not data from citizens, it's surveillance programs which include companies. I joined a purchasing conference a few weeks ago where the purchase of a large European multinational, where the purchasing director explicitly stated that usage of U.S. based tech providers for sensitive data is prohibited as a result of them finding out that they have been under surveillance. So, it's not just the citizens, there's mass -- >> There you have it, guys! We did trust you! We did have agreements there that you could have abided by, but you chose not to, you chose to abuse our trust! And you're now in a position where you are no longer trusted, and unless you can lobby your own elected representatives to actually recreate a level playing field, we're not going to continue trusting you. >> So, I think really I -- >> Well I mean that, you know, innovation has to come from somewhere, and you know, has to come from America if that's the case, you guys have to get on board, right? Is that what it -- >> Innovation without trust? >> Is that the perspective? >> I don't think it's a country thing, I mean like, it's not you or them, I think everybody -- >> I'm just bustin' Bill's chops there. >> No, but I think everybody, everybody is looking for what the privacy rules are, and that's important. And you can have that innovation with consent, and I think that's really where we're going to get to. And this is why I keep pushing that issue. I mean, privacy should be a fundamental right, and how you get paid for that privacy is interesting, or how you get compensated for that privacy if you know what the explicit value exchange is. What you're talking about here is the surveillance that's going on by companies, which shouldn't be happening, right? That shouldn't be happening at the company level. At the government level I can understand that that is happening, and I think those are treaties that the governments have to agree upon as to how much they're going to impinge on our personal privacy for the trade off for security, and I don't think they've had those discussions either. Or they decided and didn't tell any of their citizens, and I think that's probably more likely the case. >> I mean, I think what's happening here, Bill, you guys were pointing out, and Ray, you articulated there on the other side, and my kind of colorful joke aside, is that we're living a first generation modern sociology problem. I mean, this is a policy challenge that extends across multiple industries, cyber security, citizen's rights, geopolitical. I mean when would look, and even when we were doing CUBE events overseas in Europe, in North American companies we'd call it abroad, we'd just recycle the American program, and we found there's so much localization value. So, Ray, this is the digital disruption, it's the virtualization of physical for digital worlds, and it's a lot of network theory, which is computer science, a lot of sociology. This is a modern challenge, and I don't think it so much has a silver bullet, it's just that we need smart people working on this. That's my take away! >> I think we can describe the ideal endpoint being somewhere we have meaningful protection alongside the maximization of economic and social value through innovation. So, that should be what we would all agree would be the ideal endpoint. But we need both, we need meaningful protection, and we need the maximization of economic and social value through innovation! >> Can I add another axis? Another axis, security as well. >> Well, I could -- >> I put meaningful protection as becoming both security and privacy. >> Well, I'll speak for the American perspective here, and I won't speak, 'cause I'm not the President of the United States, but I will say as someone who's been from Silicon Valley and the east coast as a technical person, not a political person, our lawmakers are idiots when it comes to tech, just generally. (Ray laughing) They're not really -- (Bill laughing loudly) >> They really don't understand. They really don't understand the tech at all! >> So, the problem is -- >> I'm not claiming ours are a great deal better. (laughs) >> Well, this is why I think this is a modern problem. Like, the young people I talk to are like, "Why do we have this rules?" They're all lawyers that got into these positions of Congress on the American side, and so with the American JEDI Contract you guys have been following very closely is, it's been like the old school Oracle, IBM, and then Amazon is leading with an innovative solution, and Microsoft has come in and re-pivoted. And so what you have is a fight for the digital future of citizenship! And I think what's happening is that we're in a massive societal transition, where the people in charge don't know what the hell they're talkin' about, technically. And they don't know who to tap to solve the problems, or even shape or frame the problems. Now, there's pockets of people that are workin' on it, but to me as someone who looks at this saying, it's a pretty simple solution, no one's ever seen this before. So, there's a metaphor you can draw, but it's a completely different problem space because it's, this is all digital, data's involved. >> We've got a lobbyists out there, and we've got some tech firms spending an enormous amount of lobbying. If those lobbyists aren't trying to steer their representatives in the right direction to come up with law that aren't going to massively undermine trade and data sharing between Europe and America, then they're making a big mistake, because we got here through some really dumb lawmaking in the U.S., I mean, there are none of the laws in Europe that are a problem here. 'Cause GDPR isn't a great difference, a great deal different from some of the laws that we have already in California and elsewhere. >> Bill, Bill. >> The laws that are at issue here -- >> Bill, Bill! You have to like, back up a little bit from that rhetoric that EU is perfect and U.S. is not, that's not true actually. >> I'm not saying we're perfect! >> No, no, you say that all the time. >> But I'm saying there's a massive lack of innovation. Yeah, yeah. >> I don't, I've never said it! >> Arm wrestle! >> Yes, yes. >> When I'm being critical of some of the dumb laws in the U.S, (Sarbjeet laughing) I'm not saying Europe is perfect. What we're trying to say is that in this particular instance, I said there was an equal balance here between meaningful protection and the maximization of economic and social value. On the meaningful protection side, America's got it very wrong in terms of the meaningful protection it provides to civil European data. On the maximization of economic and social value, I think Europe's got it wrong. I think there are a lot of things we could do in Europe to actually have far more innovation. >> Yeah. >> It's a cultural issue. The Germans want rules, that's what they crave for. America's the other way, we don't want rules, I mean, pretty much is a rebel society. And that's kind of the ethos of most tech companies. But I think you know, to me the media, there's two things that go on with this tech business. The company's themselves have to be checked by say, government, and I believe in not a lot of regulation, but enough to check the power of bad actors. Media so called "checking power", both of these major roles, they don't really know what they're talking about, and this is back to the education piece. The people who are in the media so called "checking power" and the government checking power assume that the companies are bad. Right, so yeah, because eight out of ten companies like Amazon, actually try to do good things. If you don't know what good is, you don't really, (laughs) you know, you're in the wrong game. So, I think media and government have a huge education opportunity to look at this because they don't even know what they're measuring. >> I support the level of innovation -- >> I think we're unreeling from the globalization. Like, we are undoing the globalization, and that these are the side effects, these conflicts are a side effect of that. >> Yeah, so all I'm saying is I support the focus on innovation in America, and that has driven an enormous amount of wealth and value. What I'm questioning here is do you really need to spy on us, your allies, in order to help that innovation? And I'm starting to, I mean, do you need mass surveillance of your allies? I mean, I can see you may want to have some surveillance of people who are a threat to you, but wait, guys, we're meant to be on your side, and you haven't been treating our privacy with a great deal of respect! >> You know, Saudi Arabia was our ally. You know, 9/11 happened because of them, their people, right? There is no ally here, and there is no enemy, in a way. We don't know where the rogue actors are sitting, like they don't know, they can be within the walls -- >> It's well understood I think, I agree, sorry. it's well understood that nation states are enabling terrorist groups to take out cyber attacks. That's well known, the source enables it. So, I think there's the privacy versus -- >> I'm not sure it's true in your case that it's Europeans that's doing this though. >> No, no, well you know, they share -- >> I'm a former officer in the Royal Navy, I've stood shoulder to shoulder with my U.S. counterparts. I put my life on the line on NATO exercises in real war zones, and I'm now a disabled ex-serviceman as a result of that. I mean, if I put my line on the line shoulder to shoulder with Americans, why is my privacy not respected? >> Hold on -- >> I feel it's, I was going to say actually that it's not that, like even the U.S., right? Part of the spying internally is we have internal actors that are behaving poorly. >> Yeah. >> Right, we have Marxist organizations posing as, you know, whatever it is, I'll leave it at that. But my point being is we've got a lot of that, every country has that, every country has actors and citizens and people in the system that are destined to try to overthrow the system. And I think that's what that surveillance is about. The question is, we don't have treaties, or we didn't have your explicit agreements. And that's why I'm pushing really hard here, like, they're separating privacy versus security, which is the national security, and privacy versus us as citizens in terms of our data being basically taken over for free, being used for free. >> John: I agree with that. >> That I think we have some agreement on. I just think that our governments haven't really had that conversation about what surveillance means. Maybe someone agreed and said, "Okay, that's fine. You guys can go do that, we won't tell anybody." And that's what it feels like. And I don't think we deliberately are saying, "Hey, we wanted to spy on your citizens." I think someone said, "Hey, there's a benefit here too." Otherwise I don't think the EU would have let this happen for that long unless Max had made that case and started this ball rolling, so, and Edward Snowden and other folks. >> Yeah, and I totally support the need for security. >> I want to enter the -- >> I mean we need to, where there are domestic terrorists, we need to stop them, and we need to have local action in UK to stop it happening here, and in America to stop it happening there. But if we're doing that, there is absolutely no need for the Americans to be spying on us. And there's absolutely no need for the Americans to say that privacy applies to U.S. citizens only, and not to Europeans, these are daft, it's just daft! >> That's a fair point. I'm sure GCHQ and everyone else has this covered, I mean I'm sure they do. (laughs) >> Oh, Bill, I know, I've been involved, I've been involved, and I know for a fact the U.S. and the UK are discussing I know a company called IronNet, which is run by General Keith Alexander, funded by C5 Capital. There's a lot of collaboration, because again, they're tryin' to get their arms around how to frame it. And they all agree that sharing data for the security side is super important, right? And I think IronNet has this thing called Iron Dome, which is essentially like they're saying, hey, we'll just consistency around the rules of shared data, and we can both, everyone can have their own little data. So, I think there's recognition at the highest levels of some smart people on both countries. (laughs) "Hey, let's work together!" The issue I have is just policy, and I think there's a lot of clustering going on. Clustered here around just getting out of their own way. That's my take on that. >> Are we a PG show? Wait, are we a PG show? I just got to remember that. (laughs) (Bill laughing) >> It's the internet, there's no regulation, there's no rules! >> There's no regulation! >> The European rules or is it the American rules? (Ray laughing) >> I would like to jump back quickly to the purpose of the surveillance, and especially when mass surveillance is done under the cover of national security and terror prevention. I worked with five clients in the past decade who all have been targeted under mass surveillance, which was revealed by Edward Snowden, and when they did their own investigation, and partially was confirmed by Edward Snowden in person, they found out that their purchasing department, their engineering department, big parts of their pricing data was targeted in mass surveillance. There's no way that anyone can explain me that that has anything to do with preventing terror attacks, or finding the bad guys. That is economical espionage, you cannot call it in any other way. And that was authorized by the same legislation that authorizes the surveillance for the right purposes. I'm all for fighting terror, and anything that can help us prevent terror from happening, I would be the first person to welcome it. But I do not welcome when that regulation is abused for a lot of other things under the cover of national interest. I understand -- >> Back to the lawmakers again. And again, America's been victim to the Chinese some of the individual properties, well documented, well known in tech circles. >> Yeah, but just 'cause the Chinese have targeted you doesn't give you free right to target us. >> I'm not saying that, but its abuse of power -- >> If the U.S. can sort out a little bit of reform, in the Senate and the House, I think that would go a long way to solving the issues that Europeans have right now, and a long way to sort of reaching a far better place from which we can all innovate and cooperate. >> Here's the challenge that I see. If you want to be instrumenting everything, you need a closed society, because if you have a free country like America and the UK, a democracy, you're open. If you're open, you can't stop everything, right? So, there has to be a trust, to your point, Bill. As to me that I'm just, I just can't get my arms around that idea of complete lockdown and data surveillance because I don't think it's gettable in the United States, like it's a free world, it's like, open. It should be open. But here we've got the grids, and we've got the critical infrastructure that should be protected. So, that's one hand. I just can't get around that, 'cause once you start getting to locking down stuff and measuring everything, that's just a series of walled gardens. >> So, to JD's point on the procurement data and pricing data, I have been involved in some of those kind of operations, and I think it's financial espionage that they're looking at, financial security, trying to figure out a way to track down capital flows and what was purchased. I hope that was it in your client's case, but I think it's trying to figure out where the money flow is going, more so than trying to understand the pricing data from competitive purposes. If it is the latter, where they're stealing the competitive information on pricing, and data's getting back to a competitor, that is definitely a no-no! But if it's really to figure out where the money trail went, which is what I think most of those financial analysts are doing, especially in the CIA, or in the FBI, that's really what that probably would have been. >> Yeah, I don't think that the CIA is selling the data to your competitors, as a company, to Microsoft or to Google, they're not selling it to each other, right? They're not giving it to each other, right? So, I think the one big problem I studied with FISA is that they get the data, but how long they can keep the data and how long they can mine the data. So, they should use that data as exhaust. Means like, they use it and just throw it away. But they don't, they keep mining that data at a later date, and FISA is only good for five years. Like, I learned that every five years we revisit that, and that's what happened this time, that we renewed it for six years this time, not five, for some reason one extra year. So, I think we revisit all these laws -- >> Could be an election cycle. >> Huh? >> Could be an election cycle maybe. (laughs) >> Yes, exactly! So, we revisit all these laws with Congress and Senate here periodically just to make sure that they are up to date, and that they're not infringing on human rights, or citizen's rights, or stuff like that. >> When you say you update to check they're not conflicting with anything, did you not support that it was conflicting with Privacy Shield and some of the promises you made to Europeans? At what point did that fail to become obvious? >> It does, because there's heightened urgency. Every big incident happens, 9/11 caused a lot of new sort of like regulations and laws coming into the picture. And then the last time, that the Russian interference in our election, that created some sort of heightened urgency. Like, "We need to do something guys here, like if some country can topple our elections, right, that's not acceptable." So, yeah -- >> And what was it that your allies did that caused you to spy on us and to downgrade our privacy? >> I'm not expert on the political systems here. I think our allies are, okay, loose on their, okay, I call it village politics. Like, world is like a village. Like it's so only few countries, it's not millions of countries, right? That's how I see it, a city versus a village, and that's how I see the countries, like village politics. Like there are two camps, like there's Russia and China camp, and then there's U.S. camp on the other side. Like, we used to have Russia and U.S., two forces, big guys, and they managed the whole world balance somehow, right? Like some people with one camp, the other with the other, right? That's how they used to work. Now that Russia has gone, hold on, let me finish, let me finish. >> Yeah. >> Russia's gone, there's this void, right? And China's trying to fill the void. Chinese are not like, acting diplomatic enough to fill that void, and there's, it's all like we're on this imbalance, I believe. And then Russia becomes a rogue actor kind of in a way, that's how I see it, and then they are funding all these bad people. You see that all along, like what happened in the Middle East and all that stuff. >> You said there are different camps. We thought we were in your camp! We didn't expect to be spied on by you, or to have our rights downgraded by you. >> No, I understand but -- >> We thought we were on your side! >> But, but you have to guys to trust us also, like in a village. Let me tell you, I come from a village, that's why I use the villager as a hashtag in my twitter also. Like in village, there are usually one or two families which keep the village intact, that's our roles. >> Right. >> Like, I don't know if you have lived in a village or not -- >> Well, Bill, you're making some great statements. Where's the evidence on the surveillance, where can people find more information on this? Can you share? >> I think there's plenty of evidence, and I can send some stuff on, and I'm a little bit shocked given the awareness of the FISA Act, the Cloud Act, the fact that these things are in existence and they're not exactly unknown. And many people have been complaining about them for years. I mean, we've had Safe Harbor overturned, we've had Privacy Shield overturned, and these weren't just on a whim! >> Yeah, what does JD have in his hand? I want to know. >> The Edward Snowden book! (laughs) >> By Edward Snowden, which gives you plenty. But it wasn't enough, and it's something that we have to keep in mind, because we can always claim that whatever Edward Snowden wrote, that he made it up. Every publication by Edward Snowden is an avalanche of technical confirmation. One of the things that he described about the Cisco switches, which Bill prefers to quote every time, which is a proven case, there were bundles of researchers saying, "I told you guys!" Nobody paid attention to those researchers, and Edward Snowden was smart enough to get the mass media representation in there. But there's one thing, a question I have for Sabjeet, because in the two parties strategy, it is interesting that you always take out the European Union as part. And the European Union is a big player, and it will continue to grow. It has a growing amount of trade agreements with a growing amount of countries, and I still hope, and I think think Bill -- >> Well, I think the number of countries is reducing, you've just lost one! >> Only one. (Bill laughing loudly) Actually though, those are four countries under one kingdom, but that's another point. (Bill chortling heartily) >> Guys, final topic, 5G impact, 'cause you mentioned Cisco, couldn't help think about -- >> Let me finish please my question, John. >> Okay, go ahead. How would you the United States respond if the European Union would now legalize to spy on everybody and every company, and every governmental institution within the United States and say, "No, no, it's our privilege, we need that." How would the United States respond? >> You can try that and see economically what happens to you, that's how the village politics work, you have to listen to the mightier than you, and we are economically mightier, that's the fact. Actually it's hard to swallow fact for, even for anybody else. >> If you guys built a great app, I would use it, and surveil all you want. >> Yeah, but so this is going to be driven by the economics. (John laughing) But the -- >> That's exactly what John said. >> This is going to be driven by the economics here. The big U.S. cloud firms are got to find this ruling enormously difficult for them, and they are inevitably going to lobby for a level of reform. And I think a level of a reform is needed. Nobody on your side is actually arguing very vociferously that the Cloud Act and the discrimination against Europeans is actually a particularly good idea. The problem is that once you've done the reform, are we going to believe you when you say, "Oh, it's all good now, we've stopped it!" Because with Crypto AG scandal in Switzerland you weren't exactly honest about what you were doing. With the FISA courts, so I mean FISA secret courts, the secret warrants, how do we know and what proof can we have that you've stopped doing all these bad things? And I think one of the challenges, A, going to be the reform, and then B, got to be able to show that you actually got your act together and you're now clean. And until you can solve those two, many of your big tech companies are going to be at a competitive disadvantage, and they're going to be screaming for this reform. >> Well, I think that, you know, General Mattis said in his book about Trump and the United states, is that you need alliances, and I think your point about trust and executing together, without alliances, it really doesn't work. So, unless there's some sort of real alliance, (laughs) like understanding that there's going to be some teamwork here, (Bill laughing) I don't think it's going to go anywhere. So, otherwise it'll continue to be siloed and network based, right? So to the village point, if TikTok can become a massively successful app, and they're surveilling, so and then we have to decide that we're going to put up with that, I mean, that's not my decision, but that's what's goin' on here. It's like, what is TikTok, is it good or bad? Amazon sent out an email, and they've retracted it, that's because it went public. I guarantee you that they're talkin' about that at Amazon, like, "Why would we want infiltration by the Chinese?" And I'm speculating, I have no data, I'm just saying, you know. They email those out, then they pull it back, "Oh, we didn't mean to send that." Really, hmm? (laughs) You know, so this kind of -- >> But the TRA Balin's good, you always want to get TRA Balin out there. >> Yeah, exactly. There's some spying going on! So, this is the reality. >> So, John, you were talking about 5G, and I think you know, the role of 5G, you know, the battle between Cisco and Huawei, you just have to look at it this way, would you rather have the U.S. spy on you, or would you rather have China? And that's really your binary choice at this moment. And you know both is happening, and so the question is which one is better. Like, the one that you're in alliance with? The one that you're not in alliance with, the one that wants to bury you, and decimate your country, and steal all your secrets and then commercialize 'em? Or the one kind of does it, but doesn't really do it explicitly? So, you've got to choose. (laughs) >> It's supposed to be -- >> Or you can say no, we're going to create our own standard for 5G and kick both out, that's an option. >> It's probably not as straightforward a question as, or an answer to that question as you say, because if we were to fast-forward 50 years, I would argue that China is going to be the largest trading nation in the world. I believe that China is going to have the upper hand on many of these technologies, and therefore why would we not want to use some of their innovation, some of their technology, why would we not actually be more orientated around trading with them than we might be with the U.S.? I think the U.S. is throwing its weight around at this moment in time, but if we were to fast-forward I think looking in the longterm, if I had to put my money on Huawei or some of its competitors, I think given its level of investments in research and whatever, I think the better longterm bet is Huawei. >> No, no, actually you guys need to pick a camp. It's a village again. You have to pick a camp, you can't be with both guys. >> Global village. >> Oh, right, so we have to go with the guys that have been spying on us? >> How do you know the Chinese haven't been spying on you? (Ray and John laughing loudly) >> I think I'm very happy, you find a backdoor in the Huawei equipment and you show it to us, we'll take them to task on it. But don't start bullying us into making decisions based on what-ifs. >> I don't think I'm, I'm not qualified to represent the U.S., but what we would want to say is that if you look at the dynamics of what's going on, China, we've been studying that as well in terms of the geopolitical aspects of what happens in technology, they have to do what they're doing right now. Because in 20 years our population dynamics go like this, right? You've got the one child policy, and they won't have the ability to go out and fight for those same resources where they are, so what they're doing makes sense from a country perspective and country policy. But I think they're going to look like Japan in 20 years, right? Because the xenophobia, the lack of immigration, the lack of inside stuff coming in, an aging population. I mean, those are all factors that slow down your economy in the long run. And the lack of bringing new people in for ideas, I mean that's part of it, they're a closed system. And so I think the longterm dynamics of every closed system is that they tend to fail versus open systems. So, I'm not sure, they may have better technology along the way. But I think a lot of us are probably in the camp now thinking that we're not going to aid and abet them, in that sense to get there. >> You're competing a country with a company, I didn't say that China had necessarily everything rosy in its future, it'll be a bigger economy, and it'll be a bigger trading partner, but it's got its problems, the one child policy and the repercussions of that. But that is not one of the things, Huawei, I think Huawei's a massively unlimited company that has got a massive lead, certainly in 5G technology, and may continue to maintain a lead into 6G and beyond. >> Oh yeah, yeah, Huawei's done a great job on the 5G side, and I don't disagree with that. And they're ahead in many aspects compared to the U.S., and they're already working on the 6G technologies as well, and the roll outs have been further ahead. So, that's definitely -- >> And they've got a great backer too, the financer, the country China. Okay guys, (Ray laughing) let's wrap up the segment. Thanks for everyone's time. Final thoughts, just each of you on this core issue of the news that we discussed and the impact that was the conversation. What's the core issue? What should people think about? What's your solution? What's your opinion of how this plays out? Just final statements. We'll start with Bill, Ray, Sarbjeet and JD. >> All I'm going to ask you is stop spying on us, treat us equally, treat us like the allies that we are, and then I think we've got to a bright future together! >> John: Ray? >> I would say that Bill's right in that aspect in terms of how security agreements work, I think that we've needed to be more explicit about those. I can't represent the U.S. government, but I think the larger issue is really how do we view privacy, and how we do trade offs between security and convenience, and you know, what's required for personalization, and companies that are built on data. So, the sooner we get to those kind of rules, an understanding of what's possible, what's a consensus between different countries and companies, I think the better off we will all be a society. >> Yeah, I believe the most important kind of independence is the economic independence. Like, economically sound parties dictate the terms, that's what U.S. is doing. And the smaller countries have to live with it or pick the other bigger player, number two in this case is China. John said earlier, I think, also what JD said is the fine balance between national security and the privacy. You can't have, you have to strike that balance, because the rogue actors are sitting in your country, and across the boundaries of the countries, right? So, it's not that FISA is being fought by Europeans only. Our internal people are fighting that too, like how when you are mining our data, like what are you using it for? Like, I get concerned too, when you can use that data against me, that you have some data against me, right? So, I think it's the fine balance between security and privacy, we have to strike that. Awesome. JD? I'll include a little fake check, fact check, at the moment China is the largest economy, the European Union is the second largest economy, followed directly by the USA, it's a very small difference, and I recommend that these two big parties behind the largest economy start to collaborate and start to do that eye to eye, because if you want to balance the economical and manufacturing power of China, you cannot do that as being number two and number three. You have to join up forces, and that starts with sticking with the treaties that you signed, and that has not happened in the past, almost four years. So, let's go back to the table, let's work on rules where from both sides the rights and the privileges are properly reflected, and then do the most important thing, stick to them! >> Yep, I think that's awesome. I think I would say that these young kids in high school and college, they need to come up and solve the problems, this is going to be a new generational shift where the geopolitical landscape will change radically, you mentioned the top three there. And new alliances, new kinds of re-imagination has to be there, and from America's standpoint I'll just say that I'd like to see lawmakers have, instead of a LinkedIn handle, a GitHub handle. You know, when they all go out on campaign talk about what code they've written. So, I think having a technical background or some sort of knowledge of computer science and how the internet works with sociology and societal impact will be critical for our citizenships to advance. So, you know rather a lawyer, right so? (laughs) Maybe get some law involved in that, I mean the critical lawyers, but today most people are lawyers in American politics, but show me a GitHub handle of that congressman, that senator, I'd be impressed. So, that's what we need. >> Thanks, good night! >> Ray, you want to say something? >> I wanted to say something, because I thought the U.S. economy was 21 trillion, the EU is sittin' at about 16, and China was sitting about 14, but okay, I don't know. >> You need to do math man. >> Hey, we went over our 30 minutes time, we can do an hour with you guys, so you're still good. (laughs) >> Can't take anymore. >> No go on, get in there, go at it when you've got something to say. >> I don't think it's immaterial the exact size of the economy, I think that we're better off collaborating on even and fair terms, we are -- >> We're all better off collaborating. >> Yeah. >> Gentlemen -- >> But the collaboration has to be on equal and fair terms, you know. (laughs) >> How do you define fair, good point. Fair and balanced, you know, we've got the new -- >> We did define fair, we struck a treaty! We absolutely defined it, absolutely! >> Yeah. >> And then one side didn't stick to it. >> We will leave it right there, and we'll follow up (Bill laughing) in a later conversation. Gentlemen, you guys are good. Thank you. (relaxing electronic music)