 Good afternoon, good evening everyone. We are so pleased that you're joining us, Lisa Martin and Dave Vellante of theCUBE. We are covering Clickworld 2023, the first Clickworld in person since 2019. We've had a great day, day one of the event. Yesterday was partner day, today's day one. Tomorrow, as you know, from you watched the last segment with their chief product officer, lots of meat, lots of stuff coming. But we are here with one of our CUBE alumni, Stuart Bond, research vice president at IDC. We're going to kind of analyze some of the things that we're talked about today. Stuart, great to have you. Give us your take on day one from Mike Cappun's keynote this morning, the CEO at the helm, to some of the customer stories. What's your takeaways from today? Yeah, Lisa, it's been a good day and I would be amiss if I didn't say the best part of the keynote was Crawford Del Pratt, president of IDC. Shocking up to the boss. Right, right, and he was on our show with Mike. Yeah, yeah, yeah, so I have to give props to Crawford. He was good. He was good, yeah, yeah. Crawford's always been a good presenter. The thing that's really interesting, so there's a lot of merit in that in what he talked about, right? There's this shift happening right now. As we enter that intelligent automation phase, it's not a new platform. It's that our relationship with data is changing. And it's so interesting when you think about that. Data's been around for a long time. We've been working with data for a long time, but the way it's being leveraged now is new and different and giving us so many new advantages and insights that we just didn't have before. Relationships are complicated. Relationships with data are probably even more complicated. How do you see from the vision that was laid out this morning, how do you see click as a facilitator of really enabling businesses across industries to have a very successful, profitable relationship with data? Yeah, so a big part of that is who they cater to, their platform, the abilities that it has, and the different users that they cater to in the business. With the pending acquisition of Tel-En, that's going to make that relationship even better because now they're going to have a set of products that covers the entire portfolio from data ingestion through to consumption and all of the governance and quality and intelligence around that so that they have a better understanding of what making sure that that data is being used by the right person for the right reason and being used at the right time. Let's do it. Paint a picture of the taxonomy of the space. You cover it very closely. Forgetting analytics for a moment, but get into the data integration, the data governance, the data quality, your wheelhouse, what's that market look like? So the market that I cover is, I cover data intelligence and data integration. When I say data intelligence, it's intelligence about the data. So the market segments that we track in data intelligence is data quality. Tells us how good or bad our data is, what our data looks like profile, understanding where and how it's being used in the organization. Metadata management, including data lineage, data cataloging, business glossaries so that we know, we understand what the data is, where it is, what it means, what it means to the business, what it means technically, where it's come from, where it's going. Then we add to that master data intelligence. Where are the systems of record, systems of reference, systems of entry for all of the pieces of data about people, places and things that we care about the most as our business? Where is that located in the organization? And how do I reconcile the differences in all of those, between all of those technologies? Then the other segment that we have is data life cycle management, which is data archiving, which is really database archiving. So it's database archiving, some test data management and some data masking, but more focused on the structured. So I primarily focus on the structured data world. All of those same things apply to the unstructured world as well, but my focus is more on the structured side. On the data integration side, you've got the traditional ETL, ELT, you've got what we call composite data frameworks, which is data federation, data virtualization. You've got data adapters, connectors, and then, this is horrible. And then we've got dynamic data movement, which is change data capture. So if I were to look at CLIC, CLIC plays in with the acquisition of attunity, they have the change data capture, they have that flow. They play in the data life cycle management because all of the work that they do in orchestrating data warehouse creation, they also, through the acquisition of Podium, they got data catalog, they got a little bit of data quality. With the pending acquisition of Tel-En, when that comes through, they're going to have a lot more of that picture of those markets that I cover. So are these largely historically bespoke markets that are now coming together, or is CLIC sort of leading that? I know some of the big legacy guys will have one of everything, but have they historically been sort of independently funded by VCs and now they're coming together, or are they largely still independent markets? There's, a lot of them are coming together for sure. There's definitely consolidation happening in the market. What we just talked about is one of those data points. But there's others. There's been other examples of acquisitions of different companies coming together. I kind of look back at, remember the days when we had all of these best of breed business applications, we had a great payroll application, HR application, we had a great maintenance management application, we had a great manufacturing application, and then the ERP vendors came along and said, hey, we're going to put all that together for you. That's what's happening in the data world. We've got all these disparate data applications, data functionality, that's all coming together into platforms, into suites of products that organizations can then leverage to tackle their data problems. Okay, so you've got the legacy guys, IBM, SAP, SAS, Oracle, they've got, they're going to have all their stuff for their platforms. And then you've got guys like Klick that are independent, you were saying off camera, now you've got the cloud guys all coming in, of course the cloud guys are all partners with the independence, and they're doing their own thing. So how's that going to shake up? Yeah, so every single hyperscaler is working on, they're building their own data intelligence and data integration capabilities. Most of them are still focused on that cloud ecosystem. They partner with others to bring in the on-premise and the hybrid, the hybrid portion of that. But they're continuing to make that better and some of them are talking more and more about hybrid and potentially even multi-cloud. The benefit that right now the independence have is that they are cloud neutral, that they are partners with all the cloud providers, and they can offer that multi-cloud support for data that needs to move between clouds and it is a hybrid world that we are in. The surveys that we do, clearly, there's data is not just in the cloud, it's not just on-premise, it's everywhere, it's multi-cloud, it's hybrid. That is what we have today and that's what we're going to have for the foreseeable future. So the independence really have that opportunity to leverage that and say, hey, we're not any one cloud. And we had my component earlier today, asked them about the overlaps with between talent and CLIC. And he basically painted a picture that quite distinct, they're very complimentary. How much of that is true? How much overlap is there? Are they truly sort of no seams? That there is some overlap. So CLIC have been making some inroads into data integration, data intelligence, through attunity, through podium. They were missing deeper, better data quality. They were missing ETL. They were missing some of the other movement capabilities, some of the capabilities that Stitch has in the cloud. CLIC did not have that. And so this was really an opportunity for them to expand into that and to get more of that. Are there overlaps? Absolutely. Certainly in the catalog, there's CLIC catalog. There's different versions of, there's a couple of different versions of catalog in CLIC. CLIC cloud is a slightly different version than what's on-prem. Tele-end has a different version. They're going to come together. They'll figure that out. That has to be rationalized. That has to be rationalized. You got to have one catalog. But they're going to benefit a lot from the deeper quality capabilities from the ETL capabilities. People say ETL is dead, it's not. There's still a lot of ETL out there. And streaming, absolutely. The whole change data capture thing is really important, being able to leverage that to put it into data streams so we can do real-time analytics. And attunity has been doing that for a long time. Until I did an acquisition not long ago of change data capture, they'll have to do some reconciliation between those two things. But I don't think that's going to be really difficult. But at the end of the day, there's still a place for both real-time streaming analytics. There's still a place for ETL and more so ELT, which is what we're seeing more today. But you like the acquisition. You like the acquisition. I do. I do. I do. It's interesting. I haven't heard much from competitors yet about what they think about it. It'll be interesting to hear. I'll be at a few of those conferences coming up soon and I'd like to get some more impressions from them on that. How do you see Clicks value proposition evolving? You've obviously been covering them for a while. You've seen evolution. But with Telend, when customers are coming to you seeking advice and recommendations, what is that value proposition going to evolve to once they really have this end-to-end platform? Yeah. So one of the stories that we did recently gave us some insight into buying patterns. And I'm not going to give too much away because I'm going to talk about this when I'm on main stage tomorrow. But where Click is headed, where Click is right now and it's where it's headed is one of the biggest buying patterns today in that that is how these solutions are being built in data integration and data intelligence. But also what they're building towards is one of the biggest changes that we've seen over the past year. So the idea of bringing these things together, getting them into a common platform where they're all sharing metadata, they're sharing intelligence, they're sharing information about that data, getting all of that ready and available is going to increase their value proposition. But they are still competing with those vendors that are out there, that are there to solve one single problem. So even though they will have a platform, they won't have it immediately but eventually they will have a platform. They need to still be able to offer those capabilities as independent capabilities that a data engineer that would rather go down low DBT and start working with it can still look at Click or Telend to get the functionality they need to do the same thing. Okay, so you, we were talking again off camera, you said like the BI space is not one you follow, right? That's not, you do everything up to the, you enable BI, all your, the markets that you follow are enablers to BI because without what your customers do, there's no, the data's crap, right? Okay, is that fair? Yep, that's why we have data quality software. Right, well it's funny too because you and I talked at the MIT CDO IQ and we've talked about this before. It used to be a back office function, right? And it was just compliance, highly regulated industries and now it's front and center. I mean, everybody has to have a government solution but you said something that sort of struck me, people, places and things. And you know, BI, I know it's not your space but a lot of money being spent there. So we've been developing this mental model of the future of data where you've got people, places and things that are a digital representation of your business and we always use this simple example of Uber. Uber's got drivers, it's got riders, it's got destinations, it calculates an ETA. Those are different data elements or data products and they're all coherent. And you know, when you talk to people in conferences like this, so yeah, we're doing that today. Yeah, really, not the way Uber is in real time, right? And so I feel like the world wants Uber for its business, except it's more complicated, right? Like you said, people, places and things, that digital representation of your business. How do you think about that? Is that something that you feel like the technology can support that coherent semantic layer? Are people working on that? What's your thoughts? It's happening. We're seeing digital twins of businesses, digital twins of things, but it's a very delicate subject. When you start talking about having a digital twin of a person, that you start to worry about data privacy laws and lots of different interesting things when it gets there. But yes, that is something that we are seeing more and more people are starting to do that. And I think it comes back a little bit. It's not quite there, but one of the things that I've been tracking and watching is this desire to productize data, to create data products and make those data products available in internal data marketplaces and such. And that's going to, the more we can do that, I think, and have a real focus on data products in the organization, the better that data is going to get. And the further we're going to get along to having those digital twins and having the ability to use data in that way. Because I think of services, okay, we've all experienced this, okay, the cable guy is coming, or they're going to come and fix your dishwasher. And they're getting better, but they're still like, they're going to be there between nine and 12. It's like, really, you can't tell me, like within, you know, a little closer window, or you know, Amazon, you see the Amazon trucks driving around, dropping off stuff, they all drive the speed limit. Amazon knows if they go over the speed limit, so they know a lot about those people. So I hear you, we're now getting into certain privacy issues. We just, you know, tax day recently just paid my taxes, went online. IRS said I got to sign up for a new account, or else it's not going to be valid anymore. The stuff they were asking about me, I mean, it's like, okay, clear, it's got all my information, right? They know everything about us. So there is no privacy, Stuart. It's a fallacy. I can agree with that. I know all kinds of people have my information, absolutely, yep. Yeah, it's just, you know, I mean, you think of the credit agencies, how much information they have. It's just interesting now in this new world of GPT, how all of a sudden, you know, this has become so front and center, whereas, you know, we know, as I say, the credit card companies have had so much information on our credit history. You know, you read the fine print of a credit card, get ready a credit card, get people, and it says, how do you know this? The fine print says, if you're late on a payment, there's nothing to do with us, they can jack your rate up. Did you know that? Yeah, because they share information. But I'm not surprised. So, so much for your privacy. So, it's interesting now that we're living in an age where it's so front and center, and I think it's an important conversation. I don't know about Canada, Europe, I think they're much more sensitized. People, kids today, like, yeah, TikTok, bring it on. You know, no problem. I was at a dinner round table last week and we had a fella there from an airline company and the data that the airlines have on us is a little scary to think about what they can infer using that data. Yeah, so that's interesting. So, okay, so back to data products because I think it's a very powerful concept. But this idea of, you know, historically, BI is like, what happened? You know, today, last hour, last week, you know, we were talking about the example somebody was using it, were there real time changing prices? Hey, the salad's not selling. Lettuce is going to go bad, drop the price. You know, have a special, whatever. Okay, so that's a small example of limited joins. But the future is going to have so much more data. You guys publish your data universe every year. It's my boggling. Global data sphere, yeah. Data sphere. It's like, the numbers are ridiculously large, right? And then underneath that are all these data elements and data products. So we ain't seen nothing yet. No, the biggest thing that has shocked me with our data sphere, our global data sphere, is how much data, original data is replicated. So how many copies of data are created? Now, the global data sphere is all data that's being created. So it's consumer as well as business or enterprise. But enterprise makes up about half of the data sphere. So there's a lot of replication happening in business as well. You think about all of the data warehouses, the data lakes. Every copy of data that's created to work on a new problem. You think about all the spreadsheets that are out there that the data's all over the place. That's why we have so many data quality problems. It's because data's all over the place. As soon as that data comes awake, it gets copied from its original location. It gets changed. It's out of date. It's out of date. It's conflicting with the original. But this is why we talk about these data products that's coherent. That's why I use the Uber example. Their data's not out of date. That the riders, the drivers, the ETAs, the destination, they all understand each other. And that's not the digital, I like what you said, the digital twin of your business. That's not how business works today. That's not how data, that's, I mean, people talk about, yeah, we do that already, but they really don't, right? I mean, that's interesting stuff, you know? So when you think about all the data that's out there and all the different copies and what's happening today, you hear about the modern data stack a lot. I talk about the modern data environment. Modern data environment includes all the new stuff. It also includes all the old stuff. You need to be able to manage all of that. One of the things that I struggle with is that there's a lot of focus on moving data into those new cloud, those new shiny things in the cloud, right? And so that's where a lot of that focuses on that. I bring it back to, I don't think the first move that organizations should make with their data is to put it into those analytical data repositories. I think the first move they need to make is to collect all of the intelligence about their data. I come back to data intelligence. So if they know where their data is, what it means, who's accountable for it, who's using it, where it's coming from, where it's going to, then they can make the decision on whether or not they need to move that data based on the business problem that they're trying to solve. We can cut down a lot on the data replication, cut down a lot on the complex. I know, I'm on a rant here, so sorry. I feel like you're on a podcast and I'm ranting because you're right on. And if you think about, an entire industry has been built up to avoid doing complex joins because they're so hard to do. And so if you look at all the data repositories that are out there, a vast majority of them, they're caching architectures or in-memory architectures and they can only do maybe five or six joins simultaneously. And then they just spike. So new thinking, to your point, it's about the metadata. Leave the data in place until you have to move it. Move no more data than you have to kind of thing. And you can't do that in memory. It's going to be too expensive with the, what do you call it, data sphere? Data sphere. It's going to be way too expensive. You're not going to be able to use caching architectures. You're not going to be able to use in-memory architectures. It's just going to be way too expensive. And so there's going to have to be something new down to how data is laid out on the disk or in-memory. And that's, people are working on that. And because in order to see through your vision of leave it in place, because the metadata is going to determine whether or not, where it is, the latency, et cetera, that needs new, I think, new architectures. So I've been promoting an architecture or an architectural approach that I call the data control plane. The data control plane has three primary domains. It has an intelligence domain. It tells you where all your data is, all the things we talked about, what data intelligence is. That intelligence feeds into the governance domain, where you're not only doing data governance, but you're also governing the data environment, all the things that make the data move, cleanse the data, correct the data, so on. And then it's got an engineering domain where all the data engineering happens, where the data movement occurs, the ingestion, the transformation, the cleansing, and all those things work together and there's continuous observability of that data and continuous observability of the machinery that's making those pipelines flow. And we put this, we're playing with this new concept where we've got these four planes that, you know, three dimensions, they're on top of each other. At the very bottom we have the data plane, which we talk about data is highly distributed, it's all over the place, it's very diverse, there's so many different kinds of data that we're dealing with and it's very dynamic. Not only is it flowing faster, but it's always changing. So you've got the data plane at the bottom, data control plane goes on top of that to take control of the data. And on top of that you've got an analytics plane, which is leveraging the control plane to make sure you're using the right data at the right time and for the right reason and the right people are using it. And then on top of that you have a decisioning plane. What's the last one? Decisioning plane. Yeah, yeah, yeah, okay. So you're, right, all analytics at some point come down to a decision. There needs to be a decision that's made. So there's a decisioning plane on top of the analytics. And every one of those layers has its own set of technologies, has its own set of deployments, and that's tied right back to our taxonomy at IDC. And you're automating those decisions now. Yes. As opposed to automating business processes. Yes. I love the stacks. That's the planes. That's really good. You got a picture of that? We do. We do. It's still in development. We will have a final probably tomorrow. Are we going to get to see it? Oh, at the keynote? Yeah. No, so I won't be at the keynote. Okay. I don't have any slides tomorrow. I just get to talk. Last question, kind of give us some teasers about what are going to be some of the things that you're going to be talking about. Because the way that you describe the planes, but also where organizations need to start from a data intelligence perspective sounds so challenging, but to your point, absolutely the first step that they have to take. What are some of those nuggets that people can learn from you tomorrow? Yeah. So I'll likely bring up the data intelligence story because I'm really passionate about it. But again, I'll be bringing forward some of the stats that we learned from the survey that we just did on all data management. So we were looked at data engineering, data intelligence, data governance, database management, streaming data management. We looked at all of those different things. So we've got some interesting insights about what are the priorities for organizations in 2023? Where are they spending their money? Where is budget increasing? Where is it decreasing? Also, what are people buying? What's making up those solutions that people are using to take control of the modern data environment? And what has changed over the last year? And what do we think is going to, the survey doesn't tell us what we think is going to change. We'll hypothesize on that a little bit. But we asked, what has changed in the last 12 months? And the thing that has changed the most is really interesting. Ooh, what a great teaser, Sherry. Thank you so much for joining Dave and me on the program, really sharing your insights, your vision for this industry and the direction that it's going from an opportunity perspective for organizations in any industry to really become data-driven and have a culture of data that actually is successful. We really appreciate your time. Thank you for having me. It's my pleasure. Yeah, for our guests and for Dave Vellante, I'm Lisa Martin. You've been watching The Cube Live from Clickworld 2023. Stick around, Dave and I will wrap the day coming up next.