The logical path to data

Speaker A: Hello, everybody, and welcome to the Dedicated Show. This is Kate Strasney, founder of Dedicated and host of, uh, the Dedicated Show. I'm really excited for our session here today. We are going to be talking about the logical path for data and AI. Basically, if you ever wondered about logical data management and the differences between data warehouses, lake houses, we're going to get into all of that. We're going to also talk about how data management and logical data management is essential for AI. And I, uh, know we can't have a session without talking about AI, so we've got to put that in there. Now, this is going to be a really fun interactive session. I could just feel it. This is live, so if you've got comments, if you have questions, please feel free to put that into the chat. If you're joining us on LinkedIn, on YouTube, or on Twitter, we'll be able to engage with you on all platforms. Now, a really fun thing that we've got going on is we have a book giveaway. So we've got this book, the Rise of Logical Data Management. It's an essential data strategy for transforming your business in The Age of AI, an O'Reilly book by Christopher Gardner, who you're actually going to see up on stage in just a minute. Uh, the way to play is we are using a hashtag. It's called hashtag discoverlogical. And all you have to do in order to enter the raffle, it's actually an automated raffle where the system is going to pick you. So if you're feeling lucky, make sure you play is to just type in hashtag DiscoverLogical into either LinkedIn, YouTube or Twitter, and that's going to automatically enter you into a raffle. We'll announce our, uh, first winner in just a couple of minutes, and then we'll have an additional winner later on in the show, so make sure you stay tuned. All right, now, my favorite part about the show is bringing up our panelists. So I'm going to bring them up one by one so you can get a little introduction. We're going to go ahead and start with Christopher Gardner. Christopher, welcome to the Dedicated show.

Speaker B: Hey, thanks, Kate. Um, my name is Christopher Gardner. I'm the author of the book that you just saw. I'm also a data analyst at the University of Michigan. So I'm really excited to be here, really excited to talk about this.

Speaker A: Amazing. And yes, for everyone in the audience, send Christopher all the questions. Next up, we're going to bring up, uh, Alberto Pan. He is the chief technology officer of denodo, Alberto, welcome to the show.

Speaker C: Thank you. Thank you, Kate. I'm really excited to be here. Well, I'm the chief technology officer of denodo. Denodo has been working in logical data management for many, many years, helping organizations to implement these ideas in practice. So, you know, I think I am looking forward to the discussion and thanking. Thanks for having me.

Speaker A: Okay. Amazing. And of course, Mr. Ravish, welcome to the dedicated show.

Speaker D: Thanks, Kate. I'm really excited to be here as well. Um, again, I'm the chief marketing officer for denodo. I've been with denodo for a very long time and in the data space itself for the last 30 years across Denodo, Informatica and Oracle. So I'm looking forward to this discussion today.

Speaker A: Great. Well, let's, uh, Ravi, just to follow up. A very long time. How long is a very long time? I'm just curious. For 10 plus years, that is a very long time. I agree. Okay, moving on. Um, I think, you know, we, let's, let's just jump right into this. So Christopher, let's, let's start with you. We've got, we've got your book here, or at least I've got your book here. Um, what made you decide to write this book and why did you think that now was the right time?

Speaker B: I suppose the wrong answer would be all the money that I'm going to get for it. Right. And all the fame. No, no, the real reason I, I wrote the book was, uh, I work in data all the time at the University of Michigan. And uh, one of the challenges that we face is there's just so much data everywhere. There's student data, patient data, there's employee data, financial data. And for me, being able to get in and see that data is an extreme challenge. I was very interested in logical, uh, data management because I thought it might provide a way to get that data in the hands of people who really need to use it or who could use it to do their jobs. Um, and I think that's one of the big challenges we face in any business is how do you get all of the data necessary for your business in the hands of the people who could really use it.

Speaker A: Mhm, mhm. I agree. And I think it is definitely the right time. Um, I'm just looking into the comments. We've got a lot of hashtag discover logical coming in, which is perfect. That's exactly how you enter the raffle. I had a question here from Phil. Where and how do I enter the code? Follow along with what everyone else just did and just either copy, paste or type in hashtag discoverlogical to enter the raffle. All right, moving on. Um, and I think uh, after um, the next question we're going to actually pick our first winner. So everyone on the show, make sure you're getting that hashtag in. So Alberto, I think a good place for us to start, it's in the title. We've got the rise of Logical Data Management. What exactly is logical data management and why should data leaders and other leaders care about this topic?

Speaker C: Well, uh, as uh, uh, Christopher said, I think that uh, a challenge for data leaders in data management that ah, has been there for decades is uh, trying to fight data fragmentation, especially in big organizations as we are able to use the data to actually get the most of the data. Um, the primary way to fight this data fragmentation is uh, so far has been trying to copy, typically to copy everything into one central systems. Decades ago it was the data warehouse, now probably would be the data lake house and then creating even more data copies to adapt the data for each use case. Uh, but I think that that traditional centralized everything approach has limitations. It may be part of the solution but uh, it's not enough. Right. For instance it's very hard to onboard m in the central system all the data that is needed. Uh, it also creates bottlenecks. Uh, it also often strips away the business context that you really need to use the data effectively. I think that logical data management flips this model because instead of physically moving all the data, uh, logical data management builds this, I like to say, this universal semantic layer over your entire data landscape. Uh, you can think of it as a unified business view of all your data wherever it lives. Uh, and this gives you unified access and um, unified governance and also much faster time to data because you are not forced to move the data and replicate data for every new use case. You can of course still centralize data when it makes sense, but it's no longer your only option. Right, right, right. And I think this is critical now because of two massive forces, uh, uh, in, in the, in the last years. The first one would be the rise of hybrid and multi cloud architectures which have made data fragmentation actually worse than ever. And the second would be of course AI. As you said Kate, we uh, need to always talk about AI these days because AI applications and agents are just as data hungry as human users. They also need reliable real time data in the language of the business. And I think that logical data management can, can provide that.

Speaker A: And would you say this is a new topic to our space. Sorry, would you say logical data management? Is this, is this a new concept?

Speaker C: Um, no, I think that uh, has been here for a while. Um, I think this idea of um, uh, acknowledging that organizations are distributed and therefore we need a way to actually get the value of the data. Ah. Without needing to always centralize the data. I, uh, would say it has been with us for several years now actually. You can see the concept or you can see the main, the main ideas of the concept also embedded in some of the main, uh, uh, new architectural patterns that are being proposed in the market. Like for instance, the data fabric or the data mesh. But as I said, I think that this uh, today probably is more relevant than ever because of these two forces that I mentioned. But I think the idea has been around and has been an influence in these new architectural concepts for, for a few years now.

Speaker A: Mhm. That makes sense. And guess what? It is time for our first giveaway. Is everybody excited? I'm going to go put this up on screen. This is, uh, this is one of the fun parts of making this live. So we've got 27 entries in here already and once I click draw, well, the magic will happen and we'll pick our very first winner. So good luck everybody. And if you don't win now, you have a chance to win later on in the show. I love how it slows down at the end and it's like, there you go. Congratulations. Mendez Phillips, you are our first winner. Congratulations. We will follow up with you after the show. Um, and everyone else, keep playing. Did you like that, Robbie? Do you like it?

Speaker D: Yeah, awesome.

Speaker A: Uh, I want to enter too, just to see if I win.

Speaker D: I throw my hat in the ring as well.

Speaker B: Yes.

Speaker A: So yes, everybody else keep putting in Discover Logical and the hashtag and the other thing I wanted to tell everyone in the live audience, first of all, thank you for being here live. Obviously you could watch the recording after, but the best part about being here live with us is you can leave comments and ask questions directly to these experts or the panelists who have made the next 45 minutes available of their time. So if you've got questions on anything we're talking about, please feel free to put that in there. Now Ravi, let's move on to you. Now. O'Reilly published this book as a business guide. And my question to you is, who is the target audience? Who do you think who should be reading this book? I mean, I read the book, but who else should be reading this book?

Speaker D: Yeah, that's A good question. So, uh, we aim the book as a business guide, um, primarily to talk to the senior leaders, uh, in the technology space, notably whether it is a chief technology officer, chief information officer, chief data officer, chief data and analytics officer, and now there is the AI, the chief AI officer. So all these people who are spearheading data initiatives within their companies, uh, are the people that we are targeting. So why, why do we want to talk to them and what they should, why they should read this book? It's simply because the data architectures have become really complicated these days. Um, you, you will see that is because of the businesses need to be able to run faster and they need the data as the oil in order to run the businesses. And IT is the charter of the IT to provide that data to the businesses at the speed that they need. So what they do is they start creating um, copies of the data that will cater to a specific function. So for example, marketing might be needing to analyze the campaigns. So they might create an instance for marketing to do it. Finance might need IT for creating their financial books and reporting to the regulatory agencies. So they might create one for that. So like that they create these copies very quickly and provision that to all these months. And the data becomes siloed and disparate over period of time and they lose the cohesiveness of the integrated data and that becomes complex. The data architectures over the years have become rather very complex and it is time for them to stop by and simplify their architectures. And that's why we wanted to published this book. Having seen this chaos in the market, there's a better approach using a logical approach that allows the gravity of the data to stay at the sources where they are, but get a unified view of the information without having to move them all or copy them. Every time somebody asks for the data, IT provides the flexibility that is needed in the architecture because the architecture evolves very fast. Like today, the AI is moving at a very rapid pace and, and there are teams that are requesting data from the central IT team in order to power AI projects. There was this one CIO I was talking to at um, manufacturing, uh, companies, automotive manufacturing company, and he said there are 50 teams that are asking him simultaneously for the data. And how is he supposed to provide the data in a governed and a secure fashion so that they don't compromise the integrity of the data, which is a challenge. So logical data management provides that flexibility. IT provides the ability to future proof any new technology that comes on board. And um, that's why they should be reading it. So that's why we decided to publish it for them. And now.

Speaker A: Yes, and I think it is the perfect time. I just want to follow up with you, Ravi. You mentioned we want to keep the data where it is. We don't want to move it, we don't want to duplicate it. Just very briefly, tell the audience why is this? Why is it bad to duplicate and move data?

Speaker D: Three things. One, it takes time to move the data into a place and once you move it, then the data becomes duplicated and becomes out of sync over a period of time. The second one and uh, next is to like. It takes effort. It requires people to kind of write the pipelines, data pipelines, to move the data and maintain it, test it, and things change. The source and the destination changes. So they need to keep that up to date. And all this cost money, um, in order to do that. So they can save time, effort and money by going with a logical approach.

Speaker A: Mhm. Absolutely. Now before I move on to my next question, I see we have a few questions coming in from the audience. So I'll start here with Scott and he's asking how much of my existing landscape do I need to change in order to get value from this new paradigm?

Speaker C: I can take that one for instance. I think one great thing about uh, uh, logical data management is that actually it builds on top of your existing infrastructure. Um, uh, basically it provides this unified data access and governance layer on top of your existing systems. You actually don't need to replace your infrastructure. Actually logical data management gives you a way of getting value from it. Also at the same time, if you are in the process of modernization your infrastructure, for instance, moving to the cloud or whatever. Also logical data management will help you to isolate the data consumers from those changes. It will be useful for your data modernization initiatives. But actually you are not forced to do that. One of the strongest points of logical data management is getting value of what you have and also avoiding, let's say big bang approaches where you need to redefine everything and you don't start seeing value until two years happen.

Speaker B: Right.

Speaker C: Actually with logical data management you leverage what you have and you start to see the value very quickly.

Speaker D: To summarize, it is a technology that coexists with your existing technology. It is not a rip and replacement.

Speaker A: Mhm. Yeah, I was just going to say that these days no one's waiting two years for anything at all. Right. We need results and ROI instantly to be able to move forward. Um, okay, so going to Christopher. So back to your book. When I was reading it, you talked about, you had an analogy in there where the logical data layer was like a library. So first of all, if you could define what logical data layer means and then talk more about this analogy, how did you come up with this and maybe tell us about it?

Speaker B: So the logical data, like a library, is kind of where if you had the choice of going out, buying the book and having to maintain the physical copy, keep the physical copy yourself, you could do that, or you could go to the library and borrow it. Um, as I thought about this in preparation for this podcast, I actually came up with a different analogy and I want to run it past you. So, uh, if you were a manager and you needed to talk to the different domain managers within your business, uh, you have three options. You can either go to each of those domain managers physically and meet with them, at which point you're only getting one on one access. You could invite all those domain managers together to one central location and meet with them there. Or you could use a zoom and online call to talk to them virtually and communicate with them there. Hopefully you can see this is very much like logical data management. Logical data management is like the virtual call. You don't need to do all the physical lifting, the hard moving, the organizing schedules, the finding the room and all that. When you can get online and talk to people all the way across the country, like we're doing right now. Um, and I think that's a really good kind of analogy to logical data management. You're setting up a layer that's virtual, where you don't have to bring all those separate layers together to get the information from them. You can meet virtually and talk about it and get all the information you need in a phone call.

Speaker A: Uh, yeah, that's a really good analogy. I love that. Unless we're meeting in like a really nice place, then, Christopher, then I'm sorry,

Speaker B: you might miss out on lunch. I mean, that could be a draw back. But I still think that's a great example of kind of how logical data management works. You still have all the physical areas, but you can pull them all together under one logical layer and work with them together as one.

Speaker C: Yeah, and I would add to that that actually, uh, you know, we still can meet physically, you know, following with your analogy, because actually a logical data management does not prevent you to replicate data when it makes sense. It simply gives you the flexibility to do it only when it's needed and only when it makes sense. Right. So you have other options. So following with your Analogy. We still have the chance to meet for lunch in a very nice place. So no problem about that.

Speaker A: Okay then, I'm in. Sold. Uh, had another really good question in the audience here. How do you convince skeptical stakeholders to keep data in place? After so many years of being told to do it the old way, do you find a lot of resistance? I'd love to hear from, you know, all of you if possible.

Speaker D: Yeah, I can, I can actually talk to that one. So there is this concept of the data gravity and it is never old to me. Like the, there's like throwing a ball up in the air, eventually it is going to come back down. So keeping the data in the sources is adhering to the gravity because that's where the data is actually authored and created. By artificially pulling the data into a central place in another place in order to just use it for analytical or operational purposes, you're going against the gravity. It's like throwing up the ball. Eventually that data has to come back to the sources and it gets disconnected. So to me, um, it is not old fashioned, um, to um, keep the data where it is. It is old fashioned I would say in a sense trying to copy the data. This is something that we have been doing for the last 30 years. Moving into a database, moving it to a data warehouse, moving into a data lake. So this underlying repository keeps changing, whether it's a data warehouse, data lake, data lake house. These things keep changing. But the concept remains the same that to me is old. It's time to look at a new way of leaving the data wherever it is and connect to the data and provide a virtual or a logical view of it that is new.

Speaker A: And have you seen pushback, Alberto, from data leaders who say they want to do it the old way?

Speaker C: Yes, well, uh, of course I think you need to explain the value and you need to explain how it works. I think when you, for instance, when you read the book, I think Christopher, uh, did a great job explaining this. When you read the book, actually you see that this approach is giving you more options. Actually it's not, ah, precluding any solution. It's adding more tools to your toolbox. Right. Um, when for whatever reasons it makes sense to centralize data, ah, you can still do it, but you are not forced to do it. You are able to leverage the data where it lives, you are able to uh, respect data ownership and this basically translates in much faster time to data. I think at the beginning there may be some resistance. I think when you explain that actually it's not either. Or is actually giving more options, uh, and getting value of the data faster. Because we all know that sometimes centralizing all the data in a single place sometimes may be very hard for technical reasons, for legal reasons, for organizational reasons. So having this flexibility of actually having more options, I think is probably the argument that I have found more convincing. Obviously after that, after I explained this idea, people want to know the details. People want to know. Okay, but how this works. Exactly, right?

Speaker A: Mhm.

Speaker C: That's where the book hopefully will help. Because I think it, it makes a great job explaining in detail. Okay. Exactly how this works, how this performs, how, how you should design that logical layer. Right. So yeah, it's true that, uh, you know, it's not like the traditional way of doing things, but I think also it's a powerful concept and when you take the time to explain it, I, I think most people get it.

Speaker B: If I can chime in a little bit too, I would also say that the amount of data available to companies and businesses all over the world is growing exponentially. And if you're going to try to tap into some of that data, a lot of that isn't data related directly to your business, it's tangent to your business. Things like weather, um, suppliers, things that are outside. And if you want to be able to access that data and tie it together with your own data, you're not going to want to make copies of it. You're going to want to be able to directly access it wherever it is and, and tie it to your own data. And that's kind of what a logical layer will be able enable you to do.

Speaker A: Mhm. Yeah. Thank you. I appreciate everybody chiming in on this one. I just, when I saw it, I'm like, this is interesting because it has to do with people and change management and convincing and showing and educating and taking them on the journey. And I think once they see it, they're like, oh, well that's logical. Right? Then they get it. Um, so moving on here. So Ravi, question for you. There have been many books written on the topic of data management and Christopher, you've done an amazing job with your book. Ah, I read it and I really love how it was an easy read, believe it or not. It's technical, but not, I'm not sure how to describe it, it just flowed. Um, my question to you, Ravi, is why did you decide to work with O'Reilly? And uh, personally I have only love for O'Reilly. I've published my book with them as well, so we'd love to hear your story here.

Speaker D: Yeah, sure. Uh, they actually go to the heart of the data community, uh, which we want to talk to. So they have millions of readers and uh, they are famous for their animal books and in fact I was a developer in my past life, have read all the books about Java and others and uh, used it for coding. They actually talk to the way the people want to receive. Uh, fundamentally the data architectures eventually go down to the technical people and they have millions of um, such people, whether it's developers, architects, senior leadership, managers and so on, who are reading uh, their books to understand the concept and to evolve their um, whatever charter that they do, whether it's a data architecture, whether it's a development, development of AI and so on. So to us it was a no brainer to work with O'Reilly given their reach within this particular community. And we wanted to take our message directly to them. Uh, because right now I see the community uh, kind of using the old fashioned way because that's what they have been used to. But they need to think out of the box to be able to understand here is something that if I invest can save me time, effort and money. And they should be and hence our reason to go right to the heart of the community. And then we started working with O'Reilly for it.

Speaker A: Yeah, that makes sense. You're meeting the people where they are. Um, again, logical. I don't mean to say that word so many times during the show about logical data management, but it is logical. Um, great. So uh, Alberto, question.

Speaker D: An added bonus is they brought in Christopher who was great to write this book.

Speaker A: I was waiting for you to say that. I'm sure Christopher was waiting as well.

Speaker B: It's all good. I appreciate the feedback and it was fun to write about. I learned a lot and we'll hear more about that here in a little bit, I'm sure.

Speaker A: Yes, yes, for sure. Um, actually before I go on to with my next question, I see a question here from Eileen and she's asking how do you reconcile the same data in multiple sources? Who wants to take this one?

Speaker C: Um, well actually maybe I can talk a little about this one. I am not sure of understanding the question exactly.

Speaker A: If you want to rephrase Alberto, we can ask Eileen to elaborate, then we can come back. Does it that help?

Speaker C: Yes.

Speaker A: Yeah, yeah, Eileen. So if you don't mind, uh, just expanding on your question and then, and then we'll go ahead and take that for now. I, uh, Alberta, still with you. I want to ask you, you know, with the Rise of data mesh, data fabric. Um, where does logical data management actually fit in? Does it sort of complement, does it replace, does it evolve those, uh, those concepts?

Speaker C: Yeah, that, that's a question that I get a lot, right? Because, yeah, there is clearly a relationship between logical data management and these concepts. And everybody's talking about these concepts, right? For instance, saying that data fabric is the architecture of the future data management architecture of the feature data management architecture for AI. Right. Well, uh, logical data management is not a replacement. I could say it's an enabler, right? Um, I could say that it can provide the technical foundation for both the data fabric and data mesh. Because one important thing that both the data fabric and the data mesh have in common is that they accept the, this reality of a distributed data ecosystem. They acknowledge that modern organizations aren't monolithic. They are made of different units that make independent decisions. And this naturally leads to distributed data and distributed ownership. Right. Then data fabric and data mesh make the emphasis, uh, in different parts. But I think both share this insight. For instance, in the data fabric, the data fabric has this goal of creating this intelligent layer, uh, that unifies the distributed landscape with consistent semantics and they use AI for automation. The logical data management actually can be that unified layer because it provides a semantic glue and also all the metadata, uh, and the business semantics that the data fabric, uh, needs for its AI and automation capabilities in the data mesh. Again, one of the key ideas is decentralization, in this case more decentralization of the organization, giving different business domains ownership of the data and allowing them to create data products that then can be shared across the enterprise. And logical data management can be a great technical accelerator, right, for this because that's because it gives business domains the power to create and share, uh, those data products from any data source. And also because it supports in a very natural way the concept of federated governance that is crucial for the data mesh, which is basically allowing the domains to maintain the ownership of their data products while at the same time having the capability to enforce this global compliance and um, interoperability policies across, across all data products. Right. So in short, I think that uh, actually logical data management is an enabler. It provides a technical foundation to actually build uh, these concepts at a scale in big organizations.

Speaker A: Okay, yeah, that makes sense. Thank you so much for clearing that up. Eileen is back with a further explanation of her question. What she meant was, uh, several versions of, let's say, customer information, multiple sources. Which source will win? Is there a hierarchy or method to determine the correct version?

Speaker C: Well, There are, uh, several ways to do that. First, if these, uh, data sources that provide customer information provide complementary information, you can, for instance, create a unified view that unifies the data from both data sources. And you can decide in the definition of that view which pieces you will take from each data source. For instance, you can say, okay, I will take this piece of data from this particular data source and this other piece of data from this other data source. Actually, it's up to you. It's in the definition of your semantic layer what you define. This is the authoritative data source that should be used, or even you should use this source for these particular pieces and this source for these other particular pieces. You can also use different data sources for different use cases, if that's what makes sense. Actually, you have full flexibility to decide that in the definition of this logical semantic layer, uh, that you will expose to your data consumers. Hope that that helps.

Speaker A: Yes, Eileen, let us know if you need more, but hope that helps. Uh, Christopher, moving on to you and your book writing process, just tell us your journey. How long did it take you to write the book? What was your favorite chapter? How excited? What was your excitement? Level 1 to 10 for logical data management? Um, and at which point did you realize, wow, yeah, logical data management really matters.

Speaker B: And especially now, that's a tough question to answer. And, uh, I am actually an animal author for O'Reilly. I do have my own animal book, and that's something I'm very proud of. Uh, this book came to me, and I think with this book and a lot of the other articles I've written, uh, for me it was learning, uh, because there was a lot of stuff I'm familiar with, sort of in a tangential way, but learning more about what it is, how it works. When I really got into the technical details of it, kind of understanding how it worked and into my mind, envisioning how it would apply to my own career and where I currently work. I think the chapter that really, really, uh, tied it together for me was chapter nine, because that's where I could start seeing some of the applications of logical data management and where it started impacting the businesses. Um, so, you know, how does it help your executive officers? How does it help the people working on the line? How does it help your HR team? How does it help your finance team? How does it help all these individuals get access to the data? Um, a lot of the articles I've written have been about data democratization, and I'm very strong believer that people need access to data to make Decisions not just at the executive level, but throughout the business. And this seemed like a great way to do that. With regards to how long it took, uh, to write. All of my books have taken me quite a while to write. Um, I'm pretty quick at getting the first draft out, but then it goes back and forth and we got to refine it, make sure the technical parts right, clean up my, uh, writing, make it more grammar correct, etc. Um, so it does take a little while to get it to work, but hopefully I finished it, uh, in a reasonable amount of time for the, the customers and for O'Reilly, and get it out pretty good. Um, I just did notice somebody asked, uh, a question. What animal book did I wrote? Uh, did I write?

Speaker A: That was my next question for you.

Speaker B: I wrote a Tableau Certified Data Analyst Guide. I actually have a copy of it over here. So.

Speaker A: Yes, hold on, hold on. Let's. Let's make you. There you go. There it is.

Speaker B: It's backwards on my screen. But, yeah, that's, that's the book I wrote. Um, it's a study guide. Um, happy to talk more about it. Reach out to me on LinkedIn. I'm happy to tell you more about it and what it's about, but right now, let's talk about logical data management and, uh, answer any other questions you might have.

Speaker A: Awesome. Well, thank you. It seems like you enjoyed that process, so thank you for sharing. Um, there was a really interesting question from Roger here, and the question is, has the NODA been able to extrapolate hard ROI metrics from their extensive customer base to share with prospective customers? And I think before you answer the question, maybe tell the audience a bit about denodo and how do you actually help customers?

Speaker D: Yeah, I can take that one. Um, so, Roger, like, there's an appropriate question because we just completed an ROI study, uh, with our customers using Denodo and customers who are not using Denodo. And you can actually find this report on the Denodo.com website. So if you go there to the homepage, scroll all the way down to the bottom, you should see a link to it. And, uh, in which, um, it actually has the hard metrics of customers that have used the traditional data management and how much time, money and effort they're taking to do it. And with denodo, how much it is actually simplified. And we actually reported, uh, the study author, um, who was an analyst at a reputed firm, discovered about, like, close to 350% ROI and about, like seven and a half, uh, times, uh, three to four times in terms of the speed of delivery to data and about like 75% um, less effort from a data engineering perspective. So it's a great uh, report. Go ahead and read that one. And in order to like from the Denodo perspective. So Kate, I think we have been talking about it. So um, Alberto has been talking quite a bit about it. So Denodo is a provider of this logical data management, uh, technology. We have been in the business for a very long time, about 25 years. We just uh, completed our silver jubilee and uh, we have um, customers across like you know, 40 different countries and we are actually present in about 20 to 25 different countries. So we are a global company and uh, we are a recognized leader in the data management space um, by the likes of like Gartner, Forrester, IDC and so on.

Speaker A: Thank you, thank you for, for that. Yes, I think it's always good to have a reminder throughout the session people, short term memory. Right. So it's always good to, to remind people. Speaking of reminder, we are going to have the final book giveaway raffle very soon. So the hashtag logical discoverlogical. Ah, you can just enter that in the chat and we'll pick another winner. And by the way, you can also get the ebook version of this book that we're going to put the links in the um, description and we're going to put the links into the chat and I can email the link as well for anyone who signed up to make sure you actually get to download and read the book in case you don't win the physical copy. Uh, now Ravi, follow up question for you. So let's say people get the book, the ebook, they win the book, they read the book. What should they do next?

Speaker D: Maybe take a test and pass the exam.

Speaker A: I'm just kidding, Christopher. What are the answers? Tell us the answers.

Speaker D: The answer is I will jump in. Sorry, I was just kidding. So the thing for me is that people need to consider what I mentioned before in terms of the cost, effort and the time it actually takes. Business needs the data right now because it's a competitive environment. They need to be able to differentiate themselves from the competition, provide the service as quickly as needed. And it is the charter of the IT to provision the data to the um, business as quickly as possible. And this approach enables getting the data and as I mentioned to Roger, it uh, gets the data three to four times faster than the traditional data management efforts. So and also take consider right now with the AI, AI is just spinning out of control. In terms of people wanting to spin up these projects, there is board level mandates coming all the way from executives to your managers and so on. So they need to be able to provision the data quickly and that's what the logical data management allows them to do. So what I would like to do is people, you have been doing the same thing the same way, uh, for many years. The cookie cutter. Stop. Read the book. Understand the benefits of using the logical data management, which has been used by very large companies, um, up to like all the way from like you know, 5,000 employees to 150,000 employees of very large enterprises and make sure you understand and you incorporate that into your data architecture and your data practice and you would see the benefits pretty soon other than having to spend the same way the same things that you've done in the past.

Speaker A: Yeah, thank you. And you mentioned uh, the magic word AI. And this is one of the topics I really wanted to make sure we get into because the book, in fact, Christopher, my favorite chapter is chapter eight right before your chapter, Logical Data Management and AI. And the question is for uh, Alberto, when we talk about AI and logical data management, you know, Christopher, you mentioned that they work hand in hand and Alberto, I'd love for you to maybe share an example of where you've seen this in practice.

Speaker C: Yes, yes, absolutely. Uh, there are several recent ones because as Ravi said, this is actually uh, one of the hottest topic for logical data management today. Right. Because you can, um, you can get value very fast. Uh, you can make available data for AI very fast, uh, and really accelerate your data initiatives, your AI initiatives and avoid data being a bottleneck for them. For instance, one example that I like comes from a major multinational, uh, tech company that we work with. They initially used logical data management to build, I have to say in record time what they call a, uh, management cockpit for their senior executives that also include the CEO. And this cockpit has three components. Well, the first one are uh, traditional dashboards which pull data from multiple data sources. The second is an AI chatbot that allows executives to talk to their internal data in natural language. But the third component, which I think is the most exciting is a corporate AI agent that is able to perform deep, ah, open ended research, uh, tasks. Let me put you an example. An executive can ask it, for instance, analyze our sales for product X based on, I don't know, seasonality, uh, region, customer segmentation and tell me also what other factors are relevant, right. For the sales of this product. And then the agent automatically creates a plan to query all the relevant internal systems to the logical data layer and generates a complete research report that analyzes these key factors, automatically builds new dashboards to illustrate these factors, and even proposes new questions and recommendations, uh, for deeper analysis. Believe me, this is really impressive when you see it, uh, and a good illustration of the potential of AI agents. But I have to say that even if the agent is brilliant, the reason why it can be effective is because this logical data management layer is providing it with this single, ah, govern view of all the data and organization and also in the language of the business, so the agent can match the user requests to the internal data of the organization. I think, to be honest, that these systems are complete game changers for business intelligence. I think the role of the business intelligence analyst will change. It will no longer be about manually creating reports. Instead I think that the analyst will become like a guide for these automated research assistants that will collect data, perform analysis, uh, while the human focuses on guiding this process. Right. And I think to actually make this vision a reality, uh, yeah, probably Data, more than, More Than Intelligence is today devoted today the bottleneck. And I really think that logical data management can help with that.

Speaker A: Yeah, I completely agree. I think it's also an enabler, right, for all the things you talked about, the dashboards, the chatbot, the AI agent sitting next to you, which honestly I can't wait for more and more agents to come in and do, do the work. But it does have to be built on the trusted, um, data where you can access all the data that you need. Um, let's see, there's a. Okay, we're getting close to our giveaway here now. Just reminding people I'm gonna, I'm gonna go through my last question before we actually announce the winner, but just a reminder to use the hashtag. And if you don't know which hashtag, you can either look into the comments or you can look at the ticker to get the exact spelling. I saw some people misspelling M it. That's not going to enter you in there. So just, um, just letting you know that you have to use Discover Logical and include that hashtag. And if you also have any more comments or questions that you want me to address, please, um, put that in. I'm also noticing there are some comments in here with links. Feel free to go ahead and use that. This is where you get the ebook, this is where you get the ROI study. So I appreciate folks dropping that in there where it's easier for you to click and get to those research reports after. Now my My closing question to you all, and we spend about 40 minutes here talking about the topic, but for those who just tuned in, they heard nothing. What is your 30 second sort of key takeaway from this conversation? And eeny, meeny, miny, mo. I'll start with Alberto because it rhymes. Let's go.

Speaker C: Okay, so I, uh, think uh, logical data management is the key that allows every data leader to stop asking how do we move this data? And um, focus instead on how do we get the most value from it. Right. It accelerates your time to data, also adds, uh, the semantics and the business context that your users and also your AI agents need. Um, I think that the book does a great job explaining the core concepts in detail and also showing you how to, how to build it in practice.

Speaker A: Great, thank you so much for that, Christopher. Let's move on to you next.

Speaker B: Before I answer the question, I did want to also say a thank you to both, uh, Pablo, Ravi and the rest of the Denodo team for helping, helping me learn as I wrote the uh, book because, uh, there was a lot of it that I didn't know and they helped uh, me understand a lot of the concepts. Uh, I say the big takeaway for me is, uh, taking a look at the book and kind of associating it not only with your business, but how you can expand on your uh, data environment, how you make your data accessible to people within your business and how do you bring in data that's not may be part of your uh, data environment currently? How do you tie in other things to make better decisions, um, and better choices for your business? And the book has a, does uh, a great job explaining a lot of that.

Speaker A: Well, it's got a great author, so, you know, thanks.

Speaker B: Did my best.

Speaker A: Uh, and Ravi, your key takeaways.

Speaker D: Yeah. If you haven't listened, uh, to this and you're just coming in right now, my solution, uh, for that is to go back to the beginning and re watch it. So that way you missed all the important information which cannot be condensed in 30 seconds. But here I will tell you like, uh, the book is available on the website. Scott Taylor actually put. If I cannot get a physical book, can I get a logical book? I love that, Scott. So you do get the logical one. Like as Kate mentioned, there is a link that you can actually go, or you can go to the homepage, scroll all the way down to the bottom. The logical, um, representation of the book is there for you to download, but if you need a physical copy, click on the contact us on the top right hand side and just put your name and uh, address and all the other information. We'll contact you and we can ship a book for free for you.

Speaker A: Oh, look at that. I love this. Okay, well, speaking of book for free, we're about to announce our final winner. So if you're feeling lucky again, I'm going to share the screen. You're going to see the prior winner on the screen. Don't be shocked, don't say, oh my God, he won again. No, this is just the way the system works. Congratulations again to the prior winner, but we're going to have to, uh, we have 42 entries here, so good luck to everyone who has dropped in that hashtag. I'm going to click draw again. Any drum rolls here now? 3, 2, 1. Let's go. Good luck everybody.

Speaker B: Good luck.

Speaker A: Let's see. And the second winner is. Eileen. Congratulations. I love it. Okay, very nice. I love the confetti. It just looks so pretty. I love. I can do this all day. Honestly. Just keep rolling giveaways. Um, I should be in that field. Um, and then we do have more questions that are coming in from the audience. I think a great thing would be if, you know, if someone from denodo can jump in and maybe if there are any book related questions, if Christopher, you want to jump in and answer them directly on LinkedIn. I love how engaged you were as panelists and how engaged the audience were. So I really appreciate everybody tuning in. Uh, any, any additional final words, uh, Ravi, anything else you want people to, to do at the end of this live? Now is your moment.

Speaker D: Now I would just say the book, as, uh, you know, Christopher termed it as the Rise of the Logical Data Management. Data management has been around for a long time, but the rise of the logical approach to it is now. So take a look at it and it's a very interesting read. And as Kate mentioned, it, uh, simplifies it greatly from the business perspective and uh, you would really enjoy reading it and not just that, incorporate it in your data practice.

Speaker A: Great. And as I say goodbye to you all, I'm going to put up some QR codes. I highly encourage you to go ahead and follow the noto on the social media platforms that I will list, uh, LinkedIn and Instagram. So thank you all so much for the great session and I will see everybody online.

Speaker B: Thank you all for coming. Appreciate all the support on the book.

Speaker D: Thank you, Kate.

Speaker A: Thank you.

Speaker C: Thank you, Grace.

Speaker A: Thank you.

Speaker B: Okay.

Speaker D: Mhm.