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Episode 16: Changing Your Mindset to Change Your Data Strategy

Featuring Pablo Alvarez, Global Director of Product Management at Denodo

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Speakers

Tina Tang

VP of Product Marketing at Privitar

Pablo Alvarez

Global Director of Product Management at Denodo

Transcript

00:01

Intro: Welcome to InConfidence, the podcast for data ops leaders. In each episode, we ask thought leaders in futures to break down the topics and trends concerning it and data professionals today, and to give us their take on what the data landscape will look like tomorrow. Let’s join the data conversation.

00:20

Tina: today. I’m here with Pablo Alvarez. So, Pablo, thank you for joining in confidence, the podcasts for data ops leaders, let’s get warmed up with some very important questions. What is your favorite episodic binge right now? 

Pablo: All right, so now I’m back at watching the prequel of Game of Thrones and happy with that coming up. 

Tina: House of Dragons right?. 

Pablo: Yeah, exactly. It’s still only three episodes in so I go and call it a binge. And since the last thing that I binged was Stranger Things a few weeks ago, 

Tina: so epic. I don’t know why it’s like even though it’s so fantastical. I just I can’t I it’s like, oh, yeah, that could happen.

01:05

Pablo: I loved it. I loved it. It was so controversial, meaning it seemed like it looked like it was going to go in a completely different direction. And they managed to kind of tie all the loose ends and beautiful soundtrack as with previous season, so I am a big fan. 

Tina: Yeah, me too. I yeah, I couldn’t stop watching it and I had to binge it. So anyway, luckily, like Netflix encourages that behavior.

01:29

Tina: Okay, one last one, just for fun, true crime or sci fi, sci fi, 

Pablo: sci fi, put your hands down. 

Tina: All righ me toot. We’re going to start with a very, I think, a question that you haven’t been. So you’re someone who has been in the data space for a long time. I even read in your bio, that you’ve been in data virtualization for 15 years, and I’m thinking to myself, hasn’t it only been around for 15 years? around that? 

Pablo: Yeah, right. Okay. So, yeah

Tina: I think what my kids would say is an OG been around from the start, and been part of this whole movement in operationalizing data and facilitating its wider use, and, you know, taking it out of silos. So let’s start with this question. Why do you think, or what do you think makes the current moments special in the data and analytics space? 

Pablo: Yeah. So I think that we are getting to a point where we are kind of slowing down on innovation, from the perspective of there has been a lot of cycles that I think are coming to an end. And there is a moment to kind of like, rethink again, what are we doing with data? And let me explain what I mean with that. So I think in the last decade has been a crazy in many, many aspects, like the Hadoop and no SQL came a few years ago, and it was all you had to do, then that turned out not to be the case. And Cloud was where all efforts were going. And I think we’re getting to the point where those efforts or whether abandon, or those efforts are coming to fruition. Same thing with data lakes, and there were like, in the last,

03:21

Pablo: maybe 10, to five to 10 years, there’s a lot of things that got a lot of traction, got a lot of hype, some worked out, some didn’t work out. And now we’re getting to a point of consolidation where rather than trying to come up with the next cool thing, like, Okay, we have all these things, some are working, some didn’t work. Let’s now kind of work on on strategy, with our data with our processes, and make those things that we have work. And I think that’s a pivotal moment, because I think that’s a cycle that happens, you know, like, we’re talking earlier, we say we hear it happened with the data warehouse and data warehousing technologies, when like, you know, like, vertical databases appear and all of that, and then it’s okay, technology is there. Now we had like, good things, we had bad things. Now we got to something that seems that it’s working, let’s actually work on implementing things on it. Because I feel in the last 10 years in many aspects, rather than consolidating the latest strategy, it’s been like trying new thing after new thing. And now it’s time to get things to work. I think it changes a little bit, the dynamic in vendors changes the dynamics in a customers kind of like what we see happening in the market, I think reflects a little bit that consolidation point.

Tina: I love that it’s time to get things to work. I think that that is an important

takeaway from what you’re talking about this, you know, there’s a lot of experimentation. There were new ways of doing things, new technologies to do with and now come

Tina: Companies are wanting to make it work, operationalize it, and get the efficiency out of it. Because you’re right, like you mentioned, you know, Hadoop, for instance, like, what was it? Like? I don’t know, six years ago, we thought, Oh, Hadoop, the answer to everything.

Pablo: Yeah, or even less. So.

Pablo: It came and went very quickly. 

Tina: But yeah, yeah, yeah. So let’s talk about what the the biggest trends that are emerging that you’re seeing. So we’re talking, we’ve talked about, you know, what’s maturing now, can we talk about what do you see that’s emerging in the field? 

Pablo: So I think there is an area where I see a lot of evolution isn’t architectures, as I said, like they, you know, the software has gotten to a point where seeing the strengths or get into the thing.

05:52

Pablo: Analysts call that the plateau of maturity, you know, you get the hype, and then some things disappear, some things come out of that. A lot of the technologies we’ve seen are getting to the plateau of maturity, data, lakes, cloud, data science, and things like that. Some got, like, lost on the way, but many others are there, like data catalogs, and modern governance. And I think all of those things are getting into that plateau of productivity. So I think we’re I see a lot of movement these days in like trying to create architectures that enable sort of like a blueprint of what’s kind of like the, the norm for a particular industry for a particular vertical processes that you can get different as a foundation to implement something in your company. And just to put names into these things, I think, for example, terms of architectures, we see a lot of discussion with our customers around the idea of a Data Fabric, for example, as a modern New Generation architecture or like self service architectures and things like that. And as something that has gotten a lot of, at least discussion in, in the analysts, in the, in general, in the data wall, more related with the process, for example, is the ADL the data mesh, that I think tries to capture a lot of previous trends, previous discussions on ownership of data itself, and data literacy and, and things like that. So I think those are two trends that I see exemplify a little bit that consolidation of technologies and trying to make those work in, taken different angles, maybe read one more from like software stack, and the other one more from like, the process perspective. But I think there are two two good examples of what’s happening right now, in the data mesh, and the data mesh, approach to process. What is the importance of data virtualization in this paradigm? Well, data measure thing is interesting because it tries to capture what the is in their lingo, they domain knowledge. And the idea that the teams that own a certain data set are the ones that really understand what to do with that data and how to refer it to others. And what’s the true meaning of that?

08:22

Pablo: Rather than you know, you’ll want to do something with that data, passing it to a team that is purely technological, and don’t really understand what those values are in the context of the business process. So that makes total sense. And that is not a new idea. If you think about data Mart’s that’s precisely what what data mart were trying to capture. And the idea of data literacy and tribal knowledge have been out there for a while, I think it’s capitalizing on all those concepts, but truly trying to build a framework for all of this. So now you’re getting into a process that tries to capitalize on something that is highly distributed, which is the structure of the organization, you have different domains. And a big risk of trying to do that is that you are creating new silos, instead of consolidating things silos, because even though they have data meshes, that everything needs to be documented and self explanatory and whatnot. It’s a very thin line to walk, try to work on something that fosters distribution very easily can turn into something that doesn’t work with each other, you know, you have different lines of business, and now you’re giving them power. And all of a sudden, corporate reporting becomes very challenging, because things don’t interoperate. Well. So I think, going back to your question, I went a little bit on a tangent here, but I think it’s important to get that background. I think we’re totalization is a technology that helps really helps kind of keep those those two things in balance, give flexibility and give kind of a tool that  allows us lines of weakness to do things on their own, and at the same time have that aspect that allow us to manage and govern and make sure there’s interoperability between those different data products to use their lingo. So yeah, I think it’s a good way to manage, or to balance out flexibility versus risk of interoperability, just to put it in a single sentence, lots of buzzwords in that sentence, but hopefully that makes sense.

10:30

Tina: So talking, you know, we were talking about data mesh. And then we were talking about data fabric, and how their ways of architecting for efficient use and sharing as well as the process of sharing data. So what what do you think is the importance given? Like, there’s so many, so much concern around the world about data compliance and privacy, about ethical use of data, sensitivity of certain datasets, certain types of data?

Tina: How does that impact these, these approaches these paradigms, these architectures

Pablo: It’s something that sort of needs to chill over. And definitely, as you start, like, trying to the thing with these initiatives, it’s, they try to make more data accessible to a broader variety of people. Both Data Fabric and data mesh, and pretty much itself, service architecture, say any kind of modern take on on data management, tries to bring more data to more people. And that comes with a huge risk, which is that you have something that was kind of like tightly controlled environment now exploding out of that tiny box, and risks of data leaks of sensitive data being exposed, even within the company itself. Like, you know, like all of these tries to foster even a sharing of data across divisions of a company or between partner companies. So all of those risks are even even higher. All of these in, in, in a situation where like, you know, cyber attacks, data piracy, and things like that are very palpable threat. So I think at this point, we need to think of privacy and security pretty much in any step of a process or in any part of modern data architecture, is something that the you know, the idea of privacy first in or security first, in our data environment. It’s something to really consider, you know, we were talking cloud first a few years ago, and now we are there. But the privacy first security first is often what we need to think about. And that’s like, partnerships, like what we have with you really touching on that and all the different aspects and variations of what privacy means compliance means and security means when working with data that can get a lot of different flavors, depending on how you look at it.

Tina: Right. And I like, you know that you’re using the word security and privacy in the same paragraph, because, you know, I think I you know, because I think even just a few years ago, most people, even in our field looked at them as separate things, rather than being integrated and embedded in the whole landscape and architecture. I think that, well, research analysts like like Gartner have recognized that there’s a convergence happening of different capabilities in those spaces, and that they’re needed, very much needed. And that they must be woven together to have a, first of all a performant system, because you can’t have these bottlenecks. 

14:03

Tina: It just, companies and organizations cannot afford for these long delays in provisioning of the protected data. Like before, they really need it immediately. Not in seven months. So you know, capabilities that are coming together to provide that protection for sensitive data, while at the same time getting the data to those downstream value added initiatives that need it, I think is really important and denote plays such an important part of that and like you mentioned, we have a partnership with Denodo. In doing just that.

Pablo: Yeah, it’s it’s I think you’ve touched on our important topic, which is agility and you know, if you have a primary focus on security and primary focus on on that you have to because it’s not just because you’re concerned

15:00

Pablo: With data leaks, you of course are concerned with Italy’s, but there is a whole set of regulations CCPA GDPR. And you know, pretty much now every state or every, every country is coming up with an equivalent of that. So it’s, it’s the law, it’s not just like something is another use on one like some employee to see your salary. It’s a law, it’s I think the perception on all of these topics have gone past and nice to have into, like, you know, you need to it’s a requirement you have to comply with, and there is no workaround that, at the same time, you have the agility that you said, you know, if you’re doing like market analysis to create your campaigns for Christmas, and you miss Christmas, and that data is ready in March. Well, that that’s, that’s ridiculous. So you have sort of like this, this balance today that you need to think about between proper governance and governance in the broad term, including all of these topics into that work with the agility of the of the process, and you have, you know, self service pushing in that direction, while like, you know, the data stewards are pushing and other.

16:09

Pablo: At the end of the day, you know, the software is really what enabled those things. And for me, as product manager, a lot of what we’re doing and others user experience, there is optimization techniques and things like that, that we really discuss. But a big part of it is enabling these different personas in different roles to be able to do those things efficiently. And a lot of what what we do, or I do personally, as a product managers, think about ways to have agility in the process and agility come from a lot of different areas, proper integration with the ecosystem, like we have discussions with with you guys. But also like user experience,

16:47

Pablo: dashboards, and panels give you the right information with a lowest number of clicks, there’s a lot of different pieces that come to play. And back to my original comment, I think, you know, that’s why I think this is really interesting, because now it’s like, you know, it’s not just the technology evolving a little bit for the sake of it, we have really the tools are there, we need to put that into the data in front of the right people. And, and make that work. And I think that that’s what I find really exciting about this particular moment in Data Management,

Tina:  it’s also very exciting to me, because we now have the products with very efficient and effective ways of providing this functionality to data and analytics teams. And it’s now, they’re, they are helping a collaborative nature that’s necessary in order to holistically provide the right data to the right people in the right places in the right format, to comply with regulations, and at the same time, be effective and timely, to meet their business goals. And I think that’s what’s exciting is that this is not academic, this is a blueprint to use your word that is in operation today, at some very large organizations, we share some some customers as well, in this space, and it’s being done. They are doing this, and they’re doing it very effectively. So I think that that’s an important also, important point to make. It’s not theoretical, Bs, this is real life. 

Pablo: You know, this is this is this has happened. And this is you know, the tools are there, it’s a matter of putting them together are creating a plan, having the process.

18:40

Pablo: Back to my previous comment, and it’s happening. And it’s, you know, as a product manager, my past life was a lot of like getting feedback to translate into new features. And now we’re getting to the point where the features are there is more about like how you use them to, or how you make them more agile, how do you make them more efficient? How do you improve the user experience? And it’s definitely been the last couple of years, a pivotal point. And I don’t know if the pandemic has anything to do with it or not, but I feel the last couple of years having a pivotal moment. In a lot of the conversations we’re having today. 

Tina. Yeah. I feel like the pandemic was a point at which a lot of organizations realize they couldn’t hide behind this, you know, veneer of being digitalized. They actually had to have the back end to back up these kinds of initiatives that they now are not optional. They were not you know, they had to happen now, not in five years. Yeah, this transformation wave that you know, a lot of people had, you know, on the roadmap, but we’re putting it off because they they thought they had time to do it. Yeah, I think the pandemic made everyone realize no, we, we have to do it. Now. That or we  haven’t already done that there 

Pablo: That or we spend too much time alone, working from home in front more time to reflect on on that get things done. 

20:10

Tina: And so speaking about features denodo was recently recognized both by Forrester Research as Enterprise Data Fabric leader, and by Gartner research as a leader in the Data Integration Magic Quadrant. I mean, pretty impressive, because, you know, having having been part of the team that has to go through these types of assessments, I know, they’re, they’re very comprehensive, and require a lot of, you know, evaluation and time to, to, to make it onto these things. So congratulations on that part. But what is the I understand, right?

Pablo: What do you attribute to the recognition, I think our software has a very, very unique characteristic, which is that we try to blend two ideas in a way that nobody else does, which is how to manage metadata in a modern way, like similar to what data catalogs do are, you have, you know, your list of everything, you document everything you have, like data stewardship, and documentation and, you know, metadata management in the modern sense of the world. But we combine those ideas with a delivery and execution. So the thing, it’s something that hadn’t been done before. I mean, we have that that concept of data virtualization as the technology that really fuels this, this different initiatives, but really have use, I think, for the first time successfully, that technology to enable this, these different pieces and these different perspectives on on data management, em, and I think once we get customers to get into that mindset, and use the technology in that way, they’re very happy with the results and the agility that brings the better management better controlled, both in

22:13

Pablo: security, but also in many other aspects. And, and I think that that’s really what has driven that position. I mean, we started as, as a niche vendor. And slowly the technology has evolved to get into into that level, where we are we’re doing a lot of cataloging data lake management, data warehouse management, broader, that broader picture of data strategy as a whole, I think that that’s the key is like moving the the strategy not like something that is product ways, but as something that is enterprise ways, in a way that you don’t have to go. And then once you decide the strategy, go implement it in 15 different places to sell was meant to implement a strategy up an enterprise level. And I think that’s quite unique. And I think that’s what this this analysis reflect they have different angles. If you read like Forrester and Gartner one goal comes from the, the Data Fabric perspective. Gartner says more kind of focus on data integration, and what’s needed in data integration to at the end of the day enable a Data Fabric, but more from a technical standpoint. And a theme both reflected that, that aspect of that uniqueness of the software that we’ve been able to put out there. And once customers get into that mindset, they really works. And they get two very, very vertical solutions for data management. One thing we hear talked about a lot, as a goal for organizations is cloud migration. And, you know, this is kind of a simplified phrase to mean, you know, a lot of things that are, you know, behind it that make a company want to migrate to the cloud.

Tina: What are some things that you seen in this, you know, maybe some of your customers journeys, about their migration to cloud that you want to share with the audience?

Pablo:  Two things I think, are relevant to mention. One is it you know, migrations to the cloud are costly, long term process. One thing is moving one database or one application, but when you take that as the enterprise level, it might be a decade. So it’s, it’s a process that you have to take with patience and understand that there is going to be that hybrid state in some time for many years, sometimes permanently, because you might still have, you might have like the coolest Cloud Data Warehouse technology, living together with mainframe set in the 70s or like, you know, differently.

25:00

Pablo: Automation so that, and that’s a reality of a lot of people. So I think that that’s one thing I’ve seen is, you know, these things take time or are complex and is not just moving system a to system B, you have all the downstream and upstream flows and applications that you need to account for there is not just one system, there is like hundreds of them with the same implications and connectivity between them. So it’s a process, it’s quite a process, and it takes time. So I think one, one thing, one lesson learned is, you know, plan for the process, because it’s gonna be some time and you need to have a working solution, you cannot just close business for 10 years and then open again, once you’re in the cloud. The other thing I also experiencing in many, many instances is that moving to the cloud, by itself means nothing, it just means that you move from one place to another, moving to the cloud is not modernizing your data strategy, moving to the cloud is moving one thing from one place to the other. And if you want to modernize your data strategy, you need to do more things, getting into the latest version of new software, gets you some of maybe new capabilities that enable things but you need to have changing processes, changing responsibilities, changing the sign decisions, changing many other areas that are very often related more with how you do things, because moving to the cloud, by itself doesn’t just change his location. And changing location doesn’t solve any problems other than, like, you know, getting your address or something critical, which is you know, you have to maintain a data center, you don’t have to worry about the next hurricane coming because your data center is gonna get flooded. You have Amazon or Microsoft worrying about that. But in terms of data strategy, it doesn’t change anything, you have to change the data strategy 

Tina: Does it change mindset. Does it require a change in mindset? 

Pablo: A change in data strategy requires a change in mindset. The problem is when you go to the cloud without the changing mindset, then you change nothing. Just location: 

Tina: Yes. Okay. Wise words.

27:12

Tina: I feel like that came from maybe some painful personal experiences.

Pablo: Yeah, and I’ve seen it many times these Oh, no, we are a cloud first. That’s our strategy. Well, cloud first is not a strategy that’s location. And then like, you know, you see it, see what happens. Definitely, definitely personal experience.

Tina: Okay, let it all out. This is a safe space.

Tina: So Pablo, puts a takeaway that you want the audience to come away with, after listening to the show. 

Pablo: I think, trying to tie this to my initial comment, saying it’s time to get get the technology stack today. I think it’s it’s powerful. Get get the technology stack today. I think it’s powerful. Get the technology stack today. I think it’s it’s powerful. But you need to use it in the proper way. I think a as a closing statement, I think something that I see often is that in order to get like that strategy that addresses the real problems, you have to embrace the what you have. And what you have is an organization that often is very organic, and it’s highly distributed. And you know, things like data mesh, try to capitalize on that. Your data landscape is also complex and varied. And there is many different needs and teams and data scientist and data warehouses and operations and a lot of different areas, you need to go when you think about a data strategy, embrace this fact that all of these things are they’re trying to consolidate again into one single solution, one single silver bullet is not going to work. And I think like the, you know, the failures of Hadoop, for example, coming in many, many aspects, but that was one of them, you know, try to box everything into a single solution that is going to magically work is most likely to fail. A modern data strategy needs to account for the fact that the organization is distributed, the data landscape is distributed. And when you address a single problem, you don’t have a strategy, you just address a single thing. And it’s time to use the current tool sets and the current state of the art to create those brother enterprise wide data strategies. 

Tina: Thank you. Thank you, Pablo. Hopefully, that makes sense. I wanted a single sentence and that being five. Hopefully that makes sense. t does. Can we go back in? Would you mind? Maybe saying something about the mindset that’s required? Not just the changing location of your systems and your data, but what is the change and mindset that’s required? I’d like from maybe, because I’ve heard this from some CDOs is that sometimes it’s still in this day and age still hard to have data owners share their data. And it’s not because of technology or the lack of technology, it’s because they view that data as power over their fiefdom, 

Pablo: You you know we’re getting political here.

30:30

Pablo: Data is power that that’s, that’s for sure. And the other end, you have, you know, a lot of the analysis that that show that data sharing is, is key, I think, I think it was informed sorry. And like, I always like to every now and then read on the, the business side of data management, because as product manager, I tend to just wait you to focus on the technical side. And there’s all this analysis that show that data sharing a especially across divisions, or subdivisions of a company, it’s a key to, to evolve in to be competitive, even in the in the current landscape, where you have a lot of competition coming from everywhere, especially with with global supply chains, global distribution, and things like that. So there’s, you read that kind of literature, there is a lot of data sharing as a necessity to remain competitive, and from a business goal from the perspective of business goals, and there is definitely that mindset of data, spyware, and I’m not gonna let that go.

Tina: Seem self limiting.

31:37

Pablo: Funnily enough, I’ve seen that mentality also a can, if you try to analyze the technical consequences of that, what you end up having is people with hoarding Excel spreadsheets with information that is critical. And then you know, then you don’t even know where the data in the Excel spreadsheet is coming from, or who has it, who only is it was a right version of that, and things like that, that came a little bit from that runaway application of that mindset of the dice power. So I think there’s definitely a lot of value there. And if you don’t change the mindset, you don’t really change the data strategy. There is definitely always in this kind of situations, a political discussion associated with a technical one, you know, does really developed brains that we were talking about earlier, on the only world we live there is the mentality that the you know, the your your strategies have limitations. And if you don’t change them, nothing, nothing changes, and buying software alone doesn’t doesn’t change anything. So that’s a good reflection on all of that for sure. You need to be open to change, not just in so we’re changing process.

Tina: Yeah. Good. Yeah. Great observations. And in thoughts..

Pablo: What about you know, more data is more power. 

Tina: that vision, that nightmare vision of hoarding Excel spreadsheets strikes fear.

Pablo: I’m saying it is that lie that there are definitely a lot of personal experience on seeing seeing critical data that is literally maintaining an Excel spreadsheet by a person that then leaves the company and nobody knows how to actually do that thing, again, or variations. It’s, it happens more often than you think in places where you don’t. 

Tina: That’s exactly right. I think it’s especially when you think, Oh, that couldn’t happen here. You know, we’re so advanced were so this, like, you know what, it’s happening, it does happen, it will probably continue to happen. And so, I spoke to the former chief data officer of a very large business software company, and she had an impressive program, within the organization, on bringing all of the different business functions, domain, master data, domain owners, data owners, into the fold of the governance program, by helping them see the value of doing so quantifying it, studying it and honestly paying them the respect that hey, you you have, you know, you are have created these systems to capture this information. This is what we could do with it. This is how you could be recognized for, you know, being a part of this forward momentum and of helping the organization be better.

Pablo: That’s how I was taught to me, you need to recognize that this is people doing these things for a reason, and maybe you don’t understand the reasons. Try to give them something equal or better, and help them understand why it’s a better just because it’s better for it doesn’t mean that, you know, they might not adopt it if it’s not better for them or they don’t see the impact it has in other parts of the organization. So that I think that’s definitely a strategy needs to account for you can, you can just take directions and decisions without accounting, the users that are gone actually implement those decisions, because at the end of the day, they’re done by the idea, they will go back to their old ways. And a lot of times they change the mindset or the changing process need to start there. 

Tina: There has to be a carrot., there has to be a carrot and the stick, right.

35:38

Tina: We want them to come along on this journey. But you know, we want to do it in a in an effective and productive way. 

Pablo: Definitely.

Tina: Pablo, thank you so much for speaking with us on this Friday afternoon. And hopefully we can take the rest of the afternoon off page take the rest of the afternoon off. 

Pablo: I wish I wish. do you could maybe you could do it with a logger in your hand.

Tina: All have logger.

Pablo: Exactly. Now my pleasure is very good conversation. Loved it. Thanks for inviting me. 

Tina: Pleasure. As always, thanks so much.

36:25

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