Your thoughts become action; going from Thought Leadership to Practice w/ Roel and Valentijn of Vopak

Speaker 1: This is Catalog and Cocktails presented by data. world.

Tim: Hi everyone, welcome. It's time for Catalog and Cocktails presented by data.world. The enterprise data catalog for the modern data stack. This is coming to you live from Austin, Texas, where we have an honest, no BS conversation about enterprise data management. I'm Tim Gasper, longtime data nerd and product eye at data.world, joined by my co- host Juan.

Hey Tim. I'm Juan Sequela, principle scientist here at data. world, and as always it's Wednesday. We're back season four, episode two, season four, Wednesday, middle of the day, middle of the week, end of the day, time to have a drink and have an honest, no BS conversation about data. And today our special guests, we have two guests, but this is a very special guest for me personally because I've known you all for a long time. Many, many years, and we've gone back and forth, our lives have crossed path, and now we're here altogether in Austin, which is so cool. So I want to introduce Roel and Valentijn. They're both from Vopak. You're the global head of data at Vopak and one of the systems architects. And today we're just going to be talking about taking all this BS that we hear and bringing it back to reality. But first of all, let's do our tell and toast. So how are you guys doing, and what are we toasting, and what are we drinking? What are we toasting for?

Roel: Well, first of all, we're doing fine. Otherwise, we wouldn't have been here, of course. And thanks guys for having us. Great to be here. A long time listener to the podcast and good to contribute as well.

Valentijn: Yeah.

Roel: I'm having an IPA. I've been asked for an IPA, actually. We have been in a car from Houston to Austin for three hours, in 35 degrees. Not sure what it is in fahrenheit.

Tim: It's hot.

Valentijn: It was 90, 90. I saw it on the screen.

Roel: But for Dutch people that's quite hot. And it brings me some freshness.

Tim: Nice.

How about you, Valentijn?

Valentijn: I'm drinking a cider, but I actually was hoping for mojito,'cause the heat brings me back some memories of let's say the Girby and...

Juan: But there's no beach around here, so...

Valentijn: That's true. Yeah. Too bad. But then again, I can live with anything cool and fresh around here. So yeah. But thanks for having us. I listened in to the podcast once, so not familiar with all of it, but I listen to many podcasts, so hopefully I can contribute to this one.

Juan: All right Tim, how about you?

Tim: Well, I'm drinking a scotch and ginger right now. And I'm curious to just being able to meet with customers, with partners, with you all. It's awesome to be able to... It's for the last three years, it's been tough having to work from home and things like that. Being able to meet with each other, collaborate, I mean it's great to be able to do that. So cheers to that. Yeah.

Juan: Cheers.

Tim: Yeah.

Juan: And I'm drinking something that I called El Burro. And it is tequila, agave, bitters, and some ginger beer. Actually super refreshing, so we actually should have had this.

Tim: Yeah, I'll do that.

Valentijn: I'll save them on for later.

Juan: And cheers. I'm going to cheer for, we're going to be having the data. world summit in a couple weeks, on September 22nd. We have a great lineup of speakers, so many different topics. So go off to our website data. world and you can find the summit so you can sign up. All right. So warm up question of the day. If you could actually manifest something, just from thoughts alone, what mundane task would you use that, to make your life easier?

Roel: Well, in my professional life, I'm automating data, and making it easy to work with data and building systems that would automated. In my personal life, I still have to fill in my kid's school papers with paper and pencil, and I'm having to copy my personal details. And then one of my wives, and then one of my kids, over and over and over again. So if I could stop that, that would be great.

Juan: So no more pen and paper?

Roel: No,

Tim: And we're kind of almost there with technology. Just not fully implemented yet.

Roel: Not in primary schools yet. No.

Valentijn: So it's something I can do by thoughts alone, right?

Tim: Yeah.

Juan: Yeah.

Valentijn: So I thought could be transferring ideas. So that's either professional, personal. Sometimes, okay guys, we need to go, maybe on the road trip. Shall we do this? I need to do convincing, tell them things, and they don't always go my way as well. Same goes in professional life. I have to write out the idea, sketch something. It would be just easier if someone just agrees with me, oh right away. Without thinking out, or writing out everything I need to do.

Tim: I like that.

Juan: That's a little bit of Inception.

Valentijn: That's a task of by thoughts alone, and that would be very handy. But fortunately nobody can read my mind. So yeah, that's a science fiction for now.

Tim: It's a good one. That's a good idea though. I think for me, I would pick, I really enjoy cooking. But more than cooking, I enjoy a home cooked meal. So I would like to think the food I want, and I just wanted it to get cooked automatically. That would be great.

Valentijn: I enjoy. I was like oh, you're going to steal mine. I enjoy cooking. I love cooking. I don't like to do the dishes.

Roel: But that can be automated to some points.

Juan: But still, but still.

Tim: I want to automate both of those, I think.

Juan: No, I don't automate my cooking. That is my relaxation. All right. Anyways, let's get into the discussion. So okay. Honest, no BS. We all read things. There's so much stuff out there to go read. And there's books and blogs and podcasts and all that stuff. And there's a lot of people, thought leaders and hey, ourselves included. We're talking about all this stuff. We're talking about all this stuff that can frankly be heard or considered BS. Or you have to kind of sift through all that noise and what is the BS and not. But after that, you're actually going to be start finding these gems. And you hear them over and over again and you're like wait, there's something really here. Something really valuable that I need to bring into my organization. But now you got to bring it to your organization and got to convince some people. So how do you sift through all the BS, and the thought leadership? And how do you bring that into the organization?

Roel: Yeah, I think that's a great question. It is also typically what we are doing on a daily basis. I remembered this is maybe also nice to share. I remember when I was entering this field, which is I think close to 15 years ago. I had the idea that I would understand the field. Things would kind of be in control. That was like data warehousing was my area of expertise at that moment in time. And it was all being done similar by the same organizations. You had these business applications, you would extract data to a data warehouse, and you would have BI tool on top of it, and that was it. And I remembered in my first job interview actually, that's the question I was being asked. What the difference was between Inman and Kimball. There was kind of the thing that you had to know at that moment in time. But now, obviously for good reasons, it's much more difficult to keep in touch with the industry, and knows what is going on. And actually, frankly speaking, I don't think I know everything anymore what is going on. But the thing that I try to do is, not only look outside anymore. Of course, I tend to do that as well. But really try to understand what is it our organization needs. And that is actually the starting point from looking outside anymore. Because keeping all the trends together, that's in my view, next to a daytime job, that became impossible. Yeah.

Juan: There's so much noise out there. But I think one of the things that we were discussing beforehand is, even within your organization, there are things that we've always done. There's just the traditional things. And I mean, data governance is something we've always done. Then you talk about MDM, that's the old thing we've always done. But then there's these new, call it new trends, or new cool things that are the new shiny objects that, well you take the BS away, you're like," Okay, there's actually something valuable here." So how do you reconcile between the traditional things? Which frankly let's be honest, they are valuable. There's a lot of bullshitting things that you don't want to go do there. But how do you reconcile the tradition? Let's call it quote/ unquote traditional with all these new things coming out, and convincing your peers that this is actually valuable?

Roel: Yeah. I think, and I only heard that later, because I wasn't at the Gartner Conference. But I think the Gartner Conference, the keynote in the Gartner Conference started also with appreciating, but taking also more care at data as being a liability. Because we tend to talk a lot about data as being an asset. And of course we have to keep on doing it right, because that brings value and et cetera. And I think we can talk a bit more, how we do that also with info. How do we really make data and assets? But I think it has to be balanced between also data as a liability. And there indeed, you have to look at things of governance. And traditional governance looks different than governance now, but governance still is a very important part. MDM, I think technically we solved that 20 years ago. But still, I think many organizations are struggling with MDM, because it is much more an organizational thing than a technical thing. Data modeling 50 years ago, very popular. I don't know, that disappeared somehow. Now it's being taken back again. I think finding a balance indeed in data as an asset, getting value out of that. And maybe we have to go through some examples of Vopak, versus data as a liability. That I see as one of the most important things of also of my function. Yeah.

Valentijn: Yeah, maybe the rise of all these new technologies, cloud, sales vendors that tend to automate a lot of the things. There's an unlimited amount of possibilities. Maybe you tend to forget, let's say the more things that require discipline. And no machine, or no machine can ever take over the fact that someone owns certain things, that someone's entering information. So maybe they're all focused on the new things, then kind of forgot that you also need that discipline upfront. Which was more required before I started working. When you started, yeah, it's the modeling and the governance. And yeah, it's coming back at you now, Because now the amount of data has increased. But also the governance needs to be increased.

Tim: Has the data lake movement and some of that aspect, do you think taken out some of that muscle of governance data modeling? Did that kind of throw a big wrench into that, and now everybody's kind of like," Oh wait a second. We need to come back to a lot of that." Right?

Roel: Definitely.

Valentijn: If you tell an engineer," Okay, we have this place, just dump it over there." That takes away some of the things. Okay, do we need it? Where we're going to use it for? So I might use it in the future. Whereas in the area of, okay we do data warehousing, I'd say,"Okay, I need this source. I need to transform it this way. And then this is where I'm going to use it for." So it was more dedicated in what you're going to use it for. So that's a lengthy process. So I think the data lake on itself is good, but yeah, just dumping it somewhere doesn't really solve any issues. And yeah, you tend to forget, let's say the modeling and governance part on that.

Juan: Yeah. So one thing you said which is really interesting, is this difference between kind of data as an asset, and data as a liability. And I would argue that traditionally we've been looking data kind of more from that protective liability point of view, and not as much as from the asset, and as a product and so forth. So I think in that reconciliation part of it, of again, quote/ unquote legacy, old school type of stuff, falls more into the liability aspects of using the data. You got governance, so you protect it. You got to go ahead, but then we need to start thinking about it as okay, data as a product, as an asset to solve and make money, provide more business value. And I think that's where a lot of the new trends that we're seeing, that we're reading around, and all the thought leaders are talking about. That's how you want to go balance this.

Roel: Yeah, exactly. And so maybe as an example, to also understand a bit, journey that we at Vopak are doing. So I think that the first question in getting value out of data, so data really becomes an asset, is what challenges does my business run into, and what are the opportunities? And how do you translate that to a data question, or a data solution? That is hard. That's not something that you as a data team can do. That's why you have to collaborate with your business partners. So what we did last year, is we set up a data academy. And the purpose of the data academy is that we tracked down these challenges and these opportunities. And together with our business partner, we worked down, had to translate these opportunities of challenges. For example energy, everybody wants to consume energy. How do you have that business question, which is probably central to your strategy, or central to the way the board have things about your business, or things that needs to be solved. How do you translate that into a data question, and later in a data solution that really can pay benefits? The way we approach this is with a data academy, is that we set up a cross- functional team. Data persons, business persons. We train them to establish a common language. So we have created a two day foundational training. And we trained that cross- functional team, together with the stakeholders around it for two days. And what does it mean to work with data? What are typical opportunities? What are the things that you can challenge with and solve with data? How does the data game looks like? What type of analysis can you do? Where does the data come from? What's the role of an owner in there? What's the role of the analyst in there? And with a cross- functional team, you really narrow down the business challenge, that we want to save on energy, and we want to reduce the greenhouse gas emission. To what, on my terminal. Gopak has a 10 terminal organization. We have 70 10 terminals around the world. On the terminal, you will find big assets. Pumps and tanks and products that needs to be heated. What on the terminal can contribute to that saving and reusing the greenhouse gas emissions? That's the purpose of this data academy. And that is really, that's not so much technology, but that's really working together in cross- functional teams, data people with business people, to narrow that down to a data solution. I think that is an example of something that's, well they didn't exist maybe before. But I see that as a nice movement of being a mean to get more value out of our data.

Tim: Yeah. I love this idea of the academy and what y'all are doing there. I think that's a huge way to really up level sort of the data literacy and the business literacy of a lot of people in the organization. And just to tie that back to the conversation we were just having, is this academy focused a lot on more of the traditional techniques? Or is it focused more on the trendy things? Or is it really trying to hit a balance between both of those things?

Roel: It's again, that balance. It's again indeed, trying to find that balance. And I think that is also the thing that's being valued, because we typically notice that many people have great ideas about how they can make their own work more easy, if they had access to the right data. But how do you find the right data? Where do I have to go to? Who can I ask the questions, in case that I do have access to this data? That's why you have to find that balance again. And between data producer, data consumers. And I see it basically as our role to bring them together. And the mean of a data academy, how we provide this first training, and we create these cross- functional teams. Yeah, I think that's, well, that worked well, I have to say.

Juan: This is a really interesting kind of a takeaway here already. Which is, when you think about it, having something like a data academy that is bringing all the right people in the room together. And all these technologies, and all these trends, and what people are talking about everywhere. At the end of the day, though a group of people who are in that data academy, they're the ones who realize, how is this going to benefit what we're trying to go do in the business? And this has been just, I mean, I think I've seen this as a trend in the last definitely last year. But even in the last months, now people are going back to conferences and stuff. At the Gartner Conference last week, it was all the conversations we were having with people, with people who are the buyers. We were there as a vendor obviously. But with the buyers, they were like okay. They're realizing that they need to make this connection more to the business. Our last episode last week with Dip was about how to bridge the gap between business and IT. The main takeaway that I had at the MIT CDO Conference was that all the CDOs I spoke with, their on top of mind was, how am I make providing business value? And the technology conversation was just something that happened. It's an enabler, right? And I think this is now that shift that's finally happened. And I would argue that if you're that type of leader that is going in with that technology first mindset, you know what, you're not the leader anymore.

Valentijn: Now in that data academy approach, I like that you can look at it two ways. So one of the ways, okay we have this data, what things can we solve with it? And sometimes it's modeled as around a business question, and what data do we need to support that? And the latter I think is more valuable, thinking about what you need, and maybe you can solve it right away. And then I think it's up to the tech guys to find out, okay. If we want to do that, what do we need to do, to accomplish that? But there's no one from the business will ever come to us." Okay, I need a data lake. I need a limited amount of cloud storage. You need to implement something as data mesh. And I also need a catalog for that." So they just want to solve those issues and as fast as they can. They will never approach us." Okay, we need this type of technology." No, we need to convince them. Okay, we need that to support all of the questions that you guys will have.

Tim: Yeah. So that connects a lot to our conversation that we had with Dip last week about bridging between sort of what the technical folks are talking about, what the business folks are talking about. You want the business folks to understand more about the technology, you want the technologists to understand the business questions more. And ultimately the business value is what matters.

Valentijn: Ideally they need to see the opportunity. And then IT is an enabler to do that. They need to at least understand what's in there. It's like inspired.

Juan: So we have these conversations, we're having them more and more. This seems obvious. I mean, I think it's obvious, do you think it's obvious?

Valentijn: Yeah.

Juan: So why are we still talking about it? Why is it just so, why are we actually doing it? What's the struggle.

Roel: I think. Yeah, I'm just guessing. But I think bridging, because you really have to bridge the gap between your own thinking and the way the business thinks. And they probably think slightly in a different way. And if you want to bridge that gap, you also sometimes have to, you have your own beliefs on how you think, what can be, how your actions can improve the business. But then you talk to somebody that maybe have that completely difference. And how do you bridge that? How do you start a conversation? How do you find out what the challenges an organization has, or what the opportunities are? You really have to go out and try to talk to the people. And I think, I'm not going to generalize this, but if you're setting in your own comfort zone, believing that you know what you know. And you have to get out, start to talk to people, that can be a bit scary, can be a bit uncomfortable.

Valentijn: It's a language barrier.

Roel: It's a language barrier. I think that's still something we have to try.

Juan: So in your data academy, do you do this type of, what I've been calling business literacy? Trying to bridge the gap on the language, on how do these two groups of people should talk together, and not-

Valentijn: Yeah. I think yeah. We talk about the functions and some of the, let's say technical areas of the data landscape.

Juan: Wait, but from the business side, do you-

Valentijn: And the other way. Now I think in the academy, it's more like, let's say from data back to the business, these are the concepts that we are talking about. And I think once you start doing those projects, I think we expect them to supply us with information. Okay? This is what the business concepts are.

Juan: Yeah, so in the training itself, the focus is more on getting the business people to understand data better. But in setting up the cross- functional teams that works out the business challenge to what the data challenge, the focus is much more starting with the business. That's a smaller team. There the focus is much more on translating the business challenge back to, we need the data challenge.

Tim: Got it. So that's, in terms of picking which side of that, you're going to especially focus the academy on, you're like okay, let's make sure that the business folks really understand the data techniques, the data value, the data approaches, things like that?

Juan: Yeah.

Tim: And then when you kick off these projects, then you really expect that the data people will be curious. They'll get out of their comfort zone and they'll be getting that business knowledge through projects?

Juan: And we also put them in the lead. So in our case, we work together with terminals. So they have to, when we together want to start a project, they have to deliver a analytical translator. They have to deliver an as me. They are leading that track. We are supporting them. We're advising them, we are supporting them. But they are leading that. So the training is really focused on, at least on talking about data analytics, do we understand each other? Yeah. So one of the things I'm fascinated about Vopak, and I think we need to talk about what is Vopak,'cause we just started talking here. When I first met Leo brand, or the CIO of Vopak, he said something that I have repeated on this podcast many times, and I bring it up all the time. Vopak is a company who has existed for what, 400 years. It's his mission to make sure that this company exists for another 400 years. And that mindset, that what I call that's a mindset of being resilient. Not just efficient, but being resilient. That is just beautiful. I think that's something that gives you a true North Star to go strive for. And I think we need more core organizations, people thinking like that. So I'd love to kind of give us a little bit more background about Vopak. And where that kind of gives more of that context of where this whole 400 years in the passing, in the future. And how are you driving towards that? How are you taking all the thought leadership, all the blah, blah, blah, and saying," Okay, this is all cool and stuff, but what if all of this is out there that's going to make me sure that we're going to be here for another 400 years?"

Roel: Yeah. Shall I start for you?

Valentijn: Yeah. Yeah. I'll fill in.

Roel: See, I just joined Vopak almost five years ago. So I missed lots of that legacy of 400 years. But talking-

Juan: That's fair, that's fair.

Roel: Talking a bit about Vopak. Vopak is the world's leading tank storage provider. So we store oils, we store gases, we store chemicals, as long as it is built liquids, we can store it. All around the world, 70 terminals around the world. And I think, also coming a bit back to your question. Well, what makes it interesting is that we didn't start it this way. 400 years ago, there was no tank storage providing. We started in, well we call it the time of the VOC, when the Dutch went out with the boats and they started to trade with people in Asia, et cetera. And then Vopak was that company that had these....

Valentijn: Yeah. If you picture Amsterdam, you see these houses with the little canals. Those were storage houses. And at that time, that was for dry bulk.

Roel: Exactly. Yeah.

Valentijn: Like spices. Everything you couldn't get, or didn't grow here.

Roel: So Vopak reinvented itself already a few times. Well, otherwise it wouldn't be here, where it is now. What I found... I'm coming from the financial industry. What I found when I joined Vopak was actually two things, that was being communicated almost on day one. That they tried to find a balance between shorter performance, like every organization does. Long term value creation. And the second thing is being valued as important as the first thing. And I think that is part of the DNA of Vopak, that they have obviously successfully found a balance in shorter performance, and long term value creation. And I think yeah, that's probably the thing that they did well. Yeah.

Juan: I love this. So shorter performance, than the long term value. Which is what we talk about, this efficiency and resilience. And I think this is, the world lacks this. Yeah.

Valentijn: Yeah. But it's an infrastructure or asset rich company. So if you're in the financial services, next year it could be a good year. Year after that, you're done. In the world of Vopak, sometimes it's part of a chemical, an industrial complex, where these contracts can last 10 to 20, 30 years maybe., but you have to think long term in those kinds of areas.

Tim: So structurally your company actually has to think about the longer term. And there's a lot of, sort of your processes, and your money, and a lot that's built around thinking about the long term, even just by nature?

Roel: Exactly. It's capital intensive. There is lot's amount of money. And it has to be that is flowing around when you want to buy this such a tank terminal, or when you have to maintain such a tank terminal. So you have to think a bit more longer.

Tim: So has this asset rich kind of aspect of business, and just in general your journey around data, how does that impact your data strategy? Is there a lot of emphasis on internet of things, and things like that you've had to be very focused on? How else has that really impacted your data strategy a lot?

Roel: Yeah. So I think one of the things that really changed in the last few years, is that we have been tapping into sensory data. So until that time there was many other organizations, and that is also the stuff that we have been doing for tens of years. We were collecting the data from our administrative systems. And of course, extracting insights out of that, and putting that in click BI dashboards. What really is changing the game is these sensory data, because that is really causing you to understand the asset out there. And that's the capital intensive for stuff. This asset is expensive. If that asset doesn't work at the time that the customer is there loading his product, or unloading his product in our tanker mill, that's expensive. So it's the sensory data that really is going to be the game changer, I think for many industrial companies, so also for the Vopak. And better understanding the assets, what is the health of the assets? What is the health of the assets? That's what you want to know as an asset company. How is my pump doing, how is my pipeline doing, how is my tank doing? Is it still fine, or do I have to get it out of maintenance? And you want to do that at the right time, getting it out of maintenance. And there for that purpose, central data is going to be key. Yeah.

Valentijn: I think what's also good to notice, is there's companies that can produce these sensors at low cost. And there's some terminals within the Vopak network that are 70 to 80 years old. And then you have these very old installations, but it's still working. But you don't know how it's working,'cause it's disconnected. So you can either replace it, but very expensive new equipment. Or you can equip it with low cost sensors and learn more about it. And then, let's say extend the life of those machinery, of that equipment. And that's also a way, that's a different way of thinking about sustainability. I mean, you could replace everything with the latest technology, that's economically not viable. But that's also a waste. You can also extend this life with new technology.

Juan: I'm going to make a quick connect on something. I'm listening to this and connecting some dots. It's like almost managing old monolithic infrastructures of data, and mainframes, and stuff like that. And at the end of the day, you're trying to understand, what is the, understand that asset that's most important to you. Which this is actually a tank, and you want to be able to get as much knowledge about it from the original source. And the original source is to literally put a sensor at that tank. And then, if you kind of make that analogy with data, which I mean, it's kind of super meta meta here, because you guys are probably already doing that, is to think about it, I want to understand the quality of things, but not so upstream that where the dashboard is happening. It's like, let's get that back to where the actual, that data was being generated. And at some point you're like," Well, we got this old infrastructure monolithic thing, or an IBM mainframe, whatever. But it's still working, right? We should eventually get out of it, but let's try to understand as much what we can. There's knowledge in there, try to understand that." Such that the data that we do need to go move, we have a lot of knowledge about that stuff. I don't know, does that makes this, I'm trying to connect dots here with this.

Valentijn: Yeah. You have to think about IT. The software on your laptop, you can update that maybe three or four times a year, some platforms do it every day. If you think about OT software, so things like DCS, or those are distributed control systems, where you can see what pump is running there. There's no way you can do, okay let's do an upgrade over the weekend. And if it fails, I just try it again. Those things are lengthy processes. Some of that software is very old, but you can still get, yeah let's say you get the data out. And those things, you also need to think long term. I think there's machinery that doesn't, it's not even supported anymore. As in, the people who build it don't work there anymore. So some of those things are very old technology. But you can give it in your life if you actually get the data out, get the value out, and understand it more how it's behaving.

Tim: That's such an interesting set of business and data questions that y'all have. And just tying that to what you were saying about the academy, and how that's enabling a lot of people, it seems like, and correct me where I'm wrong here. It seems like you've got a lot of parts of the organization that want to do things with data, want to answer questions. Sort of decentralized empowered teams and things like that. Is that kind of the way things are set up, and you're trying to foster that? You want these different groups to really all kind of dig into the data and be empowered in that way?

Yeah. So we are focusing more on better collaboration between the teams. And so the decentralization, so it is quite decentralized. So Vopak is a very decentralized company, because we have 70 slightly different terminals around the world. So that makes us a very decentralized organization. But maybe a bit the same analogy of what's Valentijn's just thinking, by just finding ways of better collaborating between the different teams. Don't change organization, don't change architecture, just finding a way to better collaborate between the teams. We think there's a lot of value that can be getting out of here. It's also maybe, it's the sensor measurements that was the thing that I just got in my mind. It's the sensor measurements, understanding the assets better, that helps us in reducing the greenhouse gas emissions. And because now with the sensor measurements, we better understand which asset is consuming what energy. And then we find out hey, I have on paper two of the same things. Insulated, similar, contains the same products. But this tank is consuming much more energy than that tank. How is it possible? And that question, then you start to find out hey, but the insulation doesn't work. There's no way that you can see that with your eyes. But the data, the sensor, that's what they're sort of telling you.

Tim: So I want to get into some of the sensory data, but just a quick little pause here. This episode is brought to you by data. world, the catalog for your data mesh. A whole new paradigm for data empowerment. So to learn more, go to our website, to data. world. And talking about on this whole sensors, and we're talking about real time, we got a couple more episodes on this topic. Is this something, and going back to the whole thought leadership and trends and stuff. How much of this real time and sensory data do you think the world, I mean the data world is going to shift towards that? Or is it just going to be some sort of industry?'Cause right now what I'm seeing a lot is, oh real time, everybody go real time. That's one of the things that you're starting to see. How much of that is truly the world is going to go there? Or it's like," Nah, you don't need that. That's bullshitty for you." Some organizations need to go do that. What's your perspective on that?

Roel: We at Vopak, we also want to do real time. That's an important thing of our strategy. But I found out that the way business interprets real time is different than the way IT interprets real time. Business interprets real time as within 15 minutes. Not for everything, of course.

Juan: No, but this is great. When we talk about real time, what do we mean by real time?'Cause that means different for everybody.

Roel: I was from my wearing my IT hat, I thought real time, wow. That means no delays. That means milliseconds.

Tim: And you came from finance world, where real time is quite fast.

That's quite fast. But I think coming back to that, well yeah, I mean obviously we're moving more to real time, and that's also something that we are doing. But then I struggle sometimes also a bit with the question, analyzing in real time, what does it actually mean? Because if you want to analyze, you want to put context to it. You want to look at it even from different perspectives. So to what extent can you do that in real time? Well, you probably know better Valentijn. But the sensory data, it's not being analyzed in real time. I mean, it's being guarded of course continuously, but it's not being analyzed in real time. No.

Valentijn: I mean, Juan, you're a scientist. You can't write your papers in real time. You think of it as in the fly.

Juan: Well I mean, I write it in real time. No, but again, this goes back to what does real time actually mean? And this is-

Valentijn: I often find that real time is just another word for it's automated in some way. I don't have to put that much effort in it. Maybe that's how it's seen as real time.

Juan: That's an honest, no BS definition right there.

Valentijn: As it happens without my interference, then it's somehow real time. I don't need to manage it. Maybe that's-

Tim: It happens without my interference.

Roel: Yeah. Also the industrial, many industrial companies, they still come from a background where many things were being done with papers and pencils. So an operator would work over the plants, and he would have this paper and pencil. And he was saying," Does this pump still work? Yeah, still works."

Juan: That's real time right there. And it's real, but it stays right there.

Roel: And then on Friday afternoon, when they had a few hours that there was, then they would put it into a system. So I think from that, we now say, that's not real time. Then you have the information only on a weekly basis. It's being refreshed on a weekly basis. So if you can already move to having data in a half an hour basis, or 10 minutes basis, I think you're being close to real time. Yeah, yeah.

Tim: If you can do the data analysis in 15 minutes and 30 minutes, then that actually might be extremely fast. Maybe real time collection may make sense, because of the sensor data and things like that. But I think going back to a comment I think you made, and kind of both of you were implying, is that why real time? What action or insight actually becomes better with real time? And if you can't really answer the question, then it's like, well why am I asking for this to be real time? Am I really going to be staring at the sensor and doing anything about that?

Valentijn: I mean, if analytics not real time, so why should the... Oh sorry. If the decision is not real time, so why should the analytics be one? Right?

Tim: So that's a good one. How do you go through this whole, all the BS stuff, or non BS stuff, through what happens with sensors in real time. We'd be very critical about it.

Valentijn: I mean, if you're driving your car, you're in the navigation, then things need to be real time.'Cause you need to go, am I going to go left or right? Or if it takes 50 minutes for your best route thing.

Tim: Then you need that. I don't want my backup sensor to be on a 10 minute real time, that would be not good. So speaking of real time, so I think that's a very interesting topic and an interesting trend. I think in general, streaming real time continues to be sort of a trend. There's a few other things that are kind of trending. Things like knowledge graph, is it feels like it's trending. Data mesh, semantic layer, data as a product, data valuation. What are you both most interested in here? What is catching your attention? What are you most excited about? What are you cautious about?

Roel: Many of the things you just mentioned.

Juan: All right, that was good, that was good.

Tim: Let's get through that BS meter right there.

Roel: Data meshing, I think when we're going to discuss data mesh, we will touch upon a few things. What I, now what I find in the data mesh discussion, is that it is kind of a binary thing. It is either you do data mesh, or you don't do data mesh. I think of it a bit difference. I think of the way I understand data mesh, is that it is a solution to a challenge where your central data team is the bottleneck in delivering, getting value out of data. And I think it kind of sketches and approach starting from more ownership, all the way to changing your architecture and your organization. That's quite impactful, changing your architecture and changing your organization. I think there will be organizations out there, where probably that could be a solution. If I look at Vopak, I think changing the architecture and changing the organization, we don't have to do that yet. What I was saying earlier, I firmly believe in better collaboration between the teams. I firmly believe in better ownership of our data. And so the central data team cannot own the data, and they are an enabler in getting the data at the right time, to the right person. They cannot own the data. So that is something that we do emphasize on. Who is the owner of the data, not something within IT. That's the business person. We're educating them on taking more ownership about the data. So that's one. I think that's one of the principles of data mesh also. And the other one, data as a product. Certainly I also believe in that. And what I mean with data as a product, is that the data is being described, that has a certain contract or schema. It is clear what the person has data can ask questions to, if you have questions about the data. If you want to request for additional fields, there is a clear process on how to do that. Those are the things that we are putting in place at this moment in time.

Juan: So that's data mesh. What about other things around, we're talking about data, I mean semantic layers with knowledge graphs, data monetizations. And how are you figuring this out? Are you keeping it within a smaller team to consider it? Or is everybody starting to talk about this stuff, and you have to kind of, all right, let's calm down?

Valentijn: I think we're in the process of,'cause it's decentralized, so we rely more on self- service, or people working on data outside, let's say. What we can see in our own spot in the office. So then I think data products is very important. And now we're thinking about okay, if I offer data, what does it need to be? So the things Roel describes, we're thinking what kind of description would it need? I think if I refer back that to a semantic layer, I think the process where we need to go into is okay, I have this single view of a data. So with an owner, et cetera, that describes all of our customers. But how are they related to other parts of the business, the semantics? What does it mean? What's the relationship between a customer and a single product? I think that's the area where it's both a technology challenge. So let's say, what's the best technology to combine those things, because you need them both. But it's also, it's more of a knowledge or a non- technical challenge in a way. Can someone else understand the relation between, let's say the different data products?

Juan: I'm just blowing. I'm organizing my thought on this one. Titles, roles, we're talking, we're hearing a lot of people thinking about," Oh, we need this new role. Analytics engineer and data product managers and stuff." Do you see that? Are you switching the name? We're not going to call you a data steward anymore. We're going to call you a data product manager. I mean, is this helpful? Or is that just bullshitty and that's just confusing things? Or there truly needs to be a new type of role? This stuff, how are you seeing this?

Roel: Yeah, I think what we sometimes struggle with is indeed the different roles. But I think the industry is also, some with it as a whole, because roles come and go almost on a daily basis. I think what would help, and at the same time, that's very difficult, because every organization is different. But I think the role of data steward is something we're implementing as we speak. I think that is as an important role. Does it have to be a full time employee? No, not necessarily. It can be had, it's a role that can be attached to an existing function, for example. So I like to distinguish between role and function. The data product manager, maybe. But at this moment in time, I don't see that really happening within our organization. No.

Juan: So then in this case, when you're thinking about data going back to, or one of our first points, data's kind of the liability and as the assets. Who is that person, where are they working, who do they report to, who are looking, talking to the people and getting those requirements? And I'm curious, what title do they have?

Roel: Well, from an IT point of view, they were different titles. Had they're called solution managers, or product managers. I think it would be kind of a logical way to maybe add the function of data product manager. Sorry, the role of data project manager. To maybe add that to existing functions. The people that are already doing that for IT, maybe they're the best in place for data as well. I do not have the answer at this moment in time. I think also, you need a certain size and certain maturity, to have all these different data roles, and have certain people for that. So I would say some I find helpful, and some I find.

Juan: Confusing.

Roel: For confusing and not that helpful. No.

Juan: Well I think this is something, as you just said, the industry is still trying to go figure this out. And I think that we continue to evolve. And I think this is part of, we read all this stuff out there. What are we go call this? And we need to sit down. And I think having this data academy, I think for me that's the most important takeaway right now, is have a data academy, whatever you want to call it. But just bring in different people from different sides of the company, from the business, the tech side. And then, you just need to talk to your peers. And that diversity of thought is what's going to help us kind of ground us to the reality of, at the end of the day, how is this making us money, or saving us money?

Roel: Break those barriers. Right?

Valentijn: Break those barriers.

Juan: But do you feel uncomfortable?

Roel: Yeah, exactly. Try to feel uncomfortable, but yeah, don't get yourself stopped indeed, and go into the person. I think in these cross- functional teams, I'm not sure, because you just mentioned to one. I was more talking about, I think I explained it as, I have people with the business question. And I have people from our team there. We also are extending the team. So if there are many questions about the data that needs to be analyzed, we put people in the team that understand that data. So I think the notion of having that temporarily cross- functional team that is indeed trying to solve this business challenge, or business opportunity, and just finding the right people and put them in one team. I think that is at least what brings us a lot of value at this moment in time. Yeah.

Juan: All right. Well as always, we could keep talking about this. This is very, very interesting.'Cause I really appreciate is that perspective that folks, I mean you guys work at Vopak, and other companies around that, who have that long term, and I think that's a very unique perspective, and we need more of that in the world. Well, all right. Let's move on to our lightning round, which is presented by data. world, the enterprise data catalog for your data and knowledge. I'm going to start off. So are data products a more important concept than data mesh itself?

Roel: Yeah.

Tim: Yeah. Do you agree?

Valentijn: Yeah. But it's not one way or the other. But then again, yes. The products would be more important. But I didn't think it ties into the idea of ownership.

Tim: Yeah, that makes sense. All right. Next question. So we talked about the traditional stuff and we talked about more of the trendy stuff. Do we do need to do more of the so called traditional data management work? The master data management, the data modeling. Do we need to do more of that than we're doing right now?

Roel: Yes.

Valentijn: Yeah. Yeah.

Tim: All right. Hey everybody, get on that. All right?

Juan: Go back to our roots.

Tim: Easy on the trends. Easy on the trends, everybody.

Valentijn: I think my theory on all the trends and the rules, et cetera, is as tech guys, maybe we understand that the computers that one day will take over. So if we keep inventing more roles, we're going to keep confusing the computer.

Juan: That's true.

Valentijn: That's true. It's like a red rage.

Tim: You keep confusing who to impersonate, if you keep changing the titles.

Valentijn: Now I know how to be a data engineer. Okay, but now you need to be data product manager.

Tim: It's not, it's an analytics engineer.

Valentijn: Do something else.

Tim: Love it.

Juan: All right, next question. Is a catalog in a metadata a key part of a strategy for collaboration in data products?

Roel: Absolutely, yes. Are we supposed to say....

Juan: I mean, you provide some context if you want.

Roel: I think I already also explained it a bit. I think collaboration, and being facilitated by tooling, is really, really key. Yeah.

Valentijn: Yeah. You're not a computer, so you see a one or zero, you don't know what it means. You need that context.

Tim: Context.

Juan: Context, context. We call in our knowledge first world, people context relationships first.

Valentijn: Yeah. I mean the computer knows a one and a zero. We don't know that.

Tim: Yep. All right. Last question. Oh, go ahead.

Roel: Yeah. Yeah, so we were just coming from our Houston office, and we spoke to many people over there, and it was just one maintenance engineer that was asking ourselves a question." I would like to analyze pumps. At what data do I have about my pumps?" catalog, right?

Tim: Yeah. That's perfect. So last question. Can smaller companies implement a data academy as well?

Roel: Yeah, absolutely. Yeah. I think may be good to mention there, although we work together with an external company to help us set it up, in the end we develop our own training contents. And so it doesn't have to be expensive. I think if you are a data expert, maybe you need a bit of help from people that are maybe a bit more into it. Sometimes in your HR department, or your communication departments, they have probably people that can help you out. How to put content together in such a way that's being easily consumed by other people. I think people can start, if you create the training yourself, you get the people yourself, you can just do it.

Juan: You just brought up something that came up also last week, and actually spoke with our VP of employee experiences this week about it too. Which is, how does this education of the business and the data together, should probably be even part of the HR, that human employee experience onboarding process for organizations. And I think that is completely untapped opportunity. Because yes, you get onboarded, and here are the tools, here are the things we go do. But no, actually explain this. And yeah, we go through the mission, and vision of the company will exist. But no, explain to us really how this business works, and how the flow of it. And I think that's how you actually get that understanding, that context.

Valentijn: It's one of the languages you speak.

Juan: The languages.

Valentijn: So you need to be able to speak that language, if you want to communicate with your peers.

Juan: And I know how you just said, maybe you just go talk to your HR organization, because they probably already have the people in place to go help, go do this.

Valentijn: Yeah, yeah, yeah.

Juan: I love how we're just, we talk about data, but at the end of the day, it starts getting connected with so many different parts of the organization. And at the end of the day, the data, this is something, I wrote up this thing with Muhammad Osser. The 3%, the 97%. 3% of an organization, we've been analyzing LinkedIn employee data. 3% of the organization are data people. 97% aren't. We need to make sure that we're empowering, not just focusing on that 3%. And that 3% should understand what the other 97% is doing.

Valentijn: I'd argue all those 97% are also data people, but just don't know.

Juan: Yeah. They don't have the titles, is they-

Valentijn: They don't have the title.

Juan: But they all use data, and that's how sure that they can accomplish their tasks every day.

Yeah, that resonates so much with me, Valentijn. And I think the more we have these conversations, we're getting dangerously close to a hundred episodes now. It's clear that data is a mirror of the rest of the business. And I think we always think data is something separate, it's something different, all the data people. It's like no, data is actually, it's the understanding and it's the fabric of the rest of the business.

Juan: It's a fabric that turns into a mesh, and it brings us all together.

Tim: You ruined the analogy, Man.

Juan: Well you said the word fabric. I mean...

Tim: Dying it together.

Juan: All right, all right. Take aways. Tim, take us away with your takeaways. You go first.

Tim: Oh man. Amazing takeaways today. This was a great conversation. Some of my big takeaways I wrote down, is that you kind of talked about the beginning of the journey around data. First getting into data management many years ago, it seemed like in Inman versus Kimball was the big question. And obviously for good reasons, things have gotten a lot more complicated, and more interesting too, I think. But we started to talk about how we reconciled the traditional, with the new and the trendy. And you talked especially about this idea of treating data like an asset, and balancing that with treating it like a liability. And how that becomes an overarching philosophy that can be applied both to the traditional world, as well as the newer world in terms of data, in terms of business strategy. And then the past, Juan, you kind of mentioned liability may have been more the emphasis, but sort of the good asset aspect is starting to become a lot more of a big aspect here. And things like MDM, things like data modeling, these are some traditional things that I think maybe faded a little bit, or became a little out of Vogue. But now a lot of these things are roaring back. Data cataloging and governance you could also say is kind of a more traditional activity, that has really seen a new life in recent days. And we talked about this importance of breaking down these silos, breaking down these barriers, and enabling the company. And one of the big ways that you talked about Vopak doing that, is this data academy. Bringing together a cross- functional team, training them to establish a common language. What does it mean to work together and overcome challenges and gain value around the data? And the focus being around the business objective. Really focusing on the business value, the business objective. Helping people be uncomfortable, and then empowering them so as they go off and do these projects, as they kind of go back to their decentralized life, because a lot of, you mentioned a lot of Vopak is very decentralized in nature. That they're bringing that learning, those skills there. And that actually the business people, they learned about the data. Now the data people can learn about the business, as these projects kind of go underway. So those are some of the big things I wrote down. Juan, what were your big takeaways?

Well, I go back to this over and over again. The whole 400 years, and we exist for the 400 years. Focus on value creation. Short performance and long term value. I think this is a very, very important takeaway, that again, I hope, I really, really hope people think about this. And I think we need to change incentives and stuff, but this is something we should go strive towards. So how do you have that resiliency with the data strategy within Vopak? For example, I think you guys are focusing on sensors data. That is how is your strategy, or one of your strategies around that. You want to know the health of your asset. How is this pump doing, this old site? Can the data tell me how I can make an old site last longer, because now that data's visible? And we made this really cool analogy in my opinion, about how we can push consider that as pushing the health of equality back to the source. So make that dark asset visible, make that dark data visible. And I mean, even though we're thinking about it as tanks and pumps, you can even see it within the organization, with your data you have. Push that back to the original source. And another way of doing that resiliency with your data strategy, is focusing on collaboration. You'll have 70 different kind of different sites. There's 70 different large groups, all over the world spread out. You need to help them have those insights. And I think that's where kind of data academy will come in. Other kind of trends that you're seeing too is real time. We talk about real time. What is your interpretation of real time? Is it 15 minutes? Is it a millisecond? But we really need to understand what you mean by real time, and ask why. I mean, this is another trend we see, we're constantly seeing through all our episodes. Ask why, why. And how does it actually make me better with real time. And Valentijn, you said real time, it happens without my interference. So maybe that's a better definition of what real time could be. And then other times we talked about data mesh. Of course, it comes up a lot. And it's not a binary thing. It's really a solution to that central data bottleneck team. And we talked about also kind of the roles. They come and go. At the end, the industry's really struggling with this itself. And it's just us, that we need to be kind of, just be very explicit. And one of the things to think about is, is it a role or functions? Kind of have that separation, I understand that. How did we do? Did we miss anything.

Roel: Great.

Valentijn: I think that was a good recap, but I would have to listen again and make sure.

Juan: All right, we'll start over. All right, so let's throw it back to y'all. So three questions. One, what's your advice about data, about life? Very broad on purpose. Second, what resources do you follow? People, blogs, books, conference, whatever. And third, who should we invite next?

Tim: So advice, who wants to go first?

Valentijn: I have some advice, maybe in perfect for the data, and in life or jobs in general. I'd say, and especially if you hear all these buzzwords in whatever you try to do. I'd say that perfect, or being perfect, that's the enemy of just good. So if you can have a good idea of yes, you believe something is good, go for it. Don't wait or it to be perfect. You can shape it out later. I think that sometimes you're just blocking yourself, if you are looking for something perfect.

Tim: Sage advice, and useful in the data world too.

Valentijn: Yeah. It applies to many areas. You see a good bar, don't waste finding another perfect bar. Go for that one. Settle down.

Juan: That's a good one. How much time are we wasting looking for a bar? And then they all closed.

Tim: They all closed.

Valentijn: You just get that only good ones.

Roel: What helps me a lot in my job, is showing a bit of empathy, and really trying to understand that other person that you're talking to. If you're really trying to understand his concerns, even if maybe you think you might completely disagree with this person, because he has a completely different opinion than you. I think with a bit of empathy, you will find that you would better understand him, and come to a agreement on the things that you have to agree upon.

Juan: I would add empathy.

Tim: That's another topic that comes up a lot, empathy, we've discussed with a lot of people. I think this is another trend, is to understand others.

Roel: Understand others, yeah.

Tim: What resources do you follow?

Roel: I just mentioned you on forehand. I'm a books guy. I think blogs are too volatile for me. Maybe a bit old school opinion, but I'm a books guy. I read a lot of books. Currently I'm reading The Information, by James. So for the people that haven't read, it are interested in data de information by James Gleick. I think it's a very powerful book. Yeah.

Tim: Great suggestion. Yeah. What about you? Resources?

Valentijn: Not a particular resources, but I did read a good book over the summer. So I thought, well let's just recommend that one, then you'll find out.

Tim: Yeah, sure.

Valentijn: So it's completely different topic. But another fascination of mine is the Second World War, whatever happened there. So this summer I read a book by Roxane, she wrote that book. And she wrote a book about the Sisters of Auschwitz. She moved into a very old house, and behind the walls there were all these old papers. And those were newspapers from resistance parties, or Jewish families hiding, for within the war. And she reconstructed all of that with archives, talking to old family members, the things she found in her house. And it was a full on, it's a nonfiction book. So she reconstructed the whole story. It was very impressive. And I think it was translated to English. And I think that you can buy it in the US as well. And they're going to make a movie on it, so it has to be good. And it was impressive. 300 pages, one week.

Juan: Oh wow.

Valentijn: So it's not really data related, but it's at least reconstructing the story from information you find.

Tim: Oh, that's interesting. Qualitative and quantitative information. What was the name of it again?

Valentijn: Sisters of Auschwitz.

Tim: Sisters of Auschwitz. Interesting.

Valentijn: That's the English translation.

Juan: So finally, who should we invite next?

Roel: I hope that you can invite Christian Madsbjerg. He is a person that also wrote a very interesting book. He started humanities, and he has a consultancy firm that does a lot of, well oversea jobs with a lot of data. And he wrote this great book of how you can kind of blend data with humanities. What is the thing that data is good at? And what is the thing that we as humans still need to do? So if you can get him for the show, that would be great.

Tim: Interesting. That would interesting.

Juan: Do you have a suggestion?

Valentijn: I have no suggestion for you. But I'll let you know what-

Juan: Right. All right. Well, this was a fantastic conversation. Next week, we're going to be live with Joe Rice and Matt Housley, they're from Ternary Data. They also have their Monday morning data chats. We were on their podcast, I think one or two weeks ago. And now we're get the chance to have them on our podcast. They wrote the book, the Fundamentals of Data Engineering, on O'Reilly. I just got the book delivered to me yesterday, I'm really excited. So that's another book recommendation. I also like to go buy books. I don't like the eBooks either. And then also next week. No, not next week. In two weeks time, something like that, we have the data. world Summit. So go September 22nd, actually three weeks. Data. world, you can find more about our summit. And with that, thank y'all. Thank you so much.

Valentijn: Thank you.

Roel: It was awesome.

Juan: Thank you, it was awesome.

Roel: Thank you for having us.

Juan: And as always, thanks to data. world, who lets us do this every Wednesday. data. world, the data catalog for your successful cloud migration. Just visit us at data. world. And you guys, thank you so much.

Thanks Roel, thanks Valentijn.

Valentijn: Thank you.

Tim: Cheers everyone. Thanks for watching.

Juan: By the way, cheers.

Speaker 1: This is Catalog and Cocktails. A special thanks to data. world for supporting the show. Carly Burlov for producing, John Williams and Brian Jacob for the show music. And thank you to the entire Catalog and Cocktails fan base. Don't forget to subscribe, rate, and review, wherever you listen to your podcast.