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Live from Big Data London

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About this episode

For this special episode of Catalog & Cocktails, join hosts Juan Sequeda and Tim Gasper LIVE from Big Data London to close out our Fall summit 2022 and recap the event across the pond!

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

Tim Gasper: Hey everyone. Welcome. We're coming to you live from London from the Big Data London event. It is late over here and y'all that are just coming from our data.world summit, we're so excited to have you. And for those that aren't coming from the summit, go check out the summit recording, but also check out Catalog and Cocktails, we love having you. We're the honest, no BS, non- salesy conversation about enterprise data management. And today we're doing our takeaways from the conference. So I'm Tim Gasper, VP of product, product guy, data at data.world, joined by Juan Sequeda.

Juan Sequeda: Hey, I'm Juan Sequeda principal scientist at data.world. And together with Tim, we are here. We've been here in London for the last couple of days. We did Catalog and Cocktails live yesterday, and we love this so much and I can't believe that they data.world let's us do this and we're so happy and we're doing it again. And today we're doing our takeaways, but we have two special guests.

Tim Gasper: Two special guests.

Juan Sequeda: We have Emily Pick and we have-

Emily Pick: Hello.

Juan Sequeda: And Sanjeev Mohan today. How y'all doing?

Sanjeev Mohan: Hello. Very good.

Emily Pick: You know what?

Sanjeev Mohan: Yeah, it's morning in US, so it's only 11 o'clock here in London.

Juan Sequeda: Only 11 o'clock.

Sanjeev Mohan: Yeah. And we only came here a few days ago, so we still have jet lag, so we still are at the US time.

Juan Sequeda: I don't know.

Tim Gasper: Always fun.

Emily Pick: As the show runner for data.world at this event, I can say that I have a very different energy level at this point in the day. So Sanjeev...

Sanjeev Mohan: Yeah.

Emily Pick: I'm very happy that you have the energy to keep going. I just ran into Sanjeev in the hall and he was coming back from a run, while we were coming back from drinks. So it's like," This is right."

Sanjeev Mohan: But Emily has no reason to complain, because tomorrow she'll be in the Alps, in Saint Moritz.

Tim Gasper: Oh.

Emily Pick: In Chabanne.

Sanjeev Mohan: In Chabanne.

Juan Sequeda: Oh, okay. There you go.

Tim Gasper: Dang.

Emily Pick: So headed off to France in the morning and inaudible.

Juan Sequeda: All right. You're getting everybody jealous about this stuff.

Emily Pick: Listen, I got to.

Juan Sequeda: All right. But let's get everybody jealous for what they have missed at Big Data London.

Tim Gasper: Yes.

Juan Sequeda: Because this generally was a phenomenal event. I mean, so many reasons, we're going to go through them each, our individual reasons. But I think first of all, it was so exciting to get people back together.

Tim Gasper: Yes.

Juan Sequeda: We've been seeing this together, we did at Snowflake, we saw it at Gartner and just doing this together. And I met so many people, it was so awesome. So we are so happy that we did our hundredth episode yesterday. We're so happy that we just did the data.world summit. And we're here together at Big Data London. Let's start with our takeaways from Big Data London. Emily, kick us off, take us away with your takeaways.

Emily Pick: All right. Take away number one, for those of you who follow Juan on LinkedIn, you might have been aware after we went to the Gartner Data and Analytics summit, that he did a little thing where he went around and cataloged essentially, all of the taglines on all of the booths. And he pointed out that it's a little bit of word salad, in that there is a little bit confusion of in the space. And that people don't necessarily know what differentiates any one competitor for another. And then also not even competitors, but just who the providers are, what the value is, what's the overlap between platforms? So that did not get clarified at this show. I think there were 149 different vendors there. And Juan, once again went around and cataloged all of it there, but we had quite a few people who came up and were like," What's the difference between a catalog and a data quality solution? What's the difference between a time series database and a relational database?" There was a lot of confusion, especially this was heavily attended by students. And so they were trying to get their bearings for what's going on in the market and what's really interesting.

Tim Gasper: It's a harsh entrance into things, huh?

Emily Pick: It is. So I think there's some things as a marketer in the industry that we can take away from that, which is how do we really clarify what our position is? The value that we delivered, how are we doing it simply? And that's probably the biggest one. How do we simplify our messaging so that people really understand what it is that we do? Take away number two, so Juan and Tim did a little session yesterday-

Tim Gasper: Yeah, right.

Emily Pick: Called the Data Product ABCs. Line wrapped outside around the conference hall, down a couple lines. I did a little time series capture of it, because it was cute and I thought it was funny, because I'm proud of my Catalog and Cocktail boys. But people were so pumped to be there, data products, such a hot topic and they continue to be so. I mean, we've seen it so many times. How many times have you heard about data mesh? Whether you love it or you hate it, it is one of the primary topics. But treating data as a product, that's such a key element to making data usable by a wider swath of people in the enterprise. And so that actually takes me to take away number three. So one of the things that came up a few different times while we've been at the show, is that there are a lot of data analysts, data scientists, data engineers, out there who are creating these data resources for people and they're not getting adoption and they're not getting used and people don't understand, what are they supposed to do with that? And that was something that came up actually during Juan and Tim's session. And one of the things that we started talking about as a group internally at data.world, taking it from treating data as a product to, how do you add a little bit of marketing to how you promote data within the enterprise? So a big thing there. I'm trying to remember who it was, but you had a guest recently and they talked about really building empathy and how empathy is the number one thing you can do to get adoption of your different data resources. And realistically, that's what we do in marketing every day. As often as I hear people say," Oh, marketing, I hate it. All I get is targeted with ads and emails and it's terrible." We're at Big Data London, because of marketing, because they marketed to us. People came to our booth, because of marketing. Our customers came to us, because of marketing. Marketing is creating empathy within the space. So really, I think taking some lessons and we're going to be this little tease, because there's a project that Juan and Tim and my boss Thaise are all working on together with me. Be on the lookout for some new resources along the way about how you can take some marketing best practices and apply them to how you promote data within the enterprise.

Juan Sequeda: No, I think this is a topic that we're starting to go see little by little. And I'm going to place a little bet here, that data marketing is going to be something that is going to start a conversation, because if people are like," Oh, there's all this data out there, there's so much stuff." I'm like," Oh, I have all this data. Well, I need to go start promoting it. Let me see you start marketing it." And that's part of treating data as a product. I'm going to take a little bit of a vision here in saying data marketing is something that we want to be pushed and it's going to be a thing next year. With that, Sanjeev-

Sanjeev Mohan: Yeah.

Juan Sequeda: Honest, no BS, what do you think about this? And what are your honest, no BS takeaways?

Sanjeev Mohan: I live and breathe data, you know that. Every day, everything I do is data analytics. And even I find myself tripping over data. Data marketing, okay, I think we need to take a step back.

Juan Sequeda: Oh.

Sanjeev Mohan: I came to London on Tuesday and I did a webinar on data observability. Yesterday, Wednesday, I published a paper on data products. Today for your data.world Fall summit, I presented on data ops. So data catalog, data observability, data products, data ops, just getting too much, we need to clarify. And a lot of times when I'm talking to people, it's all about how do you differentiate between that? How do you differentiate between data management and data ops? And we are experts, like there are no experts, we are learning, but we at least spend our life looking at these things. And yet it takes so long and we haven't even touched upon what was a big theme at Gartner, data mesh versus data fabric. In Big Data London, there was hardly any mention of data fabric, it was all data mesh.

Juan Sequeda: I think I heard it once.

Tim Gasper: Maybe once.

Emily Pick: I don't think I heard it at any inaudible.

Juan Sequeda: I heard it once.

Sanjeev Mohan: It's like every session had to have an obligatory mention of data mesh and data products. That's how hot these two topics are. I'm data ops.

Tim Gasper: Yes.

Juan Sequeda: Tim, you take us away. What are your takeaways?

Tim Gasper: So first of all, I'll double down on data, mesh data ops, data products, that was coming up nonstop. I don't think I can remember one talk that I saw that didn't mention either data mesh or data products. That's a little bit of how maybe a little over- hyped that we're getting here, right?

Sanjeev Mohan: Yes.

Tim Gasper: We're a little over- emphasis here, getting too excited about it. I will say that I am very excited about data marketing, because I think that is something where we talk about data life cycle. And when should a data product live or die? When is it actually doing what it's meant to do versus when is it not? Sometimes we let these data products live for too long and they need to go away. But sometimes we just didn't do enough actual marketing and support and enablement around them to actually be successful. So I think that's an interesting takeaway here around data mesh and what's actually practically applicable from all of the things that we saw today. I think the two other things I'll mention is first of all, there were lots of vendors. I think there's just a lot of vendors, a lot of catalog companies, a lot of observability companies, a lot of data infrastructure companies, database companies. There's so much out there, there's a lot, there's a lot of noise. And I'm excited for that analysis, Juan, that you're going to do of all the taglines and things like that. That's going to help us navigate some of that. The last thing that I'll mention, that I feel like was a takeaway here. I felt that there was a lot of... So when you think about who's the audience? Who are we targeting? I feel like there was a lot of language that was being used and taglines and value props aimed at the data engineer persona. The person who's building the pipelines, the person who's trying to take the data, wire it from the source systems into the databases and start to do some modeling on top of that. I didn't see as much as I was expecting of data scientist oriented value props.

Sanjeev Mohan: Yeah, inaudible.

Tim Gasper: Not as much of the data analyst and BI analyst type value props, a lot of engineering speak. So I thought that was interesting. I don't know if that's like," Hey, we're in the age of the data engineer right now." Or if that just happens to be the nature of the vendors and the talks that happened. But that was definitely something that I thought the pendulum swung in that direction.

Sanjeev Mohan: Which is the wrong direction, because-

Tim Gasper: Give us a hard take on that.

Sanjeev Mohan: Yeah.

Emily Pick: I was going to say, give me your non- BS.

Sanjeev Mohan: No.

Emily Pick: Oh, no.

Sanjeev Mohan: We have been focused on data engineers for far too long data producers, ELT, ETL. And if you look at data mesh, data products, the focus needs to be on data consumers, not on data engineers. I think we've done enough of that. The whole thing about social, technical, what is that? You like that topic, right? So that's a data consumer, the people side of things. And we keep going back to technology.

Juan Sequeda: Does the emperor have no clothes?

Sanjeev Mohan: Why?

Juan Sequeda: I mean, they're all talking about, we're doing all this data stuff-

Sanjeev Mohan: Yeah, yeah.

Juan Sequeda: And then suddenly, we're satisfying the people who actually are not providing, who are disconnected from the business.

Sanjeev Mohan: Yeah.

Juan Sequeda: And I think this is for me, one of the main worries. I always start with a worry I have, is that we continue to see the lack of business, the lack of business connection, lack of business value. What you were seeing, Emily, it's like you walk through everywhere and we get all this technology and stuff. But how do people understand what is a differentiator? What is the value that everybody's providing here? Just put yourself in your shoes of," I work at an organization who has problems about, I need to organize my data and all that stuff." And you just see 150 vendors that their taglines are very similar. How do you navigate that space? I think that's one of the stuff that continues to be a trend, which by the way, is not something from today, it's something that's always been there, right?

Tim Gasper: Right.

Juan Sequeda: And we're having a lot of conversation, a lot of what I'm calling, with all due respect, the old timers. And it was the same thing over and over again. And they're like... Somebody told me... You know what? This was actually not Big Data London, this was the Data Mesh Tech Conference, which is ironic, because wait-

Emily Pick: inaudible.

Juan Sequeda: Data mesh is about social inaudible.

Tim Gasper: Though it's true that the phrase big data did not actually come up very often.

Juan Sequeda: Yeah, that's exactly-

Tim Gasper: Actually, fast data came up more than big data.

Sanjeev Mohan: Actually, I'm surprised the concept is still called big data. We retired big data as a term, long time ago.

Tim Gasper: Yeah.

Sanjeev Mohan: It just goes back to the Hadoop days and all.

Juan Sequeda: Oh, that gave me shivers and all that, thinking about that. But again, you see this and then the old timers are saying," Hey, just give it a new name for the same thing, right?" So I think that's something that is like what comes around, goes around. Now, data mesh, the value of data mesh, I think there's two things. One, and what we've said before, is the data product conversation, bringing that product thinking. And what we're excited about part of the product thinking, is we need to promote this and that's the thing where that marketing part comes in, I think number one. And I think number two around this, which gives me a lot of optimism, is that having conversations with folks is," Let's embrace the complexity." So there were definitely a handful of, let's call it, traditional vendors you would see, that there's like," Oh, single version of the truth and we're going to do that." But in the hallway conversations like," No, yeah, there's different versions, there's complexity. We need to embrace it. We need to figure out how to be decentralized. It's okay, let's go understand what works and what doesn't work." So I think that's a shift that needs to occur and that it is happening around that stuff. So as I did in the Gartner, I wrote this post on LinkedIn and let go show this over here, here is my list of all the taglines that I wrote down.

Tim Gasper: So for those that are listening-

Juan Sequeda: Right there.

Tim Gasper: He's got his iPhone and he's showing all the taglines he collected.

Juan Sequeda: I collect all the taglines and I think there's actually 149, I wrote down 136, so I missed some. And I'm going to just say the top 20 words here. Data, analytics, platform, modern, Cloud, business, build, AI database, end, time, observability, power, real, stack, one, scale, faster governance, data ops, sounds already like some weird company, but anyway...

Emily Pick: inaudible, hey, I can make you a tagline out of that in 20 seconds inaudible.

Juan Sequeda: Oh for sure, for sure. But I'm going to say something that is interesting and if I actually compare it to Gartner, I need to look at the analysis, is that observability and data ops, those two things were actually dominant in this conference. Which actually did not show up as much as I believe in Gartner. That's a very interesting observation.

Tim Gasper: Yeah.

Juan Sequeda: I don't know what to say about that, but I'm like," Okay, there's a lot of focus on observability and data ops." Another interesting observation is that the word business was actually number six on that list. But I wonder, is that because people say," Oh, we just need to use the word business to make us sound a little bit more business."

Tim Gasper: Right.

Juan Sequeda: So they're actually doing something-

Tim Gasper: Technical, blah, blah, blah, for the business kind of thing?

Juan Sequeda: Oh, there are many of those

Sanjeev Mohan: On your list, where is privacy?

Juan Sequeda: It's not in the top 20.

Sanjeev Mohan: Can you imagine that? Some years ago, GDPR was all the rage and data privacy was huge.

Tim Gasper: Probably, everything was security, protect, privacy, yeah.

Sanjeev Mohan: And there was hardly any data privacy this year.

Tim Gasper: That's true.

Juan Sequeda: And then back to the point that you said, Tim, about, hey, you're seeing more data engineers, the word build was number seven, top seven. So I feel that we are-

Tim Gasper: I don't think that even broke into the top 20 of Gartner.

Juan Sequeda: No it didn't. So I think that's an interesting kind of observation that we've seen here. So we're seeing more about the data products and the vendors to go create and help build these data products. But at the end of the day, I always say it's time to go zoom out and look at the principles, let's forget about the technology. And I truly believe that we still have those four main principles. It's data integration, moving data. Second, storing storage and compute of data. Third, just doing something with the data, dashboards, reporting analytics, ML. And fourth, it's the metadata and then if you look at all these different vendors, those 149 vendor, I believe, I truly, genuinely believe that we can go put those in those four main buckets. So I think that's a way how we need to start seeing that, such that when we go off and go into that landscape of all these vendors, we can try to be a little bit more objective and, so what do you do? Do you integrate data? Do you store and compute data? Do you do something interesting with the data? Do you govern and look at the data?

Tim Gasper: Are you the glue that going to bind it together, right?

Juan Sequeda: Are you the glue that puts it all together?

Emily Pick: Number one question at the booth," What do you do?"

Juan Sequeda: So there we go that's our takeaways. I feel that we're a bit negative, but I don't want to leave on a negative note.

Tim Gasper: Well, here's what I want to do. I want to make a positive note here and then I want to see if you all have some final comments you want to add as well. So my positive I want to put here, is that I'm really excited, I'm trying to put a little bit of a silver lining on this, but I'm really excited that data mesh and data products are actually really at the forefront. Because of everything that we can get really, really excited about and buzzy about, I would rather it be about an approach and about a culture and about a framework-

Sanjeev Mohan: Yes.

Tim Gasper: Not about a technology. And I feel like at least we're really obsessed with something that isn't tech, because you think about the Hadoop craze that we had, think about the AI craze, the deep learning craze, right?

Sanjeev Mohan: Yeah.

Tim Gasper: We are now talking about an approach and I would much rather be obsessed about that. And I think that's getting us in the right mind space, because people process technology.

Sanjeev Mohan: Yeah.

Tim Gasper: What are your takes on that?

Sanjeev Mohan: To me, the best part was networking.

Tim Gasper: Yes.

Sanjeev Mohan: Feels like people are still excited to come back together, see each other face to face, even though we've had a few events this year. But this being in Europe, so we got to see a lot of people that we normally would not see. So that was the highlight for me.

Tim Gasper: That's awesome.

Emily Pick: Yeah. I mean, my highlight is similar to Sanjeev's, I guess. I'm going to state it more is community. People were excited to be here. People were excited to learn about the different things that were going on in the space. Obviously from the vendor side, it's really great to have people come to your booths and want to learn about what you do and how you can help. But I mean again, people were queued up the length of the conference hall and the Olympia in London is massive. To consider just how long people were waiting to get into these sessions and to learn and to really collaborate with their peers, that was really encouraging. And when we talk about these things, about putting people first, I think that we are on that right track and we do understand that. It's just a matter of, how do we shift from that technology first thinking to that people first thinking?

Juan Sequeda: So to wrap up, I think what we want to challenge everybody. Everybody who's listening right now, please be more critical when it comes to treating data and please ask the why. Why is somebody asking for your data? Why are you actually doing that work? Do you know how that is going to provide value? And the moment we start really making those connections back to the end users, how the organization works, what are the business processes? Understand how we make money, save money in an organization. We are truly going to understand and have that place in this world where," Oh, we're not doing the same thing over and over again. Actually we have very tight connections."

Sanjeev Mohan: Yeah.

Juan Sequeda: I think hopefully, as you just said Tim, having these conversations about more of the approaches, not the technology is that really strong differentiating step that we have not seen in decades. And I think this is this game changer. And as much as we can talk about all the bullshit and all this stuff we hear about data mesh and that stuff, we put that aside. It is about really understanding how data is going to help connect with people. And I think this is that changing moment right now. And that's what I'm going to leave very excited about. Please, let's feel critical about it and let's understand how we're going to connect data and people together, which was the theme of our data.world summit. And I'm just super excited that we got this all together and we're all here. We're data people, data and people all together here in London.

Sanjeev Mohan: Yeah.

Juan Sequeda: And with that cheers.

Tim Gasper: Cheers.

Emily Pick: Cheers.

Sanjeev Mohan: Cheers.

Tim Gasper: Glad we could all come together and thank you everyone that attended our summit. Thank you everyone here in Big Data London. Next week, please tune in as well to Catalog and Cocktails. We are going to have Rupal Sumari. She's the head of data governance over at Penguin Random House UK. So we're so excited to have her talking with us. And we also wanted to say thank you and cheers.

Juan Sequeda: And thanks to all our listeners who came to our booth. We ran out of Catalog and Cocktails t- shirts, so if you start seeing people who have honest, no BS, t- shirts you know they're listeners to the podcasts and you're missing out.

Sanjeev Mohan: Thank you.

Emily Pick: Cheers.

Tim Gasper: Cheers.

Speaker 1: This is Catalog and Cocktails. A special thanks to data.world for supporting the show. To Carly Murdoch for producing, John Moyans and Diane 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 podcasts.

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