Episode 39: Identity Graph: The New Customer 360

Special Guests:

Transcript

Unknown Speaker 0:10 Hello, everyone, welcome to catalog and cocktails. It’s your honest no BS non salesy conversation about enterprise data management, with tasty beverages in hand. I’m Tim Gasper, longtime data nerd and product guy and joined by my co host one. Hi.

Unknown Speaker 0:27 I’m Juan cicada. I’m the principal scientist here at data world. And as always, it’s Wednesday, four o’clock Central. It’s the middle of the week, and it’s great time to take a break and talk about data. And with that, we have fantastic guest today we have some folks who are really game changers in this industry. We have Michael Murray, who is a president and Brett Harper, who is a chief data officer of wunderman Thompson data. Michael, Brett, how are you guys doing? Fantastic. I like the idea of taking a break to talk about data. And what’s the best way of doing it with some tasty beverage in hand? So how about we kick off with What are y’all drinking and we’re gonna toast for.

Unknown Speaker 1:10 Oh, I’ve got a little bourbon in the glass. I know that doesn’t feel or sound like St. Patrick’s Day kind of drink. But trust me the Irish were a fan of the bourbon.

Unknown Speaker 1:19 I think the brown and the green are both fine. I’ve got an Irish mule.

Unknown Speaker 1:26 Well, both Tim and I are dressed up in green. I think we both have green drinks. I’m doing one of my my favorites. I only drink gin and tonics. Now adding cucumber into it actually will great cucumber into squeeze as pure cucumber water and you mix it with gin and tonic. This is a great drink.

Unknown Speaker 1:42 Oh man. That’s what you fancy sounding. I looked up on Google and I was like green drinks. And I tried to map that against what I had and blue curacao and orange juice and a little bit of vodka and hey, we’re in business.

Unknown Speaker 1:55 Right? So I guess. Let’s cheer for Happy St. Patrick’s Day, everybody. Cheers. Cheers. Right. And then we got a quick warm up question we got today. Who really was St. Patrick and what he might buy if he were alive today. Michael, I think you had some thoughts about this.

Unknown Speaker 2:12 Yeah, well, you know, it being data driven, I would say and just describing him I’d say maybe three foot tall ish. I had small pot of gold may have a bit of a may have a bit of an accent. But that was told by Brad I was on completely on the wrong track. 

Unknown Speaker 2:30 probably on some cereal kind of carton or some cereal. Yeah. Some lucky charms. All my Irish relatives right now cringing at my answer, but you know, it is what it is.

Unknown Speaker 2:43 Well, hey, post in the chat. Tell us where you’re coming from. What are you drinking? What are you toasting for and who you think St. Patrick really was. And just as a quick reminder, for everybody who’s listening and everybody who’s here live on the show, please give us your review on Apple podcast. Follow us on Spotify. Rate review us, we are so excited about how the podcast is growing. And it’s all thanks to everybody who’s listening and sharing this. So all over the place, and obviously our great guests and with that, let’s just go dive into this topic today about identity graphs and customer 360. So I’m just gonna start with this. Honest, no BS honest, no BS question. What’s the deal with customer? 360? Like, isn’t this something that we’ve been wanting? This has been a thing for 10 1520 years? Isn’t this Why does it continue to be like a big topic? Like, isn’t it? Shouldn’t this be a solved topic? What’s the deal with customer? 360?

Unknown Speaker 3:39 Yeah, I think that’s a great, great question. I’ve been drawing I’ve been drawing that that diagram I won’t even say the first time I I put that picture together in terms of better understanding who customers are but I’ve got to say what’s continued to change continued continue to continue to change is the technology capabilities and support around reaching that it’s 360 is more like a practice, right? It’s it’s like golf or yoga. Right? And and you’re constantly leaning into finding better ways to actually truly deliver on that because it is not there today, but I’ll say and we’re going to talk a little bit about this in this kind of No, no BS kind of space is what we’re doing today. With knowledge graph, what we’re doing today is is I don’t know the difference between a horse and a car, the both get you there to the destination, but what we’re able to do is shockingly improved, and the rhetoric is is the capabilities are finally aligning to the rhetoric and it’s fantastic. I know we’ll cut we’ll cover off on that a little bit in a little bit more detail. But Brent What do you cuz

Unknown Speaker 4:45 No, I I think, you know, when you think about customer 360 and kind of the idea behind it is to is to build a complete and accurate picture of consumers across sorry. Had a little bit technical difficulties here, complete and accurate picture of consumers across by pulling together consumer data across a variety of structured and unstructured data. And it’s, it’s really a challenge, especially for big companies like ours, where we’re dealing with large volumes of data. And it’s we’ve got data in silos, we’ve got data from old systems, you’ve got data from all over the place, right? different formats, different quality levels. So when you think about customer 360, I think a lot of the challenge really is is is if I have data in all these different places, how do I bring them together? And and do it in such a way that it’s accurate, that it’s complete, and that it’s reliable, and then I can use it to add value. And and in, you know, add value to my customers add value to

Unknown Speaker 5:56 wondering, Thompson is such a big company and has so much rich history about it, there’s wondering constant data that you guys are part of tell us a little bit more how wunderman Thompson kind of structured? And what are the business kind of the real pain points of when it comes to data and customers? I’d love to get that, that that. That overview?

Unknown Speaker 6:14 Yeah, well, let me just kind of connect you first into wonderment Thompson. So we’re a $2 billion global agency, a part of a larger holding company called wpp. Our role with our clients is to help them understand people and communities connect that to their brands, their strategies, their and their marketing, and their media programming, and connect data to that so they can better understand who their own customers are, and who the community of customers or potential customers, they should be connecting and serving. The threat of data has now become the persistent drumbeat in every conversation, you can’t walk in the door without being able to bring the ability to enrich what they can know about their own customers or their own first party data, enrich that with with more, they know a customer very well vertically as they’re stitching that together, but horizontally, we bring that deeper understanding to the table. And then we connect that to things like machine learning to get deep on Discovery, to it’s pattern recognition and feature creation, to better understand their customers, and then connect that to how do you better serve them? How do you better support them? How do you better communicate with them? The where’s the house? And then how do you identify people that are best fit for their products, obviously, think about it from their perspective is they only have X amount of resources to be able to reach out into the marketplace. And they want to be able to connect that to people that the brands should factually resonate, doesn’t do a brand any good to communicate with somebody that they’re just completely off message or not connected. So our job are to connect those grand strategies and needs with people where it’s going to resonate matter most. And data is at the at the foundation of that. And so if you think of a $2 billion agency, we’re building the fuel and the capabilities and the resources so that we can show up and help make brands smarter, and then reach people more effectively and be able to drive value for the consumer as well as obviously the bottom line.

Unknown Speaker 8:16 That makes sense. And how is technology coming into play with that and help with that? Right. I mean, obviously, you’ve been saying that, you know, the the boxes have been showing up on the diagram for a long time. Now. Obviously, more recently, terms like identity graph come up. How does that play into into your strategy? And how y’all how y’all tackle all this?

Unknown Speaker 8:39 I mean, a jump on that. Swing? Yeah. So so as Michael said, you know, we are a pretty sizable organization, right? And just in the US alone, we are challenged with we have, you know, three historical business lines, right. So yeah, data different all kinds of different data coming in from three different business lines 1000s, of feeds very diverse array constantly changing those legacy systems or on older technologies, right? So So why did we pick a graph? Why did we go into the proof of concept and do that? Well, we needed a capability to be able to bring that that data together and connect, those legacy systems will connect those consumers in a single location, right. And so the graph technology and the way that we, the way that we did the proof of concept with with data dot world, is you know, how to describe it. We had to really go after literally 1000s of feeds, and combine them to a single view of a consumer, right, and the technology and the services. And I know this isn’t a sales pitch of the data that world team but the services component that we got by having a good partner really helped us think differently about the data. of the way it was stored, and how do we bring it together? Right, not just in the way that we’ve conventionally always done it, but also in the way that the capability allows right machine learning capabilities to bring in new math matching methodologies, things like that.

Unknown Speaker 10:14 I do I do want to let’s dive into more of that. You said the conventional way. And again, the honest no BS, I mean, everything you’re saying about the problem, the need like this is what every single marketing website or every single customer 360 or MDM company defines that that they do. Right? So why is it that traditional, quote unquote, traditional kind of customer 360 solutions, MDM solutions, customer data, platforms and stuff? Why isn’t that the solution already? Like what’s missing there?

Unknown Speaker 10:47 Yeah, great question. And I would say what’s fundamentally changed. One obviously, is the volume of data, but also the construct of high availability, high accessibility, and relationships, being able to see and discover and connect and disconnect relationships at scale. That’s a it’s, it’s, we moved out of, of, it’s not just the next chapter, but this is the new book, the new book and how you manage data at scale and create availability. So things like I mean, originally, graphs were built for fraud detection and anomaly detection. But if if identity and access management, machine learning social networking, recommendation constructs are important to you as a business, and they are to everybody, graph technology, is the is the is the provides the tech technology structure for being able to manage that. And I’ll just give you to take all the all the technical constructs away, but just the simple outcomes of this. Today, today, we can now do in 12 hours what we couldn’t do in six months, we can hypothesize something in terms of here’s a new day, a data element that we think can be meaningful to the graph, we operate a graph just so everyone here knows, but 270 million people about 20,000 different data points about who you are in the real world, what you buy what you do online, and it’s all around context around the business of selling people, we’re in the, in the business of helping people helping companies better understand people so they can serve them. So our business is all about relationships and being able to connect disparate information I saw on the chat, somebody asked about research data. So panel to population, the speed at which we can take a panel and connect it to a population and validate the the efficacy of that, we can now do that across not a small sample, not a 1% sample, or a five or 10% sample, which I’ve been living in for the last 25 years. But across the entirety of the graph in a day. And that speed, and the visibility and the transparency. these are these are words that we can’t have with an enterprise client without them actually bringing them up. First, they need hyper visibility, they need to understand what matters and why they need to have high accessibility, they need us to be able to support discovery and learning. So this is, again, if you’re not, if you’re not in a graph, technology structure or or method in practice, you’re probably not going to be doing a lot of this work for a long time. You’re making one smile? Look, I mean, I mean, this is my personal perspective is that

Unknown Speaker 13:39 the world is connected, right? And we talk a lot about a lot of the graph databases, they talk about connected data. But honestly, it’s much more than that, right? It’s connected data, it’s connected knowledge, it’s connected knowledge, with data connected people and all these things together. And the moment that you’re thinking about connections and how things relate to each other, like you’re by definition, talking about a graph. And honestly, I have to say it’s sometimes really frustrating when we start getting to these conversations. Well, well, but now this is a relational database can do this stuff and things like that. But just to kind of, I get tired of saying this, and I want to I want to get this on record here. Why? Why isn’t Why isn’t relational or traditional or MDM enough? Isn’t identity graph, just a fancy type of identity MDM, or or, or is this really like that different I want to hear? What are your take on that? Yeah.

Unknown Speaker 14:31 that’s a great question. And again, I it’s like horses for courses, right? When things matter, like, again, speed, accessibility visibility. I’ve got to say I’ve been in the relational I was in the world when it was flat files, and then it went to relational databases, and then it went to wherever it went to next and what we’re doing right now, again, is it’s a bit of a wow moment in terms of what we can do now. I will say that I’m sure that relational databases could kind of get you where you’re trying to go. Right? A horse will get me to California, a car will be much better, right? I mean, purpose built for a plane is better than a car if you want to get absolutely. Yeah. And if you’re Ilan, you know, take a rocket, right. So I just I would simply say the, the technology is meeting the need of the market where speed matters, accessibility, availability matter, both at the both at the at the at the edge, but also for the practitioner that is building staging, and managing, right. And we do both go to build stage and manage, we’ve got to have the flexibility to test into adding new value into a graph structure. And I will say this, Brett’s been doing this as long as I have. I mean, it would take us from idea to deployment in relational structure with a lot of smart people. I mean, it took weeks and months, and now it’s hours and days. So that’s an incredible change and evolution. Right. 

Unknown Speaker 16:09 I mean, I think a lot of folks are a little bit worried or scared about some of the new technological approaches. And, you know, it’s easy for those things to kind of stay a science experiment, right? How are you approaching being able to make sure that this kind of tech can actually be impactful in production with real with real products and services?

Unknown Speaker 16:31 Yeah, I’ve got it. I’ve got to say, our work touches the market, Brent, you can share how you’ve mobilized our graph into machine learning that then goes right into media and marketing activation.

Unknown Speaker 16:45 I’m going to kind of step back a little bit on that on that question. And I think it was it was, you know, how do you get started here, right? And how do you make sure that that that migration is successful? So I would say, I would say, you know, you got to be methodical and not try to boil the ocean, right? Knowing your use cases and your KPIs for the Knowledge Graph or your identity graph. hugely important, right? It could be eliminating data silos, it could be improving data quality, data governance, legal compliance, or, as Michael suggested, it’s about being able to bring better products and services for your clients. Right. I think another key, then I think it’s easy to get caught up in this is you got to be able to focus on the relevant data, right? When you look back at your systems, you got to be critical about and understand and concentrate on what’s the most impactful data you need to really be critical, because not all data is created equal. And you may not need all of it in the graph. So it’s not just a place to pick up and push everything into. But you got to think about, you know, relative to my KPIs relative to what I’m trying to get done, what is the most impactful data? Of course, you got to pick the right partner, right? I mean, it’s always important that you got to pick the right partner, someone with a proven track record, someone who’s agile, someone who’s a partner, not just a vendor, obviously, someone’s just cost competitive, right. And I think the other key there is, remember that it’s a marathon, not a sprint, right kind of project management one on one, you start at a reasonable pace, and to maintain, you know, good sustainable momentum, you got to make sure you do a proof of concept, you got to build competency and build awareness, because it’s it’s new technology, and it charts a new course, right? Learn from your mistakes, advertise your wins, you know, add data and knowledge to the graph over time. The thing is, is it’s it’s a lot of fun for the folks on our team that are working on this because it is new, and it’s different. And it’s new technology, and it’s new capabilities, and they can kind of see and see what’s possible in terms of, of new capabilities and new products and new services. And it really jazzes them up, man, when they’re when they’re a part of that. And they see what what’s possible, you know, Michael mentioned, you know, he said something kind of off the cuff, I want to, I want you to catch it, though, you know, we’re bringing in data from three different three different business lines, right systems building in, in over time, different technologies, different places, right, it would take months to build the marketing universes and months to build the attributes, and the things that we that we build, right, it can be built in 12 hours now. And we did it in six months that from start to finish. we transitioned concentrating on the most impactful data, and very tight KPIs. We made that transition in six months. We took those data feeds in and we improved and we hit all of our KPIs. It was it was awesome.

Unknown Speaker 19:39 I do want to I do want to touch on one point because you you made that you mentioned how do you make sure you’re not just building a science project, you have to design to touch the market right from the get go. Right? Otherwise, all this work by itself is just a science project unless you find a way to mobilize that and touch the market. So that was principle We started out with from day one. And that is, how are we going to mobilize this and put it in the hands of practitioners in a way that we can demonstrate real improvement, not just building improvement, which is awesome, right? And those KPIs are dead on accurate. But if you don’t build it to touch the market, so we’ve mobilized our graph into this scaled machine learning, we’ve connected it all the way through to media activation and right in the marketing activation, there was a question, do we have a callable API? The answer is yes, a callable API so that somebody can touch our graph, give us a piece of data. And we can then say, this is a, this is who this is. And here’s some data payload we can deliver back that can then again, touch the market, it can touch a call center, it can touch a personalization engine, it can touch what but you have to know that that’s what you’re trying to do from the start. Otherwise, you build an awesome graph, and it sits in a bar.

Unknown Speaker 20:54 This is spot on, I think this is one of the things that people get excited about the Knowledge Graph and identity graph, and they kind of they jump to it like a like a science experiment. And they kind of build these things up. And it’s like, yeah, this is cool. But wait, what are you using it for? What are your KPIs? But it what is the problem you’re solving? What is the business problem, but at the end of the day, you’re like, Wait, how is I love what you’re saying, and how is this connected the market? How is this making money directly for the company? I was saving money? Like, I think that’s what we need to have this notion of, of we’re delivering this graph, but we want to build products and services that consume the graph directly. And every single product and services is directly tied to revenue, increase cost savings and so forth. Yeah.

Unknown Speaker 21:33 yeah. Yeah. And any technology milestones you have along the way or on the way towards some business milestone, right, I think that’s probably a lot of mistakes that that some projects make is that they they focus on the technology, milestones, and and then they don’t get to the business goal.

Unknown Speaker 21:48 And then you run out of steam, right? Because the market so designed, touch the market, from the very beginning, everyone needs to know the work. If it doesn’t touch the market, you just didn’t actually do anything.

Unknown Speaker 22:04 And I think when there’s like questions, when people come to the questions about like, technology, again, like, Oh, can I do this with relational? I have to convince my folks, my, my team about this, I mean, at the end of the day, it’s like, well, let’s stop, let’s stop talking about the technology. Let’s talk about the the opportunities that we’re missing, right? Why are we so slow to go make this like, that’s the baseline we have, work, how we can do better with this. And that’s how we can compare.

Unknown Speaker 22:28 get them excited by the outcomes, right? The methods and the practices are improving graph is one component of it, I look, there’s a couple other moments that have happened in the last couple years for us, as we’ve designed to touch the market at scale. And with speed, the graph was a minimum requirement, the work we do with snowflake in terms of data mobilization, and accessibility is a piece of it. And then the connection into scaled machine learning the work that we’ve done with IBM around Watson and connecting our graph into that space for discovery, and then threading that all the way through into deep integrations with, you know, folks like Adobe, on the marketing, CRM side, or directly into DSPs, and ssps, etc. It’s been a number of wow moments, and it’s because the technology is catching up with the, with the intention. And, again, I don’t think that means relational databases Go Go away, I just say horses for courses, right. And this graph construct is important for these types of applications and solutions, where, again, things like high availability, high accessibility, high discoverability are our mission critical to the work that you’re trying to accomplish.

Unknown Speaker 23:40 So for something like bringing in identity graphs, knowledge, graphs, it is aspirational. I think this is kind of like the ideal way of managing your data within your organization where we were talking about your you can get on the horse to California, but you can rather get on with get 30 with a car or plane. On that aspiration. Sometimes people kind of struggle with getting that as a priority. But on the other side, there’s always like these big pains, like in the finance, there’s like regulations and fear that you’re gonna get fined. Like that’s a big motivator, catalyst to go to change. One of the things I’m seeing in your in kind of in your industry was this end of the cookies era. Right? So this is something that that, I mean, there’s an entire industry who’s afraid about this, right? How do you relate kind of the end of the cookie era? And maybe if you can also describe what that actual problem is, and how identity graphs and knowledge graphs really helped around that, because that’s something that we’re starting to see a lot.

Unknown Speaker 24:36 Yeah, I’ll jump in. And Brett and you can obviously chime in, from our perspective. Yeah, that’s, uh, there are those changes happening? And there’s a bit of a power shift, but one first party cookies aren’t going away third party cookies. All right. So yeah, thanks for the clarification. So that matters because it matters when it comes to companies needing to prioritize their own first party data and to enrich that to better understand People so that that actually plays well into the strengths of it should be about people not about devices, not about cookies, not about, not about mobile IDs, etc. It should always, it should have always been about people and understanding people and geographies and communities. It’s a challenge Don’t get me wrong is supply side publisher networks, first party cookies are going to hashed emails are going to play a greater and greater role in terms of being able to continue to better serve people with with relevance, because ultimately, consumers still want that. Right. But they also want the privacy. So our intention always in historically has been start with people. So our graph is built from people perspective up things like emails, hashed emails, first party identity connection is a part of the work that we do. And that ties into where the market is certainly going. So again, I I see the changes as they’re tectonic. But they’re in the right direction, because it it is about less about guessing. probabilistically and more about knowing deterministically. And that’s a that’s a better position for any or all of us to be in whether you’re a consumer, or a brand, or a company like ours, right?

Unknown Speaker 26:22 No, I think, I think he hit it, he hit it on the head. And then and then for us, you know, inside of wunderman Thompson, right, we, you know, the success that we’ve had to date, you know, on on what we’re doing with the graph, and by the way, you know, it’s a journey, it’s a lot of work. I mean, it’s not like this panacea, that you just, you know, it’s just magic. It’s not magic, it takes work, right. But the success that we’ve had today, you know, we’re now getting an opportunity to engage with with the media or other media companies inside of WP, and extend our graphing capabilities to address the kinds of help address the kinds of challenges that you just talked about, about the demise of the third party cookie and alternate strategies on how to how to connect to consumers, right, not as not as anonymous, you know, cookies, but as people, right, and, and with, with preferences, and with privacy concerns, and so on, and so forth. So, you know, we’ve got some, some cool stuff already lined up. And you know, we’ve only been in the graph space for about a year and a half, when we started our PLC, and it’s just, it’s just catching fire across.

Unknown Speaker 27:24 across, but we hit on something so important to put out there. And know that it’s, um, yes, I’m sure we could manage without graph support. But around privacy and consumer transparency and visibility campaigns, it’s data has always been hard to be able to see, right, and both kind of combination of knowledge graph and data catalog, actually allows us to provide an a much higher level of transparency and know the ingredients of ingredients, and to be able to trace and track where those are, and how they’re used and to be able to report on it, and also respond to consumer, sometimes demand stop this, I want to be disconnected our ability to now reach in and say, That’s not an issue. I mean, yes, it was there before. Don’t get me wrong. I’m not saying that there was no support from a privacy and compliance perspective, historically. But the ease of which that is now available to us. And the provenance and the history and the traceability and trackability exponentially improved. So, yeah, who knows mean, goes to technology for, you know, being an imposition that meets a market need, at about the exact time we needed to have it?

Unknown Speaker 28:41 Yeah, it seems like this is the perfect timing from a convergence standpoint. And, you know, where is this going going forward? Do you? Do you believe that with these trends, like the end of the third party, cookie, and the advent of graph technology really come into play? Like, you know, is everybody going to build their own identity graph? Are they going to partner with groups like yourself to to leverage, you know, great sort of solutions that are provided by awesome vendors? Like what’s what does this market look like as we go forward?

Unknown Speaker 29:10 It’s a great question. I think we’re navigating some of that with some really smart partners. But I think large enterprise clients are going to spend a lot more time energy and effort and in privatizing and expanding their own understanding of people, I think they’ll see probably a community of communities approach that will happen within enterprises and connection and sharing of of almost like a cooperative construct around first party connection where it benefits the consumer first because that is the kind of the number one goal so again, I think there’s a lot ahead for all of us and super, it’s, it’s unbelievably engaging, the people that are leaning in, and I think while while everyone’s a little shaken by the, you know, the underpinnings of an industry are changing. But I don’t think anyone is is, you know, I don’t think anyone’s gonna throw, you know, is there, it’s time for the it’s time for the construct to change anyway. So I think everyone is leaning in to create the right ecosystem for where we’re going forward is there’s opportunity here. Yeah. Oh, yeah.

Unknown Speaker 30:21 What are your What are your kind of your final words here for those skeptics? Like the I mean, you’ve you’re saying, I’m clear taking notes, you’re saying that this is not just a new chapter, right? This is a new book in Baton data management, like those folks here who are either skeptical who are doing or may not even know about this stuff. What’s your message? What What should they be paying attention to? What are they missing out? If they don’t do this?

Unknown Speaker 30:46 I would say it’s not invisible, right? I mean, I think we can, I’d be happy to demonstrate some of the things that we’re able to do that you couldn’t before. Right? And, look, it is, and I said it earlier, it’s, it’s a horses for courses kind of structure. It’s not everything. Graph doesn’t, you don’t need graph for everything. But right, these things you’re going to be in the best position to be successful doesn’t mean you will be successful, you’ll put yourself in the best position to be successful. And that’s something that is transparent and visible, and it can be demonstrated. And in some, in some cases, I would, I would, I’d put a pile on the table and say, if you can beat this, here, go, I’m gonna make this change, you make this change. And you come back in six weeks, and I’ll be done in about four hours. And, you know, it’s, again, it’s just a different tool set. So, yeah, it is a different tool set. Right. It’s Unknown Speaker 31:45 a different tool set. And I you know, I would say this, everything Michael said was, I think spot on, but again, you know, just take away from the technology just just for a minute, in talk about, talk about people, right, getting, getting the opportunity to do something really new, cutting edge, something that is going to grow and expand. I mean, to the degree that that folks on our team are engaging in the graph and learning those skill sets, right. I mean, not only are they getting to do new stuff and cool stuff, but you know, they are they are increasing their own, you know, marketability and their own professional knowledge. Right. So beyond just the technical side of it, beyond even the business outcomes, right, that the connecting to the, to the to the market, there’s also the people aspect of it, right that the human beings are saying, wow, this is really cool. There’s these cataloguing technologies and these, these identity graph technologies and or knowledge graphs, in particular. So I would suggest that that even within their own if someone’s having a hesitancy, right, even within their own technical community, go back and take a look and say, you know, let’s explore this for our business, right for our KPIs for our use cases. And is there an opportunity here not only to advance the business, but to advance the people inside of our business, right, and make it make it a better work life experience?

Unknown Speaker 33:11 I think that that’s a great way to wrap this up. And I gave my brand I told you 30 minutes fly by so we always like to wrap up with some takeaways up, Tim, how about you take away with the first takeaways?

Unknown Speaker 33:22 Yeah, sure. My first takeaways are man, there were so many things here. You know, one of the things that man one day is we take our notes as we’re kind of talking and we think about the the key things here, and we’ve got a long set of notes here. I’ll say that I love the comment that you know, this isn’t this isn’t just a new page, or a new chapter that this is a new book, that we’re really talking about a different way to do things here. And that you really want to be focused on the outcomes. Because a lot of folks, they get tripped up on new technologies. I mean, you saw this a lot, when really big data tech really came and the advent of that came, obviously now we’re kind of working through that MLA II wave. And we’re kind of coming out the other end on the pull of plateau of productivity, or whatever you want to call it, right? It’s really great to always remember that you know, where you’re headed is where you’re going to go. So make sure you’re heading the right direction. So I love that. I feel like that’s huge advice for our listeners.

Unknown Speaker 34:15 Yeah. And my takeaways is, again, following up on the it’s not a new chapter, it’s a new book, and it’s about dealing with your data and its relationships, and scale. Right. It’s I think that’s an important thing. And you talk to you said, Let’s make new relationships with this Connect relationship, want to be able to go do that at scale? And then Brett, you really talked about how to go start and I think this is this is a, this is a great list. People who really go follow this don’t boil the ocean. What are your KPIs designed to touch the market? How are we going to mobilize this? Focus on the relevant data be critical on what’s the most impactful data because not all the data needs to not all data is created equal, you don’t not everything needs to be in the graph. Pick a right partner, right? Somebody has a proven track record someone who’s agile right and not just selling you a product vendor somebody is really gonna be a partner. Again, the traditional kind of comment, but it’s very valid here. This is a marathon and not a sprint. So as always like to go wrap this up with these two questions. What’s your advice? Yes, very broad question. very broad question on purpose. And who should we invite next to, to be a guest on the show? So Michael, how about you take us first

Unknown Speaker 35:20 advice? I have just go back to a design firm designed to touch the market, right? I mean, just it’s so important. Who should you have next? I don’t know about who but from a category perspective, I’d almost pick like I think about our client roster, right? Whether there are folks in the automotive industry, the packaged goods, industry, financial services, bring in any one of those people to share their both challenges and needs. in the marketplace going forward, I think you’d have a fantastic conversation, where they could express work that they’re doing, but but really what they need. And I think you would find a pretty material connection between the capabilities and the needs. Awesome. How are you bright?

Unknown Speaker 36:07 Ah, who to invite next. I was joking with you, I think you shouldn’t like Matthew McConaughey. But be pretty great. entertaining, be entertaining, that’s for sure. Now I you know, as we as you guys talked about, you kind of leaned in on some questions about third party cookies. And we talked about privacy a little bit here, I think something that might be interesting for folks is is you might want to bring in some some of the expertise like the IAB tech lab just sat in on a webinar that they had the other day going through some of their initiatives around the third party cookie, and what’s changing there or maybe like that, some folks from the future of privacy forum or, or something like that, because, you know, privacy is consumer privacy is critical. And it’s not just not just as us as practitioners, but us as human beings, right, we want to know that our data is being protected, that it is being appropriately protected. Right. And so it’s going to become a bigger and bigger part of everything that we do in the data business. And so so maybe someone from one of those, from one of those, those entities that’s that’s overseeing and helping driving policy in response to the to the legislative initiatives

Unknown Speaker 37:17 I think they would I think they would be pleasantly shocked and surprised at what is what is coming to help. Right. Cool. Any final advice? open question, Brett?

Unknown Speaker 37:30 Oh, no, I think I think we hit it. I mean, you guys in the review hit it. It’s It’s It’s again, you know, it’s outcomes based and you really got to, I would again, start small, right? Start small with very measurable outcomes. And, you know, think critically about what it is you’re trying to get out of it. And what what are the things that you’re doing actually belong in the graph relative relative to other solutions?

Unknown Speaker 37:54 I’ll just say to everyone that like, that puts themselves on this path. I can guarantee you at some point in time in a room, somebody is going to say, wow, there’s a wow moment. And you will just it. It’s it’s happened for everyone on our team and everyone we’ve shared the work that we’re doing, even when I prep them and say somebody is going to say, Wow, it always happens. So be prepared for that. It’s fun.

Unknown Speaker 38:18 We really need to wrap up, but I need to ask this. What was your Wow, aha moment? What was it? What do you remember what it was when it was?

Unknown Speaker 38:25 Yeah, yeah. When Brett said, yeah, we, we, we thought this and we tested it. We thought it this morning. We tested it today. And we knew the answer. At the end of the day. I’m like, What? Wow. Yeah. And that just happened. Once that happened. It makes me go wow. Yeah.

Unknown Speaker 38:44 All right. Well, they’re there. There’s there. There’s a well moments ever everybody is listening. What is your wow moment, right. Anyways, thank you so much for your time. Just a quick kind of wrap up here next week is the data dot World Summit. It’s going to be next Thursday on the 25th at 11am. Central. We got folks from snowflake from Booz Allen, LinkedIn, five trans zebra data kitchen, Fishtown analytics. And I think also, both of you guys will be joining us in our summit up. So see you all next week. And then next week, we have the topic of provenance with Professor Deborah McGinnis, from RPI. So Professor Deborah McGinnis, she is an expert on provenance has been doing so much research and has a vast amount of experience and research in the whole area of semantic web and knowledge representation. So it’ll be a really great conversation. That, cheers, Michael, cheers, Brett, thanks for your time. And thank you. Thanks so much for joining us.