In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions, is joined by Travis Scoles, Executive Vice President of Advanced Advertising at Paramount. They discuss how Paramount leverages data, analytics, and AI to enhance advertising efficacy and maintain a competitive edge. The discussion also covers the integration of third-party data, the importance of privacy, and the evolution of targeted advertising.
In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions, is joined by Travis Scoles, Executive Vice President of Advanced Advertising at Paramount. They discuss how Paramount leverages data, analytics, and AI to enhance advertising efficacy and maintain a competitive edge. The discussion also covers the integration of third-party data, the importance of privacy, and the evolution of targeted advertising.
[00:00:00] Producer: Hello, and welcome to the Data Cloud Podcast. Today's episode features an interview with Travis Scoles, Executive Vice President of Advanced Advertising at Paramount, hosted by Dana Gardner, Principal Analyst at Interarbor Solutions. They discuss how Paramount leverages data analytics and AI to enhance advertising efficacy and maintain a competitive edge.
[00:00:25] Producer: The discussion also covers the integration of third party data, the importance of privacy, and the evolution of targeted advertising. So please enjoy this interview between Travis goals and your host, Dana Gardner.
[00:00:38] Dana Gardner: Welcome to the Data Cloud podcast. Travis, we're delighted to have you with us.
[00:00:41] Travis Scoles: Thank you, Dana.
[00:00:42] Dana Gardner: Hey, tell us about your role at Paramount Global. Your background in quantitative research, media analytics, and media optimization.
[00:00:50] Travis Scoles: Absolutely. So here at Paramount, I lead Advanced Advertising, which is a great way to describe data-driven advertising. So it's everything from Vantage, which is our audience targeting platform to measurement, innovation, identity strategy, kind of all the sort of hard nuts and bolts of the data area.
[00:01:07] Travis Scoles: Personally, like my personal background is I actually was a data scientist for a little while. I worked at a consulting firm and ultimately moved into to product roles. Joined Paramount to lead Product here for Advanced Advertising and, and now I run the function.
[00:01:20] Dana Gardner: Terrific. Hey, what excites you about the next five years in your area of expertise? What has sort of got you jazzed and happy to get up on Monday morning?
[00:01:29] Travis Scoles: So, right now, I think especially in the media industry, and especially for publishers like Paramount, right, who for a very long time had a very specific way of operating that's been highly disrupted through. Really a couple of things, right?
[00:01:41] Travis Scoles: Change in consumer behavior from a movement from linear consumption to a streaming consumption pattern, as well as new entrance into the market, right from the big social platforms and things like that. As a result, the industry has been in immense amounts of change. I think anybody that's sort of. Has been around that, has seen that, and I think that's really what the most exciting part of this is.
[00:02:01] Travis Scoles: Change brings an opportunity to do new things, to innovate, to try new things, and I think that's really what the most exciting part of my role is, is the ability to come in here and try to solve novel problems.
[00:02:13] Dana Gardner: And how is Paramount setting itself up for the long game and how various analytics technologies and AI in particular will help automate, enhance, and better customize ads across various media.
[00:02:25] Travis Scoles: Look, one of the reasons I love Paramount and I love working here, is that Paramount has had like a long belief that data technology capability is really critical to competition in the new world and the ability to stay in front of our competitors and the ability to stay on the top of that curve is a competitive advantage for us in the marketplace.
[00:02:44] Travis Scoles: So as we think of then about the new types of technologies that are rolling out, it's up to us to always be on the forefront of that, right? For a couple of reasons. One, we certainly wanna make sure that we have the best capabilities available to our clients to take advantage of, to help drive the efficacy of their advertising here at Paramount.
[00:03:04] Travis Scoles: But two, it's really necessary for us in order to continue to drive our own business, to understand our own inventory, our own audiences. You know, I think one of the things that people don't talk about outside of kind of the data corners is with the evolution of media and the evolution that we've seen into streaming and the different types of data in the,
[00:03:24] Travis Scoles: the richness of data you get from those digital platforms. The explosion of information around identity resolution and identity pieces across the industry, the perpetuation of linear data sets that are anchored to tens of millions of households. It's really kind of been a boon relative to, I would say, in the last several decades of data availability in the media space.
[00:03:47] Travis Scoles: And then as we think then about how companies like Paramount need to harness that. It's through technology, right? It's through things like AI, analytics, et cetera. As we look through our own journey over here, you know, I would say that machine learning, AI, those types of capabilities have been dramatically infused throughout our business.
[00:04:06] Travis Scoles: Actually, I think at, at this point, every campaign that we run is at some point touched by some automated algorithm, whether that's to forecast the delivery of a particular show or whether that's to understand the pricing implication on revenue for our yield team. All of these things are now driven by new technology.
[00:04:27] Dana Gardner: And if you would, Travis, describe Paramount generally, and then also tell us about what parts of this large company that you are focusing these efforts at and perhaps how that will expand or consolidate across the other units.
[00:04:40] Travis Scoles: So I work in ad sales, right? So everything that I do is focused on advertising.
[00:04:44] Travis Scoles: How can we make the advertising experience for brands the absolute best and most effective it can be. Here at Paramount, we have a lot of different functions, right? This company is very large. Our core business is producing content. We are an entertainment company. We are designed to make people have fun or be enthralled or learn something when they sit down, watch the big glass in the living room with their family.
[00:05:07] Travis Scoles: As we think then about the importance of things like understanding audience, right? That spans across the entire company. And as we think then about how we're trying to think through our future roadmaps, think through how we want to use data, apply it to our business that's becoming a broader and broader conversation.
[00:05:25] Travis Scoles: Because frankly, the types of application that we're seeing in data is no longer just relevant for marketing or for advertising or for even things like finance, right? It's sort of starting to span across the organization and we are seeing like, you know, just like most folks in acceleration of technology changing the way that most fun functions are done here.
[00:05:48] Dana Gardner: And I'm sure like most organizations these days, you don't just buy your analytics or AI solution off the shelf in a box prepackaged and ready to go. There's a lot of customization and work that goes into it. And you mentioned Vantage, so tell us about Vantage and how you've developed that using readily available technologies, but how you also have used your own internal expertise to improve it.
[00:06:11] Travis Scoles: Yeah, so Vantage is our audience platform and the best way to think about what Vantage does right, is it changes the concept of advertising from, Hey, we should be focused on somebody based on their age and their gender to, we should really start to measure and optimize against people that wanna buy the product that we're advertising for.
[00:06:28] Travis Scoles: Right? I know it seems fairly intuitive to say, like, if you're gonna buy advertisements to advertise a new cheeseburger for a QSR restaurant. Perhaps you should try to understand if the people that are watching television eat cheeseburgers. But this is actually a relatively novel concept in advertising, especially in the linear space.
[00:06:44] Travis Scoles: I would say it's only about 10 years old. So a lot of a Vantage is Paramount's answer to that. How can we start to leverage data and the kind of the explosion of data available in the marketplace to give richer insights and richer capabilities to our brands and to our clients so they can start to think about advertising in terms of things like,
[00:07:02] Travis Scoles: Is this driving units coming off of my shelf, right? Are the people I'm reaching the right folks for my brand, for my category as opposed to this feels like the right number of GRPs in a sort of demo range.
[00:07:17] Dana Gardner: As a consumer of media and someone who's been doing it for decades, I have to say there seems to be still a ways to go in matching my interests and needs with what I actually see.
[00:07:28] Dana Gardner: And it seems to me that it's in my best interest to improve that so that I'm not watching ads that are not relevant to me at all, and that you all get better value to your sponsors and advertisers by matching. And so how will data, analytics, and AI help close the gap between, you know, me watching a football game perhaps and seeing ads for something that I will never buy multiple times when I could be getting ads that will actually help me in my consumer journey?
[00:07:55] Dana Gardner: What is the technology gonna do to close that gap?
[00:08:00] Travis Scoles: You know, I think one of the interesting things when we think about targeted advertising is that as technology is evolving and data is becoming more prolific, we also have on the counterside of that more scrutiny and more focus on things like consumer privacy, data privacy, ensuring that we're being good stewards of the type of information's now available to us.
[00:08:19] Travis Scoles: And I would say that, as a result of that, right, the need to balance capability with the consumer, it's created an environment where a lot of the ways that we apply data and technology have become increasingly complicated and increasing complex. I'll give a great example, right? So data cleaner rooms, this was all the rage a few years ago and they're still very, you know, very much being deployed is kind of a de rigueur way to do this.
[00:08:43] Travis Scoles: You know, we leverage Snowflake, we leverage a lot of partners to do this. But it really sort of answers one core challenge for us, which is, hey, in order to understand what a consumer likes, we need to basically be able to combine data from a couple of different places, right? We need to understand what they're watching on Paramount, which is data that Paramount might have, but we also need to understand what their consumer behaviors are, which is maybe a third party.
[00:09:04] Travis Scoles: We also might work directly with an advertiser that wants to leverage first party data. How do we, how are we able to connect that to maximize the advertising experience for a consumer in a privacy compliant way through technology? Through things like clean rooms, through things like anonymization, tokenization, et cetera.
[00:09:20] Travis Scoles: So yeah, there's, it's true, right, that like certainly the world will continue to evolve as it comes to targeted advertising. Ads will continue to become more relevant for consumers, especially for consumers that opt in for the data trade. I think one of the really interesting things about streaming is this teaching consumers the value of kind of their attention, right?
[00:09:42] Travis Scoles: It's really hard to understand the value exchange that you are giving a linear data feed when it feels free, right? Like when it's a broadcast television network. But when you have to sign up and somebody's like, for an extra $6 a month, we'll make all these ads go away. Then you kind of understand, hey, like I'm actually saving money because I'm watching ads.
[00:10:02] Travis Scoles: And that money can be, that savings can be maximized if I share my information. And I think as that relationship becomes more apparent to the consumer. We'll see, you know, more interest and I think maybe less trepidation around these types of applications of data. But that doesn't change. The fact that we're always going, like every step forward in the advertising space has to really be two steps.
[00:10:22] Travis Scoles: It has to be a step forward for capability, but it also has to be a step forward for privacy.
[00:10:26] Dana Gardner: Sure. And I suppose you're also, you mentioned streaming competing with a variety of different types of businesses. Some of them are platforms and enough of themselves, and so when traditional media like television is considered passive, where I'm sitting there for half an hour or even considerably longer, I'm not giving you any information about what I'm interested in by how long I look at something or whether I click through on it or whether that relates to other activity that I've done.
[00:10:55] Dana Gardner: So again, how do you close the gap when you're competing with someone who's getting two-way information about a consumer in media versus many of your properties, which are still in that sort of passive mode? Is there a technology way to keep up and even surpass what some of these other platforms are able to do?
[00:11:13] Travis Scoles: So, you know, it's a really interesting question because Paramount, we just rolled our Always On attribution product out. And the concept of that is when you buy ads from Paramount, we're going to be able to leverage dark data through partnerships with folks like MasterCard, Circana, to show you that type of closed loop measurement, connecting with technology, connecting with data, things like clean rooms, identity resolution, et cetera.
[00:11:35] Travis Scoles: And in a lot of ways, you know, when we look at the platforms that have a certain set of data. That allows them to do that type of closed loop measurement, and we see this as a response to that. There's an interesting piece here where as we were going through to build that product, we actually had a lot of blue sky thinking that we were able to do because we had to go out and build something from scratch, right?
[00:11:53] Travis Scoles: We didn't have a, an existing data asset, an existing thing that we had to shoehorn into a advertiser solution. We instead could ask ourselves if we were an advertiser, what was the, what is the solution that we'd like, and that's what we could go out and build. I would say that the focus on performance.
[00:12:10] Travis Scoles: Which is really where a lot of this attribution stuff comes in certainly is increasing in importance. We see that with the evolution of the measurement space. We see that with the evolution of buyer KPIs and sort of their overall buy strategy. But the question is how do, what is performance, right? And you see across all of LinkedIn now folks are asking the question,
[00:12:31] Travis Scoles: what is the sort of balance between last touch and brand building and how should we be able to do that? That's why I think places like Paramount, you know, we have a very special place in this ecosystem because to your point, it's somewhat a, the big screen in the living room is, we can call it a passive experience, but it's also a co-viewing experience, right?
[00:12:47] Travis Scoles: It's also a relaxed experience. It's something where people are receptive and are looking to sort of imbibe information, whether that's storytelling from Taylor Sheridan or a 30 second creative about a new automobile. People are in the mode and receptive for that. Combine that with the types of premium content and premium viewer experience that we can offer, and it's really a unique proposition for advertisers.
[00:13:12] Travis Scoles: Our ability then to tell the story of us not only through a brand lens, but through a performance lens, through a consideration lens, through the lens of the entire consumer experience that we're able to impact is increasingly critical to us from a business perspective. And it is driving certainly, I would say, investment and a lot of building with technology and data.
[00:13:34] Dana Gardner: Yeah, and it, so it seems, Travis, that the quality of the experience for both your advertisers and your audience is really super dependent upon the quality and breadth of the data. But, unlike a factory floor where you could control all your machines, your data is not coming from your own domain, so to speak.
[00:13:55] Dana Gardner: So you have a lot of third party data and you have to rely on bringing that together and making it work in a common analytics environment. You could build buyer partner to do that. You mentioned build. How do you bring all these different data flows and even from places like a MasterCard or a Visa into your ecosystem and make it actionable?
[00:14:16] Travis Scoles: You know, one of the things when we look at kind of our distribution of, we'll say like technical folks or technology folks, it's very heavily oriented these days around data infrastructure and data connectivity being interoperable with our client systems is the number one most important thing, right?
[00:14:30] Travis Scoles: Because we need to meet our clients where they are, right? Which means we need to be able to build these data pipes. We need to be able to connect this data across the ecosystem. So it's a core area of focus for us, and it's an area that we've built an extreme competency, and frankly, by doing this over the last 10 years and building these types of teams and really becoming experts in this space.
[00:14:50] Travis Scoles: To pick up on one of the things that you mentioned in the question, like we have to use a lot of third party data sometimes, and I think it was very trendy maybe a year ago in the industry to talk about like the number of parties that your data had, if that made sense. People were talking about first party data, second party, zero party data.
[00:15:07] Travis Scoles: All of a sudden there was like a whole gradation of these data and it's felt like as the number of parties increased, people were like, well, this data feels less relevant or applicable. Yeah, at some point, guys, third party data is just somebody else's first party data, right? It's about understanding the providence, understanding the efficacy, and then understanding how to use it, how to connect it up in order to kind of deliver the outcomes advertisers are looking for.
[00:15:32] Dana Gardner: Yeah, it's funny, you know, about 10 years ago we were talking a lot about big data and I was at a conference and we had some automotive manufacturers on the stage and I said, you know, what's your big data objective? And the guy says, I don't want big data, I want all the data. And I have to assume that you're in the same boat, right?
[00:15:52] Travis Scoles: Yeah. Most, most certainly. And actually, I think it's funny because if you look at the definition of big data as it's been applied to certain areas, that itself has been changing over time. Five years ago, if you had an a CR provider or some provider that came in and said, I have, you know, 10 million households of linear viewership data, that was huge.
[00:16:10] Travis Scoles: That was a giant data set, right? Previously, we've been working on these like tiny panels that were measured into tens of thousands. Nowadays, if that number's not over 30, it's like, ah, what are we even doing here? You know? And I think that is the push to the, all the data aspect, right?
[00:16:28] Travis Scoles: Is that the bigger the data grows, the more obvious, the fact that it's not a hundred percent coverage becomes, and then that becomes kind of the North Star. Now look, I'm not sure that's a, I think the pursuit is worthwhile, but I'm not sure the outcome is possible to ever have like perfect data coverage across the universe. I also am, frankly, I'm not sure that we wanna live in a world where there's perfect data on everything about all the time, and it's all real.
[00:16:50] Travis Scoles: Like that's very Orwellian, right? But you know, I think that what we do see is kind of this evolution of the amount of information and the size of the coverage we have. And while in a lot of ways these big data sets kind of give us almost sometimes less visibility into some of the things that we may have used to have personification is a great example, right?
[00:17:12] Travis Scoles: Like big data signal. They start at the glass, not at the sofa. You know, in a lot of ways what they've done is they've allowed statistical models, the data science stuff, all of these, you know, machine learning techniques, AI, all of those things are, they require massive sets of information to get to be trained.
[00:17:29] Travis Scoles: So it's really improved, I would say, our ability to do things like forecast. How many people are gonna be watching South Park next week? Because we just have a lot more information to use and our data scientists can do more accurate, more exotic models. And the input or the output of all of that effort is better business.
[00:17:49] Dana Gardner: Sure. Now, because you're relying on data, so many different sources, and that could change in a heartbeat. You might find all of a sudden a new data avenue that you wanna pursue. It would be great if these all came in on a standardized basis, or there was a standard applied to just the media and advertising industry so that it would be clean and usable on arrival.
[00:18:10] Dana Gardner: But that's not yet the case. However, there's more than one way to go about standardization. Sometimes, uh, you can have, uh, four or five years of meetings and proposals and white papers and come up with an industry standard set that's then recognized and approved. Or there's the opportunity for someone to step up and say, I've got the best place to put all lists.
[00:18:31] Dana Gardner: And in a way, we're, we're standardizing by virtue of the fact that we do it better, at better price and make our tools available to everybody. Openness, if you will. So do you look to Snowflake as assuming that role and is it growing and how beneficial is it to have a source, a cloud source that brings in data that everybody else is using?
[00:18:53] Dana Gardner: That gives you sort of a common denominator when it comes to using data for your analytics?
[00:18:59] Travis Scoles: Yeah, so I mentioned earlier that interoperability is like a key North star for our investment pattern. And we started working with Snowflake years ago. A large driver of that from an ad sales perspective was a lot of our clients were leveraging Snowflake, right?
[00:19:12] Travis Scoles: And we wanna be fully interoperable with their systems and it's done a fantastic job of allowing us to operate and to build data-driven tools in areas where we have to leverage things like clean rooms, et cetera, right? Some of these more kind of exotic technologies due to a litany of reasons, Snowflake has been a very powerful partner for us to be able to connect data throughout that ecosystem.
[00:19:35] Travis Scoles: When I think, though, about standards, and I'll sort of use the Joint Industry Committee, which I'm very familiar with, is a good example, right? The Joint Industry Committee in the US is designed to basically evaluate transaction from various measurement data sources. One of the things that it's not doing is telling those measurement data sources.
[00:19:52] Travis Scoles: Here's all the exact standards you need to follow, because you know the flip side of standardization is commoditization, right? If everything is exactly the same, and it's all perfectly standardized, there's no competitive difference between anybody's products, and in that case, there's no urge to innovate.
[00:20:11] Travis Scoles: There's no reasons to build or to invest because it's a fully commoditized category. I look at how the ecosystem is evolving today, and I think it's a great blend, right? On the one hand, a lot of the standardization, as we would call it, is actually more about connection and interoperability, leveraging infrastructure like Snowflake.
[00:20:30] Travis Scoles: But there's still plenty of room to build custom application, custom solution to make your special sauce right, to allow buyers and sellers to have a little bit of a different setup to, to frankly compete against one another. And I think that's super critical.
[00:20:44] Dana Gardner: Yeah. And you should be focusing on those algorithms and those agents to come rather than be working the plumbing on uh, how the data's gonna assimilate and operate.
[00:20:54] Travis Scoles: Right, exactly. I think, you know, the standards question is always an interesting one because it's like, well, what do we want? Standardize. I think that's the right question because there's certainly things we don't, right? I never wanna have ad impression forecasting models totally standardized across the industry.
[00:21:11] Travis Scoles: Because I believe Paramount maintains a competitive advantage because we are so good at doing that. Right. That being said, I also don't wanna build 87 data pipelines to transfer basically the exact same information because it's coming from different companies. So like there's a balance here that I think we have to maintain to the point around Snowflake, that infrastructure layer is, I think, the right area to focus on because.
[00:21:33] Travis Scoles: That's the most plumbing E of the plumbing.
[00:21:36] Dana Gardner: Yes. And because you've been working with Snowflake for some time and because Snowflake looks at the media industry as a very important, dominant and important industry for them to be pursuing, what kind of stands out in your experience and how Snowflake improves your ability to, to know your business?
[00:21:53] Travis Scoles: Look, I think Snowflake has done a fantastic job of enabling us to connect information, not only from our walls, but also from measurable provider walls. We have very extensive Snowflake integrations with folks like VideoAmp. It allows us to connect to our client's data, et cetera. So it allows us to kind of build all of these things that are necessary for us to do this.
[00:22:15] Travis Scoles: But it also really is a response, I think, to, to the shift in the industry that happened when the platforms kind of entered. We always hear people talk about quote unquote walled gardens, right? These massive monoliths that force you to only work in their infrastructure. We don't share any data and they don't have to.
[00:22:31] Travis Scoles: They're that big and that powerful sort of in the marketplace. When I look at Snowflake and what it enables Paramount to do, it enables us to punch above our weight, right, to be a force multiplier because instead of, you know, we're competing with folks that have all of that data inside of their walls.
[00:22:48] Travis Scoles: And what it allows us to do is to bring similar capabilities and similar data sets to bear to our consumers or to our clients, but with a distributed infrastructure that's required for companies like ours.
[00:22:59] Dana Gardner: Alright, let's go up a few thousand feet in our vantage over your business and your vantage and consider that a lot of organizations got into analytics vis-a-vis reporting.
[00:23:11] Dana Gardner: They just had to know what was going on and give that report to somebody in a position to make decisions. But it's evolved quite a bit from there. And so extrapolating on that, where do you see AI and analytics enabling businesses to do things differently? And I mean, significantly differently. The headlines tell us how important AI is.
[00:23:32] Dana Gardner: Where do you see it going, and how has it moved from reporting to where it is now and how does that give us some sense of where it's gonna go next?
[00:23:40] Travis Scoles: Yeah, and I almost think it's a, like a democratization of information concept, right? So if we roll the clock back 15 years, right? BI was the thing, right? And it's all BI reporting.
[00:23:53] Travis Scoles: Microsoft Excel was sort of your most advanced tool to do this, right? It's all about spreadsheets and charts and PowerPoints, and that was a fairly democratized way to access information, right? Like if you were an analytics person, there was a set of pretty user friendly tools that you could use to unpack data sets of what at the time were considered large data sets.
[00:24:11] Travis Scoles: Now, nowadays, they wouldn't necessarily be, but it was like a very user-friendly way to do that, right? And we saw massive adoption across not just this industry, but all industries of those types of BI practices, because people realized the power that those things could bring, right?
[00:24:26] Travis Scoles: The power that information could bring. As data and technology evolved all of the sudden, because of the size of the data sets as well as kind of the way that they were set up, we saw a huge like kind of movement of importance away from things like very basic BI capabilities and into quote unquote data science areas, right?
[00:24:46] Travis Scoles: SQL. You know, NoSQL databases like Mongo, things like that, or like there was an explosion of this and all of a sudden the most important thing your company could have is this amazing data science team. Because if you really wanted the best answers and the best information that set yourself apart, you needed these really technical folks that could sit there and they worked through terminals instead of UIs, right?
[00:25:07] Travis Scoles: What AI promises is to kind of mix those worlds and take the best from both, right? The power of kind of the extreme technical tools, but combined with the ease of the sort of UI experience that we used to have in the BI space at that point. Like look, I think it's to the question around, is it used for reporting?
[00:25:27] Travis Scoles: Well, I guess everything's technically a report, right? Like, everything's just basically us trying to find out information to make better decisions about our business, whether that's pricing, whether that's how many units we have to run to fill a deal. The thing that's changing rapidly is how that information is available, how much of that information is available and who can find it quickly.
[00:25:48] Dana Gardner: Do you have any use cases among your customers, and I don't expect you to name them at all, but if there are some early adopters or harbingers of what's to come? Where can you look at and say, this customer's doing this, or we're providing them with this and that, that demonstrates where this can go?
[00:26:09] Travis Scoles: Yeah, most certainly. So when I think about our clients, right? I guess our customers, a lot of them are big agency and big holdcos, and there's sort of an investment being made right now in order to kind of chain our data together. And if you think about the explosion, focus on data capabilities that we're seeing in the agency space, right?
[00:26:27] Travis Scoles: You only have to look as far as you know, the Epsilon acquisition by Publicis or the Acxiom one by IPG, to know that these are, this is an important space moving forward, right? This is a thing that agencies are focused on, so we need to be able to interoperate with those databases. And then that's a place where infrastructure, where things like Snowflake really can come to play.
[00:26:45] Travis Scoles: And I would say that a lot of the folks in the industry over the last couple years has been around interoperability, connection, et cetera. I think the thing that is exciting, right, and is exciting about AI and all these other sort of evolutions is that it builds on top of that original approach, right?
[00:27:04] Travis Scoles: Like once we've connected our data sets, now there's a way and there's a new set of technology that allows us to maximize the utility we get from that connection to be able to learn as much as we can, not just from Paramount data or from our client's data, but from that data combined.
[00:27:21] Travis Scoles: And to do so in a way that is relatively fast, right, and relatively easy compared to maybe how it had to used to be done historically. I think that's largely the biggest shift we're gonna see. We're seeing most of our clients in some stage of that journey right now to varying degrees, but I think it's almost universal that we're all on the same journey together.
[00:27:40] Dana Gardner: Well, in our last area of inquiry, Travis, I'd like to ask you a little bit about culture, because you mentioned democratization, and that's not just about who can use data and how, but I think it's also in who has the skill sets in your organization and how can they become more data-driven without becoming data scientists, if you will.
[00:28:01] Dana Gardner: So what is it about the culture of your organization there at Paramount that you think is important and beneficial when it comes to the adoption of more of these analytics generically or embedded across the company rather than one little office with a bunch of white coated people in it?
[00:28:18] Travis Scoles: Yeah, so you know, I think, the biggest cultural shift, I think is for folks to realize that everything they do is data-driven. Everybody's sort of a data scientist to begin with, right? And maybe historically, in a lot of companies, folks don't necessarily think about it that way, so they think to themselves like, oh, the data stuff is tough.
[00:28:35] Travis Scoles: It's hard, right? It's a skill. Maybe some of those white coat guys have, but I'm in sales, or I'm in whatever. That's, that's not really the case. If you think about what data-driven advertising is or what data application, data science really is, it's looking at a bunch of information around you, right?
[00:28:52] Travis Scoles: Understanding the patterns of that information and leveraging that to make a decision based on what you think is about to happen right now. We all do that every single day, right when we wake up. You know, when I wake up, I'll give a great example. My hot water in my house, for some reason, it takes like five or 10 minutes to get hot.
[00:29:09] Travis Scoles: Now, look. I've learned that when I wake up in the morning, I need to turn the shower on before I brush my teeth, right? 'cause I need to give it a little bit of time. Well, that's me figuring out data, right? There was a historical track record of having to take cold showers, right? And then I learned from that and I said, okay, well here's a solution to that problem.
[00:29:28] Travis Scoles: I can do a few other tasks while I let this weight way up. And then now I can have a hot shower and all of my problems are solved. Before I even started my day, that is the application of data that is data science, right? In my own personal home in the first few minutes after I wake up. Culturally, what I think is happening is that folks that previously thought that they weren't data scientists are realizing they really are, right?
[00:29:50] Travis Scoles: Because they've been doing that, and then they start to realize, well, I can just extend that concept to the things that the folks in the white coats are telling me. And I can apply that to how I have conversations with my clients, and all of a sudden you have a data-driven culture, not because you're teaching everybody new skills, but you're just making 'em realize the skills they already had and you're helping them harness it in a little bit of a different way, and then they start to see the impact of that.
[00:30:15] Travis Scoles: They start to see the impact of the decision making the client sort of delight as a result, and it just becomes a self-fulfilling flywheel.
[00:30:21] Dana Gardner: Well, I think that's an excellent place to leave our discussion. Thank you so much to our latest Data Cloud podcast guest, Travis Scoles, Executive Vice President, Advanced Advertising at Paramount Global in New York.
[00:30:33] Dana Gardner: We so appreciate you sharing your thoughts, expertise, and experience, Travis.
[00:30:37] Travis Scoles: Thank you very much for having me. This was great.
[00:30:40] Producer: Calling all developers, business leaders, IT execs, and data scientists. Snowflake World Tour is your chance to learn and network. Discover how Snowflake's AI Data Cloud can transform your career and company. Experience the future. Join us on tour. Join the Snowflake World Tour to experience the future of the AI Data Cloud with Snowflake. Hear from experts, engage in breakout sessions and network with peers. Transform your business and career with Snowflake. Register today for one of our 23 stops worldwide at snowflake.com/world-tour.