In this interview with Nick Winfrey, Vice President of Data Science and Data Strategy at The Walt Disney Company, hosted by Dana Gardner, principal analyst at Interarbor Solutions we explore Disney's extensive use of AI automation and data science to optimize advertising and enhance fan experiences.
The conversation also delves into the importance of diversity in data teams, the crucial role of AI and machine learning (ML) in media and entertainment, and how Disney leverages audience behavior and loyalty across different platforms.
Dana Gardner: Welcome to the Data Cloud Podcast, Nick. We're delighted to have you with us.
Nick Winfrey: Thank you.
Dana Gardner: Few companies or brands have had the lineage, legacy, and success in multimedia storytelling than Disney, and for going on a hundred years or more now, Disney has also been at the forefront of technology to not only bring their stories to life, but to also nurture their fans' lifelong loyalty by continuously enhancing their experiences.
Now that AI, automation, and multi-channel data access are coming together to provide the best insights ever into creating compelling audience experiences, who better than Disney to take the pulse on what's possible and probable when it comes to media and entertainment innovation?
In today's discussion, we'll do just that by exploring how Disney is bringing together all the elements to match the right messages with the right audiences at just the right time across multiple channels, brands, and mediums.
Nick, tell us what Disney Advertising is, who your clients are, and how you deliver business value to them using advanced data services?
Nick Winfrey: Disney Advertising oversees all the advertising, sales, and integrated marketing for The Walt Disney Company entertainment and sports offerings. So, when you think of linear, digital, social, audio -- any of the ad-supported streaming businesses -- that would fall under our umbrella.
The lengthy list -- ABC, ABC News, Disney+, ESPN Networks, ESPN+, FX, Nat Geo, Hulu, and our eight locally owned television stations -- that is the breadth that we talk about with Disney Advertising. When we talk about who we're trying to bring value for, we're talking about the thousands of brands that run across that media and across all of that content.
We're trying to make sure that when they're working with us, that we view them as part of those brands. We want to bring that quality brand experience in the content we produce and make sure that that same quality brand experience is enabled for our advertisers so that we integrate it into that overall powerful customer experience.
Dana Gardner: And tell us, Nick, about your background and how you got to where you. How has that helped shape your team and culture?
Nick Winfrey: I have a background in economics, particularly econometrics. I studied at the University of San Diego, which puts a heavy emphasis on the philosophy, the logic, of attacking problems in a multidisciplinary way. It tries to remove silos and focus on the why.
I then took a job at the US Federal Reserve, which put a heavy focus on quantitative rigor. The importance also on the choice of words markets move on, such as the exact adjective or adverb that the chairman uses when he talks about the performance of the market. And that was really important for me to understand when you're working with a stakeholder, to really know your audience, know how you're translating, know that the particular words that you speak to matter.
After the Federal Reserve, I moved to the FBI, and that was really about how to make sure you are relevant and timely. It's great to do a robust analysis to dig into something, but if you missed your avenue to be relevant to drive a decision then your analysis is never going to be as powerful in communicating certainty and confidence in the results. When you have to move fast, there are going to be times where you are giving up on some of the accuracy, and so you have to be able to explain where you're uncertain, what makes you more confident in your results.
That all led to what I think of as a data strategy. So, when I'm building a team, I'm looking for diversity, a bit of that data unicorn of someone that we're digging for on the science, but also the why, and the strategy -- and then be able to communicate that to stakeholders. I think right now as data science has really exploded, there's a heavy -- almost too much -- emphasis on technical degrees.
In my opinion, when I'm looking at experience, art history can be a technical degree. Philosophy can be a technical degree. Neuroscience can be a technical degree. It's about how you approach the problem, how you take that toolkit that you've been trained in and how you apply it to problems with AI continuing to explode and be democratized.
The understanding of the why will be so key to the differentiating factor or a data team. For myself and my team, we're responsible for data science, data strategy, measurement strategy. We're, we're responsible not only for developing those capabilities, but translating that into tactics and levers and decisions that enable our clients to have a better brand experience when they're engaging with Disney Advertising.
Dana Gardner: And when you're bringing together these different traits and experience levels and ability to analyze, along with the new technology and data science, do you think that this is perhaps an inflection point? Are we now able to do things that we just couldn't have done before? What makes this an auspicious time for your team and the science as it's evolved?
Nick Winfrey: Yes, in doubling-down on that diversity and experience benefit, we're in a situation right now where the AI and ML toolkits have matured tremendously. And the personas and teams that leverage it have expanded the technical toolkit that you need to have.
It has adapted, and so you can bring in people who approach the problems in a different way. That really leads to new opportunities, new areas to innovate, and that get you out of that kind of group think, that boxed-in thinking. There's such a heavy focus on first-party data right now, really going into what can be observed, moving away from some of the syndicated panel-based data as well.
Everything operates in a lower-latency environment. I remember when I started out, it would take seven days to run [an analysis], and you would code something wrong and you would come back and sign in on the virtual machine and you'd see red, red, red, red.
And it was a painful experience as an analyst. That has now changed so much whereby models run so much quicker. The latency, the time to get results has declined so much. You can get quicker results, and you can be more relevant. So, personas focus on first-party data and really just the time to be able to turn around those results.
Dana Gardner: And there's a great diversity, of course, across the Disney organization -- with different brands, media, and channels -- all sorts of different business lines. How are you “following the fan,” as I've heard you describe it, across these different aspects of your business that brings you closer to a one-to-one relationship with each of your fans?
Nick Winfrey: The follow of the fan concept is one in which you're not just a fan when you're engaging. The content fanship is an essence of who someone is. You may have NBA playoffs going on, or NHL playoffs. People will focus in on how I reach that audience that is specifically a Warriors fan or a Kings fan during these sporting events. But they continue to have those characteristics long after that, that brand loyalty, that devotion, those kinds of local preferences and that becomes part of the personas that we build out.
And for us it is how do we take those friendships, those insights about the audience's behavior and translate it into something that that works for a brand and makes it actionable and real for them.
Dana Gardner: In order to do that, of course, you have to manage a lot of data complexity. We now have, as you mentioned, cutting edge AI, increased automation, and faster time-to-value from that analysis. How are you bringing these together in a way that enhances your business value for your clients? In other words, how do we get from following the fan to the bottom line?
Nick Winfrey: One of the areas that I start with when I hear that question is we are part of the business. Oftentimes data teams see themselves too much as a central service. We are not a central service. We sit under advertising leadership. We sit with the sales teams. We hear the questions, the tactics, the levers that brands are trying to pull and make decisions around.
And we honestly flip a bit of that narrative on its head in that we don't look at the AI solutions, the data capabilities, and figure out how to drive business value. We're starting from how do we drive business value and what is data science’s role in that conversation?
And so, for us, solutions get built from the client up. We're looking for those trends, those norms. I have a phrase that I like to use with the team, which is scalable customization. Where can we take the building blocks that we have and put them together in a different way? Then we can align it to the business value that we already identified.
Dana Gardner: And how important is adding more automation -- faster, at scale, and being repeatable? How is automation factoring into your ability to deliver this value across all of your various businesses?
Nick Winfrey: Automation. You know, we have a goal of 75 percent automation by 2027 for our business. But I think it's always important to remember what is behind that. We want to remove the barriers to entry. We want to free up time for innovation. We want to get things into repeatable processes.
You've got people spending a ton of time on manual tasks, or what have become more of the norm -- what we refer to sometimes as swivel chair automation, where you automate something to hand it off to someone else – and you just get into this pattern of repeating what you've always done. You don't get that opportunity to innovate, to try to find something outside of the norm and build a new capability.
Dana Gardner: Let's drill down into your data enablement journey. Tell us about some of your tools and how you've been able to develop them.
Nick Winfrey: Journeys always have multiple chapters. And I love to lean into analogies. And, so, the initial focus is on building the foundation and the pieces that you need to have in place. And just like a foundation for a house, you have to continue to invest in that foundation. For us, the foundation has always been the Audience Graph.
I've been at Disney for over 10 years. We've been investing in the Audience Graph for more than a decade now, and we continue to invest in it. And the Audience Graph is central to everything we do. It is the connection; it is the hub. It's how we pull that follow the fan and all those different engagements into one central area.
And after the investment in the foundation and that data journey, we then look at what are those interior spaces? Now that we have that foundation, we have that Audience Graph. How do we add rooms to it? How do we add the kitchen, the living room, the dining room? How do we flesh out the different functions?
That's Disney Select. When we talk about our audience segment, offering generation streams, our thought leadership on how individuals are engaging with stream platforms, all the fan following that we talked about before -- it's making sense of the data. And once we have those two pieces in place, we have the house in place.
We began thinking about what the new opportunities are, and so we started thinking about externalization. How do we expand the uses of the space? How do we make it follow the fan. It's not just us building the narrative from beginning to end. Follow the fan is co-developing that narrative with our marketers so they can own part of that story.
So, it was really about creating that short-term rental for our house, of letting someone come into a room for a period of time, understand what it looks like, and then build from there.
Dana Gardner: Any examples come to mind of when you've done all of that blocking and tackling properly and what it gets for you? Perhaps matching the context of what's going on in the media and the content with what the next advertisement might be? Is there a need for making it natural or a better experience?
Nick Winfrey: Yes, we have a product called Disney Magic Words, which is about leveraging AI capabilities against our content to provide what we refer to as hyper contextualization of the content.
It’s about understanding the moods, the emotions, the interest in those scenes, and that only gets you part of the way there. The rest of it then is understanding the brands and what type of audiences they're trying to tap into. Then you can get that creative that plays after the content and seamlessly connects the pieces together.
And the only way you can do that is by a collaborative co-development style of modeling where you are speaking data systems to data systems.
Dana Gardner: Are there any other advertising experience enhancements that come to mind? You know, that can we do now that we couldn't do before when it comes to making that experience of delivering the message about a product or a service in a way that benefits the advertiser, but also perhaps is more effective to the audience?
Nick Winfrey: My team is really focused on test and learn. A lot of the push right now in the marketplace is on how you optimize the outcomes. And optimizing to outcomes really requires getting an understanding of what those outcomes are and having the models be able to react in a actionable, timely fashion so that you can lean into the learnings that you get from it.
And you can maybe shift the narrative a little bit. Like on the measurement side. For so long it's been, “You did a good job pass. You got above a benchmark pass. You were below a benchmark fail.” We're trying to switch that narrative and move it into a conversation about how we can optimize and move things up into the right.
How can we continue to get that exponential growth in performance for brands that would've not been possible at the scale it is today. Models, as I talked about earlier, like the latency of models was too long. The ability to run these complex models within the systems, and the ability to connect systems, was just too limited.
And with the [Snowflake] AI Data Cloud and the AI the cross-cloud collaboration patterns -- they've unlocked new capabilities.
Dana Gardner: Okay. Another aspect of building out the data, the infrastructure, finding those AI models that work in the way you want, is an ability to predict audience behaviors and preferences.
Looking instead at what's working now, what might work later to deliver better insights? How do we go from this being an art to more of a science? What do we have to do to elevate predicting audience behaviors into a science?
Nick Winfrey: Predicting anything is typically tough. We all make jokes about the weather forecast or the economy. Former Chairman the Federal Reserve Alan Greenspan used the term “irrational exuberance.” It’s so difficult predicting the market because there's a human element in it. When you start predicting audiences, it's all about that human element.
It’s less about, in my opinion, necessarily having gotten into the singular process to predict better. But I think it's what I was talking about before, it’s how rapidly can you test, learn, and iterate on those processes? How well can you get to a stable model that performs well against those predictions?
Even going back to when I was talking about COVID in my career path, it’s about how well you can communicate uncertainty and confidence in your results, so you don't lose stakeholder buy-in.
And just that piece in which we're able to test, learn, and iterate has greatly expanded, and we're able to take more shots at it in a narrower timeframe. It's no longer running a batch and waiting a week; it's running a model and waiting seven minutes and understanding how you did it.
Dana Gardner: And what do you look for, Nick, in your suppliers, the people who are contributing the components that you're pulling together in order to accomplish this improvement?
Nick Winfrey: There are a few things. One, especially where cross-collaboration is so much easier, it’s so much easier to connect systems together with concepts of dynamic tables or incremental ingest. So, I'm looking for someone that starts with the ends. You know, early on in data solutions, there was a lot of like what are the means to get to the end?
So much focus is on what the architecture is, and how to connect the architecture. I want to make sure we start with the solution and then we figure out how to get there. I think there's also a balance needed of short-term and long-term goals.
Anytime we're working with a new party, we definitely have to demonstrate the easy wins. We have to demonstrate the immediate value while continuing to focus on the North Star.
Beyond that, we are going to challenge our parties to be adaptable and be transparent with us. Things become obsolete so fast and approaches shift so quickly that we need an engaged party to call us out. And when we give them feedback that maybe their systems are on a 1.0 and everything else is 2.0, we need to have a transparent and agile working relationship.
Dana Gardner: We've been talking primarily about looking at the audience and discerning inference, patterns, and behaviors. But because “measurement” is in your title, I'm going to guess that you're also involved with measuring what's working inside the company. And so, to what degree does automation and data contribute to your actually being able to know yourself and how well you're doing internally?
Nick Winfrey: With any new capability you build, you have to be willing to test it on your own internal areas first. It's how you're able to work through what happens when you pull one lever. What are the possible paths that you can go down?
I think when you're thinking about automation internally, Disney is a very complex ecosystem. I laid out all the brands that we're talking about. And in many ways, the internal teams are our stakeholders. They advertise on our platform. They try to reach their audiences on their platform, and we're able to see then if they're trying to promote new content and they're running marketing media against it. We're able to see whether they attained what they were looking for.
Did that activity drive the engagement that they're looking for? And if not, we have an ability to understand the why. So, when we get in front of advertisers, we're already starting from a grounded position from a lot of tests learned and findings that we can bring to the table.
Dana Gardner: Looking to the future, can you talk about some of your projects where these new capabilities -- and the ability to see what's going on and working internally, working better with more partners and stakeholders – deliver those better end results to the fans and enhance their experience? What's coming up on the horizon?
Nick Winfrey: Yes. We announced in January 2025 a product called Disney Compass. It's an evolution of our data journey. We started as an Audience Graph, put the rooms in the house, let people come into our house. This is almost a little bit to make a play off of Disney content.
This is almost our Wayfinder moment, like we are now taking that house and turning it to houseboat and we're following a North Star via Compass. And for us, that refocuses the narrative. We want to shift away from the past from a measurement capability. We want an optimized silo approach, but we also want to move things out of silos. We want to get out of it.
In advertising, we talk about plan, activate, and measure. We want to make all of these things self-reinforcing and to bring more transparency and consistency.
So Compass is a challenge to ourselves, our vendors, and our clients to start thinking about automation as automation from beginning to the end of the full flow. Not just what touches the Disney part of the flow, but what touches from the very beginning when an advertiser's thinking about investing in our platform to the end when they're seeing results -- and back again when they're trying to think about the audiences that they want to reach the next time, or they want to message to again.
We're challenging that automation throughout the whole process and move it toward an always-on framework. No longer is it when you think about measurement, and a measurement study is done four months later, and you're given a readout of it and you're not actionable, you're not relevant. We want to create a much more unified data platform.
Dana Gardner: When it comes to the proper infrastructure, the blocking and tackling for your data, in order to then take advantage of automation, speed ,and these higher order analytic capabilities, what do you look for? What are some of the necessary ingredients to get that right?
Nick Winfrey: The [Snowflake] AI Data Cloud is unlocking some technologies and capabilities that allow for faster path automation. The SQL and native ML capabilities are ones that we use. You know, going back to those personas, it allows for more people to leverage the ML models, but then connect it to the rest of the work that they're doing.
And so where before you had multiple handoffs, you now have a singular person that can be looking at the ML model directly to gain insights. Dynamic Tables is another one that we look at, where before you had these files moving around jobs, processing the files, picking them up, [Snowflake] Dynamic Tables allow us to have the raw data in sync, and when raw data comes in incrementally, it lands there, it's immediately attached from ingest to insights.
And in that case, the automation is done seamlessly by the Snowflake platform. For us, we don't even have to think about it as much of a pipeline from raw ingest to insights. And similarly, when we think about streams, we're working on flows that go to other platforms and data's coming in at different points.
Before we'd have a lot of data restatements, we'd have a lot of incremental pipelines, and it was a heavy lift to automation with streams. Those systems are connected and we're decreasing the data processing and data restatement. So, a lot of these tools have thoughtfully built automation into their capabilities instead of relying on the data teams to have to build them up from scratch.
Dana Gardner: I think automation builds on itself and you look to your suppliers to get that game well under way. How about digital advertising writ large? How do you see AI improving and enhancing, delivering perhaps new capabilities? What is AI going to bring to the digital advertising age?
Nick Winfrey: Going back to Disney Select, it's thousands of ML models that we run concurrently to try to think of different ways to lump personas and audience engagement patterns together. That would never have been possible at scale 10 years ago.
So when we think about what's going to happen within the digital advertising space, it's similar proof of concepts around things that can now be done in real time of understanding, with sports for example, what is a really high-intensity moment when fans are really engaged. Those types of models, again, would not have been possible a decade ago because of the amount of time they were taking to process.
The beautiful answer is, I don't know, but I think the exciting part of that is that things that were not possible before are going to start coming as actionable techniques to be used.
Dana Gardner: I have to say that number of 75 percent automation as a goal is very impressive. And so my last question is, what words of advice do you have for other organizations that would also like to up their automation game?
What are some of the basics that need to be in place? What do you recommend people do in order to put themselves in a better position to take advantage of such levels of automation?
Nick Winfrey: The foundation is key. If you're automating off of an unstable foundation, t's going to collapse right away. Context is necessary, that of building from the business solutions up so that you're not automating something that no one knows what the use case is for. And diversity of the data team is essential to get out of that box think of what the end solution looks like.
But even more when we talk about being part of sales, I think about broadening the definition of the data team and teams that are responsible for solutions. We bring legal and privacy in early and often. So in that house analogy, instead of having an inspector come in at the last step, they're inspecting along the way, so we don't have to tear down something if we've done it in a way that doesn't make sense.
You have to be automating with a purpose of the teams that are key to the end success being there early. That includes our go-to market teams that we have who simplify our messages and make sure that we're automating around what would drive the business goals. And then also our PR and communications teams, I view as part of the data team, they're making sure that they're spotlighting our successes and giving us feedback on our shortcomings.
We talk about automation as a data and tech capability, and it's the data and tech teams that sometimes get the credit for it. But you have to build a culture within the organization where everyone owns the solutions. That means everyone owns the wins and the losses and as long as you get that kind of cross-functional collaboration, moving toward automation is a lot easier than if you're trying to do it in silo.
Dana Gardner: Thanks so much to our latest Data Cloud Podcast guest, Nick Winfrey, Vice President of Data Science and Data and Measurement Strategy at The Walt Disney Company. We so much appreciate you sharing your thoughts, experience, and expertise, Nick.
Nick Winfrey: Thank you.
[Snowflake provided support for the production of this content.]
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