In advertising, getting the right message to the right audience is easier said than done. For Dana McGraw, vice president of audience modeling and data science at Disney Advertising Sales, the key is data — and how it can be shared with advertisers to enhance its value without compromising privacy and anonymity.

“It’s about how to deliver the right ad to the right consumer at the right time so we improve their ad experience as well as the experience for the brands who buy ads from us,” she says. “Our guiding light is how we use data to improve the experience for our guests.”

With distribution channels multiplying in an industry that is becoming increasingly fragmented, audience data acquisition and use remains McGraw’s biggest challenge. But, as she says, it’s also the most interesting part. Now Disney Advertising Sales has taken a data clean room approach to data governance to help advertisers leverage Disney’s audience graph — a new frontier for data sharing that protects data and privacy while generating synergistic benefits.

McGraw spoke with CIO.com’s Thor Olavsrud at Foundry’s recent Future of Data Summit to discuss Disney Advertising Sales’ approach to data sharing, as well as the importance of staying nimble as consumer habits change and maximizing the potential of data-driven products.

Here are some edited excerpts of that conversation. Watch the full video below for more insights.

On the future of data:

Dana McGraw: The future of data is particularly interesting in the advertising space because the ecosystem changes so quickly, whether through regulation, policies, distribution channels, or platforms. So we’re constantly iterating and evolving. We joke that we always work on long-range plans, but ultimately in the data space, things can get completely uprooted in six months’ time. So it’s really interesting to just think about ‘anonymity with precision.’ In advertising, you certainly want to be precise, but also have thoughts about how to anonymize things to be consistent with what consumers expect of us.

On collecting first-party data:

I think some of the challenges are industry challenges, and opportunities are obvious in the advertising space. They lend themselves to more addressability of inventory, so on a digital platform or a streaming platform, the ability to deliver the right ad at the right time is so much higher in these kinds of environments. It’s a very fragmented business and relatively nascent compared to linear television. So that fragmentation of different distribution channels, what that means to data and advertising, and how it all works is the challenging but really interesting part we get to figure out. We’re lucky we started building an audience graph years ago, and as platforms and content consumption patterns change, we’re able to be nimble. It’s not just adding more frequency across multiple platforms but thinking incrementality, which is huge for brands that advertise because they look for incremental and new audiences. So all of these different channels and our ability to look across them is a big opportunity for us.

On data clean rooms:

The clean room is really the next step as we think about how data is matched. Previously, we would call it data onboarding from a third party or from an advertiser. So if we think about data matching, data collaboration, the clean room is next to fully respect consumer privacy as we innovate. A partner we work with on the clean room side said that a clean room is the “non-movement of data.” I think that captures what it is. We’re able to leverage our proprietary audience graph, and if an advertiser or an agency is bringing data to the table and they want to understand this is a high-value audience, then we want to match it to their audiences to understand where to advertise. We’re able to do that, but the data doesn’t move or exchange hands. So we’re able to maintain that anonymity and maintain the separation of data while still being really precise in how we can find high-value audiences on behalf of advertisers.

On hiring, training, and culture:

Over the past 10 years, we’ve really changed the way we think about how we hire, structure a team, and what our team culture is like. Some of it has to do with the innovation in the data space and some with the proliferation of data. So even those who aren’t particularly adept with data or had a lot of training are using data in their day-to-day business. From an ad sales perspective, our sales team has to be well equipped to speak to data in the marketplace and to understand what we can offer. As we’ve gone through that process, we had to think about EQ [emotional intelligence] skills in addition to quant skills so we haven’t structured our team with all PhDs and quantitative folks. We have folks from very different backgrounds. But that ability to communicate and simplify has really changed the way we think about how we hire. And we pride ourselves on having a diverse team and having a culture of empathy. There’s also been a lot of investment in how we train, both on an individual level and mass trainings. On our team, for instance, we range from someone who was an analyst in the FBI to someone who was a social media manager. So it’s interesting how they interact with one another and train each other. When you’re hiring, there’s a level of aptitude that has to be there, but when you’re hiring really intellectually curious people who have a high EQ and are confident, you can start to leverage all of those resources and get them trained in the things you can teach.

Analytics, Data Science