Disney relies on clean data rooms for its marketing data

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In order to give advertisers access to a large amount of audience data while protecting user privacy, Disney Advertising Sales has adopted a data clean room approach to its data governance. The company relied on Snowflake’s offer.

Disney relies in particular on the Snowflake Data Cloud solution to poss

For years, Disney Advertising Sales has explored the frontiers of data and analytics to better help clients find the right audiences for their messages. The explosion of streaming services from The Walt Disney Company has added massive amounts of data to Disney Advertising Sales. This business, responsible for advertising sales and integrated marketing for Walt Disney’s entertainment and sports offerings, can now provide advertisers with access to more than 1,000 user segments built from this data. But with great power comes great responsibility. Disney had to reimagine its approach to data governance to ensure it protects user information and privacy.

In October 2021, Disney Ad Sales unveiled a data solution based on the concept of an essential marketing tool for this activity based on “data clean rooms” built among others with the help of providers Habu, InfoSum and Snowflake. These are secure, isolated platforms that link anonymous marketing and advertising data from multiple parties. Partners can gather data there for joint analysis under defined restrictions. Dana McGraw, vice president of audience modeling and data science at Disney Advertising Sales, explains that Disney’s relationship with its visitors is a common thread running through the company: “When we think about data, their use, to their governance, it is in fact a question of asking whether it improves the experience of our customers” she adds.

Share data securely

“Such a solution is a way for brands to access information about their own audiences, and who they want to advertise to with us, without any kind of data exchange between us,” adds Dana McGraw. Snowflake’s data cloud tool is the cornerstone of this solution. Its data sharing technology, private data exchange platform, and secure function and join capabilities help implement the partitioned solution. “The Snowflake Data Cloud solution provides the ability to provide all the protection we have from a data perspective and allows us to do some really cool things with audience graphs and other datasets, customer data , third-party datasets that we think the market is very hungry for in terms of insights, activation, and measurement,” said Lisa Valentino, executive vice president of customer solutions and addressable activation at Disney. Advertising Sales.

“The Snowflake solution allows us to manipulate data at scale, in an environment where we feel very comfortable.” Lisa Valentino says many Disney customers are looking for these kinds of siled solutions to derive pre-planning insights by connecting their own data with Disney data. Pre-scheduling information is essential for “upfronts”, meetings of television network executives, major advertisers and the media at the start of important advertising sales periods, which allow marketers to buy advertising time ” upstream “. Over the coming months, Disney plans to share information and best practices on the use of its data in these closed and secure spaces. She hopes this will help clients better understand how the group’s insights add additional value to their own data, and how to architect their data to fit into that space.

Map relevant identifiers

Snowflake’s Data Cloud empowers Disney to have a “single source of truth” about its data, while keeping it secure, available, compliant, and easily accessible to its partners. This single copy of data also gives Disney scalability and flexibility in how it prioritizes workloads, which better supports its BI (business intelligence), analytics, science and machine learning while minimizing the time data engineers must spend orchestrating, organizing, and building data pipelines to deliver data from diverse sources. Siled spaces allow Disney to define if, when, and how to provide query results to ensure data anonymity and security.

Behind this platform is Disney Select, which brings together all of Disney’s first-hand data and advanced modeling capabilities under one roof. Disney Select, in turn, is built on Disney Ad Sales Audience Graph, designed to map relevant and available identifiers on the Disney Platform for a particular household and to link attributes and engagement across all Disney endpoints. Dana McGraw notes that Disney Select gives marketers the ability to choose the audiences they want from a library of over 1,000 in-house behavioral and psycho-graphic segments.

“We work with over 100,000 attributes to inform these audience segments,” she explains. “We take advantage of advanced machine learning, which allows us to do a lot of modeling, whether from a seed of information or adding data from third parties.” As an example, Dana McGraw clarifies that Disney might not have a lot of internal data on buying automobiles, but the company could add an automotive marketer’s data to Disney’s data in the space. cloisonné to create a more suitable model. “Around each area, we think about the desired outcomes, and then we model based on those outcomes to create segments for the desired outcomes,” adds Dana McGraw.

Integrated data science is key

To succeed in this area, Disney has taken an integrated approach to data science. Dana McGraw’s data science team is housed within the company, in Lisa Valentino’s department. “We’ve had great success integrating this solutions team into our go-to-market team,” she said. For her part, Dana McGraw adds that to succeed as a data scientist within an advertising sales company, it was necessary to think about the talents and to ensure the diversity of backgrounds and skills within the team. It is not enough to hire members with quantitative training.

“We want people with a marketing background to sit next to those with deep quantitative skills,” says McGraw. “There is an exchange of ideas, understanding and workflow between these groups. Whether it’s data science, advanced analytics, or data solutions and enablement, these groups exchange ideas, skills, and work hand in hand.”

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