Every few years, the digital advertising industry is left to cope with tremendous change spurred on by tech giants. Most recently, Google’s promise to eventually deprecate third-parties cookies and Apple’s move to do away with IDFA in the name of privacy protection have presented significant targeting and tracking challenges for advertisers and marketers. Organizations have been hard at work developing alternative solutions to mitigate the impact of these changes on the industry. While there are many solutions from which advertisers can choose, one would be remiss to overlook data clean rooms.
What is a data clean room?
Digilant defines data clean rooms as privacy-safe, cloud-based environments that allow two parties to match aggregated data based on a shared identifier. This process is facilitated by an identity graph or an alternative ID, like The TradeDesk’s Unified ID 2.0 (UID 2.0) or LiveRamp’s RampID. In data clean rooms, data is matched in privacy-compliant ways so that businesses always remain in control of their data and personally identifiable information (PII) is never exposed.
Why data clean rooms?
As we near third-party cookie deprecation, data clean rooms present marketers with a privacy-safe, cookieless way to aggregate and enrich their data sets.
Take LiveRamp, for instance. Their vast network enables marketers to aggregate various data sets like CRM, ad server, publisher, and audience data via their Safe Haven enterprise platform. This privacy-safe data clean room allows for holistic, deep-level data analysis. Combining these data sets enables businesses to gain profound insight into the customer journey. This intelligence allows organizations to improve their audience modeling, enhance their targeting strategies, and analyze consumer trends. Furthermore, with LiveRamp’s RampID, businesses can combine the buy and sell sides of the ecosystem in one centralized and privacy-safe environment.
While data clean rooms may present a powerful option for navigating identity and privacy changes, organizations must perform their due diligence to understand which solution (or combination) will best equip them to navigate a cookieless, privacy-first world. Despite their potential, data clean rooms are not a one-size-fits-all solution.
Businesses with rich first-party data sets, like D2C brands and publishers, stand to reap the greatest benefits of using data clean rooms because they rely on first-party data. Furthermore, large companies with robust data assets will find data clean rooms easier to implement than smaller, leaner organizations. And, of course, the larger the data set, the more resources are required — specifically those with technical knowledge and skill — to implement the solution. Once in place, organizations must dedicate the appropriate resources, like data scientists, to examine and extract data insights. While data science teams are likely the most affected by data clean rooms, they’re not the only teams impacted. Clean rooms require agreements with each partner participating in data sharing, putting added strain on legal and partnership teams as well.
Promise, potential, and possibility
Partnership is a critical component of data clean rooms. While this may lend itself to obstacles, it opens the door to teamwork and cooperation across the entire digital ecosystem. As privacy-safe data sharing becomes increasingly popular and data clean rooms pick up steam, the industry will evolve, becoming more collaborative and making exchanging and leveraging data as frictionless as possible, opening the door to even more possibilities.
Working with a data-first company that understands evolving trends is critical to implementing and leveraging new technologies that drive business outcomes. With the right support and partners like Digilant, even smaller or new organizations can implement and leverage data clean rooms to reap the same benefits as large enterprises.