Data Clean Rooms: Empower Data Collaboration and Protect Privacy
Data Collaboration: Clean rooms address the 4 major challenges facing marketers and publishers in a cookieless, privacy-centric world.
Infrastructure is a trending topic these days. New spending for roads, bridges, public transportation, power grids, and other physical infrastructure is being considered in the chambers of Congress.
There is general agreement that massive investment is urgently needed because our aging existing infrastructure cannot meet the challenges brought on by the transition to a digital economy, the explosion of e-commerce, and the impact of climate change. As a result, our productivity and America’s competitive advantage in global business is at risk.
All the attention on infrastructure inspired me to think about the business tools that will be needed to drive the future of digital advertising. Like our nation’s infrastructure, we are at a critical moment of flux. Changing consumer attitudes about privacy mean that we have to re-architect everything we’ve done for the past two decades. It might be hard to imagine a functioning digital advertising ecosystem without third-party cookies and mobile ad identifiers, but that’s what we face.
We have to come up with new frameworks and standards for addressable digital advertising because consumer engagement will require more customer data and more effective management of that data moving forward. Marketers and publishers can start building the foundation with a technology that already exists but isn’t widely used: data clean rooms.
Think of data clean rooms as the blockchain of marketing and analytics. Clean rooms work in distributed data environments by providing secure platforms where multiple parties can share anonymized offline and online consumer data without ever exposing the raw data to any other party. Sensitive customer information also never moves between databases, eliminating the risk of data leaks.
Clean rooms address what I see are the four major challenges facing marketers and publishers in a cookieless, privacy-centric world.
Centralizing customer data management:
Brands have collected a lot of information about their customers, including personal data, engagement data, behavioral data, and attitudinal data. To be sure, data management platforms (DMPs) are good central hubs for gathering, organizing, and sharing customer, audience, and marketing data. But these platforms have their limitations. For one, DMPs don’t illuminate the full customer journey across the devices and offline touchpoints because they generally only analyze ad performance from digital channels. DMPs also weren’t built for the new era of privacy.
Implementing data governance and privacy:
Marketers don’t have effective controls over and visibility into sensitive personally identifiable information, which makes compliance with the growing raft of strict data privacy regulations problematic. While many have heard of privacy preservation and understand its general principles, deploying it in real life remains highly technical. As a result, very few marketing professionals to date have actually put privacy-preserving techniques to the test. The whole purpose behind clean rooms is to protect consumer personal data through various privacy-preserving methods. If the information from a clean room leaks out, no problem. There are no personal identifiers associated with any of it. The data can’t be connected back in any way to any source of personally identifiable information.
Carrying out strategic data collaboration:
There is growing consensus that marketers will need to enrich their data or access to more first-party data than they currently own to reach people with relevant ads — and measure the results. Matching first-party data with additional consumer behaviors, transaction data, and granular measurement data can involve several parties. But privacy concerns, coupled with the need to protect proprietary data, make it hard to write data-sharing agreements for mutual benefit. Marketers cannot get access to the data they need, nor monetize the data they have, due to ineffective and incomplete protection of sensitive and personal information. With privacy preservation, clean rooms enable the trust that is needed in any collaboration. Most clean rooms allow for data from one clean room to be compared with another, again without exposing the customer data. This means an advertiser can theoretically compare their data with a data provider or publisher to see if they share the same users and in which categories.
Opening the machine learning black box:
Current customer data management tools don’t accommodate custom modeling by data science teams, which limits their flexibility and marketing applications. Today, privacy-centric insights are mostly limited to audience sizing and overlap. Multi-party clean rooms open doors for more advanced analysis, such as multi-touch attribution. Advertisers want to measure performance along a customer’s journey, but the bulk of the data along that journey is protected behind multiple walled gardens. In a clean room, walled-garden data can come together with data from other platforms. Then, advertisers can extract valuable information about consumer engagement across all touchpoints, channels, and gardens.
Although privacy-first data clean rooms are not an entirely new concept, they haven’t been widely adopted in practice. Google and Facebook, for example, have cloud environments where stored aggregated data is accessible to their largest advertisers. But organizations find this limiting because they can’t join the data to build a full user journey to execute specific addressable media plans across channels.
But with data privacy the only way forward, marketers and publishers have to invest in the infrastructure to support their data-driven strategies. The environment reminds me of the current landscape for electric vehicles. To increase the adoption of EVs, we have also to build a cross-country network of charging stations. One won’t work without the other.
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