Data has been termed the ‘new oil’ of the digital age, but it is more than that. It’s the lifeblood of modern businesses, and its efficacy is dependent on how it’s refined, shared, and utilized. But as data privacy regulations tighten around the world, it’s getting harder and harder to tap into the full potential data can offer without compromising security. Enter the world of data clean rooms and data collaboration.
At Lotame, we are always at the forefront of next-generation data solutions, and in this piece, we take a deep dive into the realms of data clean rooms, their significance, and how they’re reshaping the digital advertising landscape.
A data clean room serves as a secure environment where companies can analyze two separate data sets without directly accessing it. Instead of merging raw data sets, different entities can derive collective insights from their data without any risk of revealing personal data or proprietary information.
Here’s an analogy from our friends at U of Digital that might help clear things up:
Think of a company hiring a contractor to examine employee data. The contractor isn’t allowed to view individual records. Instead, an HR representative, acting like a data clean room, accesses and relays the needed information. For instance, for an average salary query by city, the HR provides the answer. This ensures sensitive data remains untouched while delivering essential insights.
Data clean rooms pave the way for secure data collaboration. In the world of marketing, two heads (or data sets) are always better than one. Data clean rooms unlock potential that isolated data sets can never achieve.
Why is this collaboration imperative now? Here’s a breakdown:
Data clean rooms are a data collaboration tool that have generated a lot of hype in the industry. But not all are created equal, with several different flavors on the market. Understanding the different types of data clean rooms and their strengths and weaknesses, can help you decide which data collaboration tool is right for your business.
Here are the four main types of data clean rooms.
Platforms primarily acting as data warehouses or clouds often incorporate clean room capabilities. The stored data in these warehouses can be privately transferred into the clean room for collaboration, particularly for attribution and measurement. Tailored solutions can be designed on these platforms, given that the participating enterprise possesses robust engineering and data science resources.
A Walled Garden clean room operates within a closed ecosystem. These are often created and managed by major tech companies such as Google and Facebook. Within these walled gardens, marketers have the opportunity to merge their proprietary first-party data with the consumer data that resides exclusively within the confines of the clean room environment provided by the walled garden. This setup ensures data privacy by limiting the data’s movement outside the specific environment, but it also gives these big tech players significant control over data access and use.
In these platforms, marketers import their data, merging it with a common identifier, which could be anything from hashed emails and mobile ad identifiers to Universal IDs, underpinned by an identity backbone. This enables a private, secure collaborative environment. These platforms excel in data analysis, enrichment, modeling, and activation. Unlike walled garden clean rooms that only function within their own ecosystem, data collaborations offer a more expansive range of utilities. They are designed for marketers looking to derive insights and activate seamlessly.
Query clean rooms serve as a neutral territory for entities that wish to cooperate on their first-party datasets with other stakeholders, like a publisher collaborating with a brand. What distinguishes these cleanrooms is the “Non-Movement of Data” feature. Instead of moving and storing the data in a central location, these platforms allow brands and other entities to keep the data within their own environment. Collaborating parties can run queries and analyses without the data ever leaving its original location, ensuring both security and control over the data. Tech savvy marketers, with the right training, could find this tool valuable, but it’s primarily owned by a businesses’ tech team. One of the biggest applications of this type of clean room is discerning overlaps. They accept any common identifier available to both parties, which can limit scale of the outcome.
As with any new technology, data clean rooms have challenges that users should be aware of:
In a nutshell, while data clean rooms offer promising solutions in the realm of data collaboration, it’s essential to be aware of the existing shortcomings in the current offerings. It’s also critical to note that data clean rooms are not a comprehensive replacement for third-party cookies. They are simply a tool to be used in a marketer’s diverse tool set.
There’s a lot of industry hype surrounding the data clean room. Not every tool is created equally, and with so many different variations and platforms out there, choosing the right one can feel overwhelming. So here’s a few items to consider across these critical categories:
Understanding data clean rooms and the importance of data collaboration is just the beginning. The true value of collaboration becomes apparent when we delve into its applications, especially for marketers and media owners.
Marketers have always sought to understand their audiences better, and data collaboration enables just that. Let’s explore how:
Media owners and publishers stand to gain significantly from data collaboration:
While data collaboration and data clean rooms are a valuable tool in the marketer’s arsenal, they cannot singularly replace third-party cookies. Third-party cookies offered an “easier” way to associate user behavior to the device or domain they were exhibiting it on, and persist that information across sessions and time. Clean rooms rely on first-party data tied to a common identifier, which tends not to connect into activation platforms in a scalable way that could replace what third-party cookies have offered.
Instead, businesses need to adopt a multi-pronged approach, combining the strengths of data clean rooms with other solutions like first-party data management, audience modeling / enrichment, and identity solutions. This comprehensive strategy will ensure that marketers can continue to engage their audiences effectively while respecting user privacy in the post-cookie era.