Data-driven brands and marketers are increasingly relying on data clean rooms to deliver on the promise of modern marketing while respecting consumer privacy and complying with regulations.
Yet, unless we tackle the "messy data" problem, the way clean rooms are currently being used will actually hinder insights and ROI.
The root of the issue often lies in the metadata. Standardizing and applying the right data naming conventions and taxonomies are how marketers can make the best use of their data sets in data clean room environments.
Industry developments, such as the deprecation of third-party cookies and other identifiers, have increased marketers' interest in and need for data clean rooms. Clean rooms were a hot topic at this year's CES conference, and many top platforms announced clean room capabilities.
A common misconception, though, is the limiting context in which data clean rooms can be used. A brand doesn't have to make them its sole purpose or focus to reap their benefits.
Consider Google, Facebook, or Amazon. All are becoming major players in the clean room sector. Amazon's new clean room system even expands on the typical clean room services.
Other platforms and publishers are also launching clean rooms that enable data sharing in a privacy-forward manner. Pinterest, for example, recently launched its own clean room, citing the advantage of being able to adhere to the strict privacy requirements of media owners as well as the analytics needs of retailers.
As clean rooms become more pervasive and agencies and brands increase their reliance on them, it will be all the more imperative for organizations to deploy clean rooms correctly.
One critical requirement is data standards. Movements to create clean room standards have begun; in the meantime, here are a few ways data standardization will help solve data clean room problems.
1. Standardized data connects fields across data sets
Across all verticals, data consistency is becoming a must-have. That is directly applicable to the requirements of clean rooms, where inconsistent and unstandardized data is rampant.
Having multiple versions of the same data elements can yield unclear and unhelpful conclusions, leading to much larger problems down the line. Without adequate data standards to help make a brand's first-party data more coherent, marketers may find themselves back at square one.
Standardized data and consistent naming conventions give those who handle data the ability to connect data across data sets. Rules must be established and followed that will optimize the clean room's use for that business.
With those rules in place, data from one clean room can be compared and contrasted with data from another source. Moreover, marketers won't worry about having to normalize attributes as they've had to in the past.
Marketers tend to misstep when trying to achieve high-quality data outputs by increasing the amount of time they put into adding inputs. However, if the inputs aren't consistent across data sets, the conclusions drawn from them won't be trustworthy, and thus they are unusable.
In such cases, it is a better allocation of resources to devote time to standardizing data as opposed to focusing twice as hard on fixing faulty outcomes.
2. Connected attributes help build targeted audiences
Even if consumers' personally identifiable information (PII) is not being used in campaigns, connections across other attributes still offer marketers a chance to build targeted audiences and further personalize experiences. In the absence of consumer PII, that type of analysis and conclusion-gathering is possible only if those attributes can be connected across systems. Without an understandable, foundational naming system in place, data becomes unusable when compared across sets.
That's where the application of data IDs (or keys) is valuable. Data IDs, paired with standardized metadata, give marketers the ability to join data together that otherwise wouldn't have congealed, including in data clean rooms.
There's a variety of areas where IDs can be implemented, including campaign IDs, audience IDs, creative IDs and more.
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As data-driven brands and marketers continue to tap into the advantages that come with using clean rooms, measures need to be taken to avoid inaccurate, incomplete, or otherwise erroneous data. Considering the privacy-centric future on the horizon, that needs to be a priority if data clean rooms are to be successful.
To fully embrace how data standards could benefit data clean rooms and alleviate common issues, marketers should first align on and apply the right data naming conventions and taxonomies. Efforts to create industrywide clean room standards have begun, but in the meantime organizations will need specific in-house taxonomies to adhere to. Focusing on the foundation will help ease time needed on adjusting the inputs.
Data consistency has evolved from a nice-to-have to a need-to-have, and it is crucial to not only put standards in place but also make sure similar fields across different data sets are governed under the same standards. Once the full potential of a data clean room solution is realized and implemented, you'll be better prepared to take advantage of it.
More Resources on Data and Clean Rooms
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