Interviews, insight & analysis on digital media & marketing

Why your data scientists should be using a clean room

By Luca Bocchiardi, Director of Product Management, Cuebiq

Data is the lifeblood of any organization in any industry. Consider these facts and figures:

But the age of consumer privacy has wreaked havoc on how data can be used across a variety of use cases, from marketing through to inventory management to financial modeling. Organizations are now struggling to use their data to make both immediate and long-term decisions that could impact the very survival of their business. The way forward is rooted in enabling data science and other teams in an organization to be able to collaborate on data-driven solutions in a privacy-safe way. 

Anonymizing data is hit or miss

One much-discussed potential solution to address the impact of privacy changes is anonymizing data. But that simply doesn’t work for all situations. For example, look at location data. 

Historically, it’s been common practice for location data providers to directly share anonymized and privacy-enhanced data with researchers, who would then hold copies of the data and perform analyses locally. To protect sensitive locations, such as residential areas, the data must be shared under strict licensing agreements, where the data itself is pre-processed to add privacy-preserving noise. As a result, any analysis on changing rates of social mixing in residential neighborhoods is impossible.

Platforms that won’t fill the gap

Customer data platforms (CDPs) are often talked about as a way to securely store and scale a company’s first-party data for marketing purposes in a future without third-party cookies. However, they often focused on user-level data or identifiers. While some CDPs might offer basic audience overlaps, they do not enable collaboration with or analysis of another company’s data in a way that is both secure and protected.

Cleaning up your data house

Another solution for ensuring privacy across a range of data sets is a data clean room. 

Clean rooms are secure, collaborative environments in which two or more parties can share data. They attempt to balance availability of data with legally required protections of that data by centralizing the effort and expense of compliance, lessening the burden on individual companies to build their own internal controls just so they can use their (or others’) data in ways that advance their business. This helps prevent companies that use the clean room from incurring the threat of penalties and reputational risk that stem from non-compliance with regard to privacy and security. 

A recent report by Merkle found that 61% of organizations plan to increase their investment in data clean rooms to better connect and utilize data across distributed systems, while 23% will use them due to privacy-related changes.

What data scientists can achieve with a clean room

There are a variety of uses cases for data scientists using clean rooms:

  • Build custom audiences: Data clean rooms can be used to build custom audiences, allowing marketers to fine-tune their ad targeting.
  • Run advanced analysis: Data clean rooms allow organizations to conduct in-depth analysis on combined data sets to gain insights on customer behavior, segmentation, customer lifetime value and more.
  • Predict how certain triggers affect product usage: Data clean rooms let organizations understand how certain events, such as weather or traffic, can affect product usage.
  • Analyze your next site location: Understand the customer base of specific locations based on competition, adjacent location visits and demographic and socioeconomic characteristics.

With the use of data clean rooms, data scientists no longer have to ask how they can derive important insights without sacrificing the privacy of the users who entrust others with their data. And lawyers can sleep well knowing that compliance is certain. 

With privacy legislation on the rise, every company needs to find alternative methods for their use of data. Plain and simple, data clean rooms are the future of the data industry. With the majority of businesses looking to invest in data clean rooms, if your data science team isn’t already testing out this technology, you are falling behind your competitors. Save not just your data but also your business by exploring data clean rooms as you start planning for 2023.