As data has become one of the most prized resources for companies around the world, two vital imperatives have increasingly butted up against each other in conversations among private companies, consumers, and government regulators — the desire to harness customer data for profit and the need to keep that data safe and secure in order to protect individuals.
Enter the data clean room, a data access control and management concept designed to balance privacy with utility to help securely maximize customer data use.
How do data clean rooms work, why do they matter, and how are they used? We’ll answer all of those questions and more in this guide.
What is a Data Clean Room?
A data clean room is a place where organizations can aggregate customer data from different platforms or lines of business and combine it with first-party advertiser data to analyze and provide insights in a way that still enforces legitimate security controls.
Imagine a room on the other side of a door that you can’t see past. The room is filled with people whose identities you don’t know, but you’re allowed to slip questions through the door and receive generalized responses from the people in the room. Through this process, you get an aggregate sense of the group on the other side of the door, without them revealing any personally identifying information (PII).
This is the general purpose of a data clean room. It allows brands to safely acquire information about how ads are performing on a certain platform, and use that data to evaluate their campaigns, improve their audience targeting, and optimize their ad spend on that platform.
In the case of agency data solutions, multiple platforms may aggregate data into a larger data clean room. This allows clients to determine the efficiency and effectiveness of their ad campaigns and audiences across multiple platforms, rather than just one.
Why Do Data Clean Rooms Matter?
There are many reasons why brands and marketers are excited about data clean rooms, but they all come down to attribution and security.
Marketers use data clean rooms to refine their audiences — specifically, to determine whether they’re sending the right messages to the right people at the right times. Data clean rooms can help organizations determine whether they’re successfully serving the same messages to the same people, or whether they need to fill gaps in their audience with untapped potential customers.
Data clean rooms have also exploded in popularity for another reason — increasingly stringent data privacy laws. No company wants to be the next Cambridge Analytica with a massive data breach scandal, but they still want to benefit from the insights customer data can provide. Data clean rooms provide a middle ground where platforms can keep the valuable audience targeting information, without compromising data security. They allow companies to stay compliant, competitive, and on top of their audiences all at once.
One of the most desirable qualities of data clean rooms is that brands have complete control of the clean room environment, so they can use protected data as needed, without limitations.
How Does a Data Clean Room Work?
The first step of creating a data clean room is to compile first-party data at the user-level and load it into a secure environment. These environments may also be loaded with other sensitive information, such as transactions or historical data.
Once data goes into the clean room, it’s encrypted, secured, and protected from any access beyond what is explicitly authorized by the data owner. Only the brand itself has unlimited access to the clean room, while their clients receive output in the form of fully anonymized data that’s compliant with all regulations.
[How To] Manage Databricks Data Clean Rooms
Data Clean Room Use Cases
Here are some of the most common use cases for data clean rooms.
User-level data can be anonymized in a data clean room so that it can be securely used for measurement and other tasks. Common anonymization methods, such as encryption, hashing, and others, help ensure that data remains secure before, during, and after use.
Customer lifetime value (CLV) reporting is one of the most well-known analysis use cases for data clean rooms. It allows for user-level analysis of customers across a range of metrics, resulting in an overall assessment of how valuable that customer is, not just in terms of their specific purchases, but over the course of their entire relationship with your organization.
Thanks to the security provided by data clean rooms, these CLV data can be analyzed in a secure environment that retains the anonymity of all involved users.
Data clean rooms can help reduce the need for manual data privacy processes by incorporating automated solutions. This approach avoids draining company resources — without sacrificing effective privacy.
Data clean rooms aren’t just useful for helping ensure data regulation compliance — in many cases, they can be an essential part of the equation. A data clean room is an efficient way to meet the varied data security compliance laws and regulations required by industry, state, and federal data regulations — even far reaching compliance laws like CPRA and GDPR — by creating a secure environment where important data points are available but separated from the identifying information that’s protected by these regulations.
Drawbacks of Data Clean Rooms
So, are data clean rooms a perfect catch-all solution that every company should adopt immediately? Not necessarily. There are some potential drawbacks.
Most notably, data clean rooms are new enough that universal standards haven’t yet been adopted for their implementation. That means that platforms and advertisers may be trying to pool data that exists in multiple formats, and the prep work that goes into aggregating those different formats can be time consuming.
Another roadblock is that advertisers aren’t always eager to offer their data. For most, it’s a privacy concern — offering up transactional data in the service of a relatively new environment could prove harmful in the event of a data breach that exposes their customer data and could decimate their reputation.
Finally, poorly managed clean rooms involve substantial manual input — including emailing data sets or sharing folders. This could pose a serious privacy risk that cannot be ignored. Therefore, data clean rooms should be as automated as possible to reduce human touchpoints.
Best Practices for Your Data Clean Rooms
Whether you’re implementing a brand new data clean room or are looking to improve your existing solution, there are several best practices you can follow to ensure that you get the most value possible out of your data clean room.
First, design your data clean room with consumers in mind — not just for the present, but for the future. The best clean rooms are set up to anticipate how consumer behaviors will shift. Meanwhile, the faster you can reach an audience, the better — that makes automated audience activation essential.
The ability to analyze consumer behavior in real time is equally essential. The insights you glean from an effective data clean room will be invaluable.
Finally, never forget the overall goal of a data clean room — to provide you with a real understanding of how marketing is making an impact, and how to optimize it.
Are you looking for a cloud data access control tool that will help you implement data clean rooms within your organization to help make data safer, more secure, and more powerful for analysis? Immuta offers self-service cloud data access with automated privacy control that’s perfect for enhancing compliance, privacy, and anonymization in data clean rooms. Our other key features include:
- Universal Cloud Compatibility
- Attribute-Based Access Control
- Sensitive Data Discovery & Classification
- Dynamic Data Masking
- Data Policy Enforcement & Auditing
Find out how Immuta’s data engineering uses data clean rooms in our blog on dogfooding data governance.
Ready to learn more? Request an Immuta demo today.