Author: Nikhil Ranade
What are clean rooms?
A clean room is an engineered space that is well isolated from the outside world and actively cleansed. Rooms are usually needed for scientific research or industrial production. The main purpose of designing such clean rooms is to keep the material inside safe from contamination and unauthorized access.
This blog will give you an overview of what data clean rooms are, why we use them, and some of their advantages.
Data Clean Rooms (DCRs) are logical environments that enable multiple organizations to collaborate on their data. While doing this, DCRs ensure that no data is shared outside the environment, keeping the PII data safe and secure. Historically in data clean rooms, all the data would be stored in a single physical location or room. There would be restrictions on who can enter that room and consume the data. Still, with the emergence of cloud technologies, these physical data clean rooms have become virtual data clean rooms where data is not stored in a single location but is spread across multiple systems. These are also called distributed data clean rooms.
Why data clean rooms?
In traditional data clean rooms, all data is stored in a single physical location, which limits how the data is shared. With the recent developments of cloud technology, distributed data clean rooms eliminate the need to move data from one location to another since the data can live in the cloud. This allows each partner to control its own data while enabling analytics with other partners, or even with multiple other partners, simultaneously, which will eventually enrich their own data.
Data clean rooms have control over the following which makes the data in the clean room secure:
What data comes in
How the data in one clean room can be joined to data in the other clean room
What types of analytics each party can perform on the data
What data, if there is any, can leave
As people are becoming aware of data privacy, we are steadily moving towards a cookieless world where data clean rooms can play an important role if we want to share the data by sticking to all the security guidelines.
When to use data clean rooms?
The applications of data clean rooms are mostly where we have overlapping/intersecting data.
Ex: Organization1 has some first-party customer data and is interested in some of the customer attributes which are present with organization2. Now if organization1 wants only those attributes which are present with organization2 without sharing other PII/Raw data, they can use data clean room for this purpose. Data clean room will not only help them keep their own data more secure but also will provide upselling/cross-selling opportunities to these organizations, eventually boosting their businesses.
The following are some of the applications of data clean rooms where it can be used effectively:
Audience Overlap: As mentioned above, the value in finding an effective matching strategy without exposing participating party data.
3rd Party Enrichment: Executing approved queries, a query can be designed to return non-sensitive plain-text data from the publisher (for example) to enrich the advertiser's own data.
Campaign Attribution: Measuring the effectiveness of the campaign after the campaign itself was published.
Lookalike Analysis: Adding features such as demography, interest, and purchase intentions, needed for feeding your machine learning model audience.
Benefits of data clean room
Get deeper insights into your data by finding intersections with other organizations.
Ideate different market strategies based on the insights, such as audience targeting to monetize this data.
Generate multiple revenue streams.
Develop multi-party partnerships and collaboration between different organizations.
Data clean rooms are a logical concept that can be implemented using Snowflake's features, such as Direct data sharing, Private data exchange, etc. They help organizations to link anonymized data (for, e.g., marketing and advertising) from multiple parties for attribution. Data clean rooms don't allow data points that could be tied back to a specific user to leave the environment, allowing organizations to adhere to privacy laws. It helps organizations/brands to expand their reach, which will help them generate more revenue, understand their customers better, and develop new strategies.