Snowflake Data Clean Rooms

Snowflake Data Clean Rooms.  In this article I will explain what a Snowflake Data Clean Room really is on the Snowflake Data Cloud.  Once you have a good grasp of what it is then we will cover Data Clean Room example use cases.  We helped Snowflake pioneer this new offering a couple years ago with our client VideoAmp which we brought over to the Snowflake Data Cloud.  Our original article back in July 2020 – Shows how to analyze PII and PHI Data using the earlier Data Clean Room concepts.  Fast forward 2 years and now Snowflake has dramatically improved the initial version and scope that we put together.

What is a Data Clean Room?

Data clean rooms on Snowflake from a technical view are currently a set of data related technologies (Data Shares, Row Access Policies, Secure User Defined Functions) that work best on Snowflake to enable double blind joins of data.  This allows Snowflake to power Data Clean Rooms because it has the underlying Data Sharing technology which is based partially on Micro-Partitions. that provide features like Data Sharing and Data Cloning.  From a business standpoint, once the complexity of operating a data clean room gets easier this can provide just HUGE value to businesses by sharing data without sharing the PII part of the data.

Some of the original concepts of data clean rooms (DCRs) were around data exchanges/areas that the huge internet behemoths like Facebook and Google could share aggregated data.  The concept was to share aggregated (non-PII discernible) data with their advertisers.  So if you were an advertiser you could put in your first-party data and then see if it matches on some non-PII aggregated data. ). My view though expands the concepts of Data Clean Rooms way beyond just Media/Advertising.  There are many other areas that can achieve huge value in being able to perform “controlled” and “governed” double blind joins of data sets.

Other ways to describe a data clean room for better or worse is it is really a concept where companies and their partners can share data at an aggregated double blind join level.  (On Snowflake, its already extremely easy to share data through secure views and tables with their ground-breaking Data Share technology.  One of all-time favorite features.).  You can share double blind join previously agreed identifiers.

What are Snowflake Data Clean Room Use Cases?


  • Solving our “end of cookies” problem at some level.  Data Clean Rooms on Snowflake allow Advertisers to take their first-party data and combine it with  their publisher(s)’ viewership/exposure data for measurement of their marketing dollar.
  • Co-branding/co-marketing Promotions.  You can do customer segment overlap analysis to see where your partners customer bases have similar customer segments. and audiences.
  • Similarly, you can work with partners to do joint loyalty offerings and/or upsells where customer “interests” overlap.

Healthcare and Life Sciences:

  • There are some extremely valuable use cases where we can securely share patient data and patient outcomes across government, healthcare, and life scienes to hopefully make some huge leaps forward in healthcare and life..

Those are just a few.  There are many others.

Are you interested in how you can use a Snowflake Data Clean Room for your business?  Contact Us Today.

Want more info from others on Data Clean Rooms:

*Check out Patrick’s article here:
*Also, my friend and one of the top data clean room experts – Rachel shares some Q&A here on DCRs:
*Lastly, there is a great video here from a Snowflake Solution Architect who I met but never accepts my linkedin invitation. ha ha.

Have fun!

Also, here is an interview I provided on my view of the opportunities around Data Clean Rooms.



Analyzing PHI & PII with Snowflake’s Data Clean Rooms

Introduction – Data Clean Rooms

Sharing data can be tough. Organizations struggle to effectively manage their data internally. This problem only increases in magnitude for data sharing across multiple organizations. To make matters worse, regulations on sensitive data (PII and PHI) further complicate the process and this type of analysis is decided to be too troublesome to engage upon.

However, that’s a huge loss in opportunity. Since the business intelligence insights gained from analyzing personal health and sales datasets can be transformative to your business direction and decisions. Luckily, there is now a way to gain access and analyze this data while abiding by security laws.


Data Clean Room

A data clean room is a place to perform join analysis on sensitive data while abiding by regulations. The clean room can be set up by anyone but it will most likely be the provider. A clean room can have multiple data providers. Each provider has the ability to control:

  • Incoming Data
  • How their data can be joined with other data
  • Types of analytics that can be performed on their data
  • Outgoing data

This type of data hosting and analysis is made possible by several key Snowflake features: secure data shares, the data marketplace platform,  secure functions, and secure join capabilities.

A consumer in the data clean room has the ability to access the providers data through defined functions, joins, and queries the provider specifies. Data masking (hashing) can provide an extra layer of security so that no naked identifiable data is ever transferred between provider and consumer.


Case Analysis – Advertising to the NBA

I am the lead marketer for Weight Loss Champions. We sell weight loss pills. We want to launch an advertising campaign targeting NBA All Stars to use and endorse our product.

Company: Weight Loss Champions

Product: Weight Loss Pills

Marketing Hypothesis: Quarantine has forced a lot of people indoors. Gyms are closed. Many people are drinking and not exercising and have put on weight. We see this as a great opportunity for our business. We also realize that the NBA 2020 Season is set to restart in the Orlando bubble at the end of July.

Objective: Our goal is to get an NBA All Star to use our weight loss pills. Then try to get them to endorse our product.

Strategy: We have identified several places that these athletes will frequent, such as shopping center, restaurants. After doing a little digging, we discovered walkways between these areas that offer ad placements. We must decide what height on these walls to place our ads.

Let’s take a look at how we can accomplish this. The first step is establishing our data clean room.


How to Build Your Own Data Clean Room

Approximate time to creation is 8 hours. It took me 2 hours to submit everything to Snowflake and then 6 hours working with them to get the room up. With this guide I imagine you can do it even faster.


Make a Snowflake Account

Each participant will need a Snowflake account. Existing Snowflake customers can provide a secure sub-account. You can sign up for a 30 day free trial with $400 worth of credit here.


Submit a Support Case 

To submit a support case you will need to create an account on snowflake community and link it to your snowflake account. Once you create your account, follow these instructions to submit a case. Here’s what the case should look like.

After submitting your case, a Snowflake representative will contact you to go through the details and set up the exchange. This could take couple hours.

Create a Secure Share

A listing on your private data exchange comes from a secure share. When creating the secure share, you will specify what databases, schemas, and functions. A secure share can be made through the share tab, toggling to the outbound option, and then selecting create.

Read the full guide to Secure Shares here.

Create a New Listing

Navigate to the Data Marketplace and open your private exchange. On the left side menu it will be under Data -> Manage. From there you can create a new Listing by clicking the button on the upper right and selecting the Secure Share from before.

The provider should describe their data set here and provide documentation to how the data can be analyzed. This can be an ongoing process between provider and consumer. Where the consumer makes a request for a specific type of analysis and the provider then creates a secure function or share to fulfill that type of analysis.

Configure Roles and Access

Navigate to the Data Marketplace and click on the Admin tab on the left hand side. Click on your Private Data Exchange to configure the roles. Here you can add members and specify which accounts can be providers, consumers, and Administrators.


Analyze your Data

From here, the consumer can login on their account. They will be able to access the share and all the available schemas, tables, and functions provisioned by the provider. Here you can take a look at the share I created for a client and the functions made available. We’ll be using them later in the analysis.

For the full case analysis, check out the video below.