Secure data collaboration & safe monetization without raw data sharing
Enable governed partner analytics, media measurement, and data monetization without handing over raw datasets.
Modern cloud platforms (GCP, Snowflake, Azure) support secure data sharing, but basic sharing lacks strict analytical governance. We help you build the technical governance layer that turns your native cloud capabilities into a controlled clean room environment.
Your partners get a secure workspace to run approved queries, while you retain absolute control over raw data access, permitted logic, aggregation thresholds, and final outputs.
Key outcomes:
Reduce dependency on raw data exports.
Give partners controlled analytical flexibility.
Build on your existing cloud stack.
Start with a scoped MVP before committing to a full rollout.
The hook & the problem
Traditional data sharing creates unnecessary risk and friction. Companies want to collaborate with advertisers, retailers, and data buyers. But sharing raw data is becoming impossible to justify to security and compliance teams.
Traditional approaches force an uncomfortable trade-off:
Raw exports are flexible but risky:
Once CSVs or replicated tables leave your environment, you lose control over who copies, joins, or reuses the data.
Static reports are safe but limited:
Dashboards don't give partners the flexibility to filter segments, test hypotheses, or run custom analysis.
Basic cloud sharing isn't always enough:
Native sharing (even zero-copy) still needs an additional governance layer for join restrictions, aggregation thresholds, and privacy rules.
Custom pipelines don't scale:
Building separate masking logic and one-off exports for every partner is operationally expensive.
Who this is for
Business Teams: Exploring data monetization.
Marketing Teams: Measuring campaigns with publishers or retail media.
Data Teams: Building governed collaboration on GCP, Snowflake, or Azure.
Legal & Security: Needing concrete technical controls to review.
Why clean rooms are becoming bractical now
You do not need to buy a proprietary platform to build a secure collaboration space. The required technology is often already available in the stacks you own (GCP, Snowflake, Azure).
A clean room changes the paradigm from “sending data to the user” to “bringing approved analysis to the data.”
Zero-copy patterns:
Share or analyze data without copying raw datasets to the partner.
Governed joins:
Match datasets under defined identity controls and output restrictions.
Output Governance:
Only aggregated results or approved KPIs leave the environment.
Why clean rooms go beyond basic cloud data sharing
Feature
Basic / Native Data Sharing
Data Clean Room
Data Movement
Often zero-copy within platforms like Snowflake or BigQuery
Also uses zero-copy or limited-movement patterns where possible
Access Model
Consumers get access to shared datasets, tables, views, or listings
Consumers get governed access to approved analysis workflows
Flexibility
Mostly based on platform permissions and shared object controls
Adds query rules, join restrictions, aggregation thresholds, and output controls
Privacy Protection
Requires custom masking, anonymization, or pre-aggregated views
Privacy and governance controls are designed into the analytical workflow
Output Control
Limited unless custom-built
Defines what results can leave the environment, such as aggregated KPIs or approved outputs
Best For
Internal sharing, trusted partners, controlled dataset access
Sensitive collaboration, partner analytics, media measurement, and data monetization
Where companies use them
Data Monetization & Insights
Replace rigid static reports with a governed workspace. Buyers securely query and discover insights flexibly, without ever accessing or downloading the underlying raw rows.
Marketing & Media Measurement
Match advertiser and publisher datasets using privacy-conscious joins. Deliver aggregated campaign attribution metrics without exposing PII or raw customer lists.
Retail Media Networks (RMN)
Enable CPG brands to analyze sales performance directly within a governed retailer environment, optimizing ad spend while protecting customer privacy.
Multi-Party Collaboration
Securely join data from multiple organizations to uncover macro-level patterns, while keeping each company’s competitive raw data mathematically protected from direct exposure.
How Your team can start safely
You can reduce months of theoretical planning by starting safely with a scoped technical assessment and a targeted MVP.
- The Problem: Designing secure environments from scratch is complex and prone to misconfiguration.
- What We Do: We deploy an end-to-end “compute-to-data” framework inside your existing infrastructure (e.g., BigQuery Analytics Hub, Snowflake Clean Rooms, Azure Confidential Computing).
- Business Outcome: A scalable, IT-approved architecture based on concrete technical controls.
- The problem: Accidental exposure. A clever SQL statement can potentially expose PII or sensitive records.
- What we do: We engineer strict boundaries directly into the query layer, including query constraints, column-level masking, and minimum aggregation thresholds.
- Business outcome: Guaranteed analytical depth for partners, with raw records strictly protected from exposure.
- The problem: Native clean rooms lock you into one vendor. If your partner uses a different cloud, the project stalls.
- What we do: We assess and design secure pipelines, managed compute layers, or federated query models to bridge the cloud gap safely.
- Business outcome: Execute high-value partnerships regardless of your partner's cloud infrastructure.
The Datumo clean room delivery framework
Why work with Datumo
Datumo is a big data technology consultancy focused on delivering working cloud data solutions. Clean rooms sit at the intersection of data engineering, cloud architecture, security, governance, and analytics. These are the areas where Datumo has deep implementation experience.
We bring:
Experience across GCP, Azure, and Snowflake, supported by partnerships and certifications
Strong background in data platforms, pipelines, governance, and cloud architecture
Practical delivery focus, from assessment to working MVP
We do not only define the strategy. We help you build the environment, test the controls, and hand it over to your team.
FAQs
Timelines vary based on data readiness, cloud environment, and partner requirements, but a focused MVP can typically be scoped quickly and implemented in weeks when the business question and datasets are clear.
No. We implement your solution using existing enterprise technologies like Google Cloud, Snowflake, or Azure.
Often yes, but the right architecture depends on the platforms involved, data sensitivity, latency requirements, cost, and governance model. We assess the practical options and recommend a feasible approach.
We handle the heavy lifting of architecture and implementation. We require your IT team primarily for access provisioning, platform decisions, and final security reviews.
No. Datumo does not provide legal advice or draft data-sharing agreements. We implement the technical privacy, access, and governance controls that your legal, compliance, and security teams can review. Your legal team must approve the final data-sharing agreements.
Final action
Start with a clean room implementation consultation
Explore whether a data clean room is the right technical model for your use case. We can help you understand:
Which architecture fits your cloud stack.
What data and access controls are required.
What risks and dependencies to consider.
What a scoped MVP could look like.
Fill out the form and receive the no-obligation consultation
Have a question or want just to talk?
Rafał Rozpondek
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