Data clean room: partner data sharing without transferring personal data

Why an operator needs a clean room

The operator has one of the richest behavioural datasets in the country: location patterns, payment behaviour, device data, app usage. This data is valuable to partners (banks, insurers, retailers, media) for joint profiling and targeting.

But direct data transfer to a partner is not possible:

• Regulatorily (data protection law).
• Commercially (the partner gets an asset that belongs to the operator).
• Ethically (the customer never gave consent for transfer to a third party).

Data clean room is an architecture in which two (or more) parties jointly process their data in an isolated environment, getting only the agreed-upon analytical result as output, without cross-disclosure of source data.

Structural elements

Isolated compute environment. A zone in which both partners’ data is encrypted at rest and in motion. Neither side has direct access to the other’s raw data.

Identity matching layer. Privacy-preserving matching: hashed identifiers, secure multi-party computation, differential privacy. Parties match customers without revealing full identity.

Query interface. Approved queries (agreed upfront) — for example, “size of the intersection segment that spent >X in category Y, range buckets”, with aggregated output, no individual records.

Output controls. Minimum segment size (for example, no fewer than 100 people), suppression rules, output review before release.

Governance layer. Data Use Agreement (DUA) with consent attestation. Audit on each query: who, when, what was asked, what was returned.

Technology layer. Existing cloud-native solutions (AWS Clean Rooms, Snowflake Clean Rooms, Habu, InfoSum) or build on secure multi-party computation.

Use cases for an operator

Joint segment analysis with a bank. “How many of our premium customers use a specific banking product”. Output — sizing, not individual mapping.

Audience extension for an advertiser. “Find an audience similar to your customers”. Look-alike modelling without disclosing the source list.

Attribution for an advertiser. “How many of the people we showed your ad to converted to a purchase”. Without sharing individual tracking.

Risk modelling with a bank. A joint credit risk model trained on data from both sides without exchanging raw features.

Where it usually breaks

The clean room is launched without a clear commercial rationale — partners do not bring data because they do not see mutual benefit.

Identity matching works poorly — match rate <30%, partners see useful insights are minimal.

Approved queries are not defined upfront — every request becomes a negotiation that takes weeks. Partners lose interest.

Output controls are too strict — every query returns “not enough data”, or too loose — discloses individual data.

Governance overhead is high — every use case needs sign-off from 5 stakeholders. Adoption freezes.

Regulator interpretation is unclear. Clean room is technically privacy-preserving, but the regulator may require full consent. Without legal clarity — risky.

Operating model

Owner — Head of Data Partnerships or CDO. Partnership-side, not purely tech.

Teams:

• Platform engineering (clean room infra)
• Data legal (DUA, consent, regulator interface)
• Partnership commercial (deals with partners)
• Use case product management (what we build, for whom)

Routine — quarterly review across active rooms: utilisation, business value, regulatory issues.

What is measured

Active partner rooms — how many partnerships are running simultaneously.

Average match rate — how well identity matching works.

Queries per quarter per room — utilisation.

Revenue per room — commercial output (subscription, query-based pricing).

Audit incidents — governance breaches, false positives.

How SamaraliSoft engages

Clean Room Blueprint — 8-10 weeks. Sizing partner opportunity, governance/legal framework design, technology choice, pilot use case with one partner. This is not purely a technology project — a large part of the work is the commercial and legal framework.

Related

• /en/architecture/telecom-cdp-architecture/ — CDP as source
• /en/architecture/telecom-consent-architecture/ — consent layer
• /en/insights/telecom-data-monetization/ — data monetisation
• /en/solutions/telecom-merchant-acceptance-platform/ — merchant data