세계의 데이터 클린룸(DCR) 시장 : 예측 - 컴포넌트별, 전개 모드별, 조직 규모별, 기술별, 용도별, 최종 사용자별, 지역별 분석(-2032년)

According to Stratistics MRC, the Global Data Clean Rooms Market is accounted for $997.2 million in 2025 and is expected to reach $9748.3 million by 2032 growing at a CAGR of 38.5% during the forecast period. A Data Clean Room (DCR) is a secure, privacy-focused environment that allows multiple organizations to share, analyze, and collaborate on data without exposing personally identifiable information (PII) or raw data. It enables companies to combine datasets from different sources-such as advertisers, publishers, or retailers-while maintaining compliance with data privacy regulations like GDPR or CCPA. In a DCR, data is encrypted, anonym zed, and processed using strict access controls and aggregation techniques to ensure confidentiality. This setup helps businesses gain audience insights, measure campaign performance, and enhance data-driven decision-making without compromising user privacy or data security.

Market Dynamics:

Driver:

Rise of cloud infrastructure and scalable data platforms

Enterprises are shifting toward privacy-preserving collaboration environments that enable secure data sharing without exposing raw identifiers. Cloud-native clean rooms support scalable compute, granular access control, and real-time analytics across distributed datasets. Integration with CDPs, DMPs, and marketing automation tools enhances audience segmentation and campaign optimization. Demand for compliant and interoperable data collaboration is rising across digital-first enterprises and regulated industries. These dynamics are propelling platform deployment across privacy-centric data ecosystems.

Restraint:

High implementation cost and operational complexity

Clean room deployment requires investment in infrastructure, identity resolution, encryption, and governance frameworks. Integration with legacy systems and fragmented data sources increases setup time and technical overhead. Lack of standardized protocols and skilled personnel hampers configuration and cross-partner collaboration. Enterprises face challenges in aligning clean room architecture with existing analytics and compliance workflows. These constraints continue to hinder adoption across cost-sensitive and operationally complex organizations.

Opportunity:

Need for measurement, attribution, personalization in a post-cookie world

With third-party cookies deprecated, brands and publishers require privacy-safe environments to match audiences and measure campaign impact. Clean rooms enable deterministic matching, multi-touch attribution, and cohort analysis across first-party and partner datasets. Integration with AI and ML engines supports predictive modeling and real-time personalization across digital channels. Demand for scalable and compliant personalization infrastructure is rising across retail, OTT, and financial services. These trends are fostering innovation and platform expansion across post-cookie marketing ecosystems.

Threat:

Limited scale or data overlap

Insufficient match rates, inconsistent schema, and low audience overlap degrade analytical value and campaign precision. Enterprises struggle to identify high-value partners with complementary datasets and aligned privacy policies. Lack of interoperability across clean room vendors and identity frameworks hampers cross-platform collaboration. These limitations continue to constrain platform effectiveness and strategic alignment across multi-party data ecosystems.

Covid-19 Impact:

The pandemic accelerated interest in privacy-safe data collaboration as digital engagement surged across retail, healthcare, and media sectors. Enterprises adopted clean rooms to analyze consumer behavior, optimize digital campaigns, and manage consent across remote channels. Regulatory scrutiny and consumer awareness of data privacy increased during the crisis, reinforcing demand for secure and transparent data environments. Cloud-native architecture enabled remote deployment and scalability across distributed teams and partners. Post-pandemic strategies now include clean rooms as a core pillar of data governance, personalization, and measurement infrastructure. These shifts are reinforcing long-term investment in privacy-centric data platforms.

The federated learning segment is expected to be the largest during the forecast period

The federated learning segment is expected to account for the largest market share during the forecast period due to its ability to train models across decentralized datasets without moving raw data. Clean rooms integrate federated learning engines to support collaborative modeling, anomaly detection, and predictive analytics across privacy-sensitive environments. Platforms use secure aggregation, differential privacy, and homomorphic encryption to ensure compliance and performance. Demand for scalable and privacy-preserving AI infrastructure is rising across healthcare, finance, and retail sectors. These capabilities are boosting segment dominance across clean room-enabled machine learning deployments.

The product personalization segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the product personalization segment is predicted to witness the highest growth rate as brands and retailers adopt clean rooms to deliver tailored experiences across digital touch points. Platforms support audience segmentation, behavioural modelling, and dynamic content delivery using first-party and partner data. Integration with recommendation engines and real-time analytics enhances relevance and conversion across e-commerce and media platforms. Demand for compliant and scalable personalization infrastructure is rising across consumer goods, travel, and entertainment sectors. These dynamics are accelerating growth across personalization-focused clean room applications.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its mature digital advertising ecosystem, regulatory clarity, and enterprise investment in privacy infrastructure. U.S. and Canadian firms deploy clean rooms across retail, media, and financial services to support secure data collaboration and campaign measurement. Investment in cloud platforms, identity resolution, and consent management supports platform scalability and compliance. Presence of leading vendors, publishers, and data aggregators drives ecosystem maturity and innovation. These factors are propelling North America's leadership in clean room deployment and commercialization.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR as digital commerce, data localization, and privacy regulation converge across regional economies. Countries like India, China, Singapore, and Australia scale clean room platforms across retail, telecom, and healthcare sectors. Government-backed programs support data infrastructure, startup incubation, and cross-border compliance across digital ecosystems. Local firms launch multilingual and mobile-first solutions tailored to regional consumer behavior and regulatory frameworks. Demand for scalable and privacy-aligned data collaboration is rising across urban and rural deployments. These trends are accelerating regional growth across clean room innovation and adoption.

Key players in the market

Some of the key players in Data Clean Rooms Market include Snowflake, Google Ads Data Hub, Amazon Marketing Cloud, Habu, InfoSum, LiveRamp, Adobe Experience Platform, Salesforce Data Cloud, Neustar Fabrick, Epsilon CORE ID, Acxiom, Claravine, Lotame, The Trade Desk and Optable.

Key Developments:

In October 2025, Snowflake partnered with NIQ (formerly NielsenIQ) to deliver a dedicated clean room environment for global marketers. The collaboration enables real-time campaign measurement and consumer signal enrichment, supporting media owners, ad tech platforms, and retail networks. It reflects Snowflake's commitment to privacy-first data sharing across industries.

In September 2025, Google released updates to Ads Data Hub (ADH), enhancing its privacy-first data clean room capabilities. The platform now supports event-level ad data integration with first-party signals, enabling advertisers to measure performance across DV360, CM360, and YouTube without exposing user identities. These upgrades address attribution gaps caused by cookie deprecation and regulatory shifts.

Components Covered:

• Software
• Services

Deployment Modes Covered:

• Cloud-Based
• On-Premise

Organization Sizes Covered:

• Large Enterprises
• Small & Medium Enterprises (SMEs)

Technologies Covered:

• Secure Multi-Party Computation (SMPC)
• Differential Privacy
• Federated Learning
• Identity Resolution & Data Matching
• Other Technologies

Applications Covered:

• Advertising & Marketing Analytics
• Customer Data Enrichment
• Compliance & Risk Management
• Product Personalization
• Healthcare Data Exchange
• Other Applications

End Users Covered:

• Banking, Financial Services & Insurance (BFSI)
• Healthcare & Life Sciences
• Retail & E-Commerce
• Media & Entertainment
• IT & Telecom
• Other End Users

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Technology Analysis
• 3.7 Application Analysis
• 3.8 End User Analysis
• 3.9 Emerging Markets
• 3.10 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global Data Clean Rooms Market, By Component

• 5.1 Introduction
• 5.2 Software
  • 5.2.1 Data Collaboration Platforms
  • 5.2.2 Audience Segmentation Engines
  • 5.2.3 Measurement & Attribution Tools
• 5.3 Services
  • 5.3.1 Integration & Deployment
  • 5.3.2 Managed Services
  • 5.3.3 Consulting & Compliance Support

6 Global Data Clean Rooms Market, By Deployment Mode

• 6.1 Introduction
• 6.2 Cloud-Based
• 6.3 On-Premise

7 Global Data Clean Rooms Market, By Organization Size

• 7.1 Introduction
• 7.2 Large Enterprises
• 7.3 Small & Medium Enterprises (SMEs)

8 Global Data Clean Rooms Market, By Technology

• 8.1 Introduction
• 8.2 Secure Multi-Party Computation (SMPC)
• 8.3 Differential Privacy
• 8.4 Federated Learning
• 8.5 Identity Resolution & Data Matching
• 8.6 Other Technologies

9 Global Data Clean Rooms Market, By Application

• 9.1 Introduction
• 9.2 Advertising & Marketing Analytics
• 9.3 Customer Data Enrichment
• 9.4 Compliance & Risk Management
• 9.5 Product Personalization
• 9.6 Healthcare Data Exchange
• 9.7 Other Applications

10 Global Data Clean Rooms Market, By End User

• 10.1 Introduction
• 10.2 Banking, Financial Services & Insurance (BFSI)
• 10.3 Healthcare & Life Sciences
• 10.4 Retail & E-Commerce
• 10.5 Media & Entertainment
• 10.6 IT & Telecom
• 10.7 Other End Users

11 Global Data Clean Rooms Market, By Geography

• 11.1 Introduction
• 11.2 North America
  • 11.2.1 US
  • 11.2.2 Canada
  • 11.2.3 Mexico
• 11.3 Europe
  • 11.3.1 Germany
  • 11.3.2 UK
  • 11.3.3 Italy
  • 11.3.4 France
  • 11.3.5 Spain
  • 11.3.6 Rest of Europe
• 11.4 Asia Pacific
  • 11.4.1 Japan
  • 11.4.2 China
  • 11.4.3 India
  • 11.4.4 Australia
  • 11.4.5 New Zealand
  • 11.4.6 South Korea
  • 11.4.7 Rest of Asia Pacific
• 11.5 South America
  • 11.5.1 Argentina
  • 11.5.2 Brazil
  • 11.5.3 Chile
  • 11.5.4 Rest of South America
• 11.6 Middle East & Africa
  • 11.6.1 Saudi Arabia
  • 11.6.2 UAE
  • 11.6.3 Qatar
  • 11.6.4 South Africa
  • 11.6.5 Rest of Middle East & Africa

12 Key Developments

• 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 12.2 Acquisitions & Mergers
• 12.3 New Product Launch
• 12.4 Expansions
• 12.5 Other Key Strategies

13 Company Profiling

• 13.1 Snowflake
• 13.2 Google Ads Data Hub
• 13.3 Amazon Marketing Cloud
• 13.4 Habu
• 13.5 InfoSum
• 13.6 LiveRamp
• 13.7 Adobe Experience Platform
• 13.8 Salesforce Data Cloud
• 13.9 Neustar Fabrick
• 13.10 Epsilon CORE ID
• 13.11 Acxiom
• 13.12 Claravine
• 13.13 Lotame
• 13.14 The Trade Desk
• 13.15 Optable