데이터 분류 세계 시장 규모는 2024년에 18억 5,000만 달러로 평가되었고, 2030년까지의 CAGR은 33.45%를 나타내고, 2030년에는 104억 5,000만 달러에 달할 것으로 예측되고 있습니다.
전 세계 데이터 분류 시장은 기밀성, 가치, 규제 중요성을 바탕으로 데이터를 식별, 정리 및 라벨링하는 데 중점을 둔 사이버 보안 및 데이터 관리 솔루션의 부문을 말합니다.
| 시장 개요 | |
|---|---|
| 예측 기간 | 2026-2030년 |
| 시장 규모 : 2024년 | 18억 5,000만 달러 |
| 시장 규모 : 2030년 | 104억 5,000만 달러 |
| CAGR 2025-2030년 | 33.45% |
| 급성장 부문 | 통신 |
| 최대 시장 | 북미 |
데이터 분류는 기업이 어떤 유형의 데이터(개인정보, 기밀정보, 공개정보 등)를 소유하고, 이를 어떻게 취급, 보관, 보호해야 하는지를 이해하는 데 도움을 줍니다. 이 프로세스는 효과적인 데이터 거버넌스 구축, 데이터 보호법 준수를 보장하고 복잡한 디지털 인프라 전반에서 정보 액세스를 간소화하는 데 중요한 역할을 합니다.
기업이 엄청난 양의 구조화 및 비구조화 데이터를 생성 및 저장하게 되면서 이러한 데이터를 정확하게 분류할 필요성이 매우 중요해지고 있습니다. 일반 데이터 보호 규정(General Data Protection Regulation), 캘리포니아 주 소비자 개인정보 보호법(California Consumer Privacy Act), 다양한 산업별 기준 등의 규제를 실시함으로써 기업은 기밀 정보를 보다 책임있게 관리할 필요가 있습니다. 데이터 분류 도구를 사용하면 기업이 기밀 데이터를 식별하고 적절한 액세스 제어를 적용하며 실시간으로 사용량을 모니터링할 수 있습니다. 클라우드 채택, 원격 근무 환경 및 하이브리드 인프라는 다양한 스토리지 환경에서 작동하는 자동화되고 확장 가능한 데이터 분류 솔루션에 대한 수요를 더욱 가속화하고 있습니다.
세계의 데이터 분류 시장은 사이버 보안, 데이터 프라이버시, 인공지능의 융합에 의해 강력한 성장이 예상되고 있습니다. 머신러닝과 자연언어 처리의 진보로 자동 분류가 보다 빠르고 정확하게 이루어지게 되어 기업은 데이터량의 증대와 복잡화에 대응할 수 있게 되었습니다. 또한 데이터가 디지털 변환 전략의 중심이 됨에 따라 기업은 보안을 위해서뿐만 아니라 보다 지능적인 데이터 활용, 분석 및 의사 결정을 가능하게 하는 분류 도구에 투자하고 있습니다. 데이터의 가치와 책임에 대한 의식이 높아지는 가운데, 데이터 분류 시장은 세계 기업의 정보 관리에 필수적인 요소가 되고 있습니다.
클라우드 배포 가속화 및 데이터 스프롤링
비정형 데이터와 레거시 데이터 분류의 복잡성
분류 엔진의 인공지능과 머신러닝의 통합
Global Data Classification Market was valued at USD 1.85 Billion in 2024 and is expected to reach USD 10.45 Billion by 2030 with a CAGR of 33.45% through 2030. The Global Data Classification Market refers to the segment of cybersecurity and data management solutions focused on identifying, organizing, and labeling data based on its sensitivity, value, and regulatory importance.
| Market Overview | |
|---|---|
| Forecast Period | 2026-2030 |
| Market Size 2024 | USD 1.85 Billion |
| Market Size 2030 | USD 10.45 Billion |
| CAGR 2025-2030 | 33.45% |
| Fastest Growing Segment | Telecom |
| Largest Market | North America |
Data classification helps organizations understand what types of data they possess-such as personal, confidential, or public information-and how it should be handled, stored, and protected. This process plays a crucial role in building effective data governance, ensuring compliance with data protection laws, and streamlining information access across complex digital infrastructures.
As enterprises increasingly generate and store vast volumes of structured and unstructured data, the need to classify this data accurately has become critical. The implementation of regulations such as the General Data Protection Regulation, the California Consumer Privacy Act, and various industry-specific standards has forced organizations to manage sensitive information more responsibly. Data classification tools enable companies to locate sensitive data, apply the right access controls, and monitor usage in real-time-reducing the risk of data leaks, breaches, and non-compliance penalties. Cloud adoption, remote work environments, and hybrid infrastructures have further accelerated demand for automated, scalable data classification solutions that can function across diverse storage environments.
The Global Data Classification Market is expected to experience strong growth, driven by the convergence of cybersecurity, data privacy, and artificial intelligence. Advances in machine learning and natural language processing are making automated classification faster and more accurate, helping organizations keep pace with the growing volume and complexity of data. In addition, as data becomes central to digital transformation strategies, organizations are investing in classification tools not only for security but also to enable more intelligent data usage, analytics, and decision-making. With growing awareness about data value and responsibility, the data classification market is becoming an essential component of enterprise information management worldwide.
Key Market Drivers
Accelerating Cloud Adoption and Data Sprawl
The migration to cloud environments has unlocked scalability and agility for enterprises, but it has also created new risks in managing unstructured and dispersed data. As businesses store files across multiple cloud providers, software-as-a-service platforms, and hybrid environments, tracking sensitive or regulated information becomes more challenging. Data classification enables automated tagging and policy enforcement, helping enterprises maintain control in complex, distributed storage ecosystems. Organizations operating across multiple regions with formal data classification protocols in place reported 55% fewer compliance violations in 2024 compared to businesses without such frameworks. These organizations were able to map sensitive data to specific legal requirements, automate retention and access policies, and successfully pass audits without extensive manual intervention or risk of non-compliance penalties.
Cloud service providers often offer basic security tools, but leave ultimate data governance responsibilities to their customers. This shared responsibility model has increased the urgency for organizations to implement classification engines that can function across environments and integrate seamlessly with cloud security tools. Companies that classify data in real time can ensure it is encrypted, segmented, and stored according to internal policies and compliance mandates.
Key Market Challenges
Complexity in Classifying Unstructured and Legacy Data
One of the most pressing challenges facing the Global Data Classification Market is the growing complexity of unstructured and legacy data within organizations. Unlike structured data that resides in organized databases, unstructured data includes emails, PDFs, images, audio recordings, documents, and other formats that lack a predefined structure. As enterprises generate more digital content through remote communication tools, collaborative platforms, and customer interaction systems, unstructured data continues to grow exponentially. However, this data is also the most difficult to classify accurately, primarily because it is not easily searchable, standardized, or consistently labeled. Many legacy systems, which continue to hold decades of critical business information, were not designed to integrate with modern classification tools, adding another layer of complexity. Data residing in such environments often lacks metadata, making it nearly impossible to classify through traditional automation techniques. Without deep integration and context-aware solutions, organizations struggle to even locate, let alone classify, this information.
Further complicating the issue is the variation in content, language, and usage across business units, which makes establishing a unified classification framework highly resource-intensive. For instance, what one department considers sensitive may be routine for another, leading to inconsistencies in classification standards. Automation technologies such as artificial intelligence and natural language processing have been proposed as solutions, yet these tools often require large-scale training, fine-tuning, and validation-efforts that smaller enterprises cannot afford. Moreover, without historical classification accuracy or labeled datasets, artificial intelligence-based models produce unreliable outputs. Human intervention is frequently needed, which increases labor costs and introduces subjectivity. As a result, many organizations abandon their classification initiatives halfway or use minimal rule-based systems that do not scale. These limitations not only hinder full adoption but also dilute the return on investment in data governance platforms. In such an environment, the inability to classify unstructured and legacy data at scale remains one of the most significant bottlenecks in achieving holistic information security and compliance.
Key Market Trends
Integration of Artificial Intelligence and Machine Learning in Classification Engines
One of the most transformative trends in the Global Data Classification Market is the accelerated integration of artificial intelligence and machine learning technologies within classification engines. As enterprise data environments become more complex and diverse, traditional rule-based classification systems are proving insufficient in handling real-time decision-making, contextual analysis, and anomaly detection. Artificial intelligence and machine learning models are being deployed to understand the content and context of data, allowing for intelligent tagging, pattern recognition, and risk prioritization at scale. These systems can automatically identify sensitive information, even in unstructured formats such as free-text documents or scanned images, thereby improving classification accuracy and reducing human error.
Moreover, artificial intelligence-driven systems are continuously learning from organizational behaviors and usage patterns. As data flows through networks, classification algorithms adapt to identify evolving trends in data sensitivity and relevance. This capability not only enables dynamic policy enforcement but also reduces the workload on IT and compliance teams by automating what were previously manual, time-consuming tasks. As a result, artificial intelligence is enabling a shift from reactive to proactive data governance. Organizations that invest in artificial intelligence-enabled classification tools are positioning themselves for faster decision-making, enhanced compliance reporting, and stronger data protection frameworks-making this trend a cornerstone of future-ready data governance strategies.
In this report, the Global Data Classification Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:
Company Profiles: Detailed analysis of the major companies present in the Global Data Classification Market.
Global Data Classification Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report: