다크 애널리틱스 시장 : 점유율 분석, 산업 동향, 통계, 성장 예측(2025-2030년)

The dark analytics market is valued at USD 2.6 billion in 2025 and is forecast to reach USD 6.9 billion by 2030, advancing at a 21.6% CAGR.

This growth mirrors enterprises' realization that nearly 80% of corporate information is still unstructured and therefore invisible to conventional analytics systems. Artificial intelligence, machine learning, and cloud-native platforms now combine to turn these dormant data troves into real-time operational intelligence. Rapid proliferation of Internet-of-Things (IoT) devices, lower cloud-storage costs, and expanding regulatory mandates that require extensive log retention are further accelerating demand for dark-data processing. Competitive momentum is shifting toward providers that embed large language models, vector search, and synthetic-data generation, which together enable faster model training and stronger privacy controls.

Global Dark Analytics Market Trends and Insights

AI/ML-First Security Analytics Adoption

Security-centric architectures are redefining the dark analytics market as 91% of United States banks now use AI to detect fraud, a practice that could save USD 40 billion in losses by 2027. Chief information-security officers report that advanced threats driven by adversarial AI have made autonomous response indispensable, with 78% acknowledging material impacts on their defensive posture. Financial institutions illustrate the shift through deployments such as MongoDB's vector-search integration with OpenAI, which supports real-time analysis across structured and unstructured transaction streams. Large language models now interpret intricate security logs, but the rise of shadow AI-72% of generative-AI activity occurs outside official oversight-creates novel exposure risks that only sophisticated monitoring can address.

Exponential IoT Data Growth

IoT devices are creating zettabyte-scale unstructured data at a 40% CAGR, fundamentally reshaping storage and analytics economics. Manufacturers achieve sizable gains when they harness this dark data: Jaguar Land Rover cut supply-chain query times from three weeks to 45 minutes by applying graph analytics to real-time sensor feeds. By 2025, 75% of enterprise-generated data will be processed outside traditional data centers, a trend that is driving edge adoption. Pairing edge compute with lightweight AI allows millisecond-level pattern recognition in mission-critical settings, including automated industrial machinery and connected healthcare equipment.

Skills Gap in Data Engineering & Data Science

Fifty-eight percent of data-center operators struggled to fill engineering roles during 2025, creating a bottleneck in dark analytics market deployment. Modern projects demand multidisciplinary skills that span distributed computing, domain knowledge, and machine-learning operations. Asia-Pacific's rapid digitization inflates wages for scarce professionals, putting smaller enterprises at a disadvantage. Low-code frameworks alleviate some pressure by simplifying pipeline creation, yet advanced use cases such as multimodal inference still require seasoned talent. Many firms opt for managed platforms to bridge the gap, although this introduces concentration risk and can constrain customization flexibility.

Other drivers and restraints analyzed in the detailed report include:

1. Falling Cloud-Storage Costs
2. Zero-Trust Mandates Expanding Log Retention Windows
3. Escalating Compliance Cost (GDPR, CCPA, DORA)

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Prescriptive analytics is scaling at a 28.5% CAGR, underscoring a move from hindsight toward automated decision orchestration. Predictive methods retained the largest 43% slice of dark analytics market share in 2024 by providing probabilistic forecasts that feed planning cycles. The dark analytics market size attributable to prescriptive engines could swell to USD 2.3 billion by 2030 if current adoption momentum continues. Natural-language overlays now let business users pose conversational "what-if" questions, which models answer with ranked recommendations. Manufacturers have embraced this evolution, building digital twins that simulate entire supply networks so staff can test adjustments without interrupting production.

Descriptive and diagnostic techniques retain relevance because they uncover baseline patterns and root causes that feed higher-order optimization. Descriptive dashboards are improving through real-time connectors that fuse operational technology data with enterprise resource planning streams, broadening situational awareness. Diagnostic analytics in healthcare combines imaging notes, lab results, and clinician commentary to trace adverse outcomes back to specific process lapses, forming the foundation for later prescriptive interventions. Collectively, these layers reinforce each other, ensuring the dark analytics industry can serve both strategic foresight and daily tactical execution.

Cloud maintained a commanding 67% of dark analytics market share in 2024, benefiting from continuous service upgrades and pay-as-you-go elasticity. Even so, the segment representing edge and hybrid configurations is forecast to capture an extra USD 1.4 billion of dark analytics market size by 2030 as companies shift sensitive workloads closer to origin points. Demand is strongest in manufacturing, energy, and autonomous systems that require sub-second inference. The edge computing sector itself is expected to reach USD 61.54 billion in 2025, providing abundant processing headroom for analytics models.

Enterprises frequently blend public clouds with private on-premises resources, balancing sovereignty mandates against global scalability. This hybrid coordination raises architectural complexity: data synchronization, model governance, and zero-trust controls must function seamlessly across nodes. Providers now package turnkey edge gateways with embedded GPUs and lightweight orchestration to reduce integration overhead. Early adopters report faster anomaly detection in power grids and real-time adjustments of autonomous-guided vehicles, results that reinforce the economic case for distributed processing.

Dark Analytics Market Segmented by Analytics Type (Predictive, Prescriptive and More), Deployment Model, Data Source (Structured, Semi-Structured and Unstructured), End-User Vertical (BFSI, Healthcare and More) and by Geography. The Market Forecasts are Provided in Terms of Value (USD).

Geography Analysis

North America captured 37% of dark analytics market size in 2024 owing to its mature cloud ecosystem, early AI uptake, and supportive policy environment. Federal agencies emphasize secure data-sharing, encouraging enterprises to adopt privacy-enhanced analytics frameworks. Heavy investments in specialized hardware underline the region's commitment: Oracle alone earmarked USD 40 billion for Nvidia accelerators to back OpenAI's Texas facility, a move expected to reinforce regional leadership in AI compute. Canada focuses on natural-resources optimization, while Mexico pushes analytics in automotive and electronics manufacturing to bolster export competitiveness.

Asia-Pacific is advancing at a 24.4% CAGR as governments throughout China, India, and Southeast Asia finance next-generation data centers and talent pipelines. China accounts for 37.5% of regional big-data spending, leveraging sovereign clouds that align with national cybersecurity regulations. India's IT-services sector exports turnkey analytics solutions worldwide, using cost advantages and deep engineering pools to capture incremental demand. Japan and South Korea concentrate on industrial automation, exploiting edge AI for high-precision robotics and quality assurance. Cross-border data-flow rules remain a challenge, prompting multinationals to deploy localization strategies such as in-country edge clusters.

Europe maintains meaningful share despite stringent GDPR and proliferating AI-governance proposals. The dark analytics market benefits from legacy manufacturing bases across Germany, France, and Italy that seek predictive maintenance to lift asset uptime. DORA regulations are raising resilience standards, thereby increasing demand for advanced analytics that evaluates ICT incidents and supply-chain exposures. The United Kingdom, through its financial-services focus, accelerates adoption of synthetic data for model validation, while Nordic nations pioneer green-data-center practices to reduce analytics-related carbon footprints.

Collectively, Latin America and the Middle East & Africa represent smaller but fast-growing opportunity pools, each characterized by mobile-first consumer behaviour and fintech innovation. Both regions benefit from hyperscale expansions that lower compute costs and broaden access to sophisticated analytics tools. Telecommunications data monetization and public-sector digital identity programs are emerging as primary use cases that could elevate regional penetration in the latter half of the decade.

1. IBM Corporation
2. Microsoft Corporation
3. Amazon Web Services Inc.
4. SAP SE
5. Palantir Technologies
6. Oracle Corporation
7. Hewlett Packard Enterprise
8. SAS Institute
9. Teradata Corporation
10. Micro Focus International
11. Splunk Inc.
12. Elastic N.V.
13. Darktrace Plc.
14. Rapid7 Inc.
15. Securonix Inc.
16. Databricks Inc.
17. Snowflake Inc.
18. Google Cloud Platform
19. Cloudera Inc.
20. Exasol AG

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 AI/ML-first security analytics adoption
  • 4.2.2 Exponential IoT data growth
  • 4.2.3 Falling cloud-storage costs
  • 4.2.4 Zero-trust mandates expanding log retention windows
  • 4.2.5 Growth of synthetic data to unlock dark data
• 4.3 Market Restraints
  • 4.3.1 Skills gap in data engineering and data-science
  • 4.3.2 Escalating compliance cost (GDPR, CCPA, DORA)
  • 4.3.3 Rising carbon-footprint taxes on data at rest
• 4.4 Value / Supply-Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Buyers
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Analytics Type
  • 5.1.1 Predictive
  • 5.1.2 Prescriptive
  • 5.1.3 Diagnostic
  • 5.1.4 Descriptive
• 5.2 By Deployment Model
  • 5.2.1 On-premise
  • 5.2.2 Cloud
  • 5.2.3 Edge / Hybrid
• 5.3 By Data Source
  • 5.3.1 Structured
  • 5.3.2 Semi-Structured
  • 5.3.3 Unstructured
• 5.4 By End-user Vertical
  • 5.4.1 BFSI
  • 5.4.2 Healthcare
  • 5.4.3 Government
  • 5.4.4 Telecommunications
  • 5.4.5 Retail and E-commerce
  • 5.4.6 Manufacturing
  • 5.4.7 Others (Energy, Media, etc.)
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 South America
    • 5.5.2.1 Brazil
    • 5.5.2.2 Argentina
    • 5.5.2.3 Rest of South America
  • 5.5.3 Europe
    • 5.5.3.1 United Kingdom
    • 5.5.3.2 Germany
    • 5.5.3.3 France
    • 5.5.3.4 Italy
    • 5.5.3.5 Rest of Europe
  • 5.5.4 Asia-Pacific
    • 5.5.4.1 China
    • 5.5.4.2 Japan
    • 5.5.4.3 India
    • 5.5.4.4 South Korea
    • 5.5.4.5 Rest of Asia-Pacific
  • 5.5.5 Middle East
    • 5.5.5.1 Israel
    • 5.5.5.2 Saudi Arabia
    • 5.5.5.3 United Arab Emirates
    • 5.5.5.4 Turkey
    • 5.5.5.5 Rest of Middle East
  • 5.5.6 Africa
    • 5.5.6.1 South Africa
    • 5.5.6.2 Egypt
    • 5.5.6.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles {(includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products and Services, and Recent Developments)}
  • 6.4.1 IBM Corporation
  • 6.4.2 Microsoft Corporation
  • 6.4.3 Amazon Web Services Inc.
  • 6.4.4 SAP SE
  • 6.4.5 Palantir Technologies
  • 6.4.6 Oracle Corporation
  • 6.4.7 Hewlett Packard Enterprise
  • 6.4.8 SAS Institute
  • 6.4.9 Teradata Corporation
  • 6.4.10 Micro Focus International
  • 6.4.11 Splunk Inc.
  • 6.4.12 Elastic N.V.
  • 6.4.13 Darktrace Plc.
  • 6.4.14 Rapid7 Inc.
  • 6.4.15 Securonix Inc.
  • 6.4.16 Databricks Inc.
  • 6.4.17 Snowflake Inc.
  • 6.4.18 Google Cloud Platform
  • 6.4.19 Cloudera Inc.
  • 6.4.20 Exasol AG

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-space and Unmet-need Assessment