증강 분석 시장 : 구성요소, 기술, 전개 방식, 최종사용자, 용도, 최종 용도별 - 세계 예측(2025-2030년)

The Augmented Analytics Market was valued at USD 19.03 billion in 2024 and is projected to grow to USD 24.27 billion in 2025, with a CAGR of 29.29%, reaching USD 88.93 billion by 2030.

Unleashing the transformative potential of augmented analytics to empower data-driven decision making across diverse organizational functions

In today's data-driven world, organizations are inundated with vast quantities of structured and unstructured information. Augmented analytics represents a paradigm shift, blending machine learning, natural language processing, and advanced statistical algorithms to automate data preparation, insight generation, and explanation. This innovative approach eliminates manual bottlenecks, enabling businesses to uncover critical patterns without relying solely on specialized data teams.

By embedding artificial intelligence capabilities within intuitive analytics platforms, augmented analytics democratizes access to analytical insights, empowering users across all functional areas. From finance and operations to marketing and human resources, decision-makers can now leverage sophisticated predictive modeling and conversational interfaces to inform strategic planning, optimize resource allocation, and respond swiftly to market fluctuations.

This executive summary will guide you through the transformative shifts shaping the augmented analytics landscape, assess the implications of recent United States tariff changes, delve into key segmentation and regional insights, highlight leading technology company strategies, and offer actionable recommendations for industry leaders. A rigorous research methodology underpins every conclusion, ensuring the findings reflect the latest trends and best practices in augmented analytics adoption.

Navigating evolving dynamics as artificial intelligence and machine learning redefine the analytics landscape and democratize insights within enterprises

The analytics terrain has undergone rapid evolution, driven by exponential growth in data volumes and increased complexity in data sources. Traditional business intelligence tools, once reliant on manual querying and static dashboards, have reached their limits in delivering real-time, scalable insights. In response, organizations are embracing augmented analytics to accelerate decision cycles and maintain competitive relevance.

At the core of this shift lies the integration of machine learning and natural language processing, which automates routine analytics tasks and surfaces meaningful correlations that may otherwise remain hidden. Consequently, users can interact with data through conversational queries, graphically explore relationships, and receive AI-generated explanations that translate complex statistical outputs into business-friendly narratives.

Furthermore, self-service analytics capabilities are expanding beyond the technical elite, fostering a culture of data literacy across every level of the organization. By reducing dependency on central data teams, enterprises can simultaneously enhance agility and capacity for innovation. As these transformative dynamics continue to mature, the democratization of insights will become a cornerstone of digital initiatives across sectors.

Assessing the far-reaching consequences of United States tariff adjustments in 2025 on technology supply chains and global augmented analytics deployments

In early 2025, newly enacted United States tariffs on imported technology components and enterprise software services have introduced fresh complexities for firms that rely on global supply networks and outsourced analytics solutions. Hardware pricing has increased, prompting IT leaders to reevaluate capital expenditure plans for servers, networking gear, and on-premises infrastructure deployments. These higher costs cascade into broader analytics initiatives, where the balance between cloud migration and local hosting must be carefully assessed.

Simultaneously, service providers facing increased operational expenses are adjusting their managed and professional service rates, which can impact long-term support and implementation budgets. As a result, organizations are exploring strategic partnerships with regional service vendors to mitigate cost pressures while preserving service quality.

In light of these tariff-driven headwinds, many enterprises are bolstering their use of cloud-based analytics platforms, taking advantage of elastic consumption models to reduce upfront investments. Others are diversifying data sourcing strategies and establishing multi-vendor ecosystems to protect against supply chain disruptions. These adaptations underscore the importance of flexible procurement policies and dynamic vendor management in sustaining analytics momentum amid evolving trade policies.

Unearthing critical segmentation revelations that drive strategic adoption across components technologies deployments user profiles applications and industries

Disaggregating the market by component reveals a dual focus on platforms, which deliver core analytics engines and embedded AI capabilities, and services, which encompass both managed services that provide ongoing system administration and professional services that address implementation and customization needs. Enterprises are weighing the trade-offs between end-to-end platform ecosystems and the tailored expertise offered by specialized service providers.

When viewed through a technology lens, artificial intelligence modules are accelerating as the fastest growing segment, integrating with established business intelligence integration tools to streamline data consolidation workflows. Meanwhile, data discovery and visualization solutions play a critical role in presenting automated insights through intuitive dashboards and interactive visual interfaces, ensuring that complex findings remain accessible to diverse user audiences.

Deployment mode preferences are bifurcated between cloud-first adopters, who value on-demand scalability and reduced infrastructure overhead, and organizations with stringent regulatory or security requirements that continue to rely on on-premises installations. This duality underscores the importance of hybrid architectures that can reconcile performance, compliance, and cost considerations in tandem.

Analyzing end-user segmentation, large enterprises maintain significant investments in enterprise-wide analytics programs, leveraging scale to integrate advanced AI-driven forecasting into global operations. In contrast, small and medium-sized enterprises prioritize cost-effective self-service tools that democratize access to insights without necessitating extensive IT support.

Application-based segmentation highlights a broad spectrum of use cases. Fraud detection applications harness anomaly detection algorithms to protect financial transactions. Operations management and supply chain analytics use predictive maintenance and inventory optimization models to reduce downtime and minimize waste. Product development and innovation teams leverage customer sentiment analysis and A/B testing frameworks to refine offerings. Risk and compliance management functions integrate regulatory intelligence into workflow automation. Sales and marketing optimization tactics employ customer segmentation and propensity modeling to enhance campaign targeting. Workforce analytics facilitates talent acquisition strategies and performance management.

Finally, the end-use segmentation spans a diverse array of industries. Banking, financial services, and insurance firms adopt predictive risk analytics to underwrite policies. Educational institutions explore learning analytics to personalize student experiences. Energy and utilities players deploy sensor-based data analysis to manage grid reliability. Government agencies leverage open data platforms to drive transparency. In healthcare and life sciences, patient outcome modeling guides resource allocation. Manufacturing companies integrate IoT telemetry with AI for quality control. Media and entertainment organizations mine consumer behavior data for content personalization. Oil and gas entities utilize geospatial analytics to optimize exploration. Retail and consumer goods retailers implement demand forecasting for inventory planning. Telecommunications and IT providers monitor network performance with real-time anomaly detection. Transportation and logistics enterprises streamline route optimization and fleet management through predictive scheduling.

Revealing region-specific trends and market maturation trajectories shaping the demand for augmented analytics solutions across major global territories

The Americas region continues to lead in augmented analytics adoption, driven by high digital maturity, a robust technology infrastructure, and a competitive corporate landscape that prizes rapid innovation. Both public and private sectors are investing heavily in AI-driven data platforms to enhance customer experience, streamline operations, and comply with evolving data privacy regulations. As a result, North American enterprises are setting benchmarks for self-service capabilities and conversational analytics that peer regions seek to emulate.

In Europe, the Middle East, and Africa, market dynamics are shaped by stringent regulatory frameworks such as the General Data Protection Regulation, which place a premium on data governance and transparency. Organizations in these territories are balancing compliance with the drive to implement advanced analytics within manufacturing, energy, and government functions. Meanwhile, the rise of digital transformation initiatives across emerging markets in Africa and the Gulf Cooperation Council is fostering new opportunities for regional analytics players to offer localized solutions.

Asia-Pacific presents a fragmented yet rapidly growing market environment. High-growth economies such as China and India are investing in digital infrastructure and AI research, with government-led smart city and Industry 4.0 initiatives fueling demand for real-time analytics. Southeast Asian nations are increasingly adopting cloud-based augmented analytics platforms to support e-commerce expansion and fintech innovations. Across the region, the convergence of 5G connectivity, edge computing, and AI promises to unlock novel use cases in manufacturing automation, healthcare diagnostics, and logistics optimization.

Examining strategic initiatives and innovation trajectories of leading technology firms shaping the augmented analytics ecosystem in 2025 and beyond

Leading technology firms are forging strategic partnerships, executing targeted acquisitions, and expanding their AI-capable analytics suites to secure competitive advantage. Microsoft has integrated automated machine learning and natural language query features within its cloud platform, while IBM is embedding Watson AI capabilities into hybrid data environments to support complex enterprise use cases. SAS continues to leverage its deep statistical heritage, enhancing automated analytics pipelines with advanced model interpretability tools.

Salesforce, through its acquisition of Tableau, is focusing on seamless integration between customer relationship management data and augmented insights, enabling sales and marketing leaders to derive real-time recommendations. Qlik has championed an active intelligence approach, offering alerting and event-driven analytics that notify users of significant data deviations as they occur. ThoughtSpot is gaining traction with its search-driven analytics interface, enabling users to pose conversational queries against large-scale data warehouses.

Oracle's strategy emphasizes converged database capabilities, embedding AI-driven anomaly detection and time series forecasting directly within its autonomous cloud services. SAP's analytics cloud platform is pursuing deeper integration with enterprise resource planning systems, providing embedded insights across core business processes. TIBCO is advancing its streaming analytics solutions to support edge computing scenarios, while Yellowfin focuses on augmented storytelling features that guide users through insight narratives.

These strategic initiatives underscore a broader industry imperative to deliver AI-enriched analytics experiences that seamlessly align with existing enterprise ecosystems. As vendors continue to refine their product roadmaps, differentiated offerings centered on automation, embedded intelligence, and user-centric design will define the competitive battleground.

Delivering concrete actionable strategies to capitalize on augmented analytics advancements and overcome organizational barriers to data-driven innovation

To fully leverage the promise of augmented analytics, organizational leadership must first cultivate a culture of data literacy, investing in training programs that demystify AI-driven insights for non-technical stakeholders. Equally important is the implementation of robust data governance frameworks that safeguard data quality, privacy, and ethical use throughout the analytics lifecycle.

Industry leaders should prioritize partnerships with cloud service providers and specialized analytics vendors to accelerate deployment timelines and tap into best-in-class AI capabilities. By adopting modular, API-driven architectures, enterprises can avoid vendor lock-in and rapidly integrate emerging technologies as they become available.

Fostering cross-functional collaboration between IT, analytics, and business units is critical for aligning analytics initiatives with strategic objectives. Establishing analytics centers of excellence can serve as catalysts for standardizing methodologies, sharing reusable assets, and disseminating best practices across the organization.

Given tariff-induced cost pressures, finance and procurement teams must adopt flexible contracting strategies that hedge against pricing volatility. Exploring multi-region cloud subscriptions and leveraging spot instances can deliver cost efficiencies without compromising performance or compliance.

Technology roadmaps should incorporate hybrid deployment models that balance the agility of cloud services with the security controls of on-premises environments. This approach allows organizations to optimize workloads based on regulatory considerations, latency requirements, and operational preferences.

Finally, continuous performance monitoring combined with automated alerting mechanisms will be indispensable for maintaining the reliability and relevance of analytics outputs. By embedding feedback loops and performance metrics into analytics workflows, leaders can iteratively refine models, drive higher adoption rates, and achieve sustained business impact.

Detailing a rigorous mixed-method research approach combining qualitative insights and quantitative validation for robust augmented analytics market analysis

This research report is informed by a mixed-methods approach that blends qualitative insights with quantitative validation. Primary research involved in-depth interviews with senior analytics professionals, IT decision-makers, and industry subject-matter experts. These conversations provided firsthand perspectives on adoption drivers, technology preferences, and strategic imperatives.

Secondary research drew upon a comprehensive review of publicly available whitepapers, peer-reviewed journals, corporate press releases, and case studies. Data from technology consortiums, regulatory filings, and analyst briefings were synthesized to triangulate key market dynamics and vendor positioning.

The analytical framework employed thematic coding to identify emerging trends and pain points, followed by statistical cross-tabulation to validate the prevalence of specific use cases and technology configurations across industries. A rigorous peer review process ensured that all insights underwent validation by multiple research analysts, enhancing the reliability and objectivity of the findings.

Ethical considerations, including data privacy and disclosure compliance, were strictly adhered to throughout the research. Wherever possible, anonymized data sets were used to protect organizational confidentiality, and all vendor engagements respected non-disclosure agreements. This methodology underpins the robustness and integrity of the report's strategic recommendations.

Consolidating key findings on augmented analytics momentum and strategic imperatives to drive future organizational excellence in data utilization

The accelerated adoption of augmented analytics marks a seminal moment in the evolution of data-driven decision-making. Organizations that harness AI-powered automation will achieve faster time-to-insight, drive operational efficiencies, and unlock new revenue streams. As machine learning and natural language processing capabilities become more accessible, the divide between data specialists and business users will continue to narrow.

Strategic segmentation insights highlight the pivotal role of integrated platforms and specialized services in meeting evolving enterprise requirements. Meanwhile, regional analyses underscore the interplay between regulatory environments, digital infrastructure, and market maturity in shaping adoption patterns across the Americas, Europe, Middle East and Africa, and Asia-Pacific.

The ramifications of shifting trade policies in 2025 reinforce the need for supply chain resilience and agile procurement strategies. In parallel, leading technology firms are refining their AI-centric roadmaps to deliver differentiated user experiences and embedded intelligence across core business applications.

Looking ahead, organizations that embrace a holistic approach-combining data governance, cross-functional collaboration, flexible architectures, and continuous performance monitoring-will secure a sustainable competitive edge. By adhering to the actionable recommendations outlined herein, business leaders can navigate complexity, mitigate risk, and drive transformative outcomes.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

• 4.1. Introduction
• 4.2. Market Sizing & Forecasting

5. Market Dynamics

• 5.1. Adoption of automated data storytelling powered by natural language generation to enhance decision making
• 5.2. Implementation of no-code machine learning workflows in augmented analytics platforms for citizen data scientists
• 5.3. Integration of real-time anomaly detection capabilities leveraging streaming data and ai models
• 5.4. Expansion of embedded augmented analytics features within enterprise applications for seamless user experiences
• 5.5. Use of augmented analytics to automate data quality profiling and anomaly identification in complex datasets

6. Market Insights

• 6.1. Porter's Five Forces Analysis
• 6.2. PESTLE Analysis

7. Cumulative Impact of United States Tariffs 2025

8. Augmented Analytics Market, by Component

• 8.1. Introduction
• 8.2. Platforms
• 8.3. Services
  • 8.3.1. Managed Services
  • 8.3.2. Professional Services

9. Augmented Analytics Market, by Technology

• 9.1. Introduction
• 9.2. Artificial Intelligence (AI)
• 9.3. Business Intelligence (BI) Integration Tools
• 9.4. Data Discovery & Visualization Tools

10. Augmented Analytics Market, by Deployment Mode

• 10.1. Introduction
• 10.2. Cloud
• 10.3. On Premises

11. Augmented Analytics Market, by End User

• 11.1. Introduction
• 11.2. Large Enterprise
• 11.3. SMEs

12. Augmented Analytics Market, by Application

• 12.1. Introduction
• 12.2. Fraud Detection
• 12.3. Operations Management
• 12.4. Product Development & Innovation
• 12.5. Risk & Compliance Management
• 12.6. Sales & Marketing Optimization
• 12.7. Supply Chain Analytics
• 12.8. Workforce Analytics

13. Augmented Analytics Market, by End-use

• 13.1. Introduction
• 13.2. Banking, Financial Services, and Insurance (BFSI)
• 13.3. Education
• 13.4. Energy & Utilities
• 13.5. Government & Public Sector
• 13.6. Healthcare & Life Sciences
• 13.7. Manufacturing
• 13.8. Media & Entertainment
• 13.9. Oil & Gas
• 13.10. Retail & Consumer Goods
• 13.11. Telecommunications & Information Technology (IT)
• 13.12. Transportation & Logistics

14. Americas Augmented Analytics Market

• 14.1. Introduction
• 14.2. United States
• 14.3. Canada
• 14.4. Mexico
• 14.5. Brazil
• 14.6. Argentina

15. Europe, Middle East & Africa Augmented Analytics Market

• 15.1. Introduction
• 15.2. United Kingdom
• 15.3. Germany
• 15.4. France
• 15.5. Russia
• 15.6. Italy
• 15.7. Spain
• 15.8. United Arab Emirates
• 15.9. Saudi Arabia
• 15.10. South Africa
• 15.11. Denmark
• 15.12. Netherlands
• 15.13. Qatar
• 15.14. Finland
• 15.15. Sweden
• 15.16. Nigeria
• 15.17. Egypt
• 15.18. Turkey
• 15.19. Israel
• 15.20. Norway
• 15.21. Poland
• 15.22. Switzerland

16. Asia-Pacific Augmented Analytics Market

• 16.1. Introduction
• 16.2. China
• 16.3. India
• 16.4. Japan
• 16.5. Australia
• 16.6. South Korea
• 16.7. Indonesia
• 16.8. Thailand
• 16.9. Philippines
• 16.10. Malaysia
• 16.11. Singapore
• 16.12. Vietnam
• 16.13. Taiwan

17. Competitive Landscape

• 17.1. Market Share Analysis, 2024
• 17.2. FPNV Positioning Matrix, 2024
• 17.3. Competitive Analysis
  • 17.3.1. Actian Corporation
  • 17.3.2. Aible, Inc.
  • 17.3.3. Altair Engineering Inc.
  • 17.3.4. Alteryx, Inc.
  • 17.3.5. AtScale, Inc.
  • 17.3.6. Databricks, Inc.
  • 17.3.7. DataRobot, Inc.
  • 17.3.8. Domo, Inc.
  • 17.3.9. DotData, Inc.
  • 17.3.10. GoodData Corporation
  • 17.3.11. Infor Equity Holdings, LLC
  • 17.3.12. International Business Machines Corporation
  • 17.3.13. KNIME AG
  • 17.3.14. Microsoft Corporation
  • 17.3.15. MicroStrategy Incorporated
  • 17.3.16. Oracle Corporation
  • 17.3.17. QlikTech International AB
  • 17.3.18. Salesforce, Inc.
  • 17.3.19. SAP SE
  • 17.3.20. SAS Institute Inc.
  • 17.3.21. Sisense, Inc.
  • 17.3.22. Tellius, Inc.
  • 17.3.23. ThoughtSpot, Inc.
  • 17.3.24. Yellowfin BI
  • 17.3.25. Zoho Corporation

18. ResearchAI

19. ResearchStatistics

20. ResearchContacts

21. ResearchArticles

22. Appendix