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巨量資料

市場調查報告書

商品編碼

1624640

巨量資料市場 - 按硬體、服務、最終用戶、地區和競爭細分的全球產業規模、佔有率、趨勢、機會和預測，2019-2029F

Big Data Market - Global Industry Size, Share, Trends, Opportunity, and Forecast Segmented By Hardware, By Service, By End-User, By Region & Competition, 2019-2029F

出版日期: 2024年12月31日 | 出版商:

TechSci Research | 英文 186 Pages | 商品交期: 2-3個工作天內

價格

簡介目錄

2023年全球巨量資料市場價值為2,219.8億美元，預估至2029年將達4,317.7億美元，預測期內複合年成長率為11.56%。全球巨量資料市場是由數位設備、物聯網和社群媒體產生的資料呈指數級成長，以及分析和機器學習技術的進步所推動的。組織擴大採用數據驅動的決策來提高效率並獲得競爭優勢。向雲端運算的轉變為巨量資料儲存和處理提供了可擴展、經濟高效的解決方案。此外，監管合規性、資料貨幣化的興起以及競爭壓力正在推動企業投資巨量資料技術。物聯網和智慧型裝置的激增進一步加速了對巨量資料解決方案的需求，從而實現即時分析和可行的見解。

市場概況
預測期	2025-2029
2023 年市場規模	2219.8億美元
2029 年市場規模	4317.7億美元
2024-2029 年複合年成長率	11.56%
成長最快的細分市場	諮詢
最大的市場	北美洲

主要市場促進因素

數據爆炸和數位技術的擴散

越來越重視數據驅動的決策與商業智慧

主要市場挑戰

資料隱私和安全問題

資料管理複雜性與整合挑戰

主要市場趨勢

擴大採用基於雲端的巨量資料解決方案

細分市場洞察

最終使用者見解

區域洞察

Global Big Data Market was valued at USD 221.98 billion in 2023 and is expected to reach USD 431.77 billion by 2029 with a CAGR of 11.56% during the forecast period. The Global Big Data Market is driven by the exponential growth of data generated from digital devices, IoT, and social media, alongside advancements in analytics and machine learning technologies. Organizations are increasingly adopting data-driven decision-making to enhance efficiency and gain a competitive edge. The shift to cloud computing offers scalable, cost-effective solutions for Big Data storage and processing. Additionally, regulatory compliance, the rise of data monetization, and competitive pressures are pushing businesses to invest in Big Data technologies. The proliferation of IoT and smart devices further accelerates the demand for Big Data solutions, enabling real-time analytics and actionable insights.

Market Overview
Forecast Period	2025-2029
Market Size 2023	USD 221.98 Billion
Market Size 2029	USD 431.77 Billion
CAGR 2024-2029	11.56%
Fastest Growing Segment	Consulting
Largest Market	North America

Key Market Drivers

Data Explosion and the Proliferation of Digital Technologies

The rapid expansion of digital technologies and the subsequent data explosion are among the most significant drivers of the Global Big Data Market. The rise of internet-connected devices, social media platforms, and the Internet of Things (IoT) has led to an unprecedented surge in the amount of data generated daily. This surge is primarily driven by the continuous use of smartphones, wearables, sensors, and various IoT devices that produce vast amounts of structured and unstructured data. Social media interactions, online transactions, and user-generated content contribute significantly to this data volume, creating a vast reservoir of information that organizations can tap into for strategic insights. Every day, approximately 2.5 quintillion bytes of data are generated. The global market for Big Data analytics in healthcare is expected to reach USD79.23 billion by 2028. Currently, the digital universe contains over 44 zettabytes of data, with 70% of this data being user-generated. Additionally, annual spending on cloud computing by end-users totals around USD500 billion. These figures highlight the growing importance of data generation, analysis, and cloud technology across various industries, especially in sectors like healthcare and computing.

Moreover, the integration of digital technologies into everyday life has resulted in a data-centric culture, where organizations are compelled to analyze data to stay competitive. Businesses across all sectors, from healthcare to retail to manufacturing, are increasingly relying on data analytics to drive their strategies, improve customer experiences, and optimize operations. For instance, in retail, data analytics helps companies understand customer behavior and preferences, enabling them to tailor marketing strategies and improve product offerings. In healthcare, big data is crucial for patient care management, predictive analytics, and personalized medicine. This need to harness vast data volumes effectively is pushing the demand for advanced big data technologies, such as Hadoop, Apache Spark, and NoSQL databases, which can process and analyze data more efficiently.

The data explosion is also facilitating the growth of artificial intelligence (AI) and machine learning (ML) within the big data ecosystem. AI and ML models require large datasets to train algorithms, improve accuracy, and provide predictive analytics capabilities. As data generation continues to grow exponentially, organizations are investing heavily in big data infrastructure to support AI and ML initiatives. This trend is further accelerated by the adoption of cloud-based big data solutions, which offer scalable, flexible, and cost-effective options for storing and processing large datasets. The cloud's ability to handle massive data workloads without requiring substantial upfront investments in infrastructure makes it an attractive option for businesses of all sizes, thereby driving the market's growth.

Growing Emphasis on Data-Driven Decision Making and Business Intelligence

The increasing emphasis on data-driven decision-making and business intelligence is another key driver propelling the Global Big Data Market. In today's highly competitive business environment, companies are seeking ways to leverage data to make more informed decisions, optimize operations, and enhance customer satisfaction. Data-driven decision-making enables organizations to move beyond intuition and guesswork, allowing them to base their strategies on empirical data and analysis. This approach has proven to be highly effective in improving operational efficiency, identifying new market opportunities, and mitigating risks.

Business intelligence tools and big data analytics platforms provide organizations with the ability to collect, process, and analyze data from multiple sources in real time. This capability is particularly valuable in industries such as finance, healthcare, and retail, where timely insights can lead to significant competitive advantages. For example, in finance, big data analytics helps companies detect fraudulent activities by analyzing transaction patterns and identifying anomalies in real time. In healthcare, real-time analytics enable providers to monitor patient health data and predict potential health issues before they become critical, improving patient outcomes and reducing costs.

Moreover, the integration of big data analytics with business intelligence tools allows organizations to uncover hidden patterns and correlations within their data, leading to actionable insights. These insights can be used to enhance product development, optimize supply chains, and personalize customer experiences, ultimately driving business growth and profitability. As companies increasingly recognize the value of data as a strategic asset, there is a growing demand for robust big data solutions that can support complex analytics and provide deeper insights into market trends, customer behaviors, and operational efficiencies.

The adoption of big data technologies is also being driven by the need to remain agile and responsive in a rapidly changing market landscape. In an era where customer preferences and market dynamics are constantly evolving, organizations must be able to quickly adapt their strategies to stay ahead of the competition. Big data analytics provides the tools necessary to monitor market trends, analyze competitor actions, and respond proactively to changes in the business environment. This ability to make data-driven decisions in real-time is becoming increasingly important for businesses seeking to maintain a competitive edge, driving continued investment in big data technologies and solutions.

Key Market Challenges

Data Privacy and Security Concerns

One of the most significant challenges facing the Global Big Data Market is the growing concern over data privacy and security. As organizations collect and process vast amounts of sensitive data, including personal information, financial records, and proprietary business data, they become increasingly vulnerable to cyber threats and data breaches. High-profile data breaches have become more frequent, exposing critical vulnerabilities and leading to significant financial and reputational damage for companies. These breaches not only compromise personal and sensitive information but also erode consumer trust and bring about stringent regulatory scrutiny.

Regulatory frameworks such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA), and other regional data protection laws impose strict requirements on how organizations handle and protect data. Compliance with these regulations requires organizations to implement robust data governance frameworks, which can be complex and costly. Additionally, organizations must navigate varying legal requirements across different jurisdictions, adding to the complexity of ensuring compliance. Failing to comply can result in substantial fines, legal actions, and a loss of customer trust, further complicating the landscape for businesses operating on a global scale.

Furthermore, the integration of Big Data with emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) presents additional privacy and security challenges. These technologies often require the collection and analysis of vast amounts of data from multiple sources, increasing the potential attack surface for cyber threats. IoT devices, in particular, are often less secure and can be exploited as entry points into broader data systems. As the volume and variety of data continue to grow, ensuring data security and privacy becomes even more challenging, requiring continuous investments in advanced cybersecurity measures, encryption technologies, and secure data storage solutions.

The challenge is exacerbated by the shortage of skilled professionals in data security and privacy. Organizations often struggle to find qualified experts who can effectively implement and manage security protocols to protect large datasets. This skills gap, combined with the evolving nature of cyber threats, makes it difficult for organizations to stay ahead of potential risks. Consequently, data privacy and security concerns remain a significant barrier to the growth of the Global Big Data Market, as organizations must continually balance the need for data-driven insights with the imperative of safeguarding data integrity and privacy.

Data Management Complexity and Integration Challenges

The complexity of managing and integrating diverse datasets is another major challenge for the Global Big Data Market. As organizations collect data from a multitude of sources, including transactional databases, social media, IoT devices, and third-party providers, they are faced with the daunting task of ensuring data quality, consistency, and accessibility. The sheer volume, variety, and velocity of Big Data make it difficult to efficiently store, process, and analyze information. Traditional data management systems are often ill-equipped to handle these challenges, necessitating the adoption of more sophisticated technologies and strategies to manage data effectively.

One of the primary issues is the integration of disparate data sources. Different data types, such as structured data from relational databases and unstructured data from social media or sensor outputs, require different processing techniques and storage solutions. Integrating these data types into a cohesive system that provides comprehensive and actionable insights is a complex task. Data silos, where information is stored in isolated systems, further complicate this integration, leading to inefficiencies and a lack of a unified view of data. Organizations often need to invest in advanced data integration tools and platforms that can harmonize diverse datasets, ensuring seamless data flow and enhancing the overall analytics capabilities.

Moreover, maintaining data quality and consistency across large and varied datasets is a significant challenge. Data can often be incomplete, inaccurate, or outdated, affecting the reliability of the analytics outcomes. Ensuring high data quality requires ongoing data cleaning, validation, and enrichment processes, which can be resource-intensive and time-consuming. Additionally, as organizations increasingly rely on real-time data analytics, the need for low-latency data processing and high-speed data transfer becomes more critical. This requirement can strain existing IT infrastructure, necessitating further investments in high-performance computing resources and cloud-based solutions that can scale with the growing data demands.

The lack of standardized data management practices across different industries and regions adds another layer of complexity. Organizations must navigate diverse data standards, formats, and compliance requirements, making it difficult to establish a unified data management approach. This diversity can lead to challenges in data interoperability and sharing, hindering collaboration and the ability to derive holistic insights. As a result, organizations need to continually evolve their data management strategies, investing in new technologies and frameworks that can accommodate the growing complexity and ensure efficient and effective data integration and analysis. The ongoing need to address these challenges remains a critical barrier to maximizing the full potential of Big Data.

Key Market Trends

Increased Adoption of Cloud-Based Big Data Solutions

Another significant trend in the Global Big Data Market is the increased adoption of cloud-based Big Data solutions. As organizations generate more data than ever before, the need for scalable, flexible, and cost-effective data storage and processing solutions has become paramount. Cloud computing provides a robust platform for Big Data analytics, allowing businesses to store and process vast datasets without the substantial upfront investment in physical infrastructure. This shift to cloud-based solutions is being driven by the scalability and flexibility offered by cloud platforms, which enable organizations to adjust their resources based on demand, ensuring cost-efficiency and operational agility.

The adoption of cloud-based Big Data solutions is also accelerating due to the growing need for real-time data processing and analytics. Cloud platforms, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), offer a range of services specifically designed for Big Data, including data lakes, distributed storage systems, and high-performance computing environments. These platforms provide the computational power necessary to handle large-scale data processing tasks, such as streaming analytics, machine learning, and complex data modeling, in real time. This capability is essential for businesses looking to gain timely insights from their data to drive strategic decision-making and maintain a competitive edge in fast-paced markets.

Moreover, cloud-based Big Data solutions are enhancing data collaboration and integration across organizations. By leveraging the cloud, companies can break down data silos and enable seamless data sharing and collaboration between departments and with external partners. This increased data accessibility facilitates more comprehensive analytics and fosters innovation by allowing different stakeholders to contribute to data-driven insights. Additionally, the cloud's inherent support for multi-tenancy allows multiple organizations to share computing resources securely, reducing costs and enhancing collaboration.

The rise of hybrid and multi-cloud strategies is further influencing the adoption of cloud-based Big Data solutions. Many organizations are opting for hybrid models that combine on-premises infrastructure with public and private clouds to optimize their data management strategies. This approach provides the flexibility to keep sensitive data on-premises while leveraging the scalability of the cloud for less sensitive data processing tasks. Multi-cloud strategies, where businesses use services from multiple cloud providers, are also gaining traction, enabling companies to avoid vendor lock-in and optimize their Big Data environments for performance and cost. As a result, the continued shift towards cloud-based solutions is expected to drive significant growth in the Big Data market, providing businesses with the tools and flexibility needed to effectively manage and analyze their expanding data landscapes.

Segmental Insights

End-User Insights

The BFSI segment has emerged as the dominating segment in the global Big Data market, The Banking, Financial Services, and Insurance (BFSI) segment has emerged as the dominating segment in the global Big Data market due to its intensive reliance on data analytics to drive business strategies and maintain competitiveness. Financial institutions generate massive amounts of data daily from transactions, customer interactions, market feeds, and regulatory filings. Leveraging Big Data technologies allows these institutions to analyze this vast volume of structured and unstructured data in real time, leading to more informed decision-making, enhanced customer experiences, and improved risk management. Big Data analytics enables banks and insurers to detect fraudulent activities swiftly, assess credit risks more accurately, and develop personalized financial products that meet individual customer needs.

Furthermore, regulatory compliance is a significant driver for Big Data adoption in the BFSI sector. With stringent regulations like the Basel III, the Dodd-Frank Act, and the General Data Protection Regulation (GDPR), financial institutions must manage and analyze data effectively to ensure compliance and avoid penalties. Big Data solutions offer advanced tools for data governance, reporting, and auditing, making them essential for regulatory adherence.

The BFSI sector also benefits from predictive analytics powered by Big Data, which helps forecast market trends, optimize trading strategies, and improve investment decisions. The competitive nature of the financial industry pushes firms to continuously innovate and adopt cutting-edge technologies, further driving the demand for Big Data solutions. As the BFSI sector continues to prioritize data-driven insights for operational efficiency, risk mitigation, and customer engagement, its dominance in the global Big Data market is set to continue growing.

Regional Insights

North America has emerged as the dominating region in the global Big Data market, North America has emerged as the dominating region in the global Big Data market due to several key factors. Firstly, the region is home to many of the world's leading technology companies, such as Google, Microsoft, IBM, and Amazon, which are at the forefront of Big Data innovation. These companies have made significant investments in Big Data technologies, fostering a robust ecosystem of research, development, and deployment. Secondly, North America has a high rate of adoption of advanced technologies across various sectors, including finance, healthcare, retail, and government. Organizations in these sectors recognize the value of Big Data analytics for gaining competitive advantages, improving operational efficiencies, and enhancing customer experiences.

Moreover, the presence of a strong regulatory framework, coupled with stringent data privacy laws such as the California Consumer Privacy Act (CCPA), has pushed companies to invest heavily in data governance and management solutions. This has accelerated the adoption of Big Data tools that help ensure compliance and secure data handling. Additionally, North America's well-developed infrastructure, including widespread high-speed internet access and cloud computing facilities, supports the rapid processing and analysis of large datasets, further boosting the region's market dominance.

The growing demand for real-time analytics, driven by the need to make quick, data-driven decisions, is another factor contributing to North America's leadership in the Big Data market. As organizations continue to leverage Big Data for predictive analytics, machine learning, and AI applications, the region is likely to maintain its leading position, setting the pace for global market growth.

Key Market Players

Oracle Corporation
Microsoft Corporation
SAP SE
IBM Corporation
SAS Institute Inc.
Salesforce, Inc.
Teradata Corporation
Google LLC
Accenture PLC
Informatica LLC
Wipro Limited
Hewlett Packard Enterprise Company

Report Scope:

In this report, the Global Big Data Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Big Data Market, By Hardware:

Storage
Server
Network Equipment

Big Data Market, By Service:

Consulting
Maintenance
Training & Development

Big Data Market, By End-User:

BFSI
Manufacturing
Retail
Gaming
Telecom

Big Data Market, By Region:

North America
- United States
- Canada
- Mexico
Europe
- France
- United Kingdom
- Italy
- Germany
- Spain
- Netherlands
- Belgium
Asia-Pacific
- China
- India
- Japan
- Australia
- South Korea
- Thailand
- Malaysia
South America
- Brazil
- Argentina
- Colombia
- Chile
Middle East & Africa
- South Africa
- Saudi Arabia
- UAE
- Turkey

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Big Data Market.

Available Customizations:

Global Big Data 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:

1. Service Overview

1.1. Market Definition
1.2. Scope of the Market
- 1.2.1.Markets Covered
- 1.2.2.Years Considered for Study
- 1.2.3.Key Market Segmentations

2. Research Methodology

2.1. Objective of the Study
2.2. Baseline Methodology
2.3. Formulation of the Scope
2.4. Assumptions and Limitations
2.5. Sources of Research
- 2.5.1.Secondary Research
- 2.5.2.Primary Research
2.6. Approach for the Market Study
- 2.6.1.The Bottom-Up Approach
- 2.6.2.The Top-Down Approach
2.7. Methodology Followed for Calculation of Market Size & Market Shares
2.8. Forecasting Methodology
- 2.8.1.Data Triangulation & Validation

3. Executive Summary

4. Impact of COVID-19 on Global Big Data Market

5. Voice of Customer

6. Global Big Data Market Overview

7. Global Big Data Market Outlook

7.1. Market Size & Forecast
- 7.1.1.By Value
7.2. Market Share & Forecast
- 7.2.1.By Hardware (Storage, Server, Network Equipment)
- 7.2.2.By Service (Consulting, Maintenance, Training & Development)
- 7.2.3.By End-User (BFSI, Manufacturing, Retail, Gaming, Telecom)
- 7.2.4.By Region
7.3. By Company (2023)
7.4. Market Map

8. North America Big Data Market Outlook

8.1. Market Size & Forecast
- 8.1.1.By Value
8.2. Market Share & Forecast
- 8.2.1.By Hardware
- 8.2.2.By Service
- 8.2.3.By End-User
- 8.2.4.By Country
8.3. North America: Country Analysis
- 8.3.1.United States Big Data Market Outlook
  - 8.3.1.1. Market Size & Forecast
    - 8.3.1.1.1. By Value
  - 8.3.1.2. Market Share & Forecast
    - 8.3.1.2.1. By Hardware
    - 8.3.1.2.2. By Service
    - 8.3.1.2.3. By End-User
- 8.3.2.Canada Big Data Market Outlook
  - 8.3.2.1. Market Size & Forecast
    - 8.3.2.1.1. By Value
  - 8.3.2.2. Market Share & Forecast
    - 8.3.2.2.1. By Hardware
    - 8.3.2.2.2. By Service
    - 8.3.2.2.3. By End-User
- 8.3.3.Mexico Big Data Market Outlook
  - 8.3.3.1. Market Size & Forecast
    - 8.3.3.1.1. By Value
  - 8.3.3.2. Market Share & Forecast
    - 8.3.3.2.1. By Hardware
    - 8.3.3.2.2. By Service
    - 8.3.3.2.3. By End-User

9. Europe Big Data Market Outlook

9.1. Market Size & Forecast
- 9.1.1.By Value
9.2. Market Share & Forecast
- 9.2.1.By Hardware
- 9.2.2.By Service
- 9.2.3.By End-User
- 9.2.4.By Country
9.3. Europe: Country Analysis
- 9.3.1.Germany Big Data Market Outlook
  - 9.3.1.1. Market Size & Forecast
    - 9.3.1.1.1. By Value
  - 9.3.1.2. Market Share & Forecast
    - 9.3.1.2.1. By Hardware
    - 9.3.1.2.2. By Service
    - 9.3.1.2.3. By End-User
- 9.3.2.France Big Data Market Outlook
  - 9.3.2.1. Market Size & Forecast
    - 9.3.2.1.1. By Value
  - 9.3.2.2. Market Share & Forecast
    - 9.3.2.2.1. By Hardware
    - 9.3.2.2.2. By Service
    - 9.3.2.2.3. By End-User
- 9.3.3.United Kingdom Big Data Market Outlook
  - 9.3.3.1. Market Size & Forecast
    - 9.3.3.1.1. By Value
  - 9.3.3.2. Market Share & Forecast
    - 9.3.3.2.1. By Hardware
    - 9.3.3.2.2. By Service
    - 9.3.3.2.3. By End-User
- 9.3.4.Italy Big Data Market Outlook
  - 9.3.4.1. Market Size & Forecast
    - 9.3.4.1.1. By Value
  - 9.3.4.2. Market Share & Forecast
    - 9.3.4.2.1. By Hardware
    - 9.3.4.2.2. By Service
    - 9.3.4.2.3. By End-User
- 9.3.5.Spain Big Data Market Outlook
  - 9.3.5.1. Market Size & Forecast
    - 9.3.5.1.1. By Value
  - 9.3.5.2. Market Share & Forecast
    - 9.3.5.2.1. By Hardware
    - 9.3.5.2.2. By Service
    - 9.3.5.2.3. By End-User
- 9.3.6.Netherlands Big Data Market Outlook
  - 9.3.6.1. Market Size & Forecast
    - 9.3.6.1.1. By Value
  - 9.3.6.2. Market Share & Forecast
    - 9.3.6.2.1. By Hardware
    - 9.3.6.2.2. By Service
    - 9.3.6.2.3. By End-User
- 9.3.7.Belgium Big Data Market Outlook
  - 9.3.7.1. Market Size & Forecast
    - 9.3.7.1.1. By Value
  - 9.3.7.2. Market Share & Forecast
    - 9.3.7.2.1. By Hardware
    - 9.3.7.2.2. By Service
    - 9.3.7.2.3. By End-User

10. South America Big Data Market Outlook

10.1. Market Size & Forecast
- 10.1.1. By Value
10.2. Market Share & Forecast
- 10.2.1. By Hardware
- 10.2.2. By Service
- 10.2.3. By End-User
- 10.2.4. By Country
10.3. South America: Country Analysis
- 10.3.1. Brazil Big Data Market Outlook
  - 10.3.1.1. Market Size & Forecast
    - 10.3.1.1.1. By Value
  - 10.3.1.2. Market Share & Forecast
    - 10.3.1.2.1. By Hardware
    - 10.3.1.2.2. By Service
    - 10.3.1.2.3. By End-User
- 10.3.2. Colombia Big Data Market Outlook
  - 10.3.2.1. Market Size & Forecast
    - 10.3.2.1.1. By Value
  - 10.3.2.2. Market Share & Forecast
    - 10.3.2.2.1. By Hardware
    - 10.3.2.2.2. By Service
    - 10.3.2.2.3. By End-User
- 10.3.3. Argentina Big Data Market Outlook
  - 10.3.3.1. Market Size & Forecast
    - 10.3.3.1.1. By Value
  - 10.3.3.2. Market Share & Forecast
    - 10.3.3.2.1. By Hardware
    - 10.3.3.2.2. By Service
    - 10.3.3.2.3. By End-User
- 10.3.4. Chile Big Data Market Outlook
  - 10.3.4.1. Market Size & Forecast
    - 10.3.4.1.1. By Value
  - 10.3.4.2. Market Share & Forecast
    - 10.3.4.2.1. By Hardware
    - 10.3.4.2.2. By Service
    - 10.3.4.2.3. By End-User

11. Middle East & Africa Big Data Market Outlook

11.1. Market Size & Forecast
- 11.1.1. By Value
11.2. Market Share & Forecast
- 11.2.1. By Hardware
- 11.2.2. By Service
- 11.2.3. By End-User
- 11.2.4. By Country
11.3. Middle East & Africa: Country Analysis
- 11.3.1. Saudi Arabia Big Data Market Outlook
  - 11.3.1.1. Market Size & Forecast
    - 11.3.1.1.1. By Value
  - 11.3.1.2. Market Share & Forecast
    - 11.3.1.2.1. By Hardware
    - 11.3.1.2.2. By Service
    - 11.3.1.2.3. By End-User
- 11.3.2. UAE Big Data Market Outlook
  - 11.3.2.1. Market Size & Forecast
    - 11.3.2.1.1. By Value
  - 11.3.2.2. Market Share & Forecast
    - 11.3.2.2.1. By Hardware
    - 11.3.2.2.2. By Service
    - 11.3.2.2.3. By End-User
- 11.3.3. South Africa Big Data Market Outlook
  - 11.3.3.1. Market Size & Forecast
    - 11.3.3.1.1. By Value
  - 11.3.3.2. Market Share & Forecast
    - 11.3.3.2.1. By Hardware
    - 11.3.3.2.2. By Service
    - 11.3.3.2.3. By End-User
- 11.3.4. Turkey Big Data Market Outlook
  - 11.3.4.1. Market Size & Forecast
    - 11.3.4.1.1. By Value
  - 11.3.4.2. Market Share & Forecast
    - 11.3.4.2.1. By Hardware
    - 11.3.4.2.2. By Service
    - 11.3.4.2.3. By End-User

12. Asia-Pacific Big Data Market Outlook

12.1. Market Size & Forecast
- 12.1.1. By Value
12.2. Market Share & Forecast
- 12.2.1. By Hardware
- 12.2.2. By Service
- 12.2.3. By End-User
- 12.2.4. By Country
12.3. Asia-Pacific: Country Analysis
- 12.3.1. China Big Data Market Outlook
  - 12.3.1.1. Market Size & Forecast
    - 12.3.1.1.1. By Value
  - 12.3.1.2. Market Share & Forecast
    - 12.3.1.2.1. By Hardware
    - 12.3.1.2.2. By Service
    - 12.3.1.2.3. By End-User
- 12.3.2. India Big Data Market Outlook
  - 12.3.2.1. Market Size & Forecast
    - 12.3.2.1.1. By Value
  - 12.3.2.2. Market Share & Forecast
    - 12.3.2.2.1. By Hardware
    - 12.3.2.2.2. By Service
    - 12.3.2.2.3. By End-User
- 12.3.3. Japan Big Data Market Outlook
  - 12.3.3.1. Market Size & Forecast
    - 12.3.3.1.1. By Value
  - 12.3.3.2. Market Share & Forecast
    - 12.3.3.2.1. By Hardware
    - 12.3.3.2.2. By Service
    - 12.3.3.2.3. By End-User
- 12.3.4. South Korea Big Data Market Outlook
  - 12.3.4.1. Market Size & Forecast
    - 12.3.4.1.1. By Value
  - 12.3.4.2. Market Share & Forecast
    - 12.3.4.2.1. By Hardware
    - 12.3.4.2.2. By Service
    - 12.3.4.2.3. By End-User
- 12.3.5. Australia Big Data Market Outlook
  - 12.3.5.1. Market Size & Forecast
    - 12.3.5.1.1. By Value
  - 12.3.5.2. Market Share & Forecast
    - 12.3.5.2.1. By Hardware
    - 12.3.5.2.2. By Service
    - 12.3.5.2.3. By End-User
- 12.3.6. Thailand Big Data Market Outlook
  - 12.3.6.1. Market Size & Forecast
    - 12.3.6.1.1. By Value
  - 12.3.6.2. Market Share & Forecast
    - 12.3.6.2.1. By Hardware
    - 12.3.6.2.2. By Service
    - 12.3.6.2.3. By End-User
- 12.3.7. Malaysia Big Data Market Outlook
  - 12.3.7.1. Market Size & Forecast
    - 12.3.7.1.1. By Value
  - 12.3.7.2. Market Share & Forecast
    - 12.3.7.2.1. By Hardware
    - 12.3.7.2.2. By Service
    - 12.3.7.2.3. By End-User

13. Market Dynamics

13.1. Drivers
13.2. Challenges

14. Market Trends and Developments

15. Company Profiles

15.1. Oracle Corporation
- 15.1.1. Business Overview
- 15.1.2. Key Revenue and Financials
- 15.1.3. Recent Developments
- 15.1.4. Key Personnel/Key Contact Person
- 15.1.5. Key Product/Services Offered
15.2. Microsoft Corporation
- 15.2.1. Business Overview
- 15.2.2. Key Revenue and Financials
- 15.2.3. Recent Developments
- 15.2.4. Key Personnel/Key Contact Person
- 15.2.5. Key Product/Services Offered
15.3. SAP SE
- 15.3.1. Business Overview
- 15.3.2. Key Revenue and Financials
- 15.3.3. Recent Developments
- 15.3.4. Key Personnel/Key Contact Person
- 15.3.5. Key Product/Services Offered
15.4. IBM Corporation
- 15.4.1. Business Overview
- 15.4.2. Key Revenue and Financials
- 15.4.3. Recent Developments
- 15.4.4. Key Personnel/Key Contact Person
- 15.4.5. Key Product/Services Offered
15.5. SAS Institute Inc.
- 15.5.1. Business Overview
- 15.5.2. Key Revenue and Financials
- 15.5.3. Recent Developments
- 15.5.4. Key Personnel/Key Contact Person
- 15.5.5. Key Product/Services Offered
15.6. Salesforce, Inc.
- 15.6.1. Business Overview
- 15.6.2. Key Revenue and Financials
- 15.6.3. Recent Developments
- 15.6.4. Key Personnel/Key Contact Person
- 15.6.5. Key Product/Services Offered
15.7. Teradata Corporation
- 15.7.1. Business Overview
- 15.7.2. Key Revenue and Financials
- 15.7.3. Recent Developments
- 15.7.4. Key Personnel/Key Contact Person
- 15.7.5. Key Product/Services Offered
15.8. Google LLC
- 15.8.1. Business Overview
- 15.8.2. Key Revenue and Financials
- 15.8.3. Recent Developments
- 15.8.4. Key Personnel/Key Contact Person
- 15.8.5. Key Product/Services Offered
15.9. Accenture PLC
- 15.9.1. Business Overview
- 15.9.2. Key Revenue and Financials
- 15.9.3. Recent Developments
- 15.9.4. Key Personnel/Key Contact Person
- 15.9.5. Key Product/Services Offered
15.10. Informatica LLC
- 15.10.1. Business Overview
- 15.10.2. Key Revenue and Financials
- 15.10.3. Recent Developments
- 15.10.4. Key Personnel/Key Contact Person
- 15.10.5. Key Product/Services Offered
15.11. Wipro Limited
- 15.11.1. Business Overview
- 15.11.2. Key Revenue and Financials
- 15.11.3. Recent Developments
- 15.11.4. Key Personnel/Key Contact Person
- 15.11.5. Key Product/Services Offered
15.12. Hewlett Packard Enterprise Company
- 15.12.1. Business Overview
- 15.12.2. Key Revenue and Financials
- 15.12.3. Recent Developments
- 15.12.4. Key Personnel/Key Contact Person
- 15.12.5. Key Product/Services Offered