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Report
Big Data Technology - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
Market Report I 2025-04-28 I 120 Pages I Mordor Intelligence
The Big Data Technology Market size is estimated at USD 234.27 billion in 2025, and is expected to reach USD 375.76 billion by 2030, at a CAGR of 9.91% during the forecast period (2025-2030).
Key Highlights
- Big data technology is defined as software utility. This technology is primarily designed to analyze, process, and extract information from a large data set and a huge set of extremely complex structures.
- The amount of data produced is rising quickly yearly due to improvements in new technology, gadgets, and communication. The market for big data technologies and services is primarily driven by the demand for actionable insights that can be used for future decision-making processes by evaluating the constantly growing amounts of structured and unstructured data.
- The automobile sector is positioning itself to be industry 4.0-ready with the rise of industrial IoT and M2M connectivity. Robots, sensors, barcode readers, and RFIDs are now commonplace in this sector's factory floor. Data generation points have drastically risen as a result of these gadgets.
- Supply and demand factors play a significant role in the consumer electronics business. This industry sector has significantly benefited from big data analytics, allowing it to convert from a push market strategy to a pull market strategy.
- Furthermore, the enormous amount of data produced and stored makes it vulnerable to hacking and modification by outside parties or insiders. This will jeopardize the safety of any saved data. The vendors of big data technology, whose reputation would be in danger, will be immediately impacted.
Big Data Technology Market Trends
Retail Industry to Dominate the Market
- With Big Data technologies and sophisticated analytics, the retail sector is undergoing a significant revolution. Retailers are using Big Data analytics to stay competitive due to the growth of e-commerce, online purchasing, and high levels of rivalry.
- Big Data is used throughout the whole retail process in the business to study customer behavior, forecast demand, and improve prices. Most big data applications in retail today are for system-wide cost reduction, enhancing the customer experience both online and in-store, data-driven adaptive supply chains, and real-time analytics and targeting.
- With the use of big data analytics, the industry is now more aware of consumer behavior patterns and can plan production based on these. Using big data from the public internet as user-generated content (UGC) or online customer reviews (OCR) data presents an up-and-coming alternative for analyzing retail customer behavior.
- In January 2022, J.D. Power, a global provider of data analytics and consumer intelligence, relaunched three major automotive data products maintained by the company's Autodata Solutions division. These include solutions for vehicle identification number (VIN) descriptions, inventory management, and payment and incentives data for digital retail and desking applications.
- Retailers may store, integrate, and analyze a wide range of online and offline customer data, e-commerce transactions, clickstream data, email, social media, and call center records using a big data platform provided by vendors like MapR Technologies.
Asia-Pacific to Witness the Highest Growth
- Asia-Pacific is seeing a boom in data produced from digital goods and services due to population expansion and increased e-commerce. While big data has been widely used by Asian-Pacific international banks (APAC), more local institutions are increasingly doing the same to achieve a "second-mover advantage."
- Due to increased Internet usage, organizations can access a vast amount of structured and unstructured data. Due to these advantages, substantial multinational corporations now have their big data evaluated for practical knowledge.
- End users are also embracing big data analytics outsourcing service models. Data analytics outsourcing is a business strategy where a data-driven organization entrusts a service provider with its data in exchange for access to insightful reporting. The provider handles infrastructure setup and maintenance, data management, and data analysis. The need for immediate insights is driving the need for significant data analytics outsourcing because managing the data generated by enterprises takes time.
- More physical servers must be added to the cluster to accommodate the growing data, which takes time and money. A cloud platform's complete scalability gives businesses access to limitless storage capacity on demand. As a result of its advantages, the technology is expanding in areas where it is being used.
- Moreover, In February 2023, Guizhou Province, a major big data hub in southwest China, announced its plan to invest 20 billion yuan (USD 2.9 billion) in big data-related initiatives in 2023. The director of the province announced that the province will accelerate the construction of 5G, computing networks, data centers, and other forms of advanced digital infrastructure. Such developments are expected to boost the growth of big data technology in the region.
Big Data Technology Industry Overview
The market is concentrated, with significant legacy players dominating the market, like IBM, Microsoft, and SAP. Since companies are concerned about the privacy and management of their employee/customer data, they trust established vendors more than new entrants.
- December 2022 - Hewlett-Packard Company has announced new application, analytics, and developer services for HPE GreenLake. With edge-to-cloud technology, businesses can implement a modernization plan that puts data first for production workloads across hybrid cloud environments. Through Amazon Elastic Kubernetes Service (Amazon EKS) Anywhere from Amazon Web Services (AWS), infrastructure-as-code, and cloud-native toolchains, HPE GreenLake for Private Cloud Enterprise offers expanded container deployment options for Kubernetes to support customers' DevOps and continuous integration and continuous deployment (CI/CD) environments.
- September 2022 - The SAS Viya analytics platform is now pay-as-you-go on the Microsoft Azure Marketplace. Customers from all over the world have access to vital data exploration, machine learning, and model deployment analytics thanks to full-featured SAS Viya on Microsoft Azure. It features a rich in-app learning center to help both quick onboarding and long-term success, and it is available in many translated languages.
Additional Benefits:
- The market estimate (ME) sheet in Excel format
- 3 months of analyst support
1 INTRODUCTION
1.1 Study Assumptions and Market Definition
1.2 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET INSIGHT
4.1 Market Overview
4.2 Industry Attractiveness - Porter's Five Force Analysis
4.2.1 Bargaining Power of Suppliers
4.2.2 Bargaining Power of Consumers
4.2.3 Threat of New Entrants
4.2.4 Intensity of Competitive Rivalry
4.2.5 Threat of Substitute Products
5 MARKET DYNAMICS
5.1 Introduction to Market Drivers and Restraints
5.2 Market Drivers
5.2.1 Increasing Adoption of Data Discovery and Visualization Tools is Expanding the Market Growth
5.3 Market Restraints
5.3.1 Hacking and Tampering of Generated Data by Insiders or Third Party is Challenging the Market Growth
6 MARKET SEGMENTATION
6.1 By Delivery Mode
6.1.1 On-Premise
6.1.2 Cloud
6.2 By End-user Vertical
6.2.1 Telecom & IT
6.2.2 Energy & Power
6.2.3 BFSI
6.2.4 Retail
6.2.5 Manufacturing
6.2.6 Aerospace & Defense
6.2.7 Engineering & Construction
6.2.8 Healthcare & Pharmaceuticals
6.2.9 Other End -user Verticals (Transportation & Logistics, Media & Entertainment)
6.3 By Geography***
6.3.1 North America
6.3.1.1 United States
6.3.1.2 Canada
6.3.2 Europe
6.3.2.1 United Kingdom
6.3.2.2 Germany
6.3.2.3 France
6.3.3 Asia
6.3.3.1 China
6.3.3.2 Japan
6.3.3.3 India
6.3.3.4 South Korea
6.3.4 Australia and New Zealand
6.3.5 Latin America
6.3.6 Middle East and Africa
7 COMPETITIVE LANDSCAPE
7.1 Company Profiles*
7.1.1 IBM Corporation
7.1.2 Microsoft Corporation
7.1.3 Oracle Corporation
7.1.4 SAP SE
7.1.5 Hewlett-Packard Company
7.1.6 Cisco Systems Inc.
7.1.7 SAS Institute
7.1.8 Information Builders Inc.
7.1.9 MicroStrategy Incorporated
7.1.10 Accenture PLC
8 INVESTMENT ANALYSIS
9 MARKET OPPORTUNITIES AND FUTURE TRENDS
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