The “Big Data Market with COVID-19 Impact Analysis, by Component, Deployment Mode, Organization Size, Business Function (Finance, Marketing & Sales), Industry Vertical (BFSI, Manufacturing, Healthcare & Life Sciences) and Region – Global Forecast to 2026″ report has been added to ResearchAndMarkets.com’s offering.
The Big Data market size is projected to grow from USD 162.6 billion in 2021 to USD 273.4 billion in 2026, at a Compound Annual Growth Rate (CAGR) of 11.0% during the forecast period.
The Big Data industry is driven by sharp increase in data volume. However, rise in data connectivity through cloud computing and incorporation of digital transformation in top-level strategies.
Based on Component, the service segment is expected to grow at a higher CAGR during the forecast period
The service segment of the Big Data market is further segmented into professional services (consulting, support and maintenance, and deployment and integration) and managed services. This section discusses each service subsegment’s market size and growth rate based on type (for selected subsegments) and region.
Based on deployment mode, on-premises segment is segmented to account for a larger market size during the forecast period
Most cloud computing refers to the storage, management, and processing of data via networks of remote servers, which are typically accessed via the Internet. Enterprises mostly in heavily regulated industry verticals, such as BFSI, healthcare and life sciences, and manufacturing, opt for the on-premises deployment model of big data solutions. Furthermore, large enterprises with sufficient IT resources are expected to opt for the on-premises deployment model. On-premises is the most reliable deployment mode, which an enterprise can rely on for a high level of control and security. Enterprises need to purchase a license or a copy to deploy cloud-based solutions.
Based on business function operations segment to grow at a higher CAGR during the forecast period
The Big Data market based on business function is segmented into Finance, Marketing and Sales, Human Resources and Operations. The operations segment is expected to grow at a rapid pace during the forecast period. Big Data can help to improve the performance of order picking and stock-taking process in warehouse management with high accuracy. It aims at improving operational efficiency and effectiveness by enabling data-driven decisions at strategic, operational, and tactical levels. It assists companies in delivering revenue growth, improving margins, managing working capital in a better way, and enhancing overall control points across the supply chain.
Based on organization size, large enterprise segment to account for a larger market size during the forecast period Most The adoption of Big Data software and services among large enterprises is high due to the ever-increasing adoption of the cloud, and the trend is expected to continue during the forecast period. Large enterprises accumulate huge chunks of data that can be attributed to the widespread client base. In large enterprises, data plays a major role in evaluating the overall performance of organizations. Large enterprises are leveraging real-time data coming from various sources; for instance, social media feeds or sensors and cameras, each record needs to be processed in a way that preserves its relation to other data and sequence in time.
Based on solution, big data analytics segment to grow at a higher CAGR during the forecast period
The adoption of Big Data analytics among large enterprises is high as it provides an almost limitless source of business and informational knowledge, which can lead to operational improvements and new income prospects for businesses in practically any industry. The value hidden in company data has firms trying to develop a cutting-edge analytics operation for use cases like consumer personalization, risk reduction, fraud detection, internal operations analysis, and other new use cases arising on a near-daily basis
APAC is expected to grow at a higher CAGR during the forecast period
Asia Pacific (APAC) has continually presented lucrative market opportunities for Big Data Solutions and service providers with a notable increase in Big Data across its developed and emerging countries., Japan, China, and India have displayed ample growth opportunities in the Big Data market. Owing to a rapidly proliferating technology-backed economical structure, APAC is expected to emerge as the fastest-growing region in Big Data software and services demand during the forecast period.
Key Topics Covered:
1 Introduction
2 Research Methodology
3 Executive Summary
4 Premium Insights
4.1 Attractive Opportunities in the Big Data Market
4.2 Market: Top Three Verticals
4.3 Market, by Region
4.4 North America: Market, by Application and Industry Vertical
5 Market Overview and Industry Trends
5.1 Introduction
5.2 Market Dynamics
5.2.1 Drivers
5.2.1.1 Sharp Increase in Data Volume
5.2.1.2 Rise in Data Connectivity Through Cloud Computing
5.2.1.3 Incorporation of Digital Transformation in Top-Level Strategies
5.2.2 Restraints
5.2.2.1 Data Security Concerns and Stringent Data Security Regulations
5.2.2.2 High Big Data Handling Costs
5.2.3 Opportunities
5.2.3.1 Rise in Adoption of Technologies and Big Data Analytics
5.2.3.2 The Growth of Investment in It Sectors by the Businesses
5.2.4 Challenges
5.2.4.1 Data Silos and Poor Data Quality
5.2.4.2 Lack of Data Professionals
5.3 Use Cases
5.3.1 Banking, Financial Services, and Insurance
5.3.1.1 Use Case 1: Erste Group Bank Gains a 360-Degree View of Customers Using Oracle Big Data Appliance on a Single Cloud Platform
5.3.2 Telecommunication and Information Technology
5.3.2.1 Use Case 2: Robi Axiata Limited Uses the Cloudera Platform to Enhance Customer Experience
5.3.3 Retail and Consumer Goods
5.3.3.1 Use Case 4: Myntra Uses Azure Synapse Analytics and Azure Hdinsight to Stabilize Services and Increase Year-Over-Year Traffic
5.3.4 Government and Defense
5.3.4.1 Use Case 5: Indian Oil Uses Qlik to Achieve Business Value by Providing Analytics on the Real-Time and Big Data
5.3.5 Media and Entertainment
5.3.5.1 Use Case 6: Dwango Company Ltd. Uses the Cloudera Big Data Platform, and Advanced Analytics Enables Expansion and Business Growth
5.3.6 Manufacturing
5.3.6.1 Use Case 7: Brembo Uses Tibco Spotfire to Enhance Production Processes with the Help of Big Data
5.3.7 Healthcare and Life Science
5.3.7.1 Use Case 8: Iqvia Uses Qlik Big Data Analytics to Help UK Healthcare Services
5.3.8 Transport and Logistics
5.3.8.1 Use Case 9: Cargosmart Uses Tibco Spotfire for Greater Visibility and Benchmarking
5.4 Patent Analysis
5.4.1 Methodology
5.4.2 Document Type
5.4.3 Innovation and Patent Applications
5.4.3.1 Top Applicants
5.5 Big Data: Evolution
5.6 Big Data: Architecture
5.7 Big Data Types
5.8 Big Data Analytics Types
5.9 Big Data Ecosystem
5.10 Supply Chain Analysis
5.11 Pricing Model Analysis
5.12 Trends/Disruptions Impacting Buyers/Clients in the Market
5.13 Porter’s Five Forces Analysis
5.14 Technological Analysis
5.15 Regulatory Implications
5.16 Big Data Market: COVID-19 Impact
6 Big Data Market, by Component
6.1 Introduction
6.1.1 COVID-19 Impact on the Market, by Component
6.1.2 Component: Market Drivers
6.2 Solutions
6.2.1 Big Data Analytics
6.2.2 Data Discovery
6.2.3 Data Visualization
6.2.4 Data Management
6.3 Services
6.3.1 Managed Services
6.3.2 Professional Services
6.3.2.1 Consulting
6.3.2.2 Deployment and Integration
6.3.2.3 Support and Maintenance
7 Big Data Market, by Business Function
7.1 Introduction
7.1.1 COVID-19 Impact on the Market, by Business Function
7.1.2 Business Function: Market Drivers
7.2 Marketing and Sales
7.3 Operations
7.4 Finance
7.5 Human Resources
8 Big Data Market, by Deployment Mode
8.1 Introduction
8.1.1 COVID-19 Impact on the Market, by Deployment Mode
8.1.2 Deployment Mode: Market Drivers
8.2 On-Premises
8.3 Cloud
8.3.1 Public Cloud
8.3.2 Private Cloud
8.3.3 Hybrid Cloud
9 Big Data Market, by Organization Size
9.1 Introduction
9.1.1 COVID-19 Impact on the Market, by Organization Size
9.1.2 Organizations Size: Market Drivers
9.2 Small and Medium-Sized Enterprises
9.3 Large Enterprises
10 Big Data Market, by Industry Vertical
10.1 Introduction
10.1.1 COVID-19 Impact on Market, by Industry Vertical
10.1.2 Vertical: Market Drivers
10.2 Banking, Financial Services, and Insurance
10.3 Telecommunications and It
10.4 Retail and Consumer Goods
10.5 Healthcare and Life Sciences
10.6 Government and Defense
10.7 Media and Entertainment
10.8 Manufacturing
10.9 Transportation and Logistics
10.10 Other Industry Verticals
11 Big Data Market, by Region
12 Competitive Landscape
12.1 Overview
12.2 Key Player Strategies
12.3 Market Share Analysis
12.4 Historic Revenue Analysis
12.5 Company Evaluation Quadrant
12.5.1 Stars
12.5.2 Emerging Leaders
12.5.3 Pervasive Players
12.5.4 Participants
12.6 Competitive Benchmarking
12.7 Start-Up/Sme Evaluation Quadrant
12.7.1 Progressive Companies
12.7.2 Responsive Companies
12.7.3 Dynamic Companies
12.7.4 Starting Blocks
12.8 Competitive Scenario
12.8.1 Product Launches
12.8.2 Deals
13 Company Profiles
13.1 Introduction
13.2 Major Players
13.2.1 Ibm
13.2.2 Google
13.2.3 Oracle
13.2.4 Microsoft
13.2.5 Sap
13.2.6 Sas
13.2.7 Cloudera
13.2.8 Teradata
13.2.9 Tibco
13.2.10 Aws
13.2.11 Alteryx
13.2.12 Informatica
13.2.13 Sisense
13.2.14 Salesforce
13.2.15 Hpe
13.2.16 Qlik
13.2.17 Splunk
13.2.18 Vmware
13.2.19 Accenture
13.3 Smes/Start-Ups
13.3.1 Ataccama
13.3.2 Cogito
13.3.3 Centerfield
13.3.4 Rib Datapine
13.3.5 Fusionex
13.3.6 Bigpanda
13.3.7 Bigeye
13.3.8 Imply
13.3.9 Rivery
13.3.10 Yugabyte Db
13.3.11 Airbyte
13.3.12 Cardagraph
13.3.13 Firebolt
13.3.14 Syncari
14 Adjacent and Related Markets
15 Appendix
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