Dublin, March 06, 2023 (GLOBE NEWSWIRE) -- The "Big Data Market by Leading Companies, Solutions, Use Cases, Infrastructure, Data Integration, IoT Support, Deployment Model and Services in Industry Verticals 2023 - 2028" report has been added to ResearchAndMarkets.com's offering.
This report provides an in-depth assessment of the global big data market, including business case issues/analysis, application use cases, vendor landscape, value chain analysis, and a quantitative assessment of the industry with forecasting from 2023 to 2028. This report also evaluates the components of big data infrastructure and security framework.
This report also provides an analysis of leading big data solutions with key metrics such as streaming IoT data analytics revenue for leading providers. The report evaluates, compares, and contrasts vendors, and provides a vendor ranking matrix. Analysis takes into consideration solutions integrating both structured and unstructured data.
Big data solutions are relied upon to gain insights from data files/sets so large and complex that it becomes difficult to process using traditional database management tools and data processing applications. The publisher sees key solution areas for big data as commerce, geospatial, finance, healthcare, transportation, and smart grids. Key technology integration includes AI, IoT, cloud and high-performance computing.
Industry verticals of various types have challenges in capturing, organizing, storing, searching, sharing, transferring, analyzing, and using data to improve business. Big data is making a big impact in certain industries such as the healthcare, industrial, and retail sectors. Every large corporation collects and maintains a huge amount of data associated with its customers including their preferences, purchases, habits, travels, and other personal information. In addition to the large volume, much of this data is unstructured, making it hard to manage.
Big data technology will help financial institutions maximize the value of data and gain a competitive advantage, minimize costs, convert challenges to opportunities, and minimize risk in real-time. As an example, in the transportation industry, real-time applications can match loads to a vehicle's capacity using data analytics. Bigdata provides shipping and delivery companies with real-time notifications and updates to increase efficiency and accuracy.
Big data technologies provide financial services firms with the capability to capture and analyze data, build predictive models, back-test, and simulate scenarios. Through iteration, firms will determine the most important variables and also key predictive models. Financial firms are increasingly migrating their data and analytics to the cloud, leading to reduced cost, better data management, and better customer service. Data and insights can also be transferred far quicker than before, allowing representatives to provide customers with real-time data-backed insights.
Healthcare services can be applied more accurately with big data. Decisions based on real-time data and assistance from AI/ML solutions. Private health insurance providers can gain access to previously inaccessible information and databases through big data. Healthcare customer service processes can also be streamlined while providing personalized more personalized medical care to individuals.
Big data analytics allows retail companies to examine and interact with their audience online in new ways. Predictive analytics can analyze a consumer's activity and recommend suggested items to them. Once a consumer has purchased from a company, big data can help retain that customer by better understanding what a person wants. For example, online retailers collect all their customers' data to provide a personalized experience, earning up to 40% of their revenue from their customers' data.
Customer Relationship Management benefits greatly from the use of technology for organizing, automating, and synchronizing all customer-related information like sales, marketing, services, support and more. Big data represents a big business opportunity and it is poised to do more than just improve CRM.
Data analytics is useful for Supply Chain Management because it can analyze a variety of variables across business operations. SCM service providers use advanced analytics to analyze materials, products in inventory and imports/exports to better understand needs. This helps a business to manage its assets better, saving time and money. Data analytics can predict future risks based on history and a large set of data.
Select Report Findings:
- Big data in business intelligence apps will reach $63.5 billion by 2028
- Data Integration & Quality Tools to reach $1.2 billion globally by 2028
- Enterprise performance analytics will reach $39.9 billion globally by 2028
- Big data in supply chain management will reach $8.3 billion globally by 2028
- Combination of AI and IoT (AIoT)will rely upon advanced big data analytics software
- Real-time data will be a key value position for all use cases, segments, and solutions
- Market leading companies are rapidly integrating big data technologies with IoT infrastructure
Market Drivers
- Data Volume and Variety
- Increasing Adoption of Big Data by Enterprises and Telecom
- Maturation of Big Data Software
- Continued Investments in Big Data by Web Giants
- Business Drivers
Market Barriers
- The Big Barrier: Privacy and Security Gaps
- Workforce Reskilling and Organizational Resistance
- Lack of Clear Big Data Strategies
- Scalability and Maintenance Technical Challenges
- Big Data Development Expertise
Key Topics Covered:
1.0 Executive Summary
2.0 Introduction
2.1 Big Data Overview
2.1.1 Defining Big Data
2.1.2 Big Data Ecosystem
2.1.3 Key Characteristics of Big Data
2.1.3.1 Volume
2.1.3.2 Variety
2.1.3.3 Velocity
2.1.3.4 Variability
2.1.3.5 Complexity
2.2 Research Background
3.0 Big Data Challenges and Opportunities
3.1 Securing Big Data Infrastructure
3.1.1 Big Data Infrastructure
3.1.2 Infrastructure Challenges
3.1.3 Big Data Infrastructure Opportunities
3.1.3.1 Securing State Data
3.1.3.2 Securing APIs
3.1.3.3 Securing Applications
3.1.3.4 Securing Data for Analysis
3.1.3.5 Securing User Privileges
3.1.3.6 Securing Enterprise Data
3.2 Unstructured Data and the Internet of Things
3.2.1 New Protocols, Platforms, Streaming and Parsing, Software and Analytical Tools
4.0 Big Data Technologies and Business Cases
4.1 Big Data Technology
4.1.1 Hadoop
4.1.1.1 Other Apache Projects
4.1.2 NoSQL
4.1.2.1 Hbase
4.1.2.2 Cassandra
4.1.2.3 Mongo DB
4.1.2.4 Riak
4.1.2.5 CouchDB
4.1.3 MPP Databases
4.1.4 Other Technologies
4.1.4.1 Storm
4.1.4.2 Drill
4.1.4.3 Dremel
4.1.4.4 SAP HANA
4.1.4.5 Gremlin & Giraph
4.2 Emerging Technologies, Tools, and Techniques
4.2.1 Streaming Analytics
4.2.2 Cloud Technology
4.2.3 Search Technologies
4.2.4 Customizes Analytics Tools
4.2.5 Keywords Optimization
4.3 Big Data Roadmap
5.0 Key Big Data Sectors
5.1 Industrial Automation and Internet of Things
5.2 Retail and Hospitality
5.3 Digital Media
5.4 Utilities
5.5 Financial Services
5.6 Healthcare
5.7 Information and Communications Technologies
5.8 Government: Administration and Homeland Security
5.9 Other Sectors
5.9.1 Aviation
5.9.2 Transportation and Logistics: Optimizing Fleet Usage
5.9.3 Real-Time Processing of Sports Statistics
5.9.4 Education
5.9.5 Manufacturing
5.9.6 Extraction and Natural Resources
6.0 Big Data Value Chain
6.1 Fragmentation in the Big Data Value Chain
6.2 Data Acquisitioning and Provisioning
6.3 Data Warehousing and Business Intelligence
6.4 Analytics and Visualization
6.5 Actioning and Business Process Management
6.6 Data Governance
7.0 Big Data Analytics
7.1 The Role and Importance of Big Data Analytics
7.2 Big Data Analytics Processes
7.3 Reactive vs. Proactive Analytics
7.4 Technology and Implementation Approaches
8.0 Standardization and Regulatory Issues
8.1 Cloud Standards Customer Council
8.2 National Institute of Standards and Technology
8.3 OASIS
8.4 Open Data Foundation
8.5 Open Data Center Alliance
8.6 Cloud Security Alliance
8.7 International Telecommunications Union
8.8 International Organization for Standardization
9.0 Big Data in Industry Vertical Applications
9.1 Big Data Application in Manufacturing
9.2 Retail Applications
9.3 Big Data Application: Insurance Fraud Detection
9.4 Big Data Application: Media and Entertainment Industry
9.5 Big Data Application: Weather Patterns
9.6 Big Data Application: Transportation Industry
9.7 Big Data Application: Education Industry
9.8 Big Data Application: E-Commerce Personalization
9.9 Big Data Application: Oil and Gas Industry
9.10 Big Data Application: Telecommunication Industry
10.0 Key Big Data Companies and Solutions
10.1 Vendor Assessment Matrix
10.2 Competitive Landscape of Major Big Data Vendors
10.2.1 New Products Developments
10.2.2 Partnership, Merger, Acquisition, and Collaboration
For more information about this report visit https://www.researchandmarkets.com/r/nd1sk5
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