
Region:Asia
Author(s):Yogita Sahu
Product Code:KROD1537
October 2024
99

The market can be segmented into various factors like deployment, industry vertical, and region.
By Deployment: The market is segmented by deployment into on-premise and cloud-based. In 2023, the Cloud-Based segment held a dominant market share by the growing preference for cloud-based solutions due to their scalability, cost-effectiveness, and ease of deployment.

By Region: The market is segmented by region into China, South Korea, Japan, India, Australia, and the Rest of APAC. In 2023, China led the market with its rapid technological advancements, extensive digital infrastructure. Japan and South Korea's market shares are driven by their advanced economies, high internet penetration, and significant investments in IoT and smart city projects.
By Industry Vertical: The market is segmented by industry vertical into finance, healthcare, retail, and manufacturing. In 2023, the Finance segment dominated the market by the increasing adoption of real-time analytics for risk management, fraud detection, and algorithmic trading.

|
Company Name |
Establishment Year |
Headquarters |
|
IBM Corporation |
1911 |
Armonk, New York, USA |
|
Microsoft Corporation |
1975 |
Redmond, Washington, USA |
|
Oracle Corporation |
1977 |
Redwood City, California, USA |
|
SAP SE |
1972 |
Walldorf, Germany |
|
Software AG |
1969 |
Darmstadt, Germany |
The future trends in the market include the widespread adoption of 5G-enabled streaming analytics, the growth of edge analytics, the integration of AI and ML with streaming analytics, and the expansion of smart city initiatives across the region.
|
By Industry Vertical |
Financial Services Healthcare Retail |
|
By Deployment Model |
On-Premises Cloud-Based |
|
By Type |
Software Services |
|
By Region |
China South Korea Japan India Australia Rest of APAC |
1.1. Definition and Scope
1.2. Market Taxonomy
1.3. Market Growth Rate
1.4. Market Segmentation Overview
2.1. Historical Market Size
2.2. Year-on-Year Growth Analysis
2.3. Key Market Developments and Milestones
3.1. Growth Drivers
3.1.1. Adoption in Financial Services
3.1.2. IoT Device Connectivity
3.1.3. AI and ML Integration
3.1.4. Government Initiatives
3.2. Restraints
3.2.1. Data Privacy Concerns
3.2.2. High Implementation Costs
3.2.3. Skills Gap
3.2.4. Legacy System Integration
3.3. Opportunities
3.3.1. Edge Computing Growth
3.3.2. Smart City Projects
3.3.3. 5G Network Deployment
3.3.4. Expansion in Emerging Markets
3.4. Trends
3.4.1. AI-Powered Analytics
3.4.2. Increased Cloud Adoption
3.4.3. Real-Time Customer Insights
3.4.4. Predictive Maintenance Adoption
3.5. Government Regulation
3.5.1. Data Protection Laws
3.5.2. Digital Economy Policies
3.5.3. AI and Analytics Support Programs
3.5.4. Public-Private Partnerships
3.6. SWOT Analysis
3.7. Stake Ecosystem
3.8. Competition Ecosystem
4.1. By Industry Vertical (in Value %)
4.1.1. Financial Services
4.1.2. Healthcare
4.1.3. Retail
4.1.4. Telecommunications
4.1.5. Manufacturing
4.2. By Deployment Model (in Value %)
4.2.1. On-Premises
4.2.2. Cloud-Based
4.3. By Region (in Value %)
4.3.1. China
4.3.2. Japan
4.3.3. India
4.3.4. South Korea
4.3.5. Australia
4.3.6. Rest of APAC
4.4. By Type (in Value %)
4.4.1. Software
4.4.2. Services
5.1. Detailed Profiles of Major Companies
5.1.1. IBM Corporation
5.1.2. Microsoft Corporation
5.1.3. SAP SE
5.1.4. Oracle Corporation
5.1.5. TIBCO Software Inc.
5.2. Cross Comparison Parameters (No. of Employees, Headquarters, Inception Year, Revenue)
6.1. Market Share Analysis
6.2. Strategic Initiatives
6.3. Mergers and Acquisitions
6.4. Investment Analysis
6.4.1. Venture Capital Funding
6.4.2. Government Grants
6.4.3. Private Equity Investments
7.1. Data Protection Regulations
7.2. Compliance Requirements
7.3. Certification Processes
8.1. Future Market Size Projections
8.2. Key Factors Driving Future Market Growth
9.1. By Industry Vertical (in Value %)
9.2. By Deployment Model (in Value %)
9.3. By Type (in Value %)
9.4. By Region (in Value %)
10.1. TAM/SAM/SOM Analysis
10.2. Customer Cohort Analysis
10.3. Marketing Initiatives
10.4. White Space Opportunity Analysis
Ecosystem creation for all the major entities and referring to multiple secondary and proprietary databases to perform desk research around market to collate industry level information.
Collating statistics on this industry over the years, penetration of marketplaces and service providers ratio to compute revenue generated for Asia Pacific Streaming Analytics industry. We will also review service quality statistics to understand revenue generated which can ensure accuracy behind the data points shared.
Building market hypothesis and conducting CATIs with industry experts belonging to different companies to validate statistics and seek operational and financial information from company representatives.
Our team will approach multiple telecommunication companies and understand nature of product segments and sales, consumer preference and other parameters, which will support us validate statistics derived through bottom to top approach from such telecommunication companies.
The Asia Pacific streaming analytics market size was valued at USD 4.22 billion in 2023. The market's growth is primarily driven by the increasing adoption of real-time analytics across various industries, including finance, healthcare, and retail.
The challenges in the Asia Pacific streaming analytics market include data privacy and security concerns, a shortage of skilled professionals, high implementation and operational costs, and integration issues with legacy systems.
Key players in the Asia pacific streaming analytics market include IBM Corporation, Microsoft Corporation, SAP SE, Oracle Corporation, and TIBCO Software Inc., which lead the market with advanced analytics solutions and significant regional presence.
The growth of the Asia Pacific streaming analytics market is driven by the increasing adoption of real-time analytics in financial services, the surge in IoT device connectivity, government support for digital transformation initiatives, and rising investments in AI and machine learning technologies.
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