Artificial Intelligence (AI) and Big Data Analytics (BDA) in Telecomm Industry, 2021 Update - Market Overview, Technology Ecosystem, Telco Use Cases and Monetisation Strategies

Artificial Intelligence (AI) and Big Data Analytics (BDA) in Telecomm Industry, 2021 Update - Market Overview, Technology Ecosystem, Telco Use Cases and Monetisation Strategies

Summary

Artificial Intelligence (AI) and Big Data Analytics (BDA) in Telecomm Industry, 2021 Update provides an executive-level overview of the global artificial intelligence (AI) and big data analytics (BDA) market. It defines AI and BDA and delivers qualitative insights into the AI and BDA industries, value chain, and ecosystem dynamics. It also analyzes key trends in the industry and provides insights into telco AI and BDA monetization strategies.

The telecommunications industry is being transformed by the use of artificial intelligence (AI) and big data analytics (BDA). Telcos are utilizing AI and BDA in their own operations to automate networks and improve customer experience. In addition, AI and BDA represents new revenue stream opportunities for telcos and technology companies in the way of services such as AI and BDA platform creation, integration, and management.

The report provides an in-depth analysis of the following -
- AI and BDA taxonomy & market context: definitions of AI and BDA and technology enablement. This section also provides an overview of the regulatory environment and ethical considerations.
- The AI and BDA ecosystem: an overview of the AI and BDA value chain and ecosystem players. This section also includes an analysis of telcos role within the AI and BDA value chains.
- Case studies: this section analyzes the AI and BDA value proposition, business models, and strategies of four telecom operators.
- Key findings and recommendations: the Global Outlook Report concludes with a number of key findings and a set of recommendations for AI and BDA stakeholders, including telecom service providers.

Scope

- AI and BDA are complementary technologies driving use cases such as Industry 4.0
- AI and BDA are opening up new revenue stream opportunities for telcos and technology companies
- Telcos are joining the AI and BDA race to drive digital transformation and personalized services, internal efficiency, and innovation
- AI and BDA is set to drive telco transformation towards digital service and telco as a service models
- Telcos must remain compliant to regional AI and BDA regulations as well as establish their own set of principles for the ethical use of AI and BDA

Reasons to Buy

- This Global Outlook Report provides definitions of AI and BDA as well as a comprehensive examination of the AI and BDA value chains. It helps executives fully understand the ecosystem, market dynamics, and value chain. It helps telecom decision-makers determine key AI and BDA positioning strategies, formulate effective product development plans, and optimize return on investments.
- Four case studies illustrate the findings of the report, providing insights around different telco AI and BDA value propositions across the world, including services, monetization approaches and partnerships. This will help telecom executives craft adapted AI and BDA (including cloud gaming) strategies to unlock new revenue streams and operational efficiencies.
- The report discusses concrete opportunities in the AI and BDA market, providing a number of actionable recommendations for AI and BDA ecosystem participants, including telecom service providers.
Table of Contents
List of Exhibits
Executive Summary
Section 1: Taxonomy and Market Context
Defining Big Data Analytics
Defining AI
AI Technologies Overview
Big Data Regulations, Data Governance, Protection, and Privacy
AI and Big Data Ethics
Section 2: The AI and Big Data Analytics Ecosystem
BDA Value Chain Overview
AI Value Chain Overview
Telecoms AI and BDA Use Cases Overview
Telcos Potential Roles in the BDA Value Chain
Telcos Potential Roles in the AI Value Chain
Section 3: Case Studies
Deutsche Telekom
Orange France
Singtel
China Telecom
Section 4: Key Findings and Recommendations

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