Disruptive Big Data Applications in South Africa
Disruptive Big Data Applications in South Africa
Assessing the Use of Big Data Solutions in Local Industries
19-Aug-2016
Africa
Description
Although South Africa is in a nascent phase regarding the adoption of Big Data technologies, industries have begun implementing Big Data solutions for improved operational processes and to find new business opportunities. The key driving forces behind Big Data discussed in this analysis include increased connectivity and Internet usage, for both mobile and machine-to-machine (M2M) communications; growth in cloud services; the continued desire for African insights; and reducing costs associated with Big Data solutions. This analysis focuses on the Big Data solutions market as it stands in 2016, with detailed analyses on industry adoption and uses, as well as the expectations for the market in South Africa.
Table of Contents
Key Findings
Research Aim and Objectives
Research Scope—Ecosystem
The Characteristics of Big Data
Primary Forces Enabling Big Data
- Primary Characteristics of Big Data
- Secondary Characteristics of Big Data
The Value Chain for Big Data Solutions
Overview
Sources of Big Data
Data Storage Options
Data Storage—The Shift to Cloud-based Services
Data Processing—Batch versus Streamed
Data Analytics
Data Analytics—Global Snapshot
Market Drivers
Market Restraints
Global Big Data Adoption by Industry
Big Data Solutions—Automotive and Logistics
Big Data Solutions—Extractive Industries
Big Data Solutions—Telecommunications
Big Data Solutions—Healthcare
Big Data Solutions—Retail
Big Data Solutions—Public Sector
Big Data Solutions—Financial Services and Insurance
Big Data Solutions—Entertainment Industry
Innovative Participants in the South African Market
Introducing the SKA
Applying the Big Data Value
The Impact of the PoPI Act
Incorporating Machine Learning and Artificial Intelligence (AI)
The Last Word
Legal Disclaimer
Research Acronyms
Research Acronyms—Measures of Data Size
- 1. Big Data Solutions Market: Market Drivers, South Africa, 2016–2022
- 2. Big Data Solutions Market: Market Restraints, South Africa, 2016–2022
- 3. SKA Data Analysis Process, South Africa, 2016
- 4. Convergence Between Machine Learning, AI, and Big Data, Global, 2016
- 1. Big Data Ecosystem, Global, 2016
- 2. Major ICT Trends, Global, 2016
- 3. Big Data Value Chain, Global, 2016
- 4. Big Data Value Chain Explained, Global, 2016
- 5. Big Data Solutions Market: Notable Participants, South Africa, 2016
- 6. The Complexity of Data, Global, 2016
- 7. Big Data Sources, South Africa, 2010–2020
- 8. Batch or Stream Data Processing, Global, 2016
- 9. Four Types of Data Analytics, Global, 2016
- 10. Data Analytics Market: Per cent Revenue Breakdown, Global, 2015
- 11. Big Data Solutions Market: Revenue Breakdown by Vertical Market, Global, 2014
Related Research
Popular Topics
No Index | No |
---|---|
Podcast | No |
Table of Contents | | Executive Summary~ || Key Findings~ | Market Overview~ || Research Aim and Objectives~ || Research Scope—Ecosystem~ | What is Big Data?~ || The Characteristics of Big Data~ ||| Primary Characteristics of Big Data~ ||| Secondary Characteristics of Big Data~ || Primary Forces Enabling Big Data~ || The Value Chain for Big Data Solutions~ | Data Acquisition~ || Overview~ || Sources of Big Data~ | Data Management—Storage and Processing~ || Data Storage Options~ ||| Traditional Storage~ ||| Hybrid Storage~ ||| Cloud Storage~ || Data Storage—The Shift to Cloud-based Services~ || Data Processing—Batch versus Streamed~ | Data Analytics~ || Data Analytics~ || Data Analytics—Global Snapshot~ | Market Drivers and Restraints~ || Market Drivers~ ||| Increased connectivity and Internet usage, such as M2M, IoT, and smartphones~ ||| Growth of cloud services~ ||| Reducing costs associated with Big Data~ ||| Desire for African insights~ || Market Restraints~ ||| Lack of appropriate skills and knowledge~ ||| Existing infrastructure and data sources~ ||| Weakness of the local economy~ ||| Unconvincing business case~ ||| Concerns about data security and respecting privacy~ | Industry Outlook~ || Global Big Data Adoption by Industry~ || Big Data Solutions—Automotive and Logistics~ || Big Data Solutions—Extractive Industries~ || Big Data Solutions—Telecommunications~ || Big Data Solutions—Healthcare~ || Big Data Solutions—Retail~ || Big Data Solutions—Public Sector~ || Big Data Solutions—Financial Services and Insurance~ || Big Data Solutions—Entertainment Industry~ || Innovative Participants in the South African Market~ | The Outlook for Big Data Solutions in South Africa~ || Introducing the SKA~ || Applying the Big Data Value~ || The Impact of the PoPI Act~ || Incorporating Machine Learning and Artificial Intelligence (AI)~ | Conclusion~ || The Last Word~ || Legal Disclaimer~ | Appendix~ || Research Acronyms~ || Research Acronyms—Measures of Data Size~ | The Frost & Sullivan Story~ |
List of Charts and Figures | 1. Big Data Solutions Market: Market Drivers, South Africa, 2016–2022~ 2. Big Data Solutions Market: Market Restraints, South Africa, 2016–2022~ 3. SKA Data Analysis Process, South Africa, 2016~ 4. Convergence Between Machine Learning, AI, and Big Data, Global, 2016~| 1. Big Data Ecosystem, Global, 2016~ 2. Major ICT Trends, Global, 2016~ 3. Big Data Value Chain, Global, 2016~ 4. Big Data Value Chain Explained, Global, 2016~ 5. Big Data Solutions Market: Notable Participants, South Africa, 2016~ 6. The Complexity of Data, Global, 2016~ 7. Big Data Sources, South Africa, 2010–2020~ 8. Batch or Stream Data Processing, Global, 2016~ 9. Four Types of Data Analytics, Global, 2016~ 10. Data Analytics Market: Per cent Revenue Breakdown, Global, 2015~ 11. Big Data Solutions Market: Revenue Breakdown by Vertical Market, Global, 2014~ |
Author | Mauritz Venter |
Industries | Information Technology |
WIP Number | MC75-01-00-00-00 |
Is Prebook | No |