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Disruptive Big Data Applications in South Africa

Information Technology Disruptive Big Data Applications in South Africa

Assessing the Use of Big Data Solutions in Local Industries


RELEASE DATE
19-Aug-2016
REGION
Africa
DELIVERABLE TYPE
Market Research

RESEARCH CODE
MC75-01-00-00-00
SKU
IT03126-AF-MR_18905
Yes
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Disruptive Big Data Applications in South Africa
Published on: 19-Aug-2016 | SKU: IT03126-AF-MR_18905

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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

List of Figures & Charts
List of 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
List of Charts
  • 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
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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.
More Information
Deliverable Type Market Research
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