Big Data & Analytics for Telcos Series

Series Highlights

With the explosive growth of smart phones, telecommunications network operators massive growth in the volume, variety and velocity of data travelling across their networks. Telcos have always collected voice data but today’s data is much more complex and includes photos, video, streaming music, web browsing, location data, social media, text messages, email, and more. Traditional storage and analytics solutions cannot adequately manage this expanding, diverse volume of data. Telcos need new storage and analysis solutions so they can cost-effectively store and derive insights from this growing and invaluable volume of customer data. The Ossidian Big Data and Analytics Series follows on from the Ossidian Telco Transformation Series for Telcos  and comprises six eLearning modules that highlight examples of Big Dat and Analytics use cases and that explain how to gather, in real-time, massive quantities of unstructured and structured data to gain insights, deliver location-based services and targeted promotions, improve fraud detection and leverage its customer relationships profitably.

Course Contents

Overview

  • Introduction to Big Data and Analytics (BDA)

  • Gold Mining Analogy

  • Hadoop and BDA Adoption

  • Big Data Processing, Storage, Exploration, Discovery and Visualisation

  • Five Use Cases

  • Definition

  • Characteristics

  • The Analytics Market

  • History of BDA

 

BDA Examples

  • Telecoms and Entertainment

  • Retail

  • Government and Institutions

  • Security and Fraud Prevention

  • BDA in Africa

 

BDA Market

  • Market Overview
  • Market Drivers
  • Market Restraints
  • Vertical Markets
  • Market Players
  • Advanced Analytics
  • Business Intelligence verses Data Science

BDA Platform Introduction

  • Structured and Unstructured Data
  • Types of Data
  • Sources of Data
  • Platform
  • Physical
  • Infrastructure
  • Example of a Big Data Platform

Data Analytics

  • Basics of Data Analytics
  • Data Analytics People
  • Text Analytics (search, sentiment)
  • Numerical Analytics
  • Multimodal Analytics
  • Real-time Analytics (event identification,
  • stream analysis)
  • Querying Structured and Unstructured Data
  • Computer Decision Making
  • Machine Learning
  • Automation
  • Data Visualisation
  • User Interaction (UX) and User
  • Experience (UX)

BDA in the Enterprise

  • Application Examples from a Technical Perspective
  • Legal and Ethical Considerations
  • Data Governance
  • Data Security
  • Future Scenarios