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Big Data Analytics
preview of book Big Data Analytics
text of book Big Data Analytics

Big Data Analytics

Publisher: MC Press Online
Publication Date: October 2012
Subject: Computers: Information Technology
Number of Pages: 97

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About this title
The Big Data tsunami is already hitting organizationsa set of disruptive technologies to drive game changers. Business leaders across the globe are seeking answers to the following questions:
  • What is Big Data and what are others doing with it?
  • How do we build a strategic plan for Big Data Analytics?
  • How does Big Data change our analytics architecture?
Unlike many other Big Data Analytics blogs and books that cover the basics and technological underpinnings, this book brings a practitioner's view to Big Data Analytics. The author has drawn the material from a large number of workshops and interviews with business and IT leaders.

The book examines the drivers behind Big Data, postulates a set of use cases, identifies a set of solution components enabled by Big Data, synthesizes a solution, and recommends implementation approaches. In doing so, it respects organizations' current large investments in Data Warehousing and Business Intelligence and demonstrates both evolutionary and revolutionary ways of moving forward to the brave new world of Big Data.
About author
Arvind Sathi
Dr. Arvind Sathi is the Global Communication Sector Lead Architect for the Information Agenda team at IBM. Dr. Sathi received his Ph.D. in Business Administration from Carnegie Mellon University and worked under Nobel Prize winner Dr. Herbert A. Simon. Dr. Sathi is a seasoned professional with more than 20 years of leadership in Information Management architecture and delivery. His primary focus has been in the delivery and architecture oversight of IT projects to communications organizations. He has extensive experience with many domestic as well as international communications service providers, as well as with other services industries.

Prior to joining IBM, Dr. Sathi was the pioneer in developing knowledge-based solutions for CRM at Carnegie Group. At BearingPoint, he led the development of Enterprise Integration, MDM, and Operations Support Systems/Business Support Systems (OSS/BSS) solutions for the communications market and also developed horizontal solutions for communications, financial services, and public services. At IBM, Dr. Sathi has led several Information Management programs in MDM, data security, business intelligence, and related areas and has provided strategic architecture oversight to IBM's strategic accounts. He has also delivered a number of workshops and presentations at industry conferences on technical subjects including MDM and data architecture, and he holds patents in data masking.

Foreword by Bob Keseley
Foreword by Jeff Jonas

Chapter 1: Introduction
1.1 Volume
1.2 Velocity
1.3 Variety
1.4 Veracity

Chapter 2: Drivers for Big Data?
2.1 Sophisticated Consumers
2.2 Automation
2.3 Monetization

Chapter 3: Big Data Analytics Applications
3.1 Social Media Command Center
3.2 Product Knowledge Hub
3.3 Infrastructure and Operations Studies
3.4 Product Selection, Design and Engineering
3.5 Location-Based Services
3.6 Micro-Segmentation and Next Best Action
3.7 Online Advertising
3.8 Improved Risk Management

Chapter 4: Architecture Components
4.1 Massively Parallel Processing (MPP) Platforms
4.2 Unstructured Data Analytics and Reporting
   Search and Count
   Context-Sensitive and Domain-Specific Searches
   Categories and Ontology
   Qualitative Comparisons
   Focus on Specific Time Slice or Using Other Dimensions
4.3 Big Data and Single View of Customer/Product
4.4 Data Privacy Protection
4.5 Real-Time Adaptive Analytics and Decision Engines

Chapter 5: Advanced Analytics Platform
5.1 Real-Time Architecture for Conversations
5.2 Orchestration and Synthesis Using Analytics Engines
   Entity Resolution
   Model Management
   Command Center
   Analytics Engine
5.3 Discovery Using Data at Rest
5.4 Integration Strategies

Chapter 6: Implementation of Big Data Analytics
6.1 Revolutionary, Evolutionary, or Hybrid
6.2 Big Data Governance
   Integrating Big Data with MDM
6.3 Journey, Milestones, and Maturity Levels
   Analytics Business Maturity Model

Chapter 7: Closing Thoughts

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