Obtaining Value from Big Data for Service Systems, Volume I
Big Data Management
2nd Edition
Published on 29. May 2019
136 pages
978-1-949443-56-1 (ISBN)
Description
This volume will assist readers in fitting big data analysis into their service-based organizations.
Volume I of this two-volume series focuses on the role of big data in service delivery systems. It discusses the definition and orientation to big data, applications of it in service delivery systems, how to obtain results that can affect/enhance service delivery, and how to build an effective big data organization.
This volume will assist readers in fitting big data analysis into their service-based organizations. It will also help readers understand how to improve the use of big data to enhance their service-oriented organizations.
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Stephen H. Kaisler | Frank Armour | J. Alberto Espinosa
Obtaining Value from Big Data for Service Systems, Volume I
Big Data Management
Book
05/2019
2nd Edition
Business Expert Press
€25.30
Shipment within 3-4 weeks
Persons
Stephen H. Kaisler is consultant at SHK & Associates and adjunct professor of engineering at George Washington University. He has focused his efforts on Big Data and analytics, and enterprise architecture. He has published over 45 papers and 11 books on varied subjects such as Big Data and Historical Computing Machines.
Content
- Cover
- Obtaining Value from Big Data for Service Systems, Volume I: Big Data Management
- Dedication
- Contents
- Purpose
- Acknowledgments
- List of Acronyms
- Chapter 1: Introduction
- Defining Big Data
- Getting Started with Big Data
- Adding Value to Organizations
- Outline of This Book
- Chapter 2: Applications of Big Data to Service Delivery
- Defining Services
- Service Systems
- Big Data in Service Delivery
- A Service Delivery Model
- Supporting Service Delivery with Big Data
- Data-Driven Companies
- Retail Analytics
- Health Care
- Fraud Detection
- Mass Transit
- Chapter 3: Analyzing Big Data for Successful Results
- Big Data Usage
- Big Data Analytics
- Big Data Analytics Initiatives Need a Process
- The Analytics Cycle
- Analytic Modeling Methods and Approaches
- Quantitative Analysis
- Qualitative Analysis
- Emerging Analytics Application Areas
- Sentiment Analysis
- Geospatial Analytics
- Unstructured Text Processing
- Image, Video, and Audio Processing
- Edge and Location-Specific Analytics
- Network Analytics
- Cognitive Analytics
- Key Challenges for Analytics
- Analytical Resource and Tool Survey
- Commercial Packages
- Open Source Packages
- Chapter 4: Building an Effective Big Data Organization
- Organizational Design and Practices
- Enterprise, Domain, and Application Architecture
- Business Architecture for Analytics
- Business Architecture (BA) Aligns Analytics Initiatives and Business Outcomes
- Analytics Body of Knowledge Focus
- Governanc
- Big Data Analytics Culture
- Big Data Analytics Maturity
- People
- Chief Data Officer/Chief Information Officer
- Chief Knowledge Officer
- Chief Analytic Officer
- Chief Portfolio Integration Manager
- Analytically Informed Business Managers
- Data Analyst
- Data Scientist
- Big Data Technologist-Infrastructure and Tools
- Talent Identification and Acquisition-Staffing the Analytics Operation
- Hiring Trained Staff
- Communication, Organizational Skills, and Knowledge
- In-House Training
- College and University Education Programs
- Commercial Education Opportunities
- Outsourcing the Big Data Analytics Function
- Big Data Analytics Teamwork
- Distributed Analytics-Analytics within the Business Unit
- Centralized Analytics Group
- Analytics Group within the IT Department
- Distributed Analytics Groups within the IT and Business Units
- APPENDIX: Methods-Based Analytics Taxonomy
- References
- Further Reading
- Glossary
- About the Contributors
- Index
- Ad Page
- Back Cover
