Implementing Analytics
A Blueprint for Design, Development, and Adoption
Description
Key Features
- Simplifies the understanding of analytics from a technical and functional perspective and shows a wide array of problems that can be tackled using existing technology
- Provides a detailed step by step approach to identify opportunities, extract requirements, design variables and build and test models. It further explains the business decision strategies to use analytics models and provides an overview for governance and tuning
- Helps formalize analytics projects from staffing, technology and implementation perspectives
- Emphasizes machine learning and data mining over statistics and shows how the role of a Data Scientist can be broken down and still deliver the value by building a robust development process
Readership
Data warehouse professionals, data architects, IT managers, business professionals in data-intensive jobs, business and financial analysts, project managers
Table of Contents
Acknowledgments
Author Biography
Introduction
Organization of Book
Audience
Part 1: Concept
Chapter 1. Defining Analytics
The Hype
The Challenge of Definition
Analytics Techniques
Conclusion of Definition
Chapter 2. Information Continuum
Building Blocks of the Information Continuum
Information Continuum Levels
Summary
Chapter 3. Using Analytics
Healthcare
Customer Relationship Management
Human Resource
Consumer Risk
Insurance
Telecommunication
Higher Education
Manufacturing
Energy and Utilities
Fraud Detection
Patterns of Problems
Part 2: Design
Chapter 4. Performance Variables and Model Development
Performance Variables
Model Development
Champion–Challenger: A Culture of Constant Innovation
Chapter 5. Automated Decisions and Business Innovation
Automated Decisions
Decision Strategy
Decision Automation and Intelligent Systems
Strategy Evaluation
Champion–Challenger Strategies
Chapter 6. Governance: Monitoring and Tuning of Analytics Solutions
Analytics and Automated Decisions
Audit and Control Framework
Part 3: Implementation
Chapter 7. Analytics Adoption Roadmap
Learning from Success of Data Warehousing
The Pilot
Chapter 8. Requirements Gathering for Analytics Projects
Purpose of Requirements
Requirements: Historical Perspective
Requirements Extraction
Chapter 9. Analytics Implementation Methodology
Centralized versus Decentralized
Building on the Data Warehouse
Methodology
Chapter 10. Analytics Organization and Architecture
Organizational Structure
Technical Components in Analytics Solutions
Chapter 11. Big Data, Hadoop, and Cloud Computing
Big Data
Hadoop
Cloud Computing (For Analytics)
Conclusion
Objective 1: Simplification
Objective 2: Commoditization
Objective 3: Democratization
Objective 4: Innovation
References
Index
Product details
- No. of pages: 234
- Language: English
- Copyright: © Morgan Kaufmann 2013
- Published: May 6, 2013
- Imprint: Morgan Kaufmann
- eBook ISBN: 9780124016811
- Paperback ISBN: 9780124016965