Implementing Analytics
A Blueprint for Design, Development, and Adoption
- 1st Edition - May 30, 2013
- Latest edition
- Author: Nauman Sheikh
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 4 0 1 6 9 6 - 5
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 0 1 6 8 1 - 1
Implementing Analytics demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possib… Read more
Implementing Analytics demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possible for widespread adoption across all functions of an organization. Implementing Analytics simplifies and helps democratize a very specialized discipline to foster business efficiency and innovation without investing in multi-million dollar technology and manpower. A technology agnostic methodology that breaks down complex tasks like model design and tuning and emphasizes business decisions rather than the technology behind analytics.
- 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
Data warehouse professionals, data architects, IT managers, business professionals in data-intensive jobs, business and financial analysts, project managers
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
- Edition: 1
- Latest edition
- Published: May 30, 2013
- Language: English
NS
Nauman Sheikh
Nauman Sheikh is a veteran of the data architecture profession who has built dozens of large scale operational and analytical systems over the last 18 years. He has worked in three continents solving business challenges in consumer credit, risk, fraud and direct marketing areas dealing with a variety of cultural, technological and legal challenges surrounding data and its use. He is a hands-on practitioner with skills ranging from analytical reporting to data mining models to analytics driven business decisions and their audit and control frameworks.
Affiliations and expertise
Nauman Sheikh is a veteran of the data architecture profession who has built dozens of large scale operational and data warehouse systems over the last 17 years.Read Implementing Analytics on ScienceDirect