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Organization of Book
Part 1: Concept
Chapter 1. Defining Analytics
The Challenge of Definition
Conclusion of Definition
Chapter 2. Information Continuum
Building Blocks of the Information Continuum
Information Continuum Levels
Chapter 3. Using Analytics
Customer Relationship Management
Energy and Utilities
Patterns of Problems
Part 2: Design
Chapter 4. Performance Variables and Model Development
Champion–Challenger: A Culture of Constant Innovation
Chapter 5. Automated Decisions and Business Innovation
Decision Automation and Intelligent Systems
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
Chapter 8. Requirements Gathering for Analytics Projects
Purpose of Requirements
Requirements: Historical Perspective
Chapter 9. Analytics Implementation Methodology
Centralized versus Decentralized
Building on the Data Warehouse
Chapter 10. Analytics Organization and Architecture
Technical Components in Analytics Solutions
Chapter 11. Big Data, Hadoop, and Cloud Computing
Cloud Computing (For Analytics)
Objective 1: Simplification
Objective 2: Commoditization
Objective 3: Democratization
Objective 4: Innovation
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
- No. of pages:
- © Morgan Kaufmann 2013
- 30th May 2013
- Morgan Kaufmann
- Paperback ISBN:
- eBook ISBN:
"...demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possible for widespread adoption...""…the book is approachable for a non-technical audience as a broad overview of, and as an evangelization tool for, the discipline of analytics." --ComputingReviews.com, March 2014
"The industry hype these days is all about Big Data, but the more interesting topic is what should you be doing with all of that data? This book answers that question…Sheikh does a fine job of helping to simplify a complex topic…If you are looking for a concise and readable treatment on how you can benefit from applying analytics to your data look no further than Implementing Analytics." --Data and Technology Today online, November 2013
"Intended for both IT professionals and business managers, this guide addresses how to plan, design, and build analytics solutions for solving business problems and improving decision strategies." --Reference and Research Book News, August 2013
"This book simplifies the big data challenge and satisfies the needs of various audiences from students to data warehousing professionals clarifying analytics. Organizations can glean practical advice on how to reach the next level of decision-making maturity. Nauman has taken a systematic approach to address people, process and technology challenges as well as covering functional and technical details of implementing an analytics solution." --Ahmad Malik, Vice President of Information Technology, Learning Care Group
"Whether tracking the efficiency of an E.R. visit or predicting how and when to modify staffing levels of your call center, analytics is at the core of the answer. This book helps dispel the unknowns of the black box analytics, giving real insights to make money from data." --George Stragand, V.P. Application Development, OpenSpan
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.
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.
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