The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics, by David Haertzen
Do you enjoy completing puzzles? Perhaps one of the most challenging (yet rewarding) puzzles is delivering a successful data warehouse suitable for data mining and analytics. The Analytical Puzzle describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organization. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualization and mobile devices.
Chapter 1: Data Warehousing Perspectives
Chapter 2: Business Case and Project Management
Chapter 3: Business Architecture and Requirements
Chapter 4: Data Warehousing Technical Architecture
Chapter 5: Data Attributes
Chapter 6: Data Modeling
Chapter 7: Dimensional Modeling
Chapter 8: Data Governance and Metadata Management
Chapter 9: Data Sources and Data Quality Management
Chapter 10: Database Technology
Chapter 11: Data Integration
Chapter 12: Business Intelligence Operations and Tools
Chapter 13: Number Crunching Part 1: Statistics
Chapter 14: Number Crunching Part 2: Data Mining
Chapter 15: Number Crunching Part 3: An Analytic Pattern
Chapter 16: Presenting Data: Scorecards and Dashboards
Chapter 17: Business Intelligence Applications
Chapter 18: Customer Analytics
Chapter 19: Testing, Rolling Out, and Sustaining the Data Warehouse
This book contains three sections spanning ten chapters. Section I, Foundational Concepts, will provide you with the necessary basic concepts and discuss schema integration. Section II, Preparation and Design, introduces the case study and we will reverse engineer each of the data sources to create a set of data dictionary reports which will provide us with the meta data we need to apply the schema integration process. Section III, Physical Implementation, will present scripts to populate each of the source databases and spreadsheets and use reports to create Extract, Transform, and Load (ETL) specifications.
The ten chapters within these three sections are:
David Haertzen is a seasoned data warehouse architect who has helped a diverse set of organizations from start-ups to multinationals to utilize data for their advantage. David teaches data modeling, data warehousing, data architecture, business intelligence and is also active as editor of the Infogoal.com Data Management Center. David is a graduate of the University of Minnesota and holds an MBA from the University of St. Thomas.
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