Computer science
July 2012
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.
Here are the main objectives for each of the book's 19 chapters:
Chapter 1: Develop a foundational knowledge of data warehousing, business intelligence and analytics
Chapter 2: Build the business case needed to sell your data warehousing project, and then produce a project plan that avoids common pitfalls
Chapter 3: Elicit and organize business intelligence and data warehousing business requirements
Chapter 4: Specify the technical architecture of the data warehousing system, including software and infrastructure components, technology stack, and non-functional requirements. Gain an understanding of cloud based data warehousing and data warehouse appliances
Chapter 5: Learn about data attributes including metrics and key performance indicators (KPIs), the raw material of data warehousing and business intelligence
Chapter 6: Learn about data modeling and how to apply design patterns for each part of the data warehouse
Chapter 7: Speak the dimensional modeling language of measures, dimensions, facts, cubes, stars, and snowflakes
Chapter 8: Organize a successful data governance program. Learn how to manage metadata for your data warehousing and business intelligence project
Chapter 9: Identify useful data sources and implement a data quality program
Chapter 10: Use database technology for your data warehousing project, and understand the impact of data warehouse appliances, big data, in memory databases, columnar databases and OnLine Analytical Processing (OLAP)
Chapter 11: Apply data integration and understand the role data mapping, data cleansing, data transformation, and loading data play in a successful data warehouse
Chapter 12: Use the business intelligence (BI) operations of slice, dice, drill down, roll up, and pivot to analyze and present data
Chapter 13: Learn about descriptive and predictive statistics, and calculate mean, median, mode, variance and standard deviation
Chapter 14: Harness analytical methods such as regression analysis, data mining, and statistics to make profitable decisions and anticipate the future
Chapter 15: Appreciate the components and design patterns that compose a successful analytic application
Chapter 16: Gain an understanding of the uses and benefits of scorecards and dashboards including support of mobile device users
Chapter 17: Gain insight into applications of business intelligence that could profit your organization, including risk management, finance, marketing, government, healthcare, science and sports
Chapter 18: Perform customer analytics to better understand and segment your customers
Chapter 19: Test, roll out, and sustain the data warehouse
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.
