Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors’ tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers.
After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you’ll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success.
- Provides practical guidelines for building successful BI, DW and data integration solutions.
- Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language.
- Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses
- Describes best practices and pragmatic approaches so readers can put them into action.
- Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources.
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About the Author
Table of Contents
Concepts and Context 1 Introduction Business and Technical Needs 2 Justifying BI (Building Business and Technical Case 3 Defining Requirements - Business, Data and Quality Architectural Framework 4 Architecture Introduction 5 Information Architecture 6 Data Architecture 7 Technology and Product Architectures Data Design 8 Foundational Data Modeling 9 Dimensional Modeling 10 Advanced Dimensional Modeling Data Integration Design 11 Data Integration Processes 12 Data Integration Design&Development BI Design 13 BI Applications 14 BI Design&Development 15 Advanced Analytics 16 Data Shadow Systems Organization 17 People, Process and Politics 18 Project Management 19 Centers of Excellence