Building the Unstructured Data Warehouse (Paperback)

Answers for many valuable business questions hide in text. How well can your existing reporting environment extract the necessary text from email, spreadsheets, and documents, and put it in a useful format for analytics and reporting? Transforming the traditional data warehouse into an efficient unstructured data warehouse requires additional skills from the analyst, architect, designer, and developer. This book will prepare you to successfully implement an unstructured data warehouse and, through clear explanations, examples, and case studies, you will learn new techniques and tips to successfully obtain and analyze text.

Master these ten objectives:

• Build an unstructured data warehouse using the 11-step approach
• Integrate text and describe it in terms of homogeneity, relevance, medium, volume, and structure
• Overcome challenges including blather, the Tower of Babel, and lack of natural relationships
• Avoid the Data Junkyard and combat the Spider's Web
• Reuse techniques perfected in the traditional data warehouse and Data Warehouse 2.0,including iterative development
• Apply essential techniques for textual Extract, Transform, and Load (ETL) such as phrase recognition, stop word filtering, and synonym replacement
• Design the Document Inventory system and link unstructured text to structured data
• Leverage indexes for efficient text analysis and taxonomies for useful external categorization
• Manage large volumes of data using advanced techniques such as backward pointers
• Evaluate technology choices suitable for unstructured data processing, such as data warehouse appliances

• Chapter 1 defines unstructured data and explains why text is the main focus of this book.
• Chapter 2 addresses the challenges one faces when managing unstructured data.
• Chapter 3 discusses the DW 2.0 architecture, which leads into the role of the unstructured data warehouse. The unstructured data warehouse is defined and benefits are given. There are several features of the conventional data warehouse that can be leveraged for the unstructured data warehouse, including ETL processing, textual integration, and iterative development.
• Chapter 4 focuses on the heart of the unstructured data warehouse: Textual Extract, Transform, and Load (ETL).
• Chapter 5 describes the 11 steps required to develop the unstructured data warehouse.
• Chapter 6 describes how to inventory documents for maximum analysis value, as well as link the unstructured text to structured data for even greater value.
• Chapter 7 goes through each of the different types of indexes necessary to make text analysis efficient. Indexes range from simple indexes, which are fast to create and are good if the analyst really knows what needs to be analyzed before the indexing process begins, to complex combined indexes, which can be made up of any and all of the other kinds of indexes.
• Chapter 8 explains taxonomies and how they can be used within the unstructured data warehouse.
• Chapter 9 explains ways of coping with large amounts of unstructured data. Techniques such as keeping the unstructured data at its source and using backward pointers are discussed. The chapter explains why iterative development is so important.
• Chapter 10 focuses on challenges and some technology choices that are suitable for unstructured data processing. In addition, the data warehouse appliance is discussed.
• Chapters 11, 12, and 13 put all of the previously discussed techniques and approaches in context through three case studies.