Building the Unstructured Data Warehouse: Architecture, Analysis, and Design

Building the Unstructured Data Warehouse:
Architecture, Analysis, and Design

earn essential techniques from data warehouse legend Bill
Inmon on how to build the reporting environment your business needs now!

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

The following outline briefly describes each
chapter‘s content:

• 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.

Building the Unstructured Data Warehouse: Architecture,
Analysis, and Design