Average Reviews:
(More customer reviews)I currently work for an insurance company and we are in the process of gathering requirements to bring lots of text into our data warehouse from the customer service area. This book could not have come out at a better time. Our data warehouse contains mostly structured data and our initial approach was to bring all of the text in and treat it no differently than the more structured data such as dates and codes. We did not have the knowledge base on our team to do something different with the text.
This book greatly helped our data warehouse team in two areas. The first is in explaining terms to our managers so they understand the complexities with analyzing text. The book has a clear way of explaining concepts so that we can then use this same approach when talking with management. Phrases such as the possibility of creating a "Data Junkyard" resonated very well with management. The second area is in storage and indexing strategies. Originally our plan was to bring in all of the text from the customer service area. We got the idea from this book to bring in only the essential data and then point back to the source where the actual data lives. There are over a dozen different indexing strategies discussed in this book, many of which our database team had never considered.
They say a book meets its expectations if you can garner a couple of gems from it. This book definitely has lots of gems. A must read if you currently have a data warehouse and have the business requirements to analyze text.
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Building the Unstructured Data WarehouseLearn 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.
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