Tapping into Unstructured Data: Integrating Unstructured Data and Textual Analytics into Business Intelligence

The Definitive Guide to Unstructured Data Management and Analysis--From the World’s Leading Information Management Expert A wealth of invaluable information exists in unstructured textual form, but organizations have found it difficult or impossible to access and utilize it. This is changing rapidly: new approaches finally make it possible to glean useful knowledge from virtually any collection of unstructured data.   William H. Inmon--the father of data warehousing--and Anthony Nesavich introduce the next data revolution: unstructured data management. Inmon and Nesavich cover all you need to know to make unstructured data work for your organization. You’ll learn how to bring it into your existing structured data environment, leverage existing analytical infrastructure, and implement textual analytic processing technologies to solve new problems and uncover new opportunities. Inmon and Nesavich introduce breakthrough techniques covered in no other book--including the powerful role of textual integration, new ways to integrate textual data into data warehouses, and new SQL techniques for reading and analyzing text. They also present five chapter-length, real-world case studies--demonstrating unstructured data at work in medical research, insurance, chemical manufacturing, contracting, and beyond.   This book will be indispensable to every business and technical professional trying to make sense of a large body of unstructured text: managers, database designers, data modelers, DBAs, researchers, and end users alike.   Coverage includes What unstructured data is, and how it differs from structured data First generation technology for handling unstructured data, from search engines to ECM--and its limitations Integrating text so it can be analyzed with a common, colloquial vocabulary: integration engines, ontologies, glossaries, and taxonomies Processing semistructured data: uncovering patterns, words, identifiers, and conflicts Novel processing opportunities that arise when text is freed from context Architecture and unstructured data: Data Warehousing 2.0 Building unstructured relational databases and linking them to structured data Visualizations and Self-Organizing Maps (SOMs), including Compudigm and Raptor solutions Capturing knowledge from spreadsheet data and email Implementing and managing metadata: data models, data quality, and more

Table of Contents:
Preface xvii 1          Unstructured Textual Data in the Organization 1 2          The Environments of Structured Data and Unstructured Data 15 3          First Generation Textual Analytics 33 4          Integrating Unstructured Text into the Structured Environment 47 5          Semistructured Data 73 6          Architecture and Textual Analytics 83 7          The Unstructured Database 95 8          Analyzing a Combination of Unstructured Data and Structured Data 113 9          Analyzing Text Through Visualization 127 10        Spreadsheets and Email 135 11        Metadata in Unstructured Data 147 12        A Methodology for Textual Analytics 163 13        Merging Unstructured Databases into the Data Warehouse 175 14        Using SQL to Analyze Text 185 15        Case Study--Textual Analytics in Medical Research 195 16        Case Study--A Database for Harmful Chemicals 203 17        Case Study--Managing Contracts Through an Unstructured Database 209 18        Case Study--Creating a Corporate Taxonomy (Glossary) 215 19        Case Study--Insurance Claims 219 Glossary 227 Index 233