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