Business Metadata: Capturing Enterprise Knowledge - 1st Edition - ISBN: 9780123737267, 9780080552200

Business Metadata: Capturing Enterprise Knowledge

1st Edition

Authors: W.H. Inmon Bonnie O'Neil Lowell Fryman
eBook ISBN: 9780080552200
Paperback ISBN: 9780123737267
Imprint: Morgan Kaufmann
Published Date: 27th September 2007
Page Count: 312
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Business Metadata: Capturing Enterprise Knowledge is the first book that helps businesses capture corporate (human) knowledge and unstructured data, and offer solutions for codifying it for use in IT and management. Written by Bill Inmon, one of the fathers of the data warehouse and well-known author, the book is filled with war stories, examples, and cases from current projects. It includes a complete metadata acquisition methodology and project plan to guide readers every step of the way, and sample unstructured metadata for use in self-testing and developing skills.

This book is recommended for IT professionals, including those in consulting, working on systems that will deliver better knowledge management capability. This includes people in these positions: data architects, data analysts, SOA architects, metadata analysts, repository (metadata data warehouse) managers as well as vendors that have a metadata component as part of their systems or tools.

Key Features

  • First book that helps businesses capture corporate (human) knowledge and unstructured data, and offer solutions for codifying it for use in IT and management
  • Written by Bill Inmon, one of the fathers of the data warehouse and well-known author, and filled with war stories, examples, and cases from current projects
  • Very practical, includes a complete metadata acquisition methodology and project plan to guide readers every step of the way
  • Includes sample unstructured metadata for use in self-testing and developing skills


IT professionals, including those in consulting, working on systems that will deliver better knowledge management capability. This includes people in these positions: data architects; data analysts, SOA architects; metadata analysts, repository (metadata data warehouse) managers. Also, vendors that have a metadata component as part of their systems or tools.

Table of Contents

Business Metadata The Quest for Business Understanding Section I: Rationale and Planning

  1. What is Business Metadata
    a. What is Metadata? i. A brief history of metadata ii. Types of Metadata
  2. Technical
  3. Business
  4. Structured versus Unstructured MD b. What is Business MD? i. Some examples and usage c. When does data become MD? d. Who are the users of business metadata? e. A grid of metadata f. Business metadata and reference files
  5. The Value and Benefits of Business Metadata
    a. Metadata Provides Context: i. Example: the number "42" ii. The road sign analogy iii. The library card catalog analogy b. Business Metadata Provides Historical Perspective c. Contextual Benefits in Analytical Processing i. Simple Reports ii. Drill Downs iii. Exception Reporting iv. Heuristic Analysis v. KPI Analysis vi. Multivariate Analysis vii. Pattern Analysis viii. Spreadsheets ix. Screens d. Hidden MD e. The Information Supply Chain i. The Business Feedback Loop
  6. Who is responsible for Business Metadata? a. Who Has the Most to Gain from Business Metadata? b. Stewardship versus Ownership c. Business versus Technical Ownership d. Is Stewardship of Business Metadata any different? i. Data Stewardship ii. Metadata Stewardship iii. Business Metadata Stewardship e. Stewardship Challenges f. Why should MD be funded? (Bill) i. How and why should business metadata be funded
  7. The business case for business metadata ii. The search process – from a visceral standpoint
  8. Follow up from Subsequent Chapter
  9. The end user buying departmental tools
  10. The technician buying a repository iii. Blending everything together – a combined approach iv. Life without an organized approach to business metadata v. Funding Models
  11. Should MD be funded by ROI?
  12. What are the funding options (LOB or centralized IT, usage or overhead)? vi. Funding a Corporate Knowledge Base
  13. Business Metadata, Communication and Search (BKO) a. The need for better communication b. Faulty communication causes bad business practices c. Much time is lost in the organization due to not being able to find things i. Losing Your Car Keys Analogy d. The need for structured definitions e. The Role of Taxonomies
  14. OUT; this chapter rolled into Stewardship Section II: How-To
  15. How do you initiate a MD project? a. What are the options? b. Planning Guidelines i. Examples in MSProject c. Defining the Business Metadata Strategy and Goals i. Strategy & Goals: Business Focus ii. Strategy & Goals: Technical Focus d. Complete enterprise strategy & goals e. Constructing a Strategic Plan f. Examples in MSWord
  16. Technology Infrastructure for Metadata a. MD Modeling and Design (CWM and OMG) i. Special Challenges of Business Metadata b. What does business metadata integration entail? i. Similarity to a data warehouse c. Should be treated like a data warehouse project d. Buy versus Build Alternatives e. Centralized MD Implementation i. Federated ii. Repository f. Distributed MD Implementation g. Hybrid MD Implementation h. ETL for business metadata i. Semantic integration
  17. Business Metadata Capture a. Business MD scope i. Vulcan mind meld ii. Intro to Unstructured MD iii. Business Rules iv. Definitions v. Domains b. Business Metadata Capture from Technical MD i. Enterprise Model layer ii. Conceptual Model layer iii. Logical Model layer iv. Physical Model layer c. Special Challenges of Business Metadata i. Capturing knowledge from Business People d. Capturing knowledge from Individuals e. Capturing knowledge from Groups i. The Socialization Factor ii. Wikis and Collabs f. PR: Encouraging and Incentivizing g. Special Stewardship Approaches i. Proactive vs. Reactive ii. "Governance Lite" 8.5 Business Metadata Capture from Existing Data 8.5.1 Technical Sources of MD ERP Reports Spreadsheets Documents DBMS system catalogs OLAP ETL Legacy System Data Warehouse 8.5.2 Editing the metadata as it passes into the metadata repository Automation of the editing 8.5.3 Granularizing metadata 8.5.4 Expanding definitions & descriptions 8.5.5 Synonym resolution 8.5.6 Homonym Resolution 8.5.7 Manual Metadata editing 8.5.8 Turning Technical MD into Business MD
  18. MD Data Delivery a. Avoid Roach Motel b. Who are users? How do you deliver it? c. Active vs. Passive Delivery d. MD & DW e. MD & Marts f. MD & Operational Systems g. Example: Corporate Glossary Section III: Special Categories of Business MD 9.5 Data Quality a. Why is data quality business metadata? b. Purpose of Data Quality c. Using a Data Quality Methodology d. Expressing data quality into the language of the business
  19. Semantics & Ontologies a. Semantics: The study of meaning b. Semantic frameworks i. Controlled Vocabulary ii. Taxonomy iii. Ontology iv. Chart showing Semantic Richness c. Semantics and Business Metadata d. Semantics and Technology i. The Semantic Web ii. SOA iii. Other tools iv. Standards: OWL etc e. Making semantics practical f. Two different uses i. Glossaries/CV ii. Search g. Simple implementations
  20. Unstructured MD a. Characteristics of Unstructured business metadata b. Where unstructured business metadata resides i. Reports ii. Spreadsheets iii. Text files iv. email c. Examples of unstructured business metadata d. Plucking business metadata out i. An example of finding business metadata in unstructured data e. Relationships among unstructured business metadata objects i. Familial ii. Hierarchy f. Using Unstructured business metadata i. Business metadata and understanding unstructured documents ii. Theming documents using business metadata g. Industrial recognized lists as a basis for understanding documents h. Linguistics i. Marrying structured & unstructured data
  21. Business Rules a. Why business rules are a type of business metadata b. Business rules and their role in managing the business c. Where do you find business rules? d. Purpose for managing them as metadata e. Business Rules and Rule Engine technology f. Business Rules and the Repository
  22. Metadata & Compliance
    a. Compliance – the issues b. Financial compliance c. Communications compliance d. Types of compliance i. Sarbanes Oxley ii. Basel II iii. HIPAA iv. Patriot Act e. How do you use MD to find compliance data? f. Using business metadata i. As a screen—Finding blather ii. To classify transactions iii. As a means to determine criticality g. Creating the historical record i. Preparing for the audit using business metadata h. An example of business metadata during the compliance process i. Document Retention and Compliance i. Document Retention issues ii. Maintenance of email, iii. Email as a knowledge base & the problems it creates
  23. Knowledge Management and Business Metadata a. Intersection of Business Metadata and Knowledge Management b. Knowledge Management in Practice i. Knowledge Capture ii. Knowledge Dissemination c. Explicit and Tacit Knowledge d. Building Intellectual capital and the Corporate Knowledgebase e. Social Issues i. Impact of collaboration on Knowledge ii. Graying of the Workforce

Section IV: Putting it All Together

  1. Summary a. Business Metadata is important b. Business Metadata has been ignored in general discussions of metadata c. Lessons learned in the field d. /What does the future hold? e. Trends f. Resources

Appendix: A: MD Repository Buy Methodology (Sample project plan) B: MD Repository Build Methodology (Sample project plan) C: glossary of terms (the metadata)


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Morgan Kaufmann
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About the Author

W.H. Inmon

Best known as the “Father of Data Warehousing,” Bill Inmon has become the most prolific and well-known author worldwide in the big data analysis, data warehousing and business intelligence arena. In addition to authoring more than 50 books and 650 articles, Bill has been a monthly columnist with the Business Intelligence Network, EIM Institute and Data Management Review. In 2007, Bill was named by Computerworld as one of the “Ten IT People Who Mattered in the Last 40 Years” of the computer profession. Having 35 years of experience in database technology and data warehouse design, he is known globally for his seminars on developing data warehouses and information architectures. Bill has been a keynote speaker in demand for numerous computing associations, industry conferences and trade shows. Bill Inmon also has an extensive entrepreneurial background: He founded Pine Cone Systems, later named Ambeo in 1995, and founded, and took public, Prism Solutions in 1991. Bill consults with a large number of Fortune 1000 clients, and leading IT executives on Data Warehousing, Business Intelligence, and Database Management, offering data warehouse design and database management services, as well as producing methodologies and technologies that advance the enterprise architectures of large and small organizations world-wide. He has worked for American Management Systems and Coopers & Lybrand. Bill received his Bachelor of Science degree in Mathematics from Yale University, and his Master of Science degree in Computer Science from New Mexico State University.

Affiliations and Expertise

Inmon Data Systems, Castle Rock, CO, USA

Bonnie O'Neil

Affiliations and Expertise

Project Performance Corporation, Denver, CO, USA

Lowell Fryman

Lowell is responsible for directing thought leadership and advisory services in the Customer Success practice of Collibra. He has been a practitioner in the data management industry for three decades and is recognized as a leader in data governance, analytics and data quality having hands-on experience with implementations across most industries. Lowell is a co-author of the book “Business Metadata; Capturing Enterprise Knowledge”. Lowell is a past adjunct professor at Daniels College of Business, Denver University, a past President and current VP of Education for DAMA-I Rocky Mountain Chapter (RMC), a DAMA-I Charter member and member of the Data Governance Professionals Organization. He is also an author and reviewer on the DAMA-I Data Management Book of Knowledge (DMBOK). He focuses on practical data governance practices and has trained thousands of professionals in data governance, data warehousing, data management and data quality techniques. You can read his Data Governance Blogs at

Affiliations and Expertise

Collibra, USA

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