Business Metadata: Capturing Enterprise Knowledge
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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
Readership
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
1. Technical
2. Business
3. 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
2. 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
3. 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
1. The business case for business metadata
ii. The search process – from a visceral standpoint
1. Follow up from Subsequent Chapter
2. The end user buying departmental tools
3. The technician buying a repository
iii. Blending everything together – a combined approach
iv. Life without an organized approach to business metadata
v. Funding Models
1. Should MD be funded by ROI?
2. What are the funding options (LOB or centralized IT, usage or overhead)?
vi. Funding a Corporate Knowledge Base
4. 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
5. OUT; this chapter rolled into Stewardship
Section II: How-To
6. 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
7. 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
8. 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
8.5.1.1 ERP
8.5.1.2 Reports
8.5.1.3 Spreadsheets
8.5.1.4 Documents
8.5.1.5 DBMS system catalogs
8.5.1.6 OLAP
8.5.1.7 ETL
8.5.1.8 Legacy System
8.5.1.9 Data Warehouse
8.5.2 Editing the metadata as it passes into the metadata repository
8.5.2.1 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
9. 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
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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)
Product details
- No. of pages: 312
- Language: English
- Copyright: © Morgan Kaufmann 2007
- Published: September 27, 2007
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780123737267
- eBook ISBN: 9780080552200
About the Authors
William 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 https://www.collibra.com/blog/
Affiliations and Expertise
Former Professor at Daniels College of Business, Denver University, Denver, CO, USA
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