Robert Hillard
Information-Driven Business
Robert Hillard
Information-Driven Business
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Information doesn't just provide a window on the business, increasingly it is the business. The global economy is moving from products to services which are described almost entirely electronically. Even those businesses that are traditionally associated with making things are less concerned with managing the manufacturing process (which is largely outsourced) than they are with maintaining their intellectual property. Information-Driven Business helps you to understand this change and find the value in your data. Hillard explains techniques that organizations can use and how businesses can…mehr
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Information doesn't just provide a window on the business, increasingly it is the business. The global economy is moving from products to services which are described almost entirely electronically. Even those businesses that are traditionally associated with making things are less concerned with managing the manufacturing process (which is largely outsourced) than they are with maintaining their intellectual property. Information-Driven Business helps you to understand this change and find the value in your data. Hillard explains techniques that organizations can use and how businesses can apply them immediately. For example, simple changes to the way data is described will let staff support their customers much more quickly; and two simple measures let executives know whether they will be able to use the content of a database before it is even built. This book provides the foundation on which analytical and data rich organizations can be created. Innovative and revealing, this book provides a robust description of Information Management theory and how you can pragmatically apply it to real business problems, with almost instant benefits. Information-Driven Business comprehensively tackles the challenge of managing information, starting with why information has become important and how it is encoded, through to how to measure its use.
Produktdetails
- Produktdetails
- Verlag: John Wiley & Sons / Wiley
- Seitenzahl: 240
- Erscheinungstermin: 23. August 2010
- Englisch
- Abmessung: 235mm x 157mm x 17mm
- Gewicht: 500g
- ISBN-13: 9780470625774
- ISBN-10: 0470625775
- Artikelnr.: 29742257
- Verlag: John Wiley & Sons / Wiley
- Seitenzahl: 240
- Erscheinungstermin: 23. August 2010
- Englisch
- Abmessung: 235mm x 157mm x 17mm
- Gewicht: 500g
- ISBN-13: 9780470625774
- ISBN-10: 0470625775
- Artikelnr.: 29742257
ROBERT HILLARD is an original founder of MIKE2.0 (www.openmethodology.org), which provides a standard approach for information and data management projects. He has held international consulting leadership roles and provided advice to government and private sector clients around the world. He is a partner with Deloitte with more than twenty years' experience in the discipline, focusing on standardized approaches to information management, including being one of the first to use XBRL in government regulation and the promotion of information as a business asset rather than a technology problem. Find out more at www.infodrivenbusiness.com.
Preface. Acknowledgments. Chapter 1: Understanding the Information Economy.
Did the Internet Create the Information Economy? Origins of Electronic Data
Storage. Stocks and Flows. Business Data. Changing Business Models.
Information Sharing versus Infrastructure Sharing. Governing the New
Business. Success in the Information Economy. Note. Chapter 2: The Language
of Information. Structured Query Language. Statistics. XQuery Language.
Spreadsheets. Documents and Web Pages. Knowledge, Communications, and
Information Theory. Notes. Chapter 3: Information Governance. Information
Currency. Economic Value of Data. Goals of Information Governance.
Organizational Models. Ownership of Information. Strategic Value Models.
Repackaging of Information. Lifecycle. Notes. Chapter 4: Describing
Structured Data. Networks and Graphs. Brief Introduction to Graphs.
Relational Modeling. Relational Concepts. Cardinality and
Entity-Relationship Diagrams. Normalization. Impact of Time and Date on
Relational Models. Applying Graph Theory to Data Models. Directed Graphs.
Normalized Models. Note. Chapter 5: Small Worlds Business Measure of Data.
Small Worlds. Measuring the Problem and Solution. Abstracting Information
as a Graph. Metrics. Interpreting the Results. Navigating the Information
Graph. Information Relationships Quickly Get Complex. Using the Technique.
Note. Chapter 6: Measuring the Quantity of Information. Definition of
Information. Thermal Entropy. Information Entropy. Entropy versus Storage.
Decision Entropy. Conclusion and Application. Notes. Chapter 7: Describing
the Enterprise. Size of the Undertaking. Enterprise Data Models Are All or
Nothing. The Data Model as a Panacea. Metadata. The Metadata Solution.
Master Data versus Metadata. The Metadata Model. XML Taxonomies. Metadata
Standards. Collaborative Metadata. Metadata Technology. Data Quality
Metadata. History. Executive Buy-in. Notes. Chapter 8: A Model for
Computing Based on Information Search. Function-Centric Applications. An
Information-Centric Business. Enterprise Search. Security. Metadata Search
Repository. Building the Extracts. The Result. Note. Chapter 9: Complexity,
Chaos, and System Dynamics. Early Information Management. Simple
Spreadsheets. Complexity. Chaos Theory. Why Information Is Complex.
Extending a Prototype. System Dynamics. Data as an Algorithm. Virtual
Models and Integration. Chaos or Complexity. Notes. Chapter 10: Comparing
Data Warehouse Architectures. Data Warehousing. Contrasting the Inmon and
Kimball Approaches to Data Warehouses. Quantity Implications. Usability
Implications. Historical Data. Summary. Notes. Chapter 11: Layered View of
Information. Information Layers. Are They Real? Turning the Layers into an
Architecture. The User Interface. Selling the Architecture. Chapter 12:
Master Data Management. Publish and Subscribe. About Time. Granularity,
Terminology, and Hierarchies. Rule #1: Consistent Terminology. Rule #2:
Everyone Owns the Hierarchies. Rule #3: Consistent Granularity. Reconciling
Inconsistencies. Slowly Changing Dimensions. Customer Data Integration.
Extending the Metadata Model. Technology. Chapter 13: Information and Data
Quality. Spreadsheets. Referencing. Fit for Purpose. Measuring Structured
Data Quality. A Scorecard. Metadata Quality. Extended Metadata Model.
Notes. Chapter 14: Security. Cryptography. Public Key Cryptography.
Applying PKI. Predicting the Unpredictable. Protecting an Individual's
Right to Privacy. Securing the Content versus Securing the Reference.
Chapter 15: Opening up to the Crowd. A Taxonomy for the Future. Populating
the Stakeholder Attributes. Reducing Email Traffic within Projects.
Managing Customer Email. General Email. Preparing for the Unknown.
Charters. Information Is Dynamic. Power of the Crowd Can Improve your Data
Quality. Note. Chapter 16: Building Incremental Knowledge. Bayesian
Probabilities. Information from Processes. The MIT Beer Game. Hypothesis
Testing and Confidence Levels. Business Activity Monitoring. Note. Chapter
17: Enterprise Information Architecture. Website Information Architecture.
Extending the Information Architecture. Business Context. Users. Content.
Top-Down/Bottom-Up. Presentation Format. Project Resourcing. Information to
Support Decision Making. Note. Looking to the Future. About the Author.
Index.
Did the Internet Create the Information Economy? Origins of Electronic Data
Storage. Stocks and Flows. Business Data. Changing Business Models.
Information Sharing versus Infrastructure Sharing. Governing the New
Business. Success in the Information Economy. Note. Chapter 2: The Language
of Information. Structured Query Language. Statistics. XQuery Language.
Spreadsheets. Documents and Web Pages. Knowledge, Communications, and
Information Theory. Notes. Chapter 3: Information Governance. Information
Currency. Economic Value of Data. Goals of Information Governance.
Organizational Models. Ownership of Information. Strategic Value Models.
Repackaging of Information. Lifecycle. Notes. Chapter 4: Describing
Structured Data. Networks and Graphs. Brief Introduction to Graphs.
Relational Modeling. Relational Concepts. Cardinality and
Entity-Relationship Diagrams. Normalization. Impact of Time and Date on
Relational Models. Applying Graph Theory to Data Models. Directed Graphs.
Normalized Models. Note. Chapter 5: Small Worlds Business Measure of Data.
Small Worlds. Measuring the Problem and Solution. Abstracting Information
as a Graph. Metrics. Interpreting the Results. Navigating the Information
Graph. Information Relationships Quickly Get Complex. Using the Technique.
Note. Chapter 6: Measuring the Quantity of Information. Definition of
Information. Thermal Entropy. Information Entropy. Entropy versus Storage.
Decision Entropy. Conclusion and Application. Notes. Chapter 7: Describing
the Enterprise. Size of the Undertaking. Enterprise Data Models Are All or
Nothing. The Data Model as a Panacea. Metadata. The Metadata Solution.
Master Data versus Metadata. The Metadata Model. XML Taxonomies. Metadata
Standards. Collaborative Metadata. Metadata Technology. Data Quality
Metadata. History. Executive Buy-in. Notes. Chapter 8: A Model for
Computing Based on Information Search. Function-Centric Applications. An
Information-Centric Business. Enterprise Search. Security. Metadata Search
Repository. Building the Extracts. The Result. Note. Chapter 9: Complexity,
Chaos, and System Dynamics. Early Information Management. Simple
Spreadsheets. Complexity. Chaos Theory. Why Information Is Complex.
Extending a Prototype. System Dynamics. Data as an Algorithm. Virtual
Models and Integration. Chaos or Complexity. Notes. Chapter 10: Comparing
Data Warehouse Architectures. Data Warehousing. Contrasting the Inmon and
Kimball Approaches to Data Warehouses. Quantity Implications. Usability
Implications. Historical Data. Summary. Notes. Chapter 11: Layered View of
Information. Information Layers. Are They Real? Turning the Layers into an
Architecture. The User Interface. Selling the Architecture. Chapter 12:
Master Data Management. Publish and Subscribe. About Time. Granularity,
Terminology, and Hierarchies. Rule #1: Consistent Terminology. Rule #2:
Everyone Owns the Hierarchies. Rule #3: Consistent Granularity. Reconciling
Inconsistencies. Slowly Changing Dimensions. Customer Data Integration.
Extending the Metadata Model. Technology. Chapter 13: Information and Data
Quality. Spreadsheets. Referencing. Fit for Purpose. Measuring Structured
Data Quality. A Scorecard. Metadata Quality. Extended Metadata Model.
Notes. Chapter 14: Security. Cryptography. Public Key Cryptography.
Applying PKI. Predicting the Unpredictable. Protecting an Individual's
Right to Privacy. Securing the Content versus Securing the Reference.
Chapter 15: Opening up to the Crowd. A Taxonomy for the Future. Populating
the Stakeholder Attributes. Reducing Email Traffic within Projects.
Managing Customer Email. General Email. Preparing for the Unknown.
Charters. Information Is Dynamic. Power of the Crowd Can Improve your Data
Quality. Note. Chapter 16: Building Incremental Knowledge. Bayesian
Probabilities. Information from Processes. The MIT Beer Game. Hypothesis
Testing and Confidence Levels. Business Activity Monitoring. Note. Chapter
17: Enterprise Information Architecture. Website Information Architecture.
Extending the Information Architecture. Business Context. Users. Content.
Top-Down/Bottom-Up. Presentation Format. Project Resourcing. Information to
Support Decision Making. Note. Looking to the Future. About the Author.
Index.
Preface. Acknowledgments. Chapter 1: Understanding the Information Economy.
Did the Internet Create the Information Economy? Origins of Electronic Data
Storage. Stocks and Flows. Business Data. Changing Business Models.
Information Sharing versus Infrastructure Sharing. Governing the New
Business. Success in the Information Economy. Note. Chapter 2: The Language
of Information. Structured Query Language. Statistics. XQuery Language.
Spreadsheets. Documents and Web Pages. Knowledge, Communications, and
Information Theory. Notes. Chapter 3: Information Governance. Information
Currency. Economic Value of Data. Goals of Information Governance.
Organizational Models. Ownership of Information. Strategic Value Models.
Repackaging of Information. Lifecycle. Notes. Chapter 4: Describing
Structured Data. Networks and Graphs. Brief Introduction to Graphs.
Relational Modeling. Relational Concepts. Cardinality and
Entity-Relationship Diagrams. Normalization. Impact of Time and Date on
Relational Models. Applying Graph Theory to Data Models. Directed Graphs.
Normalized Models. Note. Chapter 5: Small Worlds Business Measure of Data.
Small Worlds. Measuring the Problem and Solution. Abstracting Information
as a Graph. Metrics. Interpreting the Results. Navigating the Information
Graph. Information Relationships Quickly Get Complex. Using the Technique.
Note. Chapter 6: Measuring the Quantity of Information. Definition of
Information. Thermal Entropy. Information Entropy. Entropy versus Storage.
Decision Entropy. Conclusion and Application. Notes. Chapter 7: Describing
the Enterprise. Size of the Undertaking. Enterprise Data Models Are All or
Nothing. The Data Model as a Panacea. Metadata. The Metadata Solution.
Master Data versus Metadata. The Metadata Model. XML Taxonomies. Metadata
Standards. Collaborative Metadata. Metadata Technology. Data Quality
Metadata. History. Executive Buy-in. Notes. Chapter 8: A Model for
Computing Based on Information Search. Function-Centric Applications. An
Information-Centric Business. Enterprise Search. Security. Metadata Search
Repository. Building the Extracts. The Result. Note. Chapter 9: Complexity,
Chaos, and System Dynamics. Early Information Management. Simple
Spreadsheets. Complexity. Chaos Theory. Why Information Is Complex.
Extending a Prototype. System Dynamics. Data as an Algorithm. Virtual
Models and Integration. Chaos or Complexity. Notes. Chapter 10: Comparing
Data Warehouse Architectures. Data Warehousing. Contrasting the Inmon and
Kimball Approaches to Data Warehouses. Quantity Implications. Usability
Implications. Historical Data. Summary. Notes. Chapter 11: Layered View of
Information. Information Layers. Are They Real? Turning the Layers into an
Architecture. The User Interface. Selling the Architecture. Chapter 12:
Master Data Management. Publish and Subscribe. About Time. Granularity,
Terminology, and Hierarchies. Rule #1: Consistent Terminology. Rule #2:
Everyone Owns the Hierarchies. Rule #3: Consistent Granularity. Reconciling
Inconsistencies. Slowly Changing Dimensions. Customer Data Integration.
Extending the Metadata Model. Technology. Chapter 13: Information and Data
Quality. Spreadsheets. Referencing. Fit for Purpose. Measuring Structured
Data Quality. A Scorecard. Metadata Quality. Extended Metadata Model.
Notes. Chapter 14: Security. Cryptography. Public Key Cryptography.
Applying PKI. Predicting the Unpredictable. Protecting an Individual's
Right to Privacy. Securing the Content versus Securing the Reference.
Chapter 15: Opening up to the Crowd. A Taxonomy for the Future. Populating
the Stakeholder Attributes. Reducing Email Traffic within Projects.
Managing Customer Email. General Email. Preparing for the Unknown.
Charters. Information Is Dynamic. Power of the Crowd Can Improve your Data
Quality. Note. Chapter 16: Building Incremental Knowledge. Bayesian
Probabilities. Information from Processes. The MIT Beer Game. Hypothesis
Testing and Confidence Levels. Business Activity Monitoring. Note. Chapter
17: Enterprise Information Architecture. Website Information Architecture.
Extending the Information Architecture. Business Context. Users. Content.
Top-Down/Bottom-Up. Presentation Format. Project Resourcing. Information to
Support Decision Making. Note. Looking to the Future. About the Author.
Index.
Did the Internet Create the Information Economy? Origins of Electronic Data
Storage. Stocks and Flows. Business Data. Changing Business Models.
Information Sharing versus Infrastructure Sharing. Governing the New
Business. Success in the Information Economy. Note. Chapter 2: The Language
of Information. Structured Query Language. Statistics. XQuery Language.
Spreadsheets. Documents and Web Pages. Knowledge, Communications, and
Information Theory. Notes. Chapter 3: Information Governance. Information
Currency. Economic Value of Data. Goals of Information Governance.
Organizational Models. Ownership of Information. Strategic Value Models.
Repackaging of Information. Lifecycle. Notes. Chapter 4: Describing
Structured Data. Networks and Graphs. Brief Introduction to Graphs.
Relational Modeling. Relational Concepts. Cardinality and
Entity-Relationship Diagrams. Normalization. Impact of Time and Date on
Relational Models. Applying Graph Theory to Data Models. Directed Graphs.
Normalized Models. Note. Chapter 5: Small Worlds Business Measure of Data.
Small Worlds. Measuring the Problem and Solution. Abstracting Information
as a Graph. Metrics. Interpreting the Results. Navigating the Information
Graph. Information Relationships Quickly Get Complex. Using the Technique.
Note. Chapter 6: Measuring the Quantity of Information. Definition of
Information. Thermal Entropy. Information Entropy. Entropy versus Storage.
Decision Entropy. Conclusion and Application. Notes. Chapter 7: Describing
the Enterprise. Size of the Undertaking. Enterprise Data Models Are All or
Nothing. The Data Model as a Panacea. Metadata. The Metadata Solution.
Master Data versus Metadata. The Metadata Model. XML Taxonomies. Metadata
Standards. Collaborative Metadata. Metadata Technology. Data Quality
Metadata. History. Executive Buy-in. Notes. Chapter 8: A Model for
Computing Based on Information Search. Function-Centric Applications. An
Information-Centric Business. Enterprise Search. Security. Metadata Search
Repository. Building the Extracts. The Result. Note. Chapter 9: Complexity,
Chaos, and System Dynamics. Early Information Management. Simple
Spreadsheets. Complexity. Chaos Theory. Why Information Is Complex.
Extending a Prototype. System Dynamics. Data as an Algorithm. Virtual
Models and Integration. Chaos or Complexity. Notes. Chapter 10: Comparing
Data Warehouse Architectures. Data Warehousing. Contrasting the Inmon and
Kimball Approaches to Data Warehouses. Quantity Implications. Usability
Implications. Historical Data. Summary. Notes. Chapter 11: Layered View of
Information. Information Layers. Are They Real? Turning the Layers into an
Architecture. The User Interface. Selling the Architecture. Chapter 12:
Master Data Management. Publish and Subscribe. About Time. Granularity,
Terminology, and Hierarchies. Rule #1: Consistent Terminology. Rule #2:
Everyone Owns the Hierarchies. Rule #3: Consistent Granularity. Reconciling
Inconsistencies. Slowly Changing Dimensions. Customer Data Integration.
Extending the Metadata Model. Technology. Chapter 13: Information and Data
Quality. Spreadsheets. Referencing. Fit for Purpose. Measuring Structured
Data Quality. A Scorecard. Metadata Quality. Extended Metadata Model.
Notes. Chapter 14: Security. Cryptography. Public Key Cryptography.
Applying PKI. Predicting the Unpredictable. Protecting an Individual's
Right to Privacy. Securing the Content versus Securing the Reference.
Chapter 15: Opening up to the Crowd. A Taxonomy for the Future. Populating
the Stakeholder Attributes. Reducing Email Traffic within Projects.
Managing Customer Email. General Email. Preparing for the Unknown.
Charters. Information Is Dynamic. Power of the Crowd Can Improve your Data
Quality. Note. Chapter 16: Building Incremental Knowledge. Bayesian
Probabilities. Information from Processes. The MIT Beer Game. Hypothesis
Testing and Confidence Levels. Business Activity Monitoring. Note. Chapter
17: Enterprise Information Architecture. Website Information Architecture.
Extending the Information Architecture. Business Context. Users. Content.
Top-Down/Bottom-Up. Presentation Format. Project Resourcing. Information to
Support Decision Making. Note. Looking to the Future. About the Author.
Index.