Information-Driven Business How to Manage Data and Information for Maximum Advantage - encuadernado, tapa blanda

EAN (ISBN-13): 9780470625774
ISBN (ISBN-10): 0470625775
Tapa dura
Tapa blanda
Año de publicación: 2010
Editorial: John Wiley & Sons Inc
216 Páginas
Peso: 0,418 kg
Idioma: eng/Englisch

Libro en la base de datos desde 2010-11-10T11:59:39+01:00 (Madrid)
Página de detalles modificada por última vez el 2023-11-11T13:50:28+01:00 (Madrid)
ISBN/EAN: 9780470625774

ISBN - escritura alterna:
0-470-62577-5, 978-0-470-62577-4
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Autor del libro: hillard
Título del libro: driven, business advantage, data

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Datos del la editorial

Autor: Robert Hillard
Título: Information-Driven Business - How to Manage Data and Information for Maximum Advantage
Editorial: John Wiley & Sons
240 Páginas
Año de publicación: 2010-09-10
Peso: 0,420 kg
Idioma: Inglés
45,90 € (DE)
No longer receiving updates
164mm x 232mm x 23mm

BB; GB; Hardcover, Softcover / Wirtschaft/Management; Wirtschaftsmathematik und -informatik, IT-Management; Business Technology; Business & Management; Wirtschaft u. Management; Unternehmenstechnologie

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.