Information management strategies for gaining a competitive advantage with data
Information Management: Gaining a Competitive Advantage with Data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Info...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Waltham, MA :
Morgan Kaufmann, an imprint of Elsevier
2014.
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| Edición: | 1st edition |
| Colección: | Savvy manager's guides.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628089706719 |
Tabla de Contenidos:
- Front Cover; Information Management: Strategies for Gaining a Competitive Advantage with Data; Copyright Page; Contents; Foreword; In praise of Information Management; Preface; 1. You're in the Business of Information; An Architecture for Information Success; Information Technology Disintermediation; The Glue is Architecture; Workload Success; What Determines Workload Success; Information in Action; Big Box Retailer; Telecommunications Provider; Judgment Still Necessary; 2. Relational Theory In Practice; Relational Theory; The Data Page; Indexes; Multidimensional Databases; RDBMS Platforms
- Fusion-ioIBM XIV; Violin Memory; Action Plan; 3. You're in the Business of Analytics; What Distinguishes Analytics?; Predictive Analytics; Building Predictive Analytic Models; Example 1 Customer Lifetime Value; Example 2 Churn Management; Example 3 Clinical Treatment; Example 4 Fraud Detection; Example 5 Next Best Offer; Analytics and Information Architecture; Analytics Requires Analysts; Action Plan; 4. Data Quality: Passing the Standard; Data Quality Defect Categories; Referential Integrity; Uniqueness; Cardinality; Subtype/Supertype; Reasonable Domain; Multiple Meaning Columns
- Formatting ErrorsOptional Data; Derived Data; Complete Data; Incorrect Data; Data Codification; Data Profiling; Sources of Poor Data Quality; Cures for Poor Data Quality; Action Plan; 5. Columnar Databases; Columnar Operation; Compression; Dictionary Encoding; Trim Compression; Run-Length Encoding; Delta Compression; Workloads; Workload Examples; Support for Managing the Customer; Cross-company Consolidation; Fraud Prevention; Columnar Conclusions; Action Plan; 6. Data Warehouses and Appliances; Data Warehousing; Data Warehouse Architecture; The Data Warehouse Appliance
- Symmetric MultiprocessingClustering; Massively Parallel Processing; IBM Netezza Appliances; Teradata Data Warehouse Appliances; The Teradata Data Warehouse Appliance; The Teradata Data Mart Appliance; The Teradata Extreme Data Appliance; ParAccel Analytic Database; Achilles Heels of the Data Warehouse Appliance; Data Appliances and the Use of Memory; Action Plan; 7. Master Data Management: One Chapter Here, but Ramifications Everywhere; MDM Justification; A Subject-Area Culture; Mastering Data; The Architecture of MDM; MDM Governance; Data Quality and MDM; MDM Roles and Responsibilities
- MDM TechnologyAction Items; 8. Data Stream Processing: When Storing the Data Happens Later; Uses of Data Stream Processing; Data Stream Processing Brings Power; Stream SQL Extensions; In Conclusion; Action Plan; References; 9. Data Virtualization: The Perpetual Short-Term Solution; The History of Data Virtualization; Controlling Your Information Asset; Leveraging Data Virtualization; Integrated Business Intelligence; Using Data Virtualization; Use Cases for Data Virtualization; Master Data Management; Mergers and Acquisitions; Temporary Permanent Solution; Simplifying Data Access
- Combining with Historical Data
