Book description
The definitive best-practices guide to enterprise data-management strategy.
You can no longer manage enterprise data "piecemeal." To maximize the business value of your data assets, you must define a coherent, enterprise-wide data strategy that reflects all the ways you capture, store, manage, and use information.
In this book, three renowned data management experts walk you through creating the optimal data strategy for your organization. Using their proven techniques, you can reduce hardware and maintenance costs, and rein in out-of-control data spending. You can build new systems with less risk, higher quality, and improve data access. Best of all, you can learn how to integrate new applications that support your key business objectives.
Drawing on real enterprise case studies and proven best practices, the author team covers everything from goal-setting through managing security and performance. You'll learn how to:
Identify the real risks and bottlenecks you face in delivering data—and the right solutions
Integrate enterprise data and improve its quality, so it can be used more widely and effectively
Systematically secure enterprise data and protect customer privacy
Model data more effectively and take full advantage of metadata
Choose the DBMS and data storage products that fit best into your overall plan
Smoothly accommodate new Business Intelligence (BI) and unstructured data applications
Improve the performance of your enterprise database applications
Revamp your organization to streamline day-to-day data management and reduce cost
Data Strategy is indispensable for everyone who needs to manage enterprise data more efficiently—from database architects to DBAs, technical staff to senior IT decision-makers.
© Copyright Pearson Education. All rights reserved.
Table of contents
- Copyright
- Acknowledgments
- About the Authors
- Foreword
- 1. Introduction
- 2. Data Integration
- 3. Data Quality
- 4. Metadata
-
5. Data Modeling
- Origins of Data Modeling
- Significance of Data Modeling
- Logical Data Modeling Concepts
- Enterprise Logical Data Model
- Physical Data Modeling Concepts
- Physical Data Modeling Techniques
- Dimensionality
-
Factors that Influence the Physical Data Model
- Guideline 1: High Degree of Normalization for Robustness
- Guideline 2: Denormalization for Short-Term Solutions
- Guideline 3: Usage of Views on Powerful Servers
- Guideline 4: Usage of Views on Powerful RDBMS Software
- Guideline 5: Cultural Influence on Database Design
- Guideline 6: Modeling Expertise Affects Database Design
- Guideline 7: User-Friendly Structures
- Guideline 8: Metric Facts Determine Database Design
- Guideline 9: When to Mimic Source Database Design
- Conclusion
- References
- 6. Organizational Roles and Responsibilities
-
7. Performance
- Performance Requirements
- Service Level Agreements
- Capacity Planning: Performance Modeling
-
Capacity Planning: Benchmarks
- Why Pursue a Benchmark?
- Benchmark Team
- Benefits of a Good Benchmark: Goals and Objectives
- Problems with “Standard” Benchmarks
- The Cost of Running a Benchmark
- Identifying and Securing Data
- Establishing Benchmark Criteria and Methodology
- Evaluating and Measuring Results
- Verifying and Reconciling Results
- Communicating Results Effectively
- Application Packages: Enterprise Resource Planning (ERPs)
- Designing, Coding, and Implementing
- Setting User Expectations
- Monitoring (Measurement)
- Tuning
- Case Studies
- Performance Tasks
- Conclusion
- References
- 8. Security and Privacy of Data
-
9. DBMS Selection
- Existing Environment
- DBMS Choices
- Why Standardize the DBMS?
- Total Cost of Ownership
- Application Packages and ERPs
- Criteria for Selection
- Selection Process
- Reference Checking
- RFPs for DBMSs
- Response Format
- Evaluating Vendors
- Dealing with the Vendor
- Exercise—How Well Are You Using Your DBMS?
- Conclusion
- References
- 10. Business Intelligence
- 11. Strategies for Managing Unstructured Data
-
12. Business Value of Data and ROI
- The Business Value of Data
- Align Data with Strategic Goals
- The Cost of Developing a Data Strategy
-
Benefits of a Data Strategy
- The Data Warehouse
-
Estimating Tangible Benefits
- Revenue Enhancement
- Cash Flow Acceleration
- Analyst Productivity
- Cost Containment
- Demand Chain Management
- Fraud Reduction
- Customer Conversion Rates
- Customer Attrition and Retention Rates
- Marketing Campaign Selection and Response Rates
- Better Relationships with Suppliers and Customers
- Data Mart Consolidation
- Estimating Intangible Benefits
- Post-Implementation Benefits Measurement
- Conclusion
- Reference
- A. ROI Calculation Process, Cost Template, and Intangible Benefits Template
- B. Resources
Product information
- Title: Data Strategy
- Author(s):
- Release date: June 2005
- Publisher(s): Addison-Wesley Professional
- ISBN: None
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