Data Strategy (Paperback)

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.

 

Table of Contents:

Acknowledgments.

About the Authors.

Foreword.

1. Introduction.

    Current Status in Contemporary Organizations.

    Why a Data strategy Is Needed.

    Value of Data as an Organizational Asset.

    Vision and Goals of the Enterprise.

    Support of the IT Strategy.

    Components of a Data Strategy.

    Data Integration.

    Data Quality.

    Metadata.

    Data Modeling.

    Organizational Roles and Responsibilities.

    Performance and Measurement.

    Security and Privacy.

    DBMS Selection.

    Business Intelligence.

    Unstructured Data.

    Business Value of Data and ROI.

    How Will You Develop and Implement a Data Strategy?

    Data Environment Assessment.

    References.

2. Data Integration.

    Ineffective “Silver-Bullet” Technology Solutions.

    Enterprise Resource Planning (ERP).

    Data Warehousing (DW).

    Customer Relationship Management (CRM).

    Enterprise Application Integration (EAI).

    Gaining Management Support.

    Business Case for Data Integration.

    Integrating Business Data.

    Know Your Business Entities.

    Mergers and Acquisitions.

    Data Redundancy.

    Data Lineage.

    Multiple DBMSs and Their Impact.

    Deciding What Data Should Be Integrated.

    Data Integration Prioritization.

    Risks of Data Integration.

    Consolidation and Federation.

    Data Consolidation.

    Data Federation.

    Data Integration Strategy Capability Maturity Model.

    Getting Started.

    Conclusion.

    References.

3. Data Quality.

    Current State of Data Quality.

    Recognizing Dirty Data.

    Data Quality Rules.

    Business Entity Rules.

    Business Attribute Rules.

    Data Dependency Rules.

    Data Validity Rules.

    Data Quality Improvement Practices.

    Data Profiling.

    Data Cleansing.

    Data Defect Prevention.

    Enterprise-Wide Data Quality Disciplines.

    Data Quality Maturity Levels.

    Standards and Guidelines.

    Development Methodology.

    Data Naming and Abbreviations.

    Metadata.

    Data Modeling.

    Data Quality.

    Testing.

    Reconciliation.

    Security.

    Data Quality Metrics.

    Enterprise Architecture.

    Data Quality Improvement Process.

    Business Sponsorship.

    Business Responsibility for Data Quality.

    Conclusion.

    References.

4. Metadata.

    Why Metadata Is Critical to the Business.

    Metadata as the Keystone.

    Management Support for Metadata.

    Starting a Metadata Management Initiative.

    Metadata Categories.

    Business Metadata.

    Technical Metadata.

    Process Metadata.

    Usage Metadata.

    Metadata Sources.

    Metadata Repository.

    Buying a Metadata Repository Product.

    Building a Metadata Repository.

    Centralized Metadata Repository.

    Distributed Metadata Repository.

    XML-Enabled Metadata Repository.

    Developing a Metadata Repository.

    Justification.

    Planning.

    Analysis.

    Design.

    Construction.

    Deployment.

    Managed Metadata Environment.

    Metadata Sourcing.

    Metadata Integration.

    Metadata Management.

    Metadata Marts.

    Metadata Delivery.

    Communicating and Selling Metadata.

    Conclusion.

    References.

5. Data Modeling.

    Origins of Data Modeling.

    Significance of Data Modeling.

    Logical Data Modeling Concepts.

    Process-Independence.

    Business-Focused Data Analysis.

    Data Integration (Single Version of Truth).

    Data Quality.

    Enterprise Logical Data Model.

    Big-Bang Versus Incremental.

    Top-Down versus Bottom-Up.

    Physical Data Modeling Concepts.

   Process-Dependence.

    Database Design.

    Physical Data Modeling Techniques.

   Denormalization.

    Surrogate Keys.

    Indexing.

    Partitioning.

    Database Views.

    Dimensionality.

    Star Schema.

    Snowflake.

    Starflake.

    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.

    Building the Teams Who Create and Maintain the Strategy.

    Resistance to Change.

    Existing Organization.

    Resistance to Standards.

     “Reasons” for Resistance.

    Optimal Organizational Structures.

    Distributed Organizations.

    Outsourced Personnel.

    Training.

    Who Should Attend.

    Mindset.

    Choice of Class.

    Timing.

    Roles and Responsibilities.

    Data Strategist.

    Database Administrator.

    Data Administrator.

    Metadata Administrator.

    Data Quality Steward.

    Consultants and Contractors.

    Security Officer.

    Sharing Data.

    Strategic Data Architect.

    Technical Services.

    Data Ownership.

    Domains.

    Security and Privacy.

    Availability Requirements.

    Timeliness and Periodicity Requirements.

    Performance Requirements.

    Data Quality Requirements.

    Business Rules.

    Information Stewardship.

    Steward Deliverables.

    Key Skills and Competencies.

    Worst Practices.

    Agenda for Weekly Data Strategy Team Meeting.

    Conclusion.

7. Performance.

    Performance Requirements.

    Service Level Agreements.

    Response Time.

    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.

    Designing.

    Coding.

    Implementation.

    Design Reviews.

    Setting User Expectations.

    Monitoring (Measurement).

    Conformance to Measures of Success191

    Types of Metrics191

    Responsibility for Measurement.

    Means to Measure.

    Use of Measurements.

    Return on Investment (ROI).

    Reporting Results to Management.

    Tuning.

    Tuning Options.

    Reporting Performance Results.

    Selling Management on Performance.

    Case Studies.

    Performance Tasks.

    Conclusion.

    References.

8. Security and Privacy of Data.

    Data Identification for Security and Privacy.

    User Role.

   Roles and Responsibilities.

    Security Officer.

    Data Owner.

    System Administrator.

    Regulatory Compliance.

    Auditing Procedures.

    Security Audits.

    External Users of Your Data.

    Design Solutions.

    Database Controls.

    Security Databases.

    Test and Production Data.

    Data Encryption.

    Standards for Data Usage.

    Impact of the Data Warehouse.

    Vendor Issues.

    Software.

    External Data.

    Communicating and Selling Security.

    Security and Privacy Indoctrination.

    Monitoring Employees.

    Training.

    Communication.

    Best Practices and Worst Practices.

    Identify Your Own Sensitive Data Exercise.

    Conclusion.

9. DBMS Selection.

    Existing Environment.

    Capabilities and Functions.

    DBMS Choices.

    Why Standardize the DBMS?

    Integration Problems.

    Greater Staff Expense.

    Software Expense.

    Total Cost of Ownership.

    Hardware.

    Network Usage.

    DBMS.

    Consultants and Contractors.

    Internal Staff.

    Help Desk Support.

    Operations and System Administration.

    IT Training.

    Application Packages and ERPs.

    Criteria for Selection.

    Selection Process.

    Reference Checking.

    Alternatives to Reference Checking.

    Selecting and Gathering References.

    Desired Types of References.

    The Process of Reference Checking.

    Questions to Ask.

    RFPs for DBMSs.

    RFP Best Practices.

    Response Format.

    Evaluating Vendors.

    Dealing with the Vendor.

    Performance.

    Vendor’s Level of Service.

    Early Code.

    Rules of Engagement.

    Set the Agenda for Meetings and Presentations.

    Professional Employee Information.

    Financial Information.

    Selection Matrix—–Categorize Capabilities and Functions.

    Exercise–How Well Are You Using Your DBMS?255

    Conclusion.

    References.

10. Business Intelligence.

    What Is Business Intelligence?

    A Brief History.

    Importance of BI.

    BI Components.

    Data Warehouse.

    Metadata Repository.

    Data Transformation and Cleansing.

    OLAP and Analytics.

    Data Presentation and Visualization.

    Important BI Tools and Processes.

    Data Mining.

    Rule-Based Analytics.

    Balanced Scorecard.

    Digital Dashboard.

    Emerging Trends and Technologies.

    Mining Structured and Unstructured Data.

    Radio Frequency Identification.

    BI Myths and Pitfalls.

    Conclusion.

    References.

11. Strategies for Managing Unstructured Data.

    What Is Unstructured Data?

    A Brief History.

    Why Now?

   Current State of Unstructured Data in Organizations.

    A Unified Content Strategy for the Organization.

    Definition of a Unified Content Strategy.

    Storage and Administration.

    Content Reusability.

    Search and Delivery.

    Combining Structured and Unstructured Data.

    Emerging Technologies.

    Digital Asset Management Software.

    Digital Rights Management Software.

    Electronic Medical Records.

    Conclusion.

    References.

12. Business Value of Data and ROI.

    The Business Value of Data.

    Companies that Sell Customer Data.

    Internal Information Gathered About Customers.

    Call Center Data.

    Click-Stream Data.

    Demographics.

    Channel Preferences.

    Direct Retailers.

    Loyalty Cards.

    Travel Data.

    Align Data with Strategic Goals.

    ROI Process.

    The Cost of Developing a Data Strategy.

    Data Warehouse.

    Hardware.

    Software.

    Personnel Costs.

    Training.

    Operations and System Administration.

    Total Cost of Ownership.

    Benefits of a Data Strategy.

    The Data Warehouse.

    Estimating Tangible Benefits.

    Estimating Intangible Benefits.

    Post-Implementation Benefits Measurement.

    Conclusion.

    Reference.

Appendix A: ROI Calculation Process, Cost Template, and Intangible Benefits Template.

    Cost of Capital.

    Risk.

    ROI Example.

    Net Present Value.

    Internal Rate of Return.

    Payback Period.

    Cost Calculation Template.

    Intangible Benefits Calculation Template.

    Reference.

Appendix B: Resources.

    Publications.

    Websites.

Index.

描述：
這本書是企業數據管理策略的權威最佳實踐指南。

您不能再以“零散”的方式管理企業數據。為了最大化數據資產的商業價值，您必須制定一個統一的、全面的企業級數據策略，該策略反映了您捕獲、存儲、管理和使用信息的所有方式。

在這本書中，三位知名的數據管理專家將引導您為您的組織創建最佳的數據策略。使用他們的成熟技術，您可以降低硬件和維護成本，控制不受控制的數據支出。您可以以更低的風險、更高的質量建立新系統，並改善數據訪問。最重要的是，您可以學習如何集成支持您的主要業務目標的新應用程序。

作者團隊借鑒了真實的企業案例和成熟的最佳實踐，涵蓋了從目標設定到安全性和性能管理的所有內容。您將學習如何：

- 確定在交付數據時面臨的真正風險和瓶頸，以及正確的解決方案
- 整合企業數據並提高其質量，使其能夠更廣泛有效地使用
- 系統地保護企業數據並保護客戶隱私
- 更有效地建模數據並充分利用元數據
- 選擇最適合您整體計劃的數據庫管理系統和數據存儲產品
- 順利適應新的商業智能（BI）和非結構化數據應用程序
- 提高企業數據庫應用程序的性能
- 重組您的組織，以簡化日常數據管理並降低成本

《數據策略》對於所有需要更高效地管理企業數據的人來說都是必不可少的，從數據庫架構師到數據庫管理員，從技術人員到高級IT決策者。

目錄：
- 致謝
- 關於作者
- 前言
- 1. 簡介
- 當代組織的現狀
- 為什麼需要數據策略
- 數據作為組織資產的價值