Data strategy:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Upper Saddle River, NJ [u.a.]
Addison-Wesley
2005
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXIV, 342 S. |
| ISBN: | 0321240995 |
Internformat
MARC
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| 300 | |a XXIV, 342 S. | ||
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
Acknowledgments xix
About the Authors xxi
Foreword xxiii
Chapter: Introduction 1
Current Status in Contemporary Organizations 1
Why a Data strategy Is Needed 3
Value of Data as an Organizational Asset 4
Vision and Goals of the Enterprise 4
Support of the IT Strategy 5
Components of a Data Strategy 6
Data Integration 6
Data Quality 7
Metadata 8
Data Modeling 9
Organizational Roles and Responsibilities 10
Performance and Measurement 10
Security and Privacy 11
DBMS Selection 12
Business Intelligence 13
Unstructured Data 14
Business Value of Data and ROI 14
How Will You Develop and Implement a Data Strategy? 15
Data Environment Assessment 16
References 21
Chapter 2: Data Integration 23
Ineffective Silver-Bullet Technology Solutions 24
Enterprise Resource Planning (ERP) 24
Data Warehousing (DW) 26
Customer Relationship Management (CRM) 27
Enterprise Application Integration (EAI) 28
yHI Data Strategy
Gaining Management Support 29
Business Case for Data Integration 31
Integrating Business Data 35
Know Your Business Entities 35
Mergers and Acquisitions 36
Data Redundancy 37
Data Lineage 37
Multiple DBMSs and Their Impact 38
Deciding What Data Should Be Integrated 38
Data Integration Prioritization 39
Risks of Data Integration 40
Consolidation and Federation 42
Data Consolidation 42
Data Federation 42
Data Integration Strategy Capability Maturity Model 43
Getting Started 44
Conclusion 45
References 46
Chapter 3: Data Quality 47
Current State of Data Quality 48
Recognizing Dirty Data 49
Data Quality Rules 51
Business Entity Rules 51
Business Attribute Rules 53
Data Dependency Rules 54
Data Validity Rules 55
Data Quality Improvement Practices 57
Data Profiling 57
Data Cleansing 58
Data Defect Prevention 59
Enterprise-Wide Data Quality Disciplines 60
Data Quality Maturity Levels 61
Standards and Guidelines 62
Development Methodology 63
Data Naming and Abbreviations 63
Contents ix
Metadata 63
Data Modeling 64
Data Quality 65
Testing 65
Reconciliation 65
Security 66
Data Quality Metrics 66
Enterprise Architecture 66
Data Quality Improvement Process 68
Business Sponsorship 69
Business Responsibility for Data Quality 70
Conclusion 71
References 71
Chapter 4: Metadata 73
Why Metadata Is Critical to the Business 74
Metadata as the Keystone 74
Management Support for Metadata 75
Starting a Metadata Management Initiative 76
Metadata Categories 78
Business Metadata 79
Technical Metadata 81
Process Metadata 81
Usage Metadata 82
Metadata Sources 82
Metadata Repository 84
Buying a Metadata Repository Product 84
Building a Metadata Repository 85
Centralized Metadata Repository 85
Distributed Metadata Repository 86
XML-Enabled Metadata Repository 87
Developing a Metadata Repository 88
Justification 88
Planning 88
Analysis 89
Design 90
x Data Strategy
Construction 91
Deployment 92
Managed Metadata Environment 93
Metadata Sourcing 94
Metadata Integration 95
Metadata Management 95
Metadata Marts 96
Metadata Delivery 97
Communicating and Selling Metadata 97
Conclusion 97
References 98
Chapter 5: Data Modeling 99
Origins of Data Modeling 100
Significance of Data Modeling 102
Logical Data Modeling Concepts 105
Process-Independence 105
Business-Focused Data Analysis 106
Data Integration (Single Version of Truth) 108
Data Quality 109
Enterprise Logical Data Model 109
Big-Bang Versus Incremental 109
Top-Down versus Bottom-Up 112
Physical Data Modeling Concepts 115
Process-Dependence 116
Database Design 117
Physical Data Modeling Techniques 117
Denormalization 117
Surrogate Keys 120
Indexing 121
Partitioning 121
Database Views 122
Dimensionality 122
Star Schema 124
Snowflake 125
Starflake 126
Contents xi
Factors that Influence the Physical Data Model 126
Guideline 1: High Degree of Normalization for Robustness 126
Guideline 2: Denormalization for Short-Term Solutions 127
Guideline 3: Usage of Views on Powerful Servers 127
Guideline 4: Usage of Views on Powerful RDBMS Software 127
Guideline 5: Cultural Influence on Database Design 128
Guideline 6: Modeling Expertise Affects Database Design 128
Guideline 7: User-Friendly Structures 129
Guideline 8: Metric Facts Determine Database Design 129
Guideline 9: When to Mimic Source Database Design 130
Conclusion 130
References 131
Chapter 6: Organizational Roles and Responsibilities 133
Building the Teams Who Create and Maintain the Strategy 134
Resistance to Change 134
Existing Organization 134
Resistance to Standards 135
Reasons for Resistance 135
Optimal Organizational Structures 135
Distributed Organizations 137
Outsourced Personnel 137
Training 138
Who Should Attend 138
Mindset 139
Choice of Class 139
Timing 140
Roles and Responsibilities 140
Data Strategist 140
Database Administrator 141
Data Administrator 142
Metadata Administrator 142
Data Quality Steward 143
Consultants and Contractors 145
Security Officer 145
Sharing Data 146
Strategic Data Architect 147
xjj Data Strategy
Technical Services 147
Data Ownership 148
Domains 148
Security and Privacy 148
Availability Requirements 149
Timeliness and Periodicity Requirements 149
Performance Requirements 150
Data Quality Requirements 150
Business Rules 151
Information Stewardship 151
Steward Deliverables 155
Key Skills and Competencies 155
Worst Practices 156
Agenda for Weekly Data Strategy Team Meeting 158
Conclusion 159
Chapter 7: Performance 161
Performance Requirements 163
Service Level Agreements 164
Response Time 165
Capacity Planning: Performance Modeling 166
Capacity Planning: Benchmarks 168
Why Pursue a Benchmark? 168
Benchmark Team 169
Benefits of a Good Benchmark: Goals and Objectives 170
Problems with Standard Benchmarks 170
The Cost of Running a Benchmark 171
Identifying and Securing Data 171
Establishing Benchmark Criteria and Methodology 171
Evaluating and Measuring Results 174
Verifying and Reconciling Results 175
Communicating Results Effectively 175
Application Packages: Enterprise Resource Planning (ERPs) 176
Designing, Coding, and Implementing 177
Designing 178
Coding 179
Contents xiii
Implementation 180
Design Reviews 180
Setting User Expectations 189
Monitoring (Measurement) 190
Conformance to Measures of Success 191
Types of Metrics 191
Responsibility for Measurement 193
Means to Measure 193
Use of Measurements 193
Return on Investment (ROI) 194
Reporting Results to Management 194
Tuning 195
Tuning Options 196
Reporting Performance Results 197
Selling Management on Performance 198
Case Studies 198
Performance Tasks 201
Conclusion 202
References 202
Chapter 8: Security and Privacy of Data 205
Data Identification for Security and Privacy 206
User Role 207
Roles and Responsibilities 208
Security Officer 208
Data Owner 209
System Administrator 209
Regulatory Compliance 210
Auditing Procedures 211
Security Audits 211
External Users of Your Data 212
Design Solutions 213
Database Controls 213
Security Databases 213
Test and Production Data 213
xiv Data Strategy
Data Encryption 214
Standards for Data Usage 214
Impact of the Data Warehouse 215
Vendor Issues 215
Software 215
External Data 216
Communicating and Selling Security 217
Security and Privacy Indoctrination 217
Monitoring Employees 217
Training 217
Communication 218
Best Practices and Worst Practices 218
Identify Your Own Sensitive Data Exercise 219
Conclusion 220
Chapter 9: DBMS Selection 223
Existing Environment 223
Capabilities and Functions 224
DBMS Choices 226
Why Standardize the DBMS? 227
Integration Problems 227
Greater Staff Expense 228
Software Expense 228
Total Cost of Ownership 228
Hardware 230
Network Usage 230
DBMS 230
Consultants and Contractors 231
Internal Staff 231
Help Desk Support 231
Operations and System Administration 232
IT Training 232
Application Packages and ERPs 232
Criteria for Selection 233
Selection Process 234
Reference Checking 236
Contents xv
Alternatives to Reference Checking 236
Selecting and Gathering References 237
Desired Types of References 237
The Process of Reference Checking 238
Questions to Ask 239
RFPs for DBMSs 242
RFP Best Practices 242
Response Format 246
Evaluating Vendors 246
Dealing with the Vendor 247
Performance 249
Vendor s Level of Service 250
Early Code 250
Rules of Engagement 250
Set the Agenda for Meetings and Presentations 252
Professional Employee Information 253
Financial Information 254
Selection Matrix Categorize Capabilities and Functions 254
Exercise—How Well Are You Using Your DBMS? 255
Conclusion 257
References 257
Chapter 10: Business Intelligence 259
What Is Business Intelligence? 260
A Brief History 261
Importance of BI 262
BI Components 264
Data Warehouse 264
Metadata Repository 265
Data Transformation and Cleansing 265
OLAP and Analytics 266
Data Presentation and Visualization 267
Important BI Tools and Processes 267
Data Mining 267
Rule-Based Analytics 268
Balanced Scorecard 268
Digital Dashboard 269
xvi Data Strategy
Emerging Trends and Technologies 269
Mining Structured and Unstructured Data 270
Radio Frequency Identification 271
BI Myths and Pitfalls 272
Conclusion 274
References 274
Chapter 11: Strategies for Managing Unstructured Data 277
What Is Unstructured Data? 278
A Brief History 278
Why Now? 280
Current State of Unstructured Data in Organizations 282
A Unified Content Strategy for the Organization 282
Definition of a Unified Content Strategy 282
Storage and Administration 283
Content Reusability 286
Search and Delivery 286
Combining Structured and Unstructured Data 287
Emerging Technologies 287
Digital Asset Management Software 287
Digital Rights Management Software 288
Electronic Medical Records 290
Conclusion 290
References 290
Chapter 12: Business Value of Data and ROI 291
The Business Value of Data 291
Companies that Sell Customer Data 292
Internal Information Gathered About Customers 292
Call Center Data 292
Click-Stream Data 293
Demographics 293
Channel Preferences 293
Direct Retailers 294
Loyalty Cards 294
Travel Data 294
Contents xvii
Align Data with Strategic Goals 295
ROI Process 295
The Cost of Developing a Data Strategy 295
Data Warehouse 296
Hardware 297
Software 297
Personnel Costs 298
Training 299
Operations and System Administration 299
Total Cost of Ownership 299
Benefits of a Data Strategy 300
The Data Warehouse 301
Estimating Tangible Benefits 301
Estimating Intangible Benefits 305
Post-Implementation Benefits Measurement 307
Conclusion 308
Reference 308
Appendix A: ROI Calculation Process, Cost Template, and Intangible
Benefits Template 309
Cost of Capital 309
Risk 310
ROI Example 310
Net Present Value 311
Internal Rate of Return 311
Payback Period 312
Cost Calculation Template 312
Intangible Benefits Calculation Template 315
Reference 315
Appendix B: Resources 317
Publications 317
Websites 320
Index 323
|
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| title | Data strategy |
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