A primer in financial data management

목차 (Table of Contents)

• CONTENTS
• Foreword = xi
• Preface = xvii
• 1. The Changing Financial Services Landscape
• 1.1 Data as the Lifeblood of the Industry = 1
• 1.2 Developments in Information Management = 3
• 1.2.1 Regulatory Demands = 6
• 1.2.2 Customer Preferences = 7
• 1.3 The Supply Chain View of Data Management = 8
• 1.3.1 Ultrashort History of Automation in Financial Services = 8
• 1.3.2 The Information Supply Chain = 12
• 1.4 The Data Management Problem = 12
• 1.5 Outline of This Book's Chapters = 17
• References = 18
• 2. Taxonomy of Financial Data
• 2.1 Introduction = 19
• 2.2 Master Data Versus Transactional Data = 20
• 2.3 Structured Data Versus Unstructured Data = 21
• 2.4 Sources of Financial Information = 22
• 2.4.1 Classification by Source Type = 22
• 2.4.2 Short Overview of Data Vendors = 25
• 2.4.3 Fragmentation and Consolidation of Information Sources = 26
• 2.5 Data Creation Processes and Information Life Cycle = 27
• 2.5.1 Data Origination = 27
• 2.5.2 Data Vendors and Licensing = 28
• 2.6 Overview of Information Sets = 32
• 2.6.1 Security Master Data = 32
• 2.6.2 Trade Support Data = 35
• 2.6.3 Corporate Actions = 36
• 2.6.4 Prices, Quotes, and Liquidity = 38
• 2.6.5 Analytics = 42
• 2.6.6 Legal Entity Data and Entity Relationships = 46
• 2.6.7 Portfolio Data = 49
• 2.6.8 Transactions = 52
• 2.6.9 Financial Statements = 53
• 2.6.10 Risk and Regulatory Reports = 54
• 2.6.11 Tax Information = 55
• 2.6.12 Other Documentation = 56
• 2.6.13 Communication Logs = 57
• 2.6.14 News = 58
• 2.6.15 Credit Information = 59
• 2.6.16 Miscellaneous = 62
• 2.7 Conclusions = 63
• Reference = 64
• 3. Information as the Fuel for Financial Services' Business Processes
• 3.1 Steps in the Information Sourcing Process = 66
• 3.1.1 The Information Supply Chain = 68
• 3.1.2 Technical Challenges = 72
• 3.1.3 Data Deployment = 72
• 3.2 Data Management From the Instrument Lifecycle Perspective = 74
• 3.2.1 Issuing = 75
• 3.2.2 Asset Servicing = 75
• 3.2.3 Custody = 76
• 3.2.4 Case Study : Information Issues in Corporate Actions Handling = 78
• 3.2.5 Securities Lending = 79
• 3.2.6 Collateral Management = 81
• 3.2.7 Fund Administration = 81
• 3.3 Data Management From the Trade Lifecycle Perspective = 82
• 3.3.1 Pretrade = 86
• 3.3.2 Trade = 89
• 3.3.3 Trading Styles and Data Needs = 89
• 3.3.4 Clearing and Settlement = 90
• 3.4 Data Management From the Customer Interaction Perspective = 92
• 3.5 Data Management From the Regulatory Reporting Perspective = 95
• 3.5.1 Regulatory Themes = 95
• 3.5.2 Regulatory Ecosystem = 95
• 3.5.3 Diversification of Regulatory Attention = 96
• 3.5.4 Increased Process Focus and Data Requirements = 97
• 3.5.5 Example Basel Regulation : Fundamental Review of the Trading Book = 100
• 3.5.6 Example : Higher Standards in Valuation = 101
• 3.5.7 Summary = 102
• 3.6 Business Data Architecture = 103
• 3.7 Conclusions = 105
• References = 106
• 4. Challenges and Trends in the Financial Data Management Agenda
• 4.1 Introduction = 107
• 4.2 Changing Business Demands = 109
• 4.2.1 Key Information Metrics = 109
• 4.2.2 The Changing Role of Sourcing Departments : The Era of Creative Sourcing = 110
• 4.3 Changing Customer Demands = 112
• 4.3.1 Data Integration Demands = 112
• 4.3.2 Client Decision Making = 113
• 4.4 Changing Regulatory Demands = 114
• 4.4.1 Example : Record Keeping = 116
• 4.4.2 Example : Valuation Policies = 117
• 4.4.3 Example : KYC = 119
• 4.4.4 Example : EU Transaction Reporting-EMIR and MiFID II = 120
• 4.5 Supply Chain Developments = 122
• 4.6 Big Data and Big Data Management = 123
• 4.7 Conclusions and Future Outlook = 124
• 4.7.1 Commonalities in Client, Business, and Regulatory Demands = 124
• 4.7.2 Toward a New IT Organization Model-Securing Pockets of Innovation = 125
• 4.7.3 Concluding Comments = 126
• Reference = 126
• 5. Data Management Tools and Techniques
• 5.1 Introduction : Technology Enablers = 128
• 5.1.1 Different Levels in an IT Infrastructure = 128
• 5.1.2 A Short Taxonomy of Data Management Tooling = 131
• 5.1.3 Data Governance Tools = 133
• 5.1.4 Analytics Tools = 134
• 5.1.5 Data Distribution Methods = 135
• 5.1.6 EUDA = 137
• 5.2 Data Storage Models = 138
• 5.2.1 Data Modeling and Databases = 140
• 5.2.2 NoSQL Databases = 146
• 5.2.3 Data Curation = 150
• 5.2.4 Data Warehouses Versus Data Marts = 151
• 5.2.5 Data Lakes = 152
• 5.2.6 Social Media Meets Banks = 154
• 5.3 Big Data Technology for Financial Institutions = 154
• 5.3.1 Developments in Analytical Capabilities = 157
• 5.3.2 Use Cases in Financial Services for Big Data Technologies = 158
• 5.3.3 Conclusions = 159
• 5.4 Data Security = 160
• 5.4.1 Risks and Threats = 160
• 5.4.2 Mitigating Actions = 161
• 5.5 Blockchain = 162
• 5.5.1 Definitions = 163
• 5.5.2 Current Infrastructure Challenges = 164
• 5.5.3 Advantages of Blockchain = 164
• 5.5.4 Blockchain in Master Data = 165
• 5.5.5 Other Application Areas = 165
• 5.5.6 Challenges = 166
• 5.5.7 Conclusions = 167
• 5.6 Cloud and Information Access = 167
• 5.6.1 Cloud Models = 167
• 5.6.2 Data Center Requirements = 168
• 5.6.3 Assurance Standards and Certification = 170
• 5.7 IT Management and Buy Versus Build Considerations = 172
• 5.8 Conclusions and Future Outlook = 176
• References = 177
• 6. Data Management Processes and Quality Management
• 6.1 Introduction : Metadata Classification and Data Management Processes = 180
• 6.2 Data Quality Fundamentals = 180
• 6.3 Data Quality Dimensions = 182
• 6.4 Data Quality Business Rules = 186
• 6.4.1 Transforming Information = 187
• 6.4.2 Financial Instrument-Type Specific Rules = 188
• 6.4.3 Staged Quality Process Rules = 188
• 6.4.4 Example : Market Data Rules = 190
• 6.5 Quality Metrics : Information Management Supply Chain KPIs = 192
• 6.5.1 Throughput = 194
• 6.5.2 Fill Rate = 194
• 6.5.3 Balanced Scorecard = 195
• 6.5.4 Cycle Time = 195
• 6.5.5 Defects Per Million Opportunities (DPMO) = 196
• 6.5.6 Perfect Order Measure = 197
• 6.5.7 Inventory Turns = 197
• 6.5.8 COPQ = 198
• 6.5.9 Other Measures = 198
• 6.5.10 KPIs and Root-Cause Analysis = 199
• 6.5.11 Defining and Monitoring KPIs and Their Use in an SLA = 201
• 6.5.12 KPI Best Practices = 203
• 6.6 Exposing and Controlling Information Uncertainty = 207
• 6.7 Quality Augmentation and Remediation Processes : What to Do With KPIs? = 209
• 6.8 The Role of Data Standards = 211
• 6.9 Data Management Maturity Models = 215
• 6.10 ROI of Data Management Processes and Quality Management = 217
• 6.11 Conclusions and Future Outlook = 220
• References = 223
• 7. Data Management Organization
• 7.1 Introduction : Changing Demands on Organizations = 225
• 7.2 Information Governance = 227
• 7.2.1 What Is Data Governance? = 230
• 7.2.2 Data Governance Models = 231
• 7.2.3 Data Governance and Cost Allocation = 233
• 7.3 Organizational Approaches = 234
• 7.3.1 What Organizational Models? = 234
• 7.3.2 New Roles in Data Management : CDOs and Data Stewards = 235
• 7.3.3 Turning Employees into Responsible "Data Citizens" = 239
• 7.4 Outsourcing and Service Options in Data Management = 239
• 7.4.1 Different Service Models = 241
• 7.4.2 1：1 Models = 243
• 7.4.3 1：N models：Industry Shared Services and Utility Models = 243
• 7.4.4 Redrawing the Borders of the Back Office : What's In and What's Out = 245
• 7.4.5 Implications for Service Providers = 246
• 7.4.6 Next Developments：N：M Models? = 248
• 7.5 Change Management Programs for Shared Data Services = 249
• 7.5.1 Best Practices = 250
• 7.5.2 Phases of Change = 252
• 7.5.3 Governance = 256
• 7.6 Conclusions and Future Outlook = 258
• References = 260
• 8. What's Next?
• 8.1 Structural Changes in the Financial Services Industry = 261
• 8.2 The Supply Chain Perspective of Information Management = 263
• 8.3 Data Management Outlook = 267
• Bibliography = 269
• Index = 271