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Table of Contents |
How to Benefit from this Book 4
Case Study: 3M a Global Manufacturing Firm 8
Companion Website and Templates 9
Chapter 1: Data Warehousing Perspectives 11
What are Business Intelligence (BI) and Analytics? 11
Decisions Impact the Bottom Line 12
Examples of Business Intelligence Results 13
What is Enterprise Data Warehousing (EDW)? 14
What a Data Warehouse Is and Is Not 14
Assessing Data Warehouse BI Maturity 15
DW and BI are Management Disciplines 18
Operational Data vs. Data Warehouse Data 18
Data Warehousing Enterprise Architecture 20
Data Warehousing Trends and Hot Topics 22
Chapter 2: Business Case and Project Management 25
Six Steps to Developing the Business Case 26
BC Step 1.0 – Initiate Business Case 26
BC Step 2.0 – Analyze Current Approach 27
BC Step 3.0 – Analyze Alternative Approaches 28
BC Step 4.0 – Determine Transition Costs 30
BC Step 5.0 – Assemble Business Case 31
BC Step 6.0 – Present Business Case 32
Case Study: 3M, Global EDW Business Case 32
Data Warehousing Project Management 33
Defining Scope and Objectives 33
Producing the Project Roadmap and Plans 35
Case Study: 3M, Enterprise Data Warehouse Continued 45
Avoiding Major Data Warehouse Missteps 46
Chapter 3: Business Architecture and Requirements 49
Data Warehousing Business Requirements 53
Homework for Data Warehouse Requirements Elicitation 54
Identify Business Intelligence User Groups 55
Interview Business Intelligence Users 55
Non-Functional Requirements 59
Case Study: 3M, a Global Manufacturing Firm 59
Data Warehousing Requirements Workshop 60
Executive Sponsor and User Manager Roles 62
Group Methods and Techniques 63
Outcomes of Effective BI Requirements Sessions 65
Requirements Session Follow Up 65
Chapter 4: Data Warehousing Technical Architecture 69
Functional and Non-functional Requirements 70
Technical Architecture Principles 70
Buy, Build, or Re-use Approach 71
Technical Architecture Roadmaps 73
Data Warehousing Technical Architecture 76
Case Study: 3M, a Global Manufacturing Firm 79
Data Warehouse vs. Data Mart 81
Data Governance, Metadata and Data Quality 87
Data warehousing Infrastructure 87
Data Warehousing Technology Stack 93
Data Warehouse in the Cloud 93
Managing, Operating, and Securing the Data Warehouse 97
Data Warehouse Architecture Tips 97
Data Warehouse Architecture Traps 98
Chapter 5: Data Attributes 101
Qualitative and Quantitative Attributes 104
Key Performance Indicators (KPIs) 108
Section 6A – Entity Relationship Modeling 113
Exclusive Supertype / Subtype 120
Non-Exclusive Supertype / Subtype 121
Summary: The Entity Relationship Model 126
Section 6B – Data Warehouse Modeling 129
Chapter 7: Dimensional Modeling 145
Facts – the Data Mart Measuring Stick 150
Fact Width and Storage Utilization 152
Mathematical Nature of Facts – Can it “Add up”? 152
Dimensions Put Data Mart Facts in Context 157
Data Modeling Slowly Changing Dimensions 159
SCD Type 0 – Unchanging Dimensions 160
One Dimension – Multiple Roles 163
Bridge Tables Support Many to Many Relationships 166
Specifying Data Mart Attributes 169
The Big Picture – Integrating the Data Mart 171
The Bigger Picture – The Federated Data Mart 173
Chapter 8: Data Governance and Metadata Management 175
Five Steps to Data Governance (DG) 175
DG Step 1.0 – Assess Data Governance Maturity 176
DG Step 2.0 – Design Data Governance Structures 178
DG Step 3.0 – Create Data Governance Strategy 178
DG Step 4.0 – Create Data Governance Policies 179
DG Step 5.0 – Monitor and Maintain Data Governance 179
Metadata – “Data about data” 179
How Can Data Warehousing Metadata be Managed? 181
Metadata Manager / Repository 181
Chapter 9: Data Sources and Data Quality Management 183
Understanding Data Sources 183
Dimension Data Sources for the Data Mart 186
Identifying Fact Data Sources for the Data Mart 187
Detailed Data Source Understanding for Data Warehousing 187
Obtain Existing Documentation 188
Model and Define the Input 189
Data Profiling and Data Quality 191
Six Steps to Data Quality Management (DQM) 192
Chapter 10: Database Technology 195
Big Data – Beyond Relational 198
ROLAP Uses SQL for Business Intelligence 207
MOLAP Business Intelligence Benefits 208
Chapter 11: Data Integration 211
Extracting Data to Staging Area 214
Applying Data Transformations 214
Loading the Atomic Data Warehouse 217
Loading Data Mart Dimensions 218
Programmer Written ETL Applications 221
Data Warehousing ETL Tools At Hand 222
Chapter 12: Business Intelligence Operations and Tools 227
Section 12A – Business Intelligence Operations 227
BI Operations Tips and Traps 230
Section 12B – Business Intelligence Tools 232
Interactive Query and Analysis Tools 232
Data Mining and Statistical Tools 233
Chapter 13: Number Crunching Part 1: Statistics 237
Inferential Statistics – Regression Models 243
R and Octave Open Source Software 244
Chapter 14: Number Crunching Part 2: Data Mining 247
Predictive Data Mining – Decision Trees 251
Predictive Data Mining – Neural Nets 252
Data Mining Tips and Traps 253
Chapter 15: Number Crunching Part 3: An Analytic Pattern 257
Analytical Application Example Architecture 257
Chapter 16: Presenting Data: Scorecards and Dashboards 263
Data Visualization Graphics Types 272
Mobile Device Considerations 275
Chapter 17: Business Intelligence Applications 279
Supply Chain and Manufacturing BI Applications 280
Operations BI Applications 281
Performance Management BI Applications 282
Risk Management BI Applications 282
Government BI Applications 283
Case Study: Richmond, VA Policy Department 283
Chapter 18: Customer Analytics 285
Common Customer Data Model 289
Relationships / Hierarchies 291
Single Customer View Tips and Traps 291
Chapter 19: Testing, Rolling Out, and Sustaining the Data Warehouse 299
Section 19A – Testing the Data Warehouse 299
Data Warehouse Testing Responsibilities 300
Business Requirements and Testing 300
Data Warehousing Test Plan 300
Testing Environments and Infrastructure 300
Unit Testing for the Data Warehouse 301
QA Testers Perform Many Types of Tests 301
Can the Data Warehouse Perform? 302
Business Users Test Business Intelligence 302
Business Intelligence Must Be Believed 303
Section 19B – Business Intelligence Rollout 303
Pre-deployment for Business Intelligence Rollout 304
Deployment of Data Warehouses 305
Training Business Intelligence and Data Warehouse Users 305
Follow-up Support for the Data Warehouse 306
Business Intelligence Follow-up Assessment 306
Section 19C – Sustainable Business Intelligence 307
BI / Data Warehousing People and Process Tips 308
BI / Data Warehousing People and Process Traps 308
Sustaining BI / Data Warehousing Tips 309
Sustaining BI / Data Warehousing Traps 309
Appendix B: Suggested Readings 325