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A Manager's Guide to Data Warehousing


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Table of Contents

Introduction xxiii Part One The Essentials of Data Warehousing 1 Chapter 1 Gaining Data Warehouse Success 3 The Essentials of Data Warehousing 3 What Is a Data Warehouse? 4 Differences Between Operational and DW Systems 4 The DataWarehousing Environment 4 What Is a Data Model? 6 Understanding Industry Perspectives 7 Design and Development Sequence 8 Why Build a Data Warehouse? 11 The Value of Data Warehousing 12 The Promises of DataWarehousing 15 Keys to Success 16 Developing and Maintaining Strong Business and Technology Partnerships 17 Identifying True Business Requirements 17 Shifting to a Global Perspective 18 Overcoming Unrealistic Expectations 19 Providing Clear Communication 20 Treating Data As a Corporate Asset 21 Effectively Leveraging Technology 21 Roadblocks to Success 22 Believing the Myth: "If You Build It, They Will Come" 22 Falling into the Project Deadline Trap 23 Failing to Uphold Organizational Discipline 23 Lacking Business Process Change 24 Narrowing the Focus Too Much 25 Resting on Your Laurels 27 Relying on the Technology Fix 27 Getting the Right People Involved 28 Finding Lost Institutional Knowledge 29 Summary 30 Chapter 2 The Executive's FAQ for Data Warehousing 31 Question:What is the business benefit of a data warehouse? 32 Answer 32 Question: How much will it cost? 33 Answer 33 Question: How long will it take? 34 Answer 35 Question: How can I ensure success? 36 Answer 36 Question: Do other companies really build these in 90 days? 37 Answer 37 Question: How will we know we are doing this right? 38 Answer 38 Question:Why didn't this work last time?What is different this time? 39 Answer 39 Question: Do we have the right technology in place? 39 Answer 40 Question: Are we the only company with data warehouse problems? 40 Answer 41 Question:Will I get one version of the truth? 41 Answer 42 Question:Why can't we just use our current systems? 43 Answer 44 Question:Will the data warehouse replace our old systems? 45 Question:Who needs to be involved? 45 Question: Do we know where we are going? How will we know when we get there? 46 Answer 46 Question: How do we get started and stay focused? 47 Answer 47 Summary 48 Part Two The Business Side of Data Warehousing 49 Chapter 3 Understanding Where You Are and Finding Your Way 51 Assessing Your Current State 51 What Is Your Company's Strategic Direction? 52 What Are the Company's Top Initiatives? 54 How Healthy Is Your Data? 55 Does the Business Place Value on Analysis? 56 Reflecting on Your Data Warehouse History 57 Understanding Your Existing Reporting Environment 58 Finding the Reporting Systems 59 Compiling an Inventory 60 Identifying the Business Purpose 61 Discovering the Data You Already Have 63 Understanding the People 65 Tracking Technology and Tools 65 Understanding Enterprise Resources 66 Netting It All Out 68 Introducing the Case Studies 70 The Call Center Data Warehouse Project 70 In Real Life 70 Giant Company 71 Agile, Inc. 72 Summary 72 Chapter 4 Successful IT--Business Partnerships 75 What a Partnership Really Means 75 What the Business Partners Should Expect to Do 76 Business Executives and Senior Management 78 The Executive Business Sponsor 78 Business Managers 81 The Business Champion 82 Business Analysts 83 Helping the Business Analyst Deal with Change 85 Business User Audience 86 Project Manager 86 What You Should Expect from IT 88 CIO/IT Executive Sponsor 89 Data Warehouse Manager 89 Business Systems Analyst 90 Source System Analyst 91 Data Modeler/Data Architect 92 ETL Developer(s) 93 Business Intelligence Application Developer 94 Other Supporting Roles 95 Tips for Building and Sustaining a Partnership 95 Leveraging External Consulting 97 Building Strong Project Teams 98 Effective Communication 99 Netting Out Key Messages 99 Presenting in Business Terms 100 Meeting Preparation 101 Presentation Tips 102 When to Communicate 103 Partnerships Beyond a Project 104 The Decision-Making Process 104 Executive Steering Committee 104 DW Business Support Team 106 Enterprise Considerations 107 In Real Life 107 A Glimpse into Giant, Co. 107 Insight from Agile, Inc. 108 Summary 109 Chapter 5 Setting Up a Successful Project 111 Defining the Project 111 Setting Up the Project Charter 112 Documenting Project Scope 117 Developing a Statement of Work 117 How Much Will It Cost? 120 Project Approval 122 Starting the Project 122 Launching the Project 123 Managing a Successful Project 124 Issue Tracking 124 Using Project Change Control 125 Discussing Change in Business Terms 126 Managing Expectations 128 In Real Life 129 Structured Projects with Giant 129 Freedom for Creativity at Agile, Inc. 130 Summary 131 Chapter 6 Providing Business Requirements 133 What Requirements Are Needed? 134 Peeling Back the Layers of Requirements Gathering 134 Who Provides Input? 137 Who Gathers the Requirements? 137 Providing Business Requirements 138 Strategic Requirements 138 Broad Business Requirements 140 Business Analyses 143 Business Data Requirements 145 Systems and Technical Requirements 147 Communicating What You Really Need 149 What Else Would Help the Project Team? 150 Data Integration Challenges 151 Assess Organizational Motivation 151 Complete Picture of the Data 152 What If No One Is Asking? 152 Practical Techniques for Gathering Requirements 153 Interview Session Characteristics 153 Individual Interviews 153 Group Interviews 153 Project Team Participation 154 Interview Tips 154 Who Needs to Be Included? 155 Setting a Good Example 156 Preparing for Interview Sessions 157 Conducting the Interview Sessions 157 Capturing Content: Notes vs. Tapes 157 Running the Interview 158 Concluding the Interview 158 Putting the Pieces Together 158 Individual Interview Documentation 159 Responsibilities 159 Business Themes 159 Business Data 160 Consolidated Requirements Documentation 161 Executive Summary 161 Consolidated Business Themes 162 Candidate Business Analyses 162 Consolidated Business Data Requirements 162 Identification of Non-Data Warehouse Requirements 163 Common Requirements Gathering Challenges 163 Sifting Through Reports 163 Listing Data Elements 164 Developing Functional Specifications 164 Moving Beyond Immediate 164 Lack of Requirements 165 The Cynic 165 Setting Attainable Goals 166 Exploring Alternatives 167 Setting Priorities 168 In Real Life 170 A Glimpse into Giant Company 170 Insight from Agile, Inc. 170 Summary 171 Part Three Dealing with the Data 173 Chapter 7 Modeling the Data for your Business 175 The Purpose of Dimensional Models 176 Ease of Use 176 Query Performance 177 Understanding Your Data 177 What Is a Dimensional Model? 178 Dimensions 178 Facts 180 Using Both Parts of the Model 180 Implementing a Dimensional Model 181 Diagramming Your Dimensional Model 182 The Business Dimensional Model 182 Business Dimensions 183 Fact Groups 184 A Call Center Case Study 186 Call Center Dimensions 187 Date Dimension 187 Time Dimension 187 Customer Dimension 189 Employee Dimension 191 Call Dimension 191 Call Outcome Dimension 194 Employee Task Dimension 195 Call Center Fact Groups 196 Calls Fact Group 196 Call Center Time Tracking Fact Group 196 Call Forecast Fact Group 198 Working with the Model 199 Business Dimensional Model Index 200 Enterprise Considerations 200 Conformed Dimensions 200 Conformed Facts 202 Practical Guidelines 202 Guidelines for a Single Dimension 202 Guidelines for a Single Fact Group 203 Characteristics of the Model across the Enterprise 204 Business Participation in the Modeling Process 205 Creating the First Draft 205 Preparing for Modeling Sessions 205 Brainstorming the Framework 206 Drafting the Initial Dimensions 206 Drafting the Initial Fact Groups 207 Documenting the Model 208 Logging Questions and Issues 208 Building the Business MeasuresWorksheet 209 Preliminary Source to Target Data Map 211 Completing or Fleshing Out the Model 211 Working Through the Issues 211 Completing the Documentation 212 Working Through All the Data Elements 212 Refining the Model 213 Business Reviews of the Model 213 Small Business Reviews 214 When Are You Done? 214 Gaining Final Commitment 215 Expanding Business Data Over Time 215 Enhancing Dimensions 215 Adding More Fact Groups 215 Reflecting on Business Realities: Advanced Concepts 216 Supporting Multiple Perspectives: Multiple Hierarchies 216 Tracking Changes in the Dimension: Slowly Changing Dimensions 216 Depicting the Existence of a Relationship: Factless Fact Tables 218 Linking Parts of a Transaction: Degenerate Dimensions 219 Pulling Together Components: Junk Dimensions 221 Multiple Instances of a Dimension: Role Playing 222 Other Notation 224 Dimension Connectors 224 Clusters of Future Attributes 225 Notation Summary 225 Taking the Model Forward 225 Translating the Business Dimensional Model 226 Dimension Table Design 226 Translating Fact Groups 227 Physical Database Design 228 In Real Life 228 A Glimpse into Giant Co. 229 Insight from Agile, Inc. 229 Summary 230 Chapter 8 Managing Data As a Corporate Asset 231 What Is Information Management? 232 Information Management Example--Customer Data 235 IM Beyond the Data Warehouse 239 Master Data Management 240 Master Data Feeds the Data Warehouse 242 Finding the Right Resources 242 Data Governance 243 Data Ownership 243 Who Really Owns the Data? 244 Your Responsibilities If You Are "the Owner" 246 What are IT's Responsibilities? 247 Challenges with Data Ownership 247 Data Quality 248 Profiling the Data 249 How Clean Does the Data Really Need to Be? 250 Measuring Quality 250 Quality of Historical Data 251 Cleansing at the Source 253 Cleaning Up for Reporting 254 Managing the Integrity of Data Integration 254 Quality Improves When It Matters 256 Example: Data Quality and Grocery Checkout Scanners 257 Example: Data Quality and the Evaluation of Public Education 257 Realizing the Value of Data Quality 258 Implementing a Data Dictionary 259 The Data Dictionary Application 259 Populating the Data Dictionary 261 Accessing the Data Dictionary 263 Maintaining the Data Dictionary 263 Getting Started with Information Management 264 Understanding Your Current Data Environment 264 What Data Do You Have? 265 What Already Exists? 266 Where Do You Want to Be? 267 Develop a Realistic Strategy 268 Sharing the Information Management Strategy 269 Setting Up a Sustainable Process 270 Enterprise Commitment 270 The Data Governance Committee 270 Revising the Strategy 271 In Real Life 271 A Glimpse into Giant, Co. 272 Insight from Agile, Inc. 272 Summary 274 Part Four Building the Project 275 Chapter 9 Architecture, Infrastructure, and Tools 277 What Is Architecture? 278 Why Do We Need Architecture? 278 Making Architecture Work 281 Data Architecture 282 Revisiting DW Goals 283 Components of DW Data Architecture 285 A Closer Look at Common Data Warehouse Architectures 286 Bottom-Up Data Architecture 286 Top-Down Data Architecture 290 Publish the Data: Data Marts 294 Adopting an Architecture 295 Technical Architecture 297 Technical Architecture Basics 298 Components of Technical Architecture 299 Infrastructure 300 Technical Architecture in Action 300 What You Need to Know about Technical Architecture 301 Navigating the Technology Jungle 302 Weighing Technology Options 303 Best of Breed 303 End-to-End Solutions 303 Deciding Not to Buy a Tool 304 Finding the Right Products 304 Requests for Information or Proposals 305 Business Participation in the Selection Process 305 Understanding Product Genealogy 306 Understanding Value and Evaluating Your Options 306 Cutting through the Marketing Hype 308 The Value of References 309 Making ArchitectureWork for You 310 Just-In-Time Architecture 311 In Real Life 311 Architecture at Giant 311 Agile Ignores the Need for Architecture 312 Summary 313 Chapter 10 Implementation: Building the Database 315 Extract, Transform, and Load (ETL) Fundamentals 315 What Work Is Being Done? 315 ETL System Functionality 317 Extraction 318 Transformation 318 Load 322 The Business Role in ETL 323 Why Does the Business Need to Help? 323 Defining Business Rules 324 Defining Expected Results--The Test Plan 325 Development Support 326 Testing the ETL System--Is the data Right? 326 Why Does It Take So Long and Cost So Much? 327 Balancing Requirements and Data Reality 329 Discovering the Flaws in Your Current Systems 330 Applying New Business Rules 331 Working Toward Long-Term Solutions 332 Manually Including Business Data 333 Tracking Progress--AreWe There Yet? 333 What Else Can You Do to Help? 334 Encouragement and Support 334 Ensuring Continued Business Participation 335 Proactive Communication 336 In Real Life 337 Building the Data Warehouse at Giant, Co. 337 Agile, Inc., Builds a Data Warehouse Quickly 338 Summary 339 Chapter 11 Data Delivery: What you Finally See 341 What Is Business Intelligence? 341 Business Intelligence without a DW 342 BI in Action 343 Tabular Reports 343 Parameter-Driven Reports 343 Interactive Reports--Drilling Down and Across 344 Exception Reports 344 Other BI Capabilities 345 Complex Analysis 345 BI Building Blocks 346 Data Content--UnderstandingWhat You Have 346 Navigation--Finding What You Need 347 Presentation--How Do YouWant to See Results? 347 Delivery--How Do You Receive the Results? 351 Supporting Different Levels of Use 352 Construction of the BI Solution 354 Planning for Business Change 354 Design--What Needs to Be Delivered? 355 Development 357 Testing BI Applications and Validating Data 358 Additional Responsibilities 359 Security--Who Can Look at the Data? 359 System Controls--Who Can Change What? 360 Planning a Successful Launch 361 Marketing the Solution 361 Learning to Use the Data without a Technical Degree 362 Learning about the Data 362 Learning about the BI Tool/Application 362 Ensuring That the Right Help Is Available 363 In Real Life 364 BI at Giant Company 364 Agile, Inc. Dives into BI 365 Summary 366 Part Five Next Steps--Expanding On Success 367 Chapter 12 Managing the Production Data Warehouse 369 Finishing the Project 369 Recapping the BI Application Launch 369 Post-Implementation Review 370 Looking Back--Did you Accomplish Your Objectives? 371 Adopting the Solution 371 Tracking Data Warehouse Use 372 Getting the Rest of the Business Community on Board 372 Business Process Change 374 Changing How Data Is Used 374 Streamlining Business Processes 374 Encouraging Change 375 The Production DataWarehouse 375 Staffing Production Activities 376 Maintaining the Environment 376 Keeping Up with Technology 376 Monitoring Performance and Capacity Planning 378 Maintaining the Data Warehouse 380 Maintaining the ETL System 380 Maintaining the BI Application 381 Tracking Questions and Problems 382 Fixing Bugs 384 When the DataWarehouse Falls Short 384 Common Causes for a StalledWarehouse 385 Jump-Starting a Stalled Data Warehouse 388 Conducting an Assessment 388 DeterminingWhat Can Be Salvaged 389 Developing a Plan to Move On 390 Aligning DW Objectives with Business Goals 391 Getting It Right This Time 392 Launching the Improved Data Warehouse and BI Solution 393 In Real Life 394 Lack of Support for the Production DW at Giant Co. 394 Unleashing BI at Agile, Inc. 395 Summary 396 Chapter 13 Achieving Long-Term Success 397 Planning for Expansion and Growth 397 Exploring Expansion Opportunities 398 Prioritization of Feedback 399 Managing Enterprise DWResources 400 Creating an Enterprise Data Warehouse Team 400 The Centralized Enterprise Data Warehouse Team 401 The Virtual Enterprise Data Warehouse Team 401 Enterprise DW Team Responsibilities 403 Funding the Enterprise DW Team 404 Pushing into the Future 405 Embedded Business Intelligence 405 Operational Business Intelligence 406 Real-Time DataWarehousing 407 Unstructured Data 408 Monitoring Industry Innovation 409 Moving Toward Business Value 410 Measuring Success One Step at a Time 410 Adjusting Expectations to Reality 412 Keeping the Momentum Going 413 Celebrating Progress 416 Success Can Be Attained 417 Conclusion 419 Glossary 421 Index 429

About the Author

Laura L. Reeves, coauthor of The Data Warehouse Lifecycle Toolkit, has over 23 years of experience in end-to-end data warehouse development focused on developing comprehensive project plans, collecting business requirements, designing business dimensional models and database schemas, and creating enterprise data warehouse strategies and data architectures.

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