{"product_id":"building-the-data-warehouse-paperback-softback-9780764599446","title":"Building the Data Warehouse (Paperback \/ softback) 9780764599446","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eBuilding the Data Warehouse\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eW. H. Inmon (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780764599446, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 11 October 2005\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e576 pages\u003cbr\u003e23.6 x 18.8 x 3.8 cm, 0.748 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cul\u003e \u003cli\u003eThe new edition of the classic bestseller that launched the data warehousing industry covers new approaches and technologies, many of which have been pioneered by Inmon himself\u003c\/li\u003e \u003cli\u003eIn addition to explaining the fundamentals of data warehouse systems, the book covers new topics such as methods for handling unstructured data in a data warehouse and storing data across multiple storage media\u003c\/li\u003e \u003cli\u003eDiscusses the pros and cons of relational versus multidimensional design and how to measure return on investment in planning data warehouse projects\u003c\/li\u003e \u003cli\u003eCovers advanced topics, including data monitoring and testing\u003c\/li\u003e \u003cli\u003eAlthough the book includes an extra 100 pages worth of valuable content, the price has actually been reduced from $65 to $55\u003c\/li\u003e \u003c\/ul\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAcknowledgments xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Evolution of Decision Support Systems 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Evolution 2\u003c\/p\u003e \u003cp\u003eThe Advent of DASD 4\u003c\/p\u003e \u003cp\u003ePC\/4GL Technology 4\u003c\/p\u003e \u003cp\u003eEnter the Extract Program 5\u003c\/p\u003e \u003cp\u003eThe Spider Web 6\u003c\/p\u003e \u003cp\u003eProblems with the Naturally Evolving Architecture 7\u003c\/p\u003e \u003cp\u003eLack of Data Credibility 7\u003c\/p\u003e \u003cp\u003eProblems with Productivity 9\u003c\/p\u003e \u003cp\u003eFrom Data to Information 12\u003c\/p\u003e \u003cp\u003eA Change in Approach 14\u003c\/p\u003e \u003cp\u003eThe Architected Environment 16\u003c\/p\u003e \u003cp\u003eData Integration in the Architected Environment 18\u003c\/p\u003e \u003cp\u003eWho Is the User? 20\u003c\/p\u003e \u003cp\u003eThe Development Life Cycle 20\u003c\/p\u003e \u003cp\u003ePatterns of Hardware Utilization 22\u003c\/p\u003e \u003cp\u003eSetting the Stage for Re-engineering 23\u003c\/p\u003e \u003cp\u003eMonitoring the Data Warehouse Environment 25\u003c\/p\u003e \u003cp\u003eSummary 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 The Data Warehouse Environment 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Structure of the Data Warehouse 33\u003c\/p\u003e \u003cp\u003eSubject Orientation 34\u003c\/p\u003e \u003cp\u003eDay 1 to Day n Phenomenon 39\u003c\/p\u003e \u003cp\u003eGranularity 41\u003c\/p\u003e \u003cp\u003eThe Benefits of Granularity 42\u003c\/p\u003e \u003cp\u003eAn Example of Granularity 43\u003c\/p\u003e \u003cp\u003eDual Levels of Granularity 46\u003c\/p\u003e \u003cp\u003eExploration and Data Mining 50\u003c\/p\u003e \u003cp\u003eLiving Sample Database 50\u003c\/p\u003e \u003cp\u003ePartitioning as a Design Approach 53\u003c\/p\u003e \u003cp\u003ePartitioning of Data 53\u003c\/p\u003e \u003cp\u003eStructuring Data in the Data Warehouse 56\u003c\/p\u003e \u003cp\u003eAuditing and the Data Warehouse 61\u003c\/p\u003e \u003cp\u003eData Homogeneity and Heterogeneity 61\u003c\/p\u003e \u003cp\u003ePurging Warehouse Data 64\u003c\/p\u003e \u003cp\u003eReporting and the Architected Environment 64\u003c\/p\u003e \u003cp\u003eThe Operational Window of Opportunity 65\u003c\/p\u003e \u003cp\u003eIncorrect Data in the Data Warehouse 67\u003c\/p\u003e \u003cp\u003eSummary 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 The Data Warehouse and Design 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBeginning with Operational Data 71\u003c\/p\u003e \u003cp\u003eProcess and Data Models and the Architected Environment 78\u003c\/p\u003e \u003cp\u003eThe Data Warehouse and Data Models 79\u003c\/p\u003e \u003cp\u003eThe Data Warehouse Data Model 81\u003c\/p\u003e \u003cp\u003eThe Midlevel Data Model 84\u003c\/p\u003e \u003cp\u003eThe Physical Data Model 88\u003c\/p\u003e \u003cp\u003eThe Data Model and Iterative Development 91\u003c\/p\u003e \u003cp\u003eNormalization and Denormalization 94\u003c\/p\u003e \u003cp\u003eSnapshots in the Data Warehouse 100\u003c\/p\u003e \u003cp\u003eMetadata 102\u003c\/p\u003e \u003cp\u003eManaging Reference Tables in a Data Warehouse 103\u003c\/p\u003e \u003cp\u003eCyclicity of Data — The Wrinkle of Time 105\u003c\/p\u003e \u003cp\u003eComplexity of Transformation and Integration 108\u003c\/p\u003e \u003cp\u003eTriggering the Data Warehouse Record 112\u003c\/p\u003e \u003cp\u003eEvents 112\u003c\/p\u003e \u003cp\u003eComponents of the Snapshot 113\u003c\/p\u003e \u003cp\u003eSome Examples 113\u003c\/p\u003e \u003cp\u003eProfile Records 114\u003c\/p\u003e \u003cp\u003eManaging Volume 115\u003c\/p\u003e \u003cp\u003eCreating Multiple Profile Records 117\u003c\/p\u003e \u003cp\u003eGoing from the Data Warehouse to the Operational Environment 117\u003c\/p\u003e \u003cp\u003eDirect Operational Access of Data Warehouse Data 118\u003c\/p\u003e \u003cp\u003eIndirect Access of Data Warehouse Data 119\u003c\/p\u003e \u003cp\u003eAn Airline Commission Calculation System 119\u003c\/p\u003e \u003cp\u003eA Retail Personalization System 121\u003c\/p\u003e \u003cp\u003eCredit Scoring 123\u003c\/p\u003e \u003cp\u003eIndirect Use of Data Warehouse Data 125\u003c\/p\u003e \u003cp\u003eStar Joins 126\u003c\/p\u003e \u003cp\u003eSupporting the ODS 133\u003c\/p\u003e \u003cp\u003eRequirements and the Zachman Framework 134\u003c\/p\u003e \u003cp\u003eSummary 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Granularity in the Data Warehouse 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRaw Estimates 140\u003c\/p\u003e \u003cp\u003eInput to the Planning Process 141\u003c\/p\u003e \u003cp\u003eData in Overflow 142\u003c\/p\u003e \u003cp\u003eOverflow Storage 144\u003c\/p\u003e \u003cp\u003eWhat the Levels of Granularity Will Be 147\u003c\/p\u003e \u003cp\u003eSome Feedback Loop Techniques 148\u003c\/p\u003e \u003cp\u003eLevels of Granularity — Banking Environment 150\u003c\/p\u003e \u003cp\u003eFeeding the Data Marts 157\u003c\/p\u003e \u003cp\u003eSummary 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 The Data Warehouse and Technology 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eManaging Large Amounts of Data 159\u003c\/p\u003e \u003cp\u003eManaging Multiple Media 161\u003c\/p\u003e \u003cp\u003eIndexing and Monitoring Data 162\u003c\/p\u003e \u003cp\u003eInterfaces to Many Technologies 162\u003c\/p\u003e \u003cp\u003eProgrammer or Designer Control of Data Placement 163\u003c\/p\u003e \u003cp\u003eParallel Storage and Management of Data 164\u003c\/p\u003e \u003cp\u003eMetadata Management 165\u003c\/p\u003e \u003cp\u003eLanguage Interface 166\u003c\/p\u003e \u003cp\u003eEfficient Loading of Data 166\u003c\/p\u003e \u003cp\u003eEfficient Index Utilization 168\u003c\/p\u003e \u003cp\u003eCompaction of Data 169\u003c\/p\u003e \u003cp\u003eCompound Keys 169\u003c\/p\u003e \u003cp\u003eVariable-Length Data 169\u003c\/p\u003e \u003cp\u003eLock Management 171\u003c\/p\u003e \u003cp\u003eIndex-Only Processing 171\u003c\/p\u003e \u003cp\u003eFast Restore 171\u003c\/p\u003e \u003cp\u003eOther Technological Features 172\u003c\/p\u003e \u003cp\u003eDBMS Types and the Data Warehouse 172\u003c\/p\u003e \u003cp\u003eChanging DBMS Technology 174\u003c\/p\u003e \u003cp\u003eMultidimensional DBMS and the Data Warehouse 175\u003c\/p\u003e \u003cp\u003eData Warehousing across Multiple Storage Media 182\u003c\/p\u003e \u003cp\u003eThe Role of Metadata in the Data Warehouse Environment 182\u003c\/p\u003e \u003cp\u003eContext and Content 185\u003c\/p\u003e \u003cp\u003eThree Types of Contextual Information 186\u003c\/p\u003e \u003cp\u003eCapturing and Managing Contextual Information 187\u003c\/p\u003e \u003cp\u003eLooking at the Past 187\u003c\/p\u003e \u003cp\u003eRefreshing the Data Warehouse 188\u003c\/p\u003e \u003cp\u003eTesting 190\u003c\/p\u003e \u003cp\u003eSummary 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 The Distributed Data Warehouse 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTypes of Distributed Data Warehouses 193\u003c\/p\u003e \u003cp\u003eLocal and Global Data Warehouses 194\u003c\/p\u003e \u003cp\u003eThe Local Data Warehouse 197\u003c\/p\u003e \u003cp\u003eThe Global Data Warehouse 198\u003c\/p\u003e \u003cp\u003eIntersection of Global and Local Data 201\u003c\/p\u003e \u003cp\u003eRedundancy 206\u003c\/p\u003e \u003cp\u003eAccess of Local and Global Data 207\u003c\/p\u003e \u003cp\u003eThe Technologically Distributed Data Warehouse 211\u003c\/p\u003e \u003cp\u003eThe Independently Evolving Distributed Data Warehouse 213\u003c\/p\u003e \u003cp\u003eThe Nature of the Development Efforts 213\u003c\/p\u003e \u003cp\u003eCompletely Unrelated Warehouses 215\u003c\/p\u003e \u003cp\u003eDistributed Data Warehouse Development 217\u003c\/p\u003e \u003cp\u003eCoordinating Development across Distributed Locations 218\u003c\/p\u003e \u003cp\u003eThe Corporate Data Model — Distributed 219\u003c\/p\u003e \u003cp\u003eMetadata in the Distributed Warehouse 223\u003c\/p\u003e \u003cp\u003eBuilding the Warehouse on Multiple Levels 223\u003c\/p\u003e \u003cp\u003eMultiple Groups Building the Current Level of Detail 226\u003c\/p\u003e \u003cp\u003eDifferent Requirements at Different Levels 228\u003c\/p\u003e \u003cp\u003eOther Types of Detailed Data 232\u003c\/p\u003e \u003cp\u003eMetadata 234\u003c\/p\u003e \u003cp\u003eMultiple Platforms for Common Detail Data 235\u003c\/p\u003e \u003cp\u003eSummary 236\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Executive Information Systems and the Data Warehouse 239\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEIS — The Promise 240\u003c\/p\u003e \u003cp\u003eA Simple Example 240\u003c\/p\u003e \u003cp\u003eDrill-Down Analysis 243\u003c\/p\u003e \u003cp\u003eSupporting the Drill-Down Process 245\u003c\/p\u003e \u003cp\u003eThe Data Warehouse as a Basis for EIS 247\u003c\/p\u003e \u003cp\u003eWhere to Turn 248\u003c\/p\u003e \u003cp\u003eEvent Mapping 251\u003c\/p\u003e \u003cp\u003eDetailed Data and EIS 253\u003c\/p\u003e \u003cp\u003eKeeping Only Summary Data in the EIS 254\u003c\/p\u003e \u003cp\u003eSummary 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 External Data and the Data Warehouse 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExternal Data in the Data Warehouse 260\u003c\/p\u003e \u003cp\u003eMetadata and External Data 261\u003c\/p\u003e \u003cp\u003eStoring External Data 263\u003c\/p\u003e \u003cp\u003eDifferent Components of External Data 264\u003c\/p\u003e \u003cp\u003eModeling and External Data 265\u003c\/p\u003e \u003cp\u003eSecondary Reports 266\u003c\/p\u003e \u003cp\u003eArchiving External Data 267\u003c\/p\u003e \u003cp\u003eComparing Internal Data to External Data 267\u003c\/p\u003e \u003cp\u003eSummary 268\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Migration to the Architected Environment 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Migration Plan 270\u003c\/p\u003e \u003cp\u003eThe Feedback Loop 278\u003c\/p\u003e \u003cp\u003eStrategic Considerations 280\u003c\/p\u003e \u003cp\u003eMethodology and Migration 283\u003c\/p\u003e \u003cp\u003eA Data-Driven Development Methodology 283\u003c\/p\u003e \u003cp\u003eData-Driven Methodology 286\u003c\/p\u003e \u003cp\u003eSystem Development Life Cycles 286\u003c\/p\u003e \u003cp\u003eA Philosophical Observation 286\u003c\/p\u003e \u003cp\u003eSummary 287\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 The Data Warehouse and the Web 289\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSupporting the eBusiness Environment 299\u003c\/p\u003e \u003cp\u003eMoving Data from the Web to the Data Warehouse 300\u003c\/p\u003e \u003cp\u003eMoving Data from the Data Warehouse to the Web 301\u003c\/p\u003e \u003cp\u003eWeb Support 302\u003c\/p\u003e \u003cp\u003eSummary 302\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Unstructured Data and the Data Warehouse 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntegrating the Two Worlds 307\u003c\/p\u003e \u003cp\u003eText — The Common Link 308\u003c\/p\u003e \u003cp\u003eA Fundamental Mismatch 310\u003c\/p\u003e \u003cp\u003eMatching Text across the Environments 310\u003c\/p\u003e \u003cp\u003eA Probabilistic Match 311\u003c\/p\u003e \u003cp\u003eMatching All the Information 312\u003c\/p\u003e \u003cp\u003eA Themed Match 313\u003c\/p\u003e \u003cp\u003eIndustrially Recognized Themes 313\u003c\/p\u003e \u003cp\u003eNaturally Occurring Themes 316\u003c\/p\u003e \u003cp\u003eLinkage through Themes and Themed Words 317\u003c\/p\u003e \u003cp\u003eLinkage through Abstraction and Metadata 318\u003c\/p\u003e \u003cp\u003eA Two-Tiered Data Warehouse 320\u003c\/p\u003e \u003cp\u003eDividing the Unstructured Data Warehouse 321\u003c\/p\u003e \u003cp\u003eDocuments in the Unstructured Data Warehouse 322\u003c\/p\u003e \u003cp\u003eVisualizing Unstructured Data 323\u003c\/p\u003e \u003cp\u003eA Self-Organizing Map (SOM) 324\u003c\/p\u003e \u003cp\u003eThe Unstructured Data Warehouse 325\u003c\/p\u003e \u003cp\u003eVolumes of Data and the Unstructured Data Warehouse 326\u003c\/p\u003e \u003cp\u003eFitting the Two Environments Together 327\u003c\/p\u003e \u003cp\u003eSummary 330\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 The Really Large Data Warehouse 331\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy the Rapid Growth? 332\u003c\/p\u003e \u003cp\u003eThe Impact of Large Volumes of Data 333\u003c\/p\u003e \u003cp\u003eBasic Data-Management Activities 334\u003c\/p\u003e \u003cp\u003eThe Cost of Storage 335\u003c\/p\u003e \u003cp\u003eThe Real Costs of Storage 336\u003c\/p\u003e \u003cp\u003eThe Usage Pattern of Data in the Face of Large Volumes 336\u003c\/p\u003e \u003cp\u003eA Simple Calculation 337\u003c\/p\u003e \u003cp\u003eTwo Classes of Data 338\u003c\/p\u003e \u003cp\u003eImplications of Separating Data into Two Classes 339\u003c\/p\u003e \u003cp\u003eDisk Storage in the Face of Data Separation 340\u003c\/p\u003e \u003cp\u003eNear-Line Storage 341\u003c\/p\u003e \u003cp\u003eAccess Speed and Disk Storage 342\u003c\/p\u003e \u003cp\u003eArchival Storage 343\u003c\/p\u003e \u003cp\u003eImplications of Transparency 345\u003c\/p\u003e \u003cp\u003eMoving Data from One Environment to Another 346\u003c\/p\u003e \u003cp\u003eThe CMSM Approach 347\u003c\/p\u003e \u003cp\u003eA Data Warehouse Usage Monitor 348\u003c\/p\u003e \u003cp\u003eThe Extension of the Data Warehouse across Different Storage Media 349\u003c\/p\u003e \u003cp\u003eInverting the Data Warehouse 350\u003c\/p\u003e \u003cp\u003eTotal Cost 351\u003c\/p\u003e \u003cp\u003eMaximum Capacity 352\u003c\/p\u003e \u003cp\u003eSummary 354\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 The Relational and the Multidimensional Models as a Basis for Database Design 357\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Relational Model 357\u003c\/p\u003e \u003cp\u003eThe Multidimensional Model 360\u003c\/p\u003e \u003cp\u003eSnowflake Structures 361\u003c\/p\u003e \u003cp\u003eDifferences between the Models 362\u003c\/p\u003e \u003cp\u003eThe Roots of the Differences 363\u003c\/p\u003e \u003cp\u003eReshaping Relational Data 364\u003c\/p\u003e \u003cp\u003eIndirect Access and Direct Access of Data 365\u003c\/p\u003e \u003cp\u003eServicing Future Unknown Needs 366\u003c\/p\u003e \u003cp\u003eServicing the Need to Change Gracefully 367\u003c\/p\u003e \u003cp\u003eIndependent Data Marts 370\u003c\/p\u003e \u003cp\u003eBuilding Independent Data Marts 371\u003c\/p\u003e \u003cp\u003eSummary 375\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Data Warehouse Advanced Topics 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEnd-User Requirements and the Data Warehouse 377\u003c\/p\u003e \u003cp\u003eThe Data Warehouse and the Data Model 378\u003c\/p\u003e \u003cp\u003eThe Relational Foundation 378\u003c\/p\u003e \u003cp\u003eThe Data Warehouse and Statistical Processing 379\u003c\/p\u003e \u003cp\u003eResource Contention in the Data Warehouse 380\u003c\/p\u003e \u003cp\u003eThe Exploration Warehouse 380\u003c\/p\u003e \u003cp\u003eThe Data Mining Warehouse 382\u003c\/p\u003e \u003cp\u003eFreezing the Exploration Warehouse 383\u003c\/p\u003e \u003cp\u003eExternal Data and the Exploration Warehouse 384\u003c\/p\u003e \u003cp\u003eData Marts and Data Warehouses in the Same Processor 384\u003c\/p\u003e \u003cp\u003eThe Life Cycle of Data 386\u003c\/p\u003e \u003cp\u003eMapping the Life Cycle to the Data Warehouse Environment 387\u003c\/p\u003e \u003cp\u003eTesting and the Data Warehouse 388\u003c\/p\u003e \u003cp\u003eTracing the Flow of Data through the Data Warehouse 390\u003c\/p\u003e \u003cp\u003eData Velocity in the Data Warehouse 391\u003c\/p\u003e \u003cp\u003e“Pushing” and “Pulling” Data 393\u003c\/p\u003e \u003cp\u003eData Warehouse and the Web-Based eBusiness Environment 393\u003c\/p\u003e \u003cp\u003eThe Interface between the Two Environments 394\u003c\/p\u003e \u003cp\u003eThe Granularity Manager 394\u003c\/p\u003e \u003cp\u003eProfile Records 396\u003c\/p\u003e \u003cp\u003eThe ODS, Profile Records, and Performance 397\u003c\/p\u003e \u003cp\u003eThe Financial Data Warehouse 397\u003c\/p\u003e \u003cp\u003eThe System of Record 399\u003c\/p\u003e \u003cp\u003eA Brief History of Architecture — Evolving to the Corporate Information Factory 402\u003c\/p\u003e \u003cp\u003eEvolving from the CIF 404\u003c\/p\u003e \u003cp\u003eObstacles 406\u003c\/p\u003e \u003cp\u003eCIF — Into the Future 406\u003c\/p\u003e \u003cp\u003eAnalytics 406\u003c\/p\u003e \u003cp\u003eErp\/sap 407\u003c\/p\u003e \u003cp\u003eUnstructured Data 408\u003c\/p\u003e \u003cp\u003eVolumes of Data 409\u003c\/p\u003e \u003cp\u003eSummary 410\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Cost-Justification and Return on Investment for a Data Warehouse 413\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCopying the Competition 413\u003c\/p\u003e \u003cp\u003eThe Macro Level of Cost-Justification 414\u003c\/p\u003e \u003cp\u003eA Micro Level Cost-Justification 415\u003c\/p\u003e \u003cp\u003eInformation from the Legacy Environment 418\u003c\/p\u003e \u003cp\u003eThe Cost of New Information 419\u003c\/p\u003e \u003cp\u003eGathering Information with a Data Warehouse 419\u003c\/p\u003e \u003cp\u003eComparing the Costs 420\u003c\/p\u003e \u003cp\u003eBuilding the Data Warehouse 420\u003c\/p\u003e \u003cp\u003eA Complete Picture 421\u003c\/p\u003e \u003cp\u003eInformation Frustration 422\u003c\/p\u003e \u003cp\u003eThe Time Value of Data 422\u003c\/p\u003e \u003cp\u003eThe Speed of Information 423\u003c\/p\u003e \u003cp\u003eIntegrated Information 424\u003c\/p\u003e \u003cp\u003eThe Value of Historical Data 425\u003c\/p\u003e \u003cp\u003eHistorical Data and CRM 426\u003c\/p\u003e \u003cp\u003eSummary 426\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 The Data Warehouse and the ODS 429\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eComplementary Structures 430\u003c\/p\u003e \u003cp\u003eUpdates in the ODS 430\u003c\/p\u003e \u003cp\u003eHistorical Data and the ODS 431\u003c\/p\u003e \u003cp\u003eProfile Records 432\u003c\/p\u003e \u003cp\u003eDifferent Classes of ODS 434\u003c\/p\u003e \u003cp\u003eDatabase Design — A Hybrid Approach 435\u003c\/p\u003e \u003cp\u003eDrawn to Proportion 436\u003c\/p\u003e \u003cp\u003eTransaction Integrity in the ODS 437\u003c\/p\u003e \u003cp\u003eTime Slicing the ODS Day 438\u003c\/p\u003e \u003cp\u003eMultiple ODS 439\u003c\/p\u003e \u003cp\u003eODS and the Web Environment 439\u003c\/p\u003e \u003cp\u003eAn Example of an ODS 440\u003c\/p\u003e \u003cp\u003eSummary 441\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17 Corporate Information Compliance and Data Warehousing 443\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTwo Basic Activities 445\u003c\/p\u003e \u003cp\u003eFinancial Compliance 446\u003c\/p\u003e \u003cp\u003eThe “What” 447\u003c\/p\u003e \u003cp\u003eThe “Why” 449\u003c\/p\u003e \u003cp\u003eAuditing Corporate Communications 452\u003c\/p\u003e \u003cp\u003eSummary 454\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18 The End-User Community 457\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Farmer 458\u003c\/p\u003e \u003cp\u003eThe Explorer 458\u003c\/p\u003e \u003cp\u003eThe Miner 459\u003c\/p\u003e \u003cp\u003eThe Tourist 459\u003c\/p\u003e \u003cp\u003eThe Community 459\u003c\/p\u003e \u003cp\u003eDifferent Types of Data 460\u003c\/p\u003e \u003cp\u003eCost-Justification and ROI Analysis 461\u003c\/p\u003e \u003cp\u003eSummary 462\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19 Data Warehouse Design Review Checklist 463\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhen to Do a Design Review 464\u003c\/p\u003e \u003cp\u003eWho Should Be in the Design Review? 465\u003c\/p\u003e \u003cp\u003eWhat Should the Agenda Be? 465\u003c\/p\u003e \u003cp\u003eThe Results 465\u003c\/p\u003e \u003cp\u003eAdministering the Review 466\u003c\/p\u003e \u003cp\u003eA Typical Data Warehouse Design Review 466\u003c\/p\u003e \u003cp\u003eSummary 488\u003c\/p\u003e \u003cp\u003eGlossary 489\u003c\/p\u003e \u003cp\u003eReferences 507\u003c\/p\u003e \u003cp\u003eArticles 507\u003c\/p\u003e \u003cp\u003eBooks 510\u003c\/p\u003e \u003cp\u003eWhite Papers 512\u003c\/p\u003e \u003cp\u003eIndex 517\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer networking \u0026amp; communications [\u003ca title=\"See our other books on Computer networking \u0026amp; communications\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20networking%20\u0026amp;%20communications%20%5BUT%5D%22\"\u003eUT\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52462971322648,"sku":"9780764599446","price":35.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780764599446.jpg?v=1785543914","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/building-the-data-warehouse-paperback-softback-9780764599446","provider":"Freshly Printed Books","version":"1.0","type":"link"}