The data warehouse mentor: practical data warehouse and business intelligence insights
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
McGraw-Hill
2011
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXV, 388 S. graph. Darst. |
| ISBN: | 9780071745321 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | IMAGE 1
CONTENTS
ACKNOWLEDGMENTS XXI INTRODUCTION XXIII PART I PREPARATION CHAPTER 1 DATA
WAREHOUSE AND BUSINESS INTELLIGENCE OVERVIEW 3 BUSINESS INTELLIGENCE
OVERVIEW 4DEFINITION 4VALUE OF BUSINESS INTELLIGENCE 6BREAKDOWN OF
BUSINESS AND INTELLIGENCE 7BUSINESS INTELLIGENCE SUCCESS FACTORS 8
PURPOSE OF BI 9 BL USER PRESENTATION 11 BI TOOL AND ARCHITECTURE
15ADVANCEMENTS DUE TO GLOBALIZATION 17DATA WAREHOUSE OVERVIEW
17DEFINITION 18DATA WAREHOUSE SYSTEM 19DATA WAREHOUSE ARCHITECTURE
20DATA FLOW TERMINOLOGY 22DATA WAREHOUSE PURPOSE 23DATA STRUCTURE
STRATEGY 26DATA WAREHOUSE BUSINESS 27FREQUENTLY ASKED QUESTIONS
28CURRENT SYSTEMS GOOD ENOUGH? 29WHAT IS THE VALUE OF A DATA WAREHOUSE?
30HOW MUCH WILL IT COST? 32HOW LONG WILL IT TAKE? 32WHAT WILL MAKE US
SUCCESSFUL? 34 XI
IMAGE 2
X II T HE D OT A- W A R E H O U SE M E N T OR
CHAPTER 2 DATA IN THE ORGANIZATION 39
CORPORATE ASSET 40
DATA IN CONTEXT 40
DATA QUALITY 42
DATA VOCABULARY 44
DATA COMPONENTS 46
ORGANIZING THE DATA 50
STRUCTURING THE DATA 50
DATA MODELS 51
DATA ARCHITECTURE 57
COMPETITIVE ADVANTAGE 62
DATA MODEL BUILD OR BUY 63
MENTORING THE BUSINESS 66
CHAPTER 3 REASONS FOR BUILDING 69
PLATFORM MIGRATION 71
BUSINESS CONTINUITY 72
REVERSE ENGINEERING 72
DATA QUALITY 73
PARALLEL ENVIRONMENTS 74
ADDEDVALUE 75
DATA WAREHOUSE CENTRALIZATION 75
CORPORATE MERGER 76
IN-HOUSE MERGING 76
CENTRAL DESIGN AND LOCAL USAGE 76
DATA MART CONSOLIDATION 77
NEW INITIATIVE 79
NEW INITIATIVE: DYNAMIC REPORTING 81
JUST BUILD IT 82
DATA FLOUNDATION 84
REASONS (OR NOT BUILDING A DATA WAREHOUSE 86
POOR DATA QUALITY 86
LACK OF BUSINESS INTEREST 86
TACK OF SPONSORSHIP 87
UNCLEAR FOCUS 87
SUFFICIENCY OF CURRENT SYSTEMS 87
LACK OF RESOURCES 87
IMAGE 3
C O N T E N TS X I II
UNSTABLE ENVIRONMENT 88
TOO COSTLY 88
POOR MANAGEMENT 88
CHAPTER 4 DATA WAREHOUSE AND BUSINESS INTELLIGENCE STRATEGY 89
BUSINESS INTELLIGENCE STRATEGY 90
BUSINESS PURPOSE 90
BUSINESS USAGE 91
ARCHITECTURE OVERVIEW 93
DATA WAREHOUSE STRATEGY 94
USAGE 95
DW ARCHITECTURE 96
FOCUS AND SUCCESS 99
ENTERPRISE OR LINE OF BUSINESS? 99
GOAL FOCUSED 100
SUCCESS: WHEN ARE WE DONE? 100
WHERETOSTART? 101
FOR BL 102
FORDW 103
HOWTOSTART? 104
FOR BL 104
FORDW 106
PROJECT PHASING 109
HOW LONG WILL IT TAKE, REVISITED 110
POINTS OF INTEREST 112
TYPICAL FAILURE REASONS 112
BASICVALUES 117
CHAPTER 5 PROJECT RESOURCES: ROLES AND INSIGHTS 119
KEY OBSERVATIONS 120
PROJECTTEAMS 120
SENIOR EXPERTISE 121
LEADERSHIP 122
PROJECT SPONSOR 124
DATA WAREHOUSE EXECUTIVE 125
TEAM STRUCTURE 125
EXECUTIVE SPONSORSHIP 125
DATA STEWARDS 127
BASIC RESOURCES 127
IMAGE 4
T HE D A TA W A R E H O U SE M E N T OR
PERIODIC REVIEWS: PROGRESS AUDIT 132
CENTER OF COMPETENCE 132
CHAPTER 6 WRITE-LF-UP OVERVIEW 135
PROJECT CHARTER 136
PROJECT SCOPE 138
STATEMENT OF WORK (SOW) 139
PART II COMPONENTS
CHAPTER 7 BUSINESS INTELLIGENCE: DATA MARTS AND USAGE 143
WHY MODEL THE DATA? 145
TYPES OF DATA MODELS 146
DESIGN OF DATA 149
FACT TABLES 157
TYPES OF FACTS 158
TYPES OF FACT TABLES 160
SOURCE OF MEASURES 162
FACTLABKKEY 163
GRAIN OF FACT TABLE 163
FACT TABLE DENSITY 164
FACTLESS FACT TABLE 164
DIMENSIONS 165
DIMENSION OR MEASURE 166
HISTORY AND DATES 166
DIMENSION TABLE KEY 171
GRAIN OF DIMENSION 172
SOURCE AND VALUE OF DIMENSION ATTRIBUTES 173
TYPES OF DIMENSIONS 174
HIERARCHIES AND HELPER TABLES 184
PROFILE TABLES 186
NUMBER OF DIMENSIONS 188
SIZING 188
CHAPTER 8 ENTERPRISE DATA MODELS 191
DATA MODELS OVERVIEW 192
INMON AND KIMBALL 195
EDMPURPOSE 196
IMAGE 5
C O N T E N TS XV
EDM BENEFIT 196
DATA MODEL: WHERE TO START 197
FULL TOP-DOWN DATA MODEL 198
SUBJECT AREA MODEL 199
CONCEPT MODEL 201
ENTITY RELATIONSHIP MODEL 202
BUS ARCHITECTURE 203
PURCHASED DATA MODEL 205
MODEL INSIGHTS 207
DATA COMPONENTS 207
NORMALIZING A DATA MODEL 208
SUPERTYPE/SUBTYPE MODELS 214
CAPTURING HISTORY IN A NORMALIZED DATA MODEL 217
SURROGATE KEYS 222
LOGICAL VS. PHYSICAL DATA MODEL 222
REFERENTIAL INTEGRITY OR NOT 224
OTHER DATA MODELS 225
INPUT DATA MODEL 225
STAGING DATA MODEL 225
FINAL THOUGHTS 226
CHAPTER 9 DATA WAREHOUSE ARCHITECTURE: COMPONENTS 227
ARCHITECTURE OVERVIEW 228
ARCHITECT ROLES 228
SOLUTION ARCHITECT 229
DATA WAREHOUSE ARCHITECT 229
TECHNICAL ARCHITECT 230
DATA ARCHITECT 230
ETL ARCHITECT 230
BL ARCHITECT 231
OVERALL 231
ARCHITECTURE TIERS 232
SINGLE-TIER ARCHITECTURE 232
CLASSIC TWO-TIER ARCHITECTURE 233
ADVANCED THREE-TIER ARCHITECTURE 235
IMAGE 6
X VI T HE D A TA W A R E H O U SE M E N T OR
DATA WAREHOUSE ARCHITECTURES 236
SOLO DATA MART ARCHITECTURE 236
BUS ARCHITECTURE 237
CENTRAL REPOSITORY ARCHITECTURE 238
FEDERATED ARCHITECTURE 239
COMPONENTS (LAYERS) 240
DATA SOURCES 240
DATA POPULATION 240
DATA ORGANIZATION 241
DATA DISTRIBUTION 241
INFORMATION OUT 241
IMPLEMENTATION APPROACHES 242
DATA DESIGN AND DATA FLOW 243
LOGICAL VS. PHYSICAL MODELS 243
TOP-DOWN APPROACH 246
BOTTOM-UP APPROACH 246
HYBRID APPROACH 249
ACCELERATORS 250
DATA ACQUISITION LAYER 251
CENTRALIZED DATA LAYER 251
DATA DISTRIBUTION LAYER 252
PERFORMANCE LAYER 252
USER PRESENTATION LAYER 253
METHODOLOGY 253
OUT-OF-THE-BOX SOLUTION 254
CHAPTER 10 ETL AND DATA QUALITY 255
ARCHITECTURE 257
DATA POPULATION 258
DATA DISTRIBUTION 259
ETL MAPPING 260
INITIAL AND INCREMENTAL LOADS 262
EILVS.ELTVS.ENL 263
PARALLEL OPERATIONS 265
ETL ROLES 265
DATA FLOW DIAGRAMS 267
OPERATIONAL DATA STORE (ODS) 267
IMAGE 7
C O N T E N TS X V LI
SOURCE SYSTEMS 268
NO SOURCE 268
MULTIPLE SOURCES 269
ALTERNATE SOURCES (SIFS) 269
UNSTRUCTURED DATA 271
DATA PROFILING 271
DATA CAPTURE 2 72
MULTIPLE LARGE FILES 272
SWITCH FILES 272
FAILSAFE STRATEGY 273
TRONSFORMATION AND STAGING 274
PREPARATION 275
SURROGATE KEYS 277
REFERENTIAL INTEGRITY 280
AGGREGATING, PROFILING, AND SUMMARIZING 280
CODE TABLES 281
LOADING 281
HISTORY VS. NO HISTORY 282
INSERT/UPDATE/UPSERT/DELETE 282
POPULATION INFORMATION 282
LOAD SCHEDULING 283
STAGING FOR EDW VS. STAGING FOR BUS ARCHITECTURE 283
DATA DISTRIBUTION 285
3NF TO STAR 285
DATA QUALITY 287
FJL TOOLS 289
PROJECT PLANNING AND METHODOLOGY 291
FUNDAMENTALS 294
RISK: PHASED DEVELOPMENT 295
RISK: DATA QUALITY 296
RISK: RESOURCES 296
RISK: COST 297
CHANGE MANAGEMENT 297
BEST PRACTICES 298
MISTAKES 298
IMAGE 8
X V I II T HE D A TA W A R E H O U SE M E N T OR
PROJECT PLAN METHODOLOGY 299
BUSINESS REQUIREMENTS 301
STRATEGY AND PLAN 302
SOLUTION OUTLINE 304
DESIGN 304
BUILD 305
DEPLOY 306
USE 306
PART III LET S BUILD
CHAPTER 12 WORKING SCENARIOS 309
THE CHEF: LET S GET COOKING! 312
TOP-DOWN (ENTERPRISE REPOSITORY) 315
VOCABULARY 317
CENTRALIZED DATA MODEL 318
DATA ARCHITECTURE 319
SOURCES 321
DATA MODEL 321
DATABASE 323
ACQUISITION 323
SOLUTION OVERVIEW 324
BOFFOM-(/P (OWP REPORTING) 325
END RESULT 327
VOCABULARY 332
DATA ARCHITECTURE 332
CONFORMED DIMENSION ADMINISTRATION 334
SOURCES 334
SOLUTION OVERVIEW 334
HYBRID (NORMALIZED DESIGN AND OLAP) 336
FIRST EFFORTS 338
DATA MODELS 338
DATA ARCHITECTURE 339
SOLUTION OVERVIEW 341
MERGING 342
PLAN OF ACTION 343
NO INPUT: STRUCTURED INPUT FILES 345
IMAGE 9
C O N T E N TS X IX
INTEGRATING PHASE 2 347
CHANGE MANAGEMENT 348
THE BIGGER PICTURE: ENTERPRISE INFORMATION ARCHITECTURE (EIA) 348
CHAPTER 13 DATA GOVERNANCE 351
WHAT IS DATA GOVERNANCE? 352
DEFINITION 352
REASONS FOR DATA GOVERNANCE 353
ORGANIZATIONAL STRUCTURE 354
DRIVERS AND INITIATIVES 356
DATA GOVERNANCE: MAJOR POINTS 357
SECURITY AND SENSITIVITY 357
DATA QUALITY 358
OWNERSHIP 359
CHANGE CONTROL 360
DATA GOVERNANCE READINESS 361
CHAPTER 14 POST-PROJECT REVIEW 363
SYNOPSIS 364
PROJECT REVIEW 365
NEXT PHASE 370
INDEX 371
|
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| title | The data warehouse mentor practical data warehouse and business intelligence insights |
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