Rick Sherman is the founder of Athena IT Solutions, which provides consulting, training and vendor services for business intelligence, analytics, data integration and data warehousing. He is an adjunct faculty member at Northeastern University's Graduate School of Engineering and is a frequent contributor to industry publications, events, and webinars.
Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors' tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled - projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget turning the deluge of data into actionable information that fuels business knowledge. Finally, you'll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. - Provides practical guidelines for building successful BI, DW and data integration solutions. - Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. - Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses- Describes best practices and pragmatic approaches so readers can put them into action. - Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources.
Front
1
Business Intelligence
4
Copyright 5
Contents 6
Foreword 18
How to Use This Book 20
CHAPTER SUMMARIES 20
Acknowledgments 24
PART I -
26
CHAPTER 1
28
JUST ONE WORD: DATA 28
WELCOME TO THE DATA DELUGE 29
TAMING THE ANALYTICS DELUGE 31
TOO MUCH DATA, TOO LITTLE INFORMATION 33
DATA CAPTURE VERSUS INFORMATION ANALYSIS 35
THE FIVE CS OF DATA 37
COMMON TERMINOLOGY FROM OUR PERSPECTIVE 39
REFERENCES 44
PART II -
46
CHAPTER 2 - JUSTIFYING BI: BUILDING THE BUSINESS AND TECHNICAL CASE 48
WHY JUSTIFICATION IS NEEDED 48
BUILDING THE BUSINESS CASE 49
BUILDING THE TECHNICAL CASE 53
ASSESSING READINESS 57
CREATING A BI ROAD MAP 60
DEVELOPING SCOPE, PRELIMINARY PLAN, AND BUDGET 60
OBTAINING APPROVAL 65
COMMON JUSTIFICATION PITFALLS 65
CHAPTER 3 - DEFINING REQUIREMENTS—BUSINESS, DATA AND QUALITY 68
THE PURPOSE OF DEFINING REQUIREMENTS 68
GOALS 69
DELIVERABLES 70
ROLES 72
DEFINING REQUIREMENTS WORKFLOW 74
INTERVIEWING 81
DOCUMENTING REQUIREMENTS 85
PART III -
88
CHAPTER 4 - ARCHITECTURE FRAMEWORK 90
THE NEED FOR ARCHITECTURAL BLUEPRINTS 90
ARCHITECTURAL FRAMEWORK 91
INFORMATION ARCHITECTURE 92
DATA ARCHITECTURE 93
TECHNICAL ARCHITECTURE 97
PRODUCT ARCHITECTURE 103
METADATA 103
SECURITY AND PRIVACY 105
AVOIDING ACCIDENTS WITH ARCHITECTURAL PLANNING 106
DO NOT OBSESS OVER THE ARCHITECTURE 108
CHAPTER 5 - INFORMATION ARCHITECTURE 110
THE PURPOSE OF AN INFORMATION ARCHITECTURE 110
DATA INTEGRATION FRAMEWORK 111
DIF INFORMATION ARCHITECTURE 112
OPERATIONAL BI VERSUS ANALYTICAL BI 125
MASTER DATA MANAGEMENT 128
CHAPTER 6 - DATA ARCHITECTURE 132
THE PURPOSE OF A DATA ARCHITECTURE 132
HISTORY 133
DATA ARCHITECTURAL CHOICES 143
DATA INTEGRATION WORKFLOW 153
DATA WORKFLOW—RISE OF EDW AGAIN 161
OPERATIONAL DATA STORE 162
REFERENCES 167
CHAPTER 7 - TECHNOLOGY & PRODUCT ARCHITECTURES
WHERE ARE THE PRODUCT AND VENDOR NAMES? 168
EVOLUTION NOT REVOLUTION 169
TECHNOLOGY ARCHITECTURE 172
PRODUCT AND TECHNOLOGY EVALUATIONS 190
PART IV -
196
CHAPTER 8 - FOUNDATIONAL DATA MODELING 198
THE PURPOSE OF DATA MODELING 198
DEFINITIONS—THE DIFFERENCE BETWEEN A DATA MODEL AND DATA MODELING 198
THREE LEVELS OF DATA MODELS 199
DATA MODELING WORKFLOW 202
WHERE DATA MODELS ARE USED 203
ENTITY-RELATIONSHIP (ER) MODELING OVERVIEW 204
NORMALIZATION 214
LIMITS AND PURPOSE OF NORMALIZATION 219
CHAPTER 9 - DIMENSIONAL MODELING 222
INTRODUCTION TO DIMENSIONAL MODELING 222
HIGH-LEVEL VIEW OF A DIMENSIONAL MODEL 223
FACTS 223
DIMENSIONS 228
SCHEMAS 233
ENTITY RELATIONSHIP VERSUS DIMENSIONAL MODELING 238
PURPOSE OF DIMENSIONAL MODELING 241
FACT TABLES 243
ACHIEVING CONSISTENCY 245
ADVANCED DIMENSIONS AND FACTS 246
DIMENSIONAL MODELING RECAP 259
CHAPTER 10 - BUSINESS INTELLIGENCE DIMENSIONAL MODELING 262
INTRODUCTION 262
HIERARCHIES 262
OUTRIGGER TABLES 269
SLOWLY CHANGING DIMENSIONS 270
CAUSAL DIMENSION 287
MULTIVALUED DIMENSIONS 288
JUNK DIMENSIONS 290
VALUE BAND REPORTING 293
HETEROGENEOUS PRODUCTS 294
ALTERNATE DIMENSIONS 295
TOO FEW OR TOO MANY DIMENSIONS 297
PART V -
298
CHAPTER 11 - DATA INTEGRATION DESIGN AND DEVELOPMENT 300
GETTING STARTED WITH DATA INTEGRATION 300
DATA INTEGRATION ARCHITECTURE 302
DATA INTEGRATION REQUIREMENTS 305
DATA INTEGRATION DESIGN 310
DATA INTEGRATION STANDARDS 315
LOADING HISTORICAL DATA 320
DATA INTEGRATION PROTOTYPING 323
DATA INTEGRATION TESTING 323
CHAPTER 12 - DATA INTEGRATION PROCESSES 326
INTRODUCTION: MANUAL CODING VERSUS TOOL-BASED DATA INTEGRATION 326
DATA INTEGRATION SERVICES 334
PART VI -
360
CHAPTER 13 - BUSINESS INTELLIGENCE APPLICATIONS 362
BI CONTENT SPECIFICATIONS 362
REVISE BI APPLICATIONS LIST 364
BI PERSONAS 365
BI DESIGN LAYOUT—BEST PRACTICES 368
DATA DESIGN FOR SELF-SERVICE BI 373
MATCHING TYPES OF ANALYSIS TO VISUALIZATIONS 376
CHAPTER 14 - BI DESIGN AND DEVELOPMENT 384
BI DESIGN 384
BI DEVELOPMENT 392
BI APPLICATION TESTING 397
CHAPTER 15 - ADVANCED ANALYTICS 400
ADVANCED ANALYTICS OVERVIEW AND BACKGROUND 400
PREDICTIVE ANALYTICS AND DATA MINING 402
ANALYTICAL SANDBOXES AND HUBS 408
BIG DATA ANALYTICS 420
DATA VISUALIZATION 426
REFERENCE 427
CHAPTER 16 - DATA SHADOW SYSTEMS 428
THE DATA SHADOW PROBLEM 428
ARE THERE DATA SHADOW SYSTEMS IN YOUR ORGANIZATION? 430
WHAT KIND OF DATA SHADOW SYSTEMS DO YOU HAVE? 431
DATA SHADOW SYSTEM TRIAGE 432
THE EVOLUTION OF DATA SHADOW SYSTEMS IN AN ORGANIZATION 433
DAMAGES CAUSED BY DATA SHADOW SYSTEMS 437
THE BENEFITS OF DATA SHADOW SYSTEMS 438
MOVING BEYOND DATA SHADOW SYSTEMS 439
MISGUIDED ATTEMPTS TO REPLACE DATA SHADOW SYSTEMS 442
RENOVATING DATA SHADOW SYSTEMS 443
PART VII -
448
CHAPTER 17 - PEOPLE, PROCESS AND POLITICS 450
THE TECHNOLOGY TRAP 450
THE BUSINESS AND IT RELATIONSHIP 452
ROLES AND RESPONSIBILITIES 454
BUILDING THE BI TEAM 456
TRAINING 466
DATA GOVERNANCE 469
CHAPTER 18 - PROJECT MANAGEMENT 474
THE ROLE OF PROJECT MANAGEMENT 474
ESTABLISHING A BI PROGRAM 475
BI ASSESSMENT 485
WORK BREAKDOWN STRUCTURE 490
BI ARCHITECTURAL PLAN 495
BI PROJECTS ARE DIFFERENT 497
PROJECT METHODOLOGIES 498
BI PROJECT PHASES 504
BI PROJECT SCHEDULE 509
CHAPTER 19 - CENTERS OF EXCELLENCE 518
THE PURPOSE OF CENTERS OF EXCELLENCE 518
BI COE 519
DATA INTEGRATION CENTER OF EXCELLENCE 526
ENABLING A DATA-DRIVEN ENTERPRISE 536
REFERENCE 537
Index 538
The Business Demand for Data, Information, and Analytics
Abstract
In the business world, knowledge is not just power. It is the lifeblood of a thriving enterprise. Knowledge comes from information, and that, in turn, comes from data. Many enterprises are overwhelmed by the deluge of data, which they are receiving from all directions. They are wondering if they can handle Big Data—with its expanding volume, variety, and velocity. There is a big difference between raw data, which by itself is not useful, and actionable information, which business people can use with confidence to make decisions. Data must to be transformed to make it clean, consistent, conformed, current, and comprehensive—the five Cs of data. It is up to a Business Intelligence (BI) team to gather and manage the data to empower the company’s business groups with the information they need to gain knowledge—knowledge that helps them make informed decisions about every step the company takes. While there are attempts to circumvent or replace BI with operational systems, there really is no good substitute for true BI. Operational systems may excel at data capture, but BI excels at information analysis.
Keywords
Big Data; Data; Data 5 Cs; Data capture; Data variety; Data velocity; Data volume; Information; Information analysis; Operational BI
Just One Word: Data
“I just want to say one word to you. Just one word… Are you listening? … Plastics. There’s a great future in plastics.”
Mr. McGuire in the 1967 movie The Graduate.
Welcome to the Data Deluge
Data Volume, Variety, and Velocity
Taming the Analytics Deluge
Erscheint lt. Verlag | 4.11.2014 |
---|---|
Sprache | englisch |
Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
Informatik ► Office Programme ► Outlook | |
Mathematik / Informatik ► Informatik ► Software Entwicklung | |
Sozialwissenschaften ► Kommunikation / Medien ► Buchhandel / Bibliothekswesen | |
Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
ISBN-10 | 0-12-411528-4 / 0124115284 |
ISBN-13 | 978-0-12-411528-6 / 9780124115286 |
Haben Sie eine Frage zum Produkt? |
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Größe: 22,1 MB
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