Summary

"If you are looking for a complete treatment of business intelligence, then go no further than this book. Larissa T. Moss and Shaku Atre have covered all the bases in a cohesive and logical order, making it easy for the reader to follow their line of thought. From early design to ETL to physical database design, the book ties together all the components of business intelligence." --Bill Inmon, Inmon Enterprises Business Intelligence Roadmapis a visual guide to developing an effective business intelligence (BI) decision-support application. This book outlines a methodology that takes into account the complexity of developing applications in an integrated BI environment. The authors walk readers through every step of the process--from strategic planning to the selection of new technologies and the evaluation of application releases. The book also serves as a single-source guide to the best practices of BI projects. Part I steers readers through the six stages of a BI project: justification, planning, business analysis, design, construction, and deployment. Each chapter describes one of sixteen development steps and the major activities, deliverables, roles, and responsibilities. All technical material is clearly expressed in tables, graphs, and diagrams. Part II provides five matrices that serve as references for the development process charted in Part I. Management tools, such as graphs illustrating the timing and coordination of activities, are included throughout the book. The authors conclude by crystallizing their many years of experience in a list of dos, don'ts, tips, and rules of thumb. The accompanying CD-ROM includes a complete, customizable work breakdown structure. Both the book and the methodology it describes are designed to adapt to the specific needs of individual stakeholders and organizations. The book directs business representatives, business sponsors, project managers, and technicians to the chapters that address their distinct responsibilities. The framework of the book allows organizations to begin at any step and enables projects to be scheduled and managed in a variety of ways. Business Intelligence Roadmapis a clear and comprehensive guide to negotiating the complexities inherent in the development of valuable business intelligence decision-support applications

Author Biography

Larissa T. Moss Ms. Moss is president of Method Focus, Inc. Shaku Atre Ms. Atre is president of Atre Group, Inc.

About the Authors	p. xix
Foreword	p. xxi
Preface	p. xxiii
The Purpose of This Book	p. xxiv
How This Book Is Organized	p. xxv
How to Use This Book	p. xxviii
Who Should Read This Book	p. xxviii
Comments	p. xxxii
Stages and Steps	p. 1
Guide to the Development Steps	p. 3
Business Intelligence Definition	p. 4
BI Decision-Support Initiatives	p. 5
Development Approaches	p. 5
Engineering Stages and the Development Steps	p. 11
Parallel Development Tracks	p. 17
BI Project Team Structure	p. 20
Justification for Using This Project Lifecycle Guide	p. 26
Bibliography and Additional Reading	p. 27
Step 1: Business Case Assessment	p. 29
Business Justification	p. 31
Business Drivers	p. 33
Business Analysis Issues	p. 35
Cost-Benefit Analysis	p. 37
Risk Assessment	p. 40
Business Case Assessment Activities	p. 45
Deliverable Resulting from These Activities	p. 48
Roles Involved in These Activities	p. 49
Risks of Not Performing Step 1	p. 49
Bibliography and Additional Reading	p. 50
Step 2: Enterprise Infrastructure Evaluation	p. 51
Technical Infrastructure Evaluation	p. 53
The Hardware Platform	p. 54
The Middleware Platform	p. 57
The DBMS Platform	p. 58
Technical Infrastructure Evaluation Activities	p. 61
Deliverables Resulting from These Activities	p. 62
Roles Involved in These Activities	p. 63
Risks of Not Performing Step 2, Section A	p. 63
Nontechnical Infrastructure Evaluation	p. 64
The Effects of Stovepipe Development	p. 65
The Need for Nontechnical Infrastructure	p. 66
Enterprise Architecture	p. 68
Enterprise Standards	p. 71
Nontechnical Infrastructure Evaluation Activities	p. 75
Deliverable Resulting from These Activities	p. 76
Roles Involved in These Activities	p. 77
Risks of Not Performing Step 2, Section B	p. 78
Bibliography and Additional Reading	p. 78
Step 3: Project Planning	p. 81
Managing the BI Project	p. 83
Defining the BI Project	p. 84
Planning the BI Project	p. 90
Project Planning Activities	p. 98
Deliverables Resulting from These Activities	p. 100
Roles Involved in These Activities	p. 101
Risks of Not Performing Step 3	p. 103
Bibliography and Additional Reading	p. 103
Step 4: Project Requirements Definition	p. 105
General Business Requirements	p. 108
Project-Specific Requirements	p. 112
The Interviewing Process	p. 116
Project Requirements Definition Activities	p. 118
Deliverable Resulting from These Activities	p. 121
Roles Involved in These Activities	p. 121
Risks of Not Performing Step 4	p. 122
Bibliography and Additional Reading	p. 123
Step 5: Data Analysis	p. 125
Business-Focused Data Analysis	p. 127
Top-Down Logical Data Modeling	p. 128
Bottom-Up Source Data Analysis	p. 133
Data Cleansing	p. 136
Data Analysis Activities	p. 141
Deliverables Resulting from These Activities	p. 143
Roles Involved in These Activities	p. 144
Risks of Not Performing Step 5	p. 145
Bibliography and Additional Reading	p. 146
Step 6: Application Prototyping	p. 149
Purposes of Prototyping	p. 151
Best Practices for Prototyping	p. 153
Types of Prototypes	p. 156
Building Successful Prototypes	p. 159
Application Prototyping Activities	p. 163
Deliverables Resulting from These Activities	p. 165
Roles Involved in These Activities	p. 165
Risks of Not Performing Step 6	p. 166
Bibliography and Additional Reading	p. 167
Step 7: Meta Data Repository Analysis	p. 169
The Importance of Meta Data	p. 172
Meta Data Repository as Navigation Tool	p. 174
Meta Data Classifications	p. 176
Meta Data Repository Challenges	p. 182
The Logical Meta Model	p. 184
Meta Data Repository Analysis Activities	p. 186
Deliverables Resulting from These Activities	p. 188
Roles Involved in These Activities	p. 188
Risks of Not Performing Step 7	p. 189
Bibliography and Additional Reading	p. 190
Step 8: Database Design	p. 191
Differences in Database Design Philosophies	p. 193
Logical Database Design	p. 197
Physical Database Design	p. 201
Database Design Activities	p. 204
Deliverables Resulting from These Activities	p. 207
Roles Involved in These Activities	p. 208
Risks of Not Performing Step 8	p. 209
Bibliography and Additional Reading	p. 209
Step 9: Extract/Transform/Load Design	p. 211
Implementation Strategies	p. 213
Preparing for the ETL Process	p. 215
Designing the Extract Programs	p. 219
Designing the Transformation Programs	p. 221
Designing the Load Programs	p. 223
Designing the ETL Process Flow	p. 225
Evaluating ETL Tools	p. 229
ETL Design Activities	p. 231
Deliverables Resulting from These Activities	p. 233
Roles Involved in These Activities	p. 233
Risks of Not Performing Step 9	p. 234
Bibliography and Additional Reading	p. 234
Step 10: Meta Data Repository Design	p. 237
Meta Data Silos	p. 239
Meta Data Repository Solutions	p. 242
Designing a Meta Data Repository	p. 247
Licensing (Buying) a Meta Data Repository	p. 250
Meta Data Repository Design Activities	p. 254
Deliverables Resulting from These Activities	p. 255
Roles Involved in These Activities	p. 256
Risks of Not Performing Step 10	p. 257
Bibliography and Additional Reading	p. 257
Step 11: Extract/Transform/Load Development	p. 259
Source Data Transformation	p. 261
Reconciliation	p. 263
Peer Reviews	p. 267
ETL Testing	p. 268
Formal Test Plan	p. 273
ETL Development Activities	p. 276
Deliverables Resulting from These Activities	p. 277
Roles Involved in These Activities	p. 278
Risks of Not Performing Step 11	p. 279
Bibliography and Additional Reading	p. 279
Step 12: Application Development	p. 281
Online Analytical Processing Tools	p. 283
Multidimensional Analysis Factors	p. 287
Online Analytical Processing Architecture	p. 289
Development Environments	p. 292
Application Development Activities	p. 295
Deliverables Resulting from These Activities	p. 297
Roles Involved in These Activities	p. 298
Risks of Not Performing Step 12	p. 299
Bibliography and Additional Reading	p. 299
Step 13: Data Mining	p. 301
Defining Data Mining	p. 303
Data Mining Techniques	p. 307
Data Mining Operations	p. 310
Applications of Data Mining	p. 311
Data Mining Activities	p. 313
Deliverables Resulting from These Activities	p. 315
Roles Involved in These Activites	p. 316
Risks of Not Performing Step 13	p. 316
Bibliography and Additional Reading	p. 317
Step 14: Meta Data Repository Development	p. 319
Populating the Meta Data Repository	p. 321
Meta Data Repository Interface Processes	p. 324
Meta Data Repository Testing	p. 326
Preparing for the Meta Data Repository Rollout	p. 327
Meta Data Repository Development Activities	p. 331
Deliverables Resulting from These Activities	p. 332
Roles Involved in These Activities	p. 333
Risks of Not Performing Step 14	p. 334
Bibliography and Additional Reading	p. 335
Step 15: Implementation	p. 337
Incremental Rollout	p. 339
Security Management	p. 340
Data Backup and Recovery	p. 345
Monitoring the Utilization of Resources	p. 347
Growth Management	p. 349
Implementation Activities	p. 352
Deliverables Resulting from These Activities	p. 354
Roles Involved in These Activities	p. 355
Risks of Not Performing Step 15	p. 356
Bibliography and Additional Reading	p. 356
Step 16: Release Evaluation	p. 359
The Application Release Concept	p. 361
Post-Implementation Reviews	p. 364
Release Evaluation Activities	p. 369
Deliverables Resulting from These Activities	p. 371
Roles Involved in These Activities	p. 371
Risks of Not Performing Step 16	p. 374
Bibliography and Additional Reading	p. 375
At a Glance	p. 377
Human Resource Allocation Matrix	p. 379
Entry & Exit Criteria and Deliverables Matrix	p. 387
Activity Dependency Matrix	p. 405
Task/Subtask Matrix	p. 411
Practical Guidelines Matrix	p. 455
Work Breakdown Structure	p. 491
Index	p. 525
Table of Contents provided by Ingram. All Rights Reserved.

Supplemental Materials

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Excerpts

Many organizations are already well equipped to implement successful business intelligence (BI) decision-support applications, such as data warehouses, data marts, and other business analytics applications. However, during our consulting and teaching engagements, we have encountered many ill-equipped organizations as well. We observed some common factors among them, which we address in this book: Lack of understanding of the complexity of BI decision-support projects Lack of recognizing BI decision-support projects as cross-organizational business initiatives and not understanding that cross-organizational initiatives are different from stand-alone solutions Unavailable or unwilling business representatives Unengaged business sponsors or business sponsors who have little or no authority due to their low-level positions within the organization Lack of skilled and available staff as well as suboptimum staff utilization Inappropriate project team structure and dynamics No software release concept (no iterative development method) No work breakdown structure (no methodology) Ineffective project management (only project administration) No business analysis and no standardization activities No appreciation of the impact of dirty data on business profitability No understanding of the necessity for and the usage of meta data Too much reliance on disparate methods and tools (the "silver bullet" syndrome) BI project managers and project teams can use this book to improve their project life cycles. They can also use it to obtain the appropriate recognition for their BI projects from the business community and to solicit the required support from their executive management. BI project team members and the business representatives assigned to them can use this book to gain a better understanding of the development effort required to build and deploy successful BI decision-support applications. THE PURPOSE OF THIS BOOK Business Intelligence Roadmapis a guide for developing BI decision-support applications. The two main purposes of this book are to Explain the complexity of BI decision-support projects Present a step-by-step guide for the entire BI project lifecycle ComplexityIn order to give you an appreciation of the complexity of BI decision-support projects, we describe all of the components that go into a BI decision- support development effort. For example: You should know what makes a BI decision-support application different from a traditional decision-support system so that you can avoid costly mistakes. You should understand the infrastructure components of your new BI decision-support application, such as the tools available (for development and for access and analysis). You should be able to recognize items that could impair the success of your new BI decision-support application. You should determine how many resources you need and what type of resources, both technical and human. You should decide on the design or architecture of your BI decision- support application, such as designing for multidimensional reporting or ad hoc querying. Step-by-Step GuideOur step-by-step guide across the breadth of a complete development lifecycle includes activities, deliverables, roles and responsibilities, dos and don'ts, and entry and exit criteria, plus tips and rules of thumb to lead you to a successful BI decision-support implementation. For example: You should choose which steps you ought to perform on your BI project because no two BI decision-support projects are exactly alike. You should know whether to start with a cross-organizational decision-support solution or a tailored departmental solution with the basis for expansion. You should understand the sequence in which to perform development activities, t