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Business Intelligence
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Author |
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Elizabeth Vitt, Michael Luckevich, Stacia Misner
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Pages |
224
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N/A
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Level |
All Levels
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Published |
04/17/2002
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ISBN |
9780735616271
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ISBN-10 |
0-7356-1627-2
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Price(USD) |
$39.99
To see this book's discounted price, select a reseller below.
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Index
A
Access. See Microsoft Access
Accrue Software Pilot solutions, 96
action, in BI cycle, 21
actionability of information, 132, 134-35
action plans, BI role in, 14
Active Directory. See Microsoft Active Directory
ad hoc analysis, 41, 57, 105-6, 157
affinity grouping, 59
aggregation method, 160, 168-69
alternate hierarchy, 42-43
analysis. See business reporting and analysis
analysis at the speed of thought, 15, 31, 34, 45
Analysis Database. See Microsoft Analysis Database
analysis gap, bridging, 15, 29-34. See also online analytical processing (OLAP)
analytical applications. See BI accelerators
analytical processing. See online analytical processing (OLAP)
Analytics Builder Workbook. See Microsoft Analytics Builder Workbook
ancestor, within hierarchies, 43
application programming interfaces (APIs), 177. See also specific interfaces
Aspirity, LLC, 75n
assets under management (AUM), 75, 78, 80
attributes, 43, 162, 166-67
Audi AG, 67-73
BI benefits, 71-72, 115, 133
BI solution, 70-71
business requirements, 68-70
company background, 67-68
future plans, 72-73
AUM (assets under management), 75, 78, 80
availability, in database evaluation, 159
B
back-end technology. See online analytical processing (OLAP)
base measures, 119, 137
benchmark, 45
benefit analysis, 140-42, 156
BI. See business intelligence (BI)
BI accelerators, 156, 161. See also Microsoft Business Intelligence Accelerator for SQL Server
BI attitude, 16-17, 27
BI blueprint, 127, 162, 168. See also opportunity areas, identifying
BI cycle, 17-22
BI opportunity scorecard, 138-40
BI solution, 145. See also implementation guidelines
BI systems, 34-35. See also online analytical processing (OLAP)
Boeing, 104
bottom-up analysis. See data mining
brainstorming sessions
arranging, 123, 131
asking business questions in, 124-27
defining team for, 123-24
organizing information requirements in, 127
results of, 132 (see also opportunity areas, identifying)
Braun, Heinz, 67, 69
B2B (business-to-business) transaction systems, 15-16
B2C (business-to-consumer) transaction systems, 16
budgets, 106
Burroughs, John, 106
Business Analytics Data Warehouse. See CompUSA Inc.
Business Intelligence Accelerator for SQL Server. See Microsoft Business Intelligence Accelerator for SQL Server
business intelligence (BI)
concept overview, 13-17
cycle of, 17-22
enabling, 23-27
future of, 175
business reporting and analysis
applying data mining, 57-59
at Audi AG, 71-72
BI accelerators providing, 161
in BI cycle, 18-20
at CompUSA, 89-90
defining user communities, 54-56
evaluating tools for, 157-58
front-end tools for, 52, 57, 153, 157-58
making data easier to analyze, 53-54
processes, 52
at Russell, 78-79
business teams. See teams
business-to-business (B2B) transaction systems, 15-16
business-to-consumer (B2C) transaction systems, 16
business unit, 114
business-unit BI applications, 115-16
"buying versus building" decision, 156
C
calculated measures, 119, 137, 168. See also measures
calculation engines, 36. See also Microsoft SQL Server Relational Database Engine
Cascade Designs, 103-10
business requirements, 105-6
company background, 103-4
ERP software, 103, 107-9
future plans, 110
lessons learned, 109-10
OLAP technology at, 155
case studies
Audi AG, 67-73
Cascade Designs, 103-10
CompUSA Inc., 85-94
Disco S.A., 95-102
Fabrikam (fictional), 3-11
Russell, 75-83
cells, for data storage, 38-40
centralized data warehouse. See data warehouse; Microsoft Data Warehousing Framework
child, within hierarchies, 43, 165
classification, 59
cleansed data, 61-62, 172
click-stream analysis, 94
closed-loop analysis, 180
clustering, 59, 98
Codd, E. F., 35
Cognos Impromptu, 76, 81
Cognos PowerPlay, 78-79
CompUSA Inc., 85-94
BI benefits, 90-92, 115, 118, 132, 134
BI challenges, 92-93
Business Analytics Data Warehouse solution, 88-90
business requirements, 86-88
company background, 85-86
future plans, 93-94
organizational structure, 114
consistency, 60-61, 85
corporate culture, 26-27
cost analysis, 140-42, 147, 155-56
CRM (customer resource management) systems, 50, 181
cross-functional BI applications, 115-16, 123, 135-36
cube storage, 38-40, 41-43, 179. See also online analytical processing (OLAP); storage
culture. See corporate culture
custom aggregation, 169
customer resource management (CRM) systems, 50, 181
customer support department KPIs, 22
customization, in database evaluation, 158, 159
cyclical design. See iterative design technique
D
Daily Tracker report, 89-90
data. See also information
analyzing existence and accessibility of, 136-37
cleansed, 61-62, 172
at CompUSA, 91, 92-93
creating information from, 15-16
dirty, 61-62, 173-74
electronic data interchange, 106
gathering (see online transaction processing [OLTP])
historical, 62, 169-70, 172
metadata, 151
reporting (see business reporting and analysis)
sell-through, 68-69
silos of, 34, 51, 151
sources of, 170-74
storage of, 23 (see also cube storage; online analytical processing [OLAP]; storage)
Data Analyzer. See Microsoft Data Analyzer
database marketing, 58
data marts
assembling pieces of, 147-48
at Audi AG, 70-71
within data warehouse framework, 50, 60
determining subject of, 146-47
importance of first, 147
integrating, 151
relational databases hosting, 63
selecting technologies to host (see technology)
data mining
applying, 57-59
at Disco S.A., 97, 98-99
implementation team knowledge of, 153
in Microsoft Data Warehousing Framework, 180
role of, 52
data model, 70
data modelers, 153, 154, 162
Data Transformation Services (DTS). See Microsoft Data Transformation Services (DTS)
data warehouse, 49-52, 60-63. See also Microsoft Data Warehousing Framework
Data Warehousing Alliance (DWA). See Microsoft Data Warehousing Alliance (DWA)
Data Warehousing Framework. See Microsoft Data Warehousing Framework
DB2, 178
decision making, 14-15, 21, 156
decision tree, 99-100
deployability, in database evaluation, 157-58
descendant, within hierarchies, 43
descriptive data mining techniques, 59, 98
desktop online analytical processing (DOLAP), 46, 47
detail, determining required level of, 121-22, 137
Diaz, Horacio, 95, 96, 101
dimensions. See also multidimensional analysis
attributes in designing, 166-67
in Audi AG data model, 70
availability of data supporting, 136
BI blueprints in designing, 162
changes in, 167
defined, 30
in opportunity analysis, 120-22, 126-27
in searching for BI opportunities, 120
in slicing and dicing, 36-40
source data not supporting, 172
terminology, 41-43
using hierarchies, 165-66
using levels, 163-65
dirty data, 61-62, 173-74
Disco S.A., 95-102
BI benefits, 100-101, 115
BI challenges, 101-2
business requirements, 96-97
company background, 95-96
DiscoPlus program enhancement, 97-100
DOLAP (desktop online analytical processing), 46, 47
drilling. See also hierarchies
at CompUSA, 89-90
hierarchies enabling, 163
and multiple dimensions, 165
in OLAP systems, 40-43
at Russell, 78
DTS (Data Transformation Services). See Microsoft Data Transformation Services (DTS)
E
EDI (electronic data interchange), 106
80/20 rule, 81
Einstein project. See Russell
electronic data interchange (EDI), 106
Ellison, Steve, 86, 89
English Query, 180-81
enterprise, 114
enterprise resource planning (ERP) systems, 50, 103, 107-9, 181. See also Cascade Designs
Espinal, Eric, 80
estimation, 59
ETL tools. See extracting, transforming, and loading (ETL) tools
Excel. See Microsoft Excel
Exchange Server. See Microsoft Exchange Server
executive sponsorship, 152, 154
Extensible Markup Language for Analysis (XML/A), 24, 155, 179-80
extracting, transforming, and loading (ETL) tools
BI accelerators providing, 161
within data warehouse framework, 52
enabling BI, 24
evaluating, 160-61
implementation team knowledge of, 153
for spreadsheets as data sources, 171
F
Fahrzeugsteuerung. See Audi AG
family relationships, within hierarchies, 43
finance department KPIs, 22
finance dimensions, 121
Fortune, 76
Frank Russell Company. See Russell
fraud prevention, 91
Fromson, Lee, 103, 104, 105-6, 107, 109-10
front-end tools, 52, 57, 153, 157-58. See also software; specific tools
full-scale projects, 148
functional area, 114, 115, 119, 120
functionality, in database evaluation, 157
G
General Electric Corporation, 114
graphics, making data accessible, 53-54, 55
Greenshields, Rod, 80
Grupo Sanborns, 85. See also CompUSA Inc.
H
Halbert, Anna, 89
heliocentric model, 20
Hewlett Packard Corporation, 114
hierarchies, 40-43, 163, 165-66. See also levels
historical data, 62, 169-70, 172
HOLAP (hybrid online analytical processing), 46, 47
Horch, August, 67
Horch AG, 67. See also Audi AG
Host Integration Server, 178
human resource dimensions, 121
hybrid online analytical processing (HOLAP), 46, 47
Hyperion Solutions Pillar, 106
I
IBM AS/400, 178
implementation guidelines, 145-74
approach to system design, 147-48
assembling team, 149-50, 152-54
"buying versus building" decision, 156
choosing technologies (see technology)
deciding on source data, 170-74
designing dimensions (see dimensions)
designing measures (see measures)
importance of first data mart, 147
integrating data marts, 151
project category options, 148-51
using opportunity areas to create data marts, 146-47
Impromptu. See Cognos Impromptu
IMS, 178
information. See also data
in BI opportunity analysis, 118-22
converting data into, 15-16
historic, 169-70
metadata, 151
Information Age, 13, 175
information consumers, 56, 157. See also user communities
Information Democracy, 24
information technology (IT)
at Audi AG, 68
in BI opportunity analysis, 124, 128
in BI solution implementation, 154, 156
building data warehouse, 60
at Cascade Designs, 107
choosing BI platform, 49
at CompUSA, 92
at Disco S.A., 96
at Microsoft, 184
operational systems spending, 34
at Russell, 81
information users, 55-56, 157. See also user communities
insight, in BI cycle, 20-21
Internet, 24, 157, 179
interoperability, 155, 175
IT. See information technology (IT)
iterative design technique, 147-48, 150
J
JD Edwards, 103, 107, 108, 109
K
key performance indicators (KPIs). See also measures
at CompUSA, 89
defined, 15
enabling BI, 26
at enterprise level, 116
examples of common, 22
introduction to, 21-22
Knox, Matthew, 75, 77-78, 80-81, 82
KPIs. See key performance indicators (KPIs)
L
Lea, Jim, 104
leaf members, 43
levels, 42-43, 163-65. See also hierarchies
Liautaud, Bernard, 24
linear development technique, 147
loss prevention, 91
M
manageability, in database evaluation, 158, 159
management
in BI solution implementation, 145
empowering, 117-18
rational approach to, 16-17
shaping corporate culture, 26
supporting BI solution, 152, 176
Matra Systems, 89
marketing department KPIs, 22
marketing dimensions, 121
materiality of information, 132-33, 134-35
MDX (Multidimensional Expression), 181
measures. See also key performance indicators (KPIs)
aggregation method, 160, 168-69
in Audi AG data model, 70
availability of data supporting, 136
base, 119, 137
in BI opportunity analysis, 119-22, 126-27
calculated, 119, 137, 168
defined, 36, 119
within OLAP, 43-45
of project success, 150-51
refresh rate and historical data, 169-70
source data not supporting, 172
member properties. See attributes
members, 43. See also attributes
mental model, 18-19, 20, 26, 118
metadata, 151. See also Microsoft SQL Server 2000 Meta Data Services
metrics. See key performance indicators (KPIs); measures
Microsoft Access, 76, 80, 178
Microsoft Active Directory, 178
Microsoft Analysis Database, 183
Microsoft Analytics Builder Workbook, 183
Microsoft Business Intelligence Accelerator for SQL Server, 182-84
Microsoft Corporation, 70, 95n, 177
Microsoft Data Analyzer, 181-82
Microsoft Data Transformation Services (DTS), 71, 178-79, 183
Microsoft Data Warehousing Alliance (DWA), 184
Microsoft Data Warehousing Framework, 177-84. See also specific components
Microsoft Excel
in BI accelerator, 183
at Cascade Designs, 107-8
at Disco S.A., 96, 99, 101
in Microsoft DTS, 178
in Microsoft DWA, 184
Microsoft Exchange Server, 178
Microsoft .NET, 180
Microsoft Office, 177
Microsoft OLAP Actions, 180
Microsoft PivotTable, 184
Microsoft Prescriptive Architecture Guides, 184
Microsoft SQL Server Analysis Services 2000
at Cascade Designs, 107
at CompUSA, 89
at Disco S.A., 98, 99
within Microsoft Data Warehousing Framework, 177, 179-80, 182
Microsoft SQL Server Relational Database Engine, 177-78. See also calculation engines
Microsoft SQL Server 2000
at Cascade Designs, 107-8, 109
at CompUSA, 89, 92
as Disco S.A., 97
in Microsoft Data Warehousing Framework, 177-81
at Russell Company, 77, 78, 82
Microsoft SQL Server 2000 Data Transformation Services (DTS), 107-8
Microsoft SQL Server 2000 Meta Data Services, 178, 181. See also metadata
Microsoft Staging Database, 78, 183
Microsoft Subject Matter Database, 183
Microsoft Visual Basic, 178
Microsoft Visual FoxPro, 96
Microsoft Windows XP, 94
MOLAP (multidimensional online analytical processing), 46, 47
Moore's law, 23
Morton, Thomas, 83
motivation, and corporate culture, 27
multidimensional analysis, 29-31. See also dimensions; online analytical processing (OLAP); online transaction processing (OLTP)
Multidimensional Expression (MDX), 181
multidimensional online analytical processing (MOLAP), 46, 47
N
Naherny, Dennis, 88, 92, 93, 94
nesting of dimensions, 37, 38, 56
.NET. See Microsoft .NET
networks, enabling BI, 24
Newton, Sir Isaac, 25
Northwestern Mutual, 76. See also Russell
O
objectives, BI role in setting, 14
Ocaranza, Carlos, 97
ODBC (open database connectivity), 24, 155, 178
Office software. See Microsoft Office
OLAP. See online analytical processing (OLAP)
OLAP Actions. See Microsoft OLAP Actions
OLE DB, 155, 178-79, 180
OLE DB for OLAP, 155
OLTP. See online transaction processing (OLTP)
Omni Parser, 89
online analytical processing (OLAP), 35-47
at Audi AG, 70-71
BI accelerators providing, 161
at Cascade Designs, 107-8, 109
complexity of calculations in, 137
at CompUSA, 88, 92
within data warehouse, 62-63 (see also data warehouse)
dimensions for slice and dice, 36-40
at Disco S.A., 96, 97, 98-99, 101
evaluating, 158-60
hierarchies for drill down, 40-43
implementation team knowledge of, 153
introduction to, 35-36
measures, 43-45
in Microsoft SQL Server Analysis Services 2000, 179-80
OLE DB for, 155
at Russell, 77, 78, 81, 82
storage modes, 45-47
testing capabilities of, 148
working with data mining, 59
online transaction processing (OLTP)
and complexity of calculations, 137
at CompUSA, 88
as data source, 171
defining information needs from, 118
and functional areas, 115, 116
implementation team knowledge of, 153
integrating data from, 45
overview, 32-33
relational database engine role in, 178
serving lower-level jobs, 117
open database connectivity (ODBC), 24, 155, 178
operational databases, in bridging analysis gap, 32-33
operational reporting, 34
operational systems, 31-34
operations department KPIs, 22
operations dimensions, 121
opportunity areas, identifying, 113-43
asking business questions, 124-27
brainstorming sessions, 123-24
in data marts, 146
defined, 128
defining information needs, 118-22
grading by difficulty, 135-38
grading by importance, 132-35
grouping requirements, 128-31
identifying areas of use, 114-16
identifying user communities, 117-18
organizing information requirements, 127
ranking opportunities, 138-42 (see also pilot projects)
Oracle, 178
P
parent, within hierarchies, 43
parent-child dimension, 165
Paxman, Barry, 107
people, enabling BI, 25-26. See also user communities
performance indicators. See key performance indicators (KPIs)
performance management framework, 17
pharmaceutical companies, 58
Picasso project. See Russell
pilot projects, 140, 148, 149
pivoting of dimensions, 37, 38, 56
PivotTable. See Microsoft PivotTable
point-of-sale systems, 87, 92
POP (preseason order program), 106, 108
power analysts, 56, 157. See also user communities
PowerPlay. See Cognos PowerPlay
predictive data mining techniques, 59, 98
Prescriptive Architecture Guides. See Microsoft Prescriptive Architecture Guides
preseason order program (POP), 106, 108
processing power, enabling BI, 23
ProClarity software, 70, 92, 183
productivity, 91-92, 182
project managers, 154
proof-of-concept projects, 148-49
Ptolemaic view, 20
Q
questions, in opportunity analysis, 124-27
R
ragged hierarchy, 42
ratio, defined, 45
rationality, and science, 16-17
RDBMS (relational database management system), 32, 63
real time, 32, 160
refresh rate, 169-70, 172, 173
relational database management system (RDBMS), 32, 63
relational databases, 158-60. See also Microsoft SQL Server Relational Database Engine
relational online analytical processing (ROLAP), 46, 47
reporting. See business reporting and analysis
reporting paradigm, 57
returns analysis, 140-42
Rockwell Automation, 69
ROLAP (relational online analytical processing), 46, 47
Russell, Frank, 76
Russell, 75-83
background, 75-76
BI benefits, 79-83, 118
business requirements, 76-78
Einstein project, 81-82, 118
OLAP technology at, 155
Picasso project, 78-79, 118
S
sales department KPIs, 22
sales dimensions, 121
scalability, 103, 157-59
science, and rationality, 16-17
security, in database evaluation, 159
segmentation, 98
sell-through data, 68-69. See also Audi AG
semiadditive aggregation, 168-69
sibling, within hierarchies, 43
silos of data, 34, 51, 151
slicing and dicing, 30, 36-40. See also multidimensional analysis
slowly changing dimensions, 167
software. See also front-end tools; specific software title
at Audi AG, 69, 70
at Cascade Designs, 103, 107-9
data mining, 58-59
enabling BI, 24
Soft Warehouse, 85. See also CompUSA Inc.
source data, 170-74
speed
analysis at the speed of thought, 15, 31, 34, 45
in data delivery, 62-63
in decision making, 14-15, 21
spreadsheets, as data source, 171
SQL Server Analysis Services 2000. See Micro-soft SQL Server Analysis Services 2000
SQL Server Relational Database Engine. See Microsoft SQL Server Relational Database Engine
SQL Server 2000. See Microsoft SQL Server 2000; Microsoft SQL Server 2000 Data Transformation Services (DTS); Microsoft SQL Server 2000 Meta Data Services
SQL (structured query language), 32
Staging Database. See Microsoft Staging Database
standards, enabling BI, 24
sticky notes, in brainstorming sessions, 123, 124-27, 131
storage, 23, 45-47. See also cube storage
stovepipe reporting, 34
strategic impact of information, 133-34, 135
structured query language (SQL), 32
Subject Matter Database. See Microsoft Subject Matter Database
subject-oriented data, 60
Sybase Adaptive Server Enterprise, 78, 80, 82
T
tactical impact of information, 133-34, 135
teams
for brainstorming, 123-24
business teams, 152-53, 154, 172-73
for implementation, 149-50, 152-54
technology
enabling BI, 23-24
ETL tools (see extracting, transforming, and loading (ETL) tools)
evaluating, 148-49, 154-56
front-end reporting and analysis tools, 52, 57, 153, 157-58
OLAP and relational databases, 158-60 (see also online analytical processing [OLAP])
thick-client tools, 157-58
thin-client tools, 157-58
top-down analysis, 41. See also front-end tools; online analytical processing (OLAP)
transactions. See online transaction processing (OLTP)
tribal wisdom, 18
U
uniform aggregation, 168
usability, in database evaluation, 158
user communities
defining, 54-56, 117-18
in defining measures, 120
at Disco S.A., 101
educating, 150
in evaluating front-end tools, 157-58
represented on implementation teams, 152-53
at Russell, 80-81
V
vision, and mental model, 18-19
Visual Basic. See Microsoft Visual Basic
Visual FoxPro. See Microsoft Visual FoxPro
visualization, 59
Volkswagen Aktiengesellschaft (AG), 67-68. See also Audi AG
VSAM, 178
W
West, Landon, 91
Windows XP. See Microsoft Windows XP
Witt, Cathy, 85, 93
write back feature, 157, 159-60
X
XML/A (Extensible Markup Language for Analysis), 24, 155, 179-80
Z
zero client footprint, 157
Last Updated: April 16, 2002
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