Index

Note: Page numbers with “f” and “t” denote figures and tables, respectively.

A
Access services, 312
Accidental architecture
See also Analytical data architecture (ADA); Information architecture; Technical architecture
addiction signs, 82
avoiding accidents, 81–83
recovering from, 83
Accumulating fact tables, 218–219
Ad hoc analysis, 150, 406
Additive facts, 202–203
Adopters, 312
Advanced analytics, 375b
See also Big Data analytics; Predictive analytics
in action, 376–377
analytical hubs, 384–387
analytical sandboxes, 383–384
architecture, 381f
data mining, 377–383
data visualization, 401–402
overview and background, 375–377
past and predictive analytics, 376
predictive analytics, 377–383
roadblocks to success, 382–383
skills, 382t, 399t
techniques and examples, 381t
window to future, 376
Advanced dimensional modeling, 237
See also Dimensional modeling
approaches, 270
custom dimension groups, 271–272, 272f
hot swappable dimension, 270–271, 271f
causal dimension, 262–263, 263f
heterogeneous products, 269–270
hierarchies, 237, 238f
balanced, 238–240
ragged, 240–241, 240f
unbalanced, 241, 241f
variable-depth, 241–244
junk dimension, 265–267
multivalued dimension, 263–265
outrigger tables, 244, 245f
too few dimensions, 272
too many dimensions, 272
value band reporting, 268–269, 268f
Aggregation subservice, 323–324, 324f
Agile methodology, 475–476
Alternate key (AK), 187
Analysts, 341–342
Analytical data architecture (ADA), 120, 123, 124f
See also Information architecture; Technical architecture
BI sources, 127
data marts, 125
data schemas, 125–126
EDW, 125
enhanced hub-and-spoke model, 124–125
physical data store combinations, 128
Analytical hubs, 384–387, 386f
advice for, 394–395
architecture options, 388, 389f
advanced analytics layers, 388–391
business analytics, 388–391
data access and integration, 393–394
platforms, 391–392
predictive modeling with, 392–393
design principles, 387–388
Analytical sandboxes, 383–384, 385f
advice for, 394–395
architecture options, 388, 390f
advanced analytics layers, 388–391
business analytics, 388–391
data access and integration, 393–394
platforms, 391–392
design principles, 387–388
Analytics, 225
challenges, 7–8
deluge, 6–8
importance, 7
strategy, 8
Application development methodology, See Systems development life cycle (SDLC)
Application programming interface (API), 109
Application vendor, 101
Architecture framework
accidental architecture, 81
addiction signs, 82
recovering from, 83
action plan, 84t
architectural blueprints, 65–66
BI framework, 66, 67f
data architecture, 68–72
information architecture, 67–68
metadata management, 78–80
privacy, 80–81
product architecture, 78
security, 80–81
technical architecture, 72–78
“as is” analysis, 247, 249, 260
“as was” analysis, 247, 249, 260
Asynchronous JavaScript and XML (AJAX), 360
Atomic level, 220
Atomicity, consistency, isolation, and durability (ACID), 163
Audit services, 329
data integration jobs, 329
data quality metrics, 330
error handling services, 330–331
table and row updates, 330
Auditable data, 277
B
Balanced hierarchy, 238–240
Banded-value approach, 255
Basically available, soft state, eventual consistency (BASE), 163
Battling project methodologies, 474
agile methodology, 475–476
waterfall methodology, 474–475
BI Competency Center (BICC), 493
Big Data, 3, 147, 163–164, 500
database, 4
projects, 396, 398
refined, 149
Big Data analytics, 151, 395
See also Advanced analytics; Predictive analytics
Big Data team, 398–400
hybrid architecture, 397–398, 398t
program, 397
scope, 396–397
structured vs. unstructured data, 395t
worst practices, 400–401
Bogged-down feedback loop, 428
“Bottom-up” approach, 451–452
Bridge table, 243–244, 243f
for multivalued dimensions, 264–265
navigation, 244f
Bring your own device (BYOD), 113
Bring your own results (BYOR), 113
Bulk loading utilities, 312
Business
analyst, 439–440
analytics, 8
benefits documentation, 27, 28f
case, 24–28
initiatives, 25
metadata, 79
processes, 25–27
sponsorship, 25–26
transformation services, 321
value determination, 27–28
Business intelligence (BI), 3, 143, 375
accidental architecture, 115–116
“in actionable” information, 10
analysis phase, 463–465
analytics, 149–151, 149f
appliances, 164–165
architectural plan, 470–472
assessment, 460–465, 460f
balancing, 457–459
building, 455–457
business involvement, 454f
business participation, 455
business sponsors, 452
COE, 433
content specifications, 337–339
data capture vs. information analysis, 10
data warehousing, 11–12
operational BI, 11
operational system, 11
data design for self-service, 348
inconsistency, 349–350
last data preparation step, 348–349
OLAP cubes and in-memory columnar databases, 350–351
data integration activities, 301
data modeling use, 173
design, 359
BI application, 362–367
privacy, security and access standards, 361–362
UI standards, 359–361
visual design methods, 362f
Web and BI development challenges, 360t
design layout, 343–348
dashboard example, 337–339, 347f
focus on purpose, 343
development, 367
application development tasks, 370–372, 371f
BI application testing, 372–374, 373f
prototyping lifecycle, 369–370, 369f
discovery phase, 461–463
distribution analysis, 357
DW and DI, 15f
enterprise and BI applications, 472t
enterprise business strategy, 451–452
and business initiatives, 450f
and plans drive business intelligence program, 451f
establishment, 450–460
evolution, 144
data management, 146–147
enterprise applications, 145–146
technology platforms, 145
internal marketing, 459–460
matching types of analysis to visualization, 351, 352f
comparative analysis, 351
contribution analysis, 354
correlation analysis, 352
geographic data, 337–3395
time-series or trending analysis, 351–352
personas, 340
analysts, 341–342
casual consumers, 341
data scientists, 342–343
power users, 342
portfolio, 452–453
program
composition, 453f
governance, 454–455
project, 472–473
management, 449
schedule, 484–492
project phases, 479–484, 479f
analysis and definition phase, 481–482
architect and design phase, 482
build, test, and refine phase, 482–483
deploy and roll-out phase, 484
implementation phase, 483
scope and plan phase, 480–481
recommendations phase, 465
revising BI applications list, 339–340
solution, 43
sources, 127
sponsorship, 454
swim lane, 487–488
targets, 152–153
technology and terms, 14–19
trade-offs, 457f
working committee, 433
Business Intelligence Centers of excellence (BI COE), 493–501
See also Centers of excellence (COEs); Data Integration Centers of excellence (DI COE)
building business case, 496
alignment of resources improvement, 498
business and IT objectives, 497
cost savings, 496–497
monitor BI performance, 497–498
value of BI, 496
business needs, 494
business preferences, 495
organizational structures drives reporting silos, 494–495
deliverables, 498–499
interactions, 499f
organization and funding, 501
skills, 499–500
Business requirements, 46, 49–51, 282–284
documenting requirements, 60–61
interviewing, 56
conducting interviews, 58
interview follow-ups, 59–60
preparation for, 57–58
reviewing interview content, 58–59
primary deliverable, 45–47
primary goals, 44–45
purpose of, 43–44
roles, 47
BI team participants, 47–48
business participants, 48
IT participants, 49
people vs., 48b
stepwise refinement, 47f
workflow, 49, 50f
compliance requirements, 53
data quality requirements, 51–52
data requirements, 51–52
functional requirements, 52–53
IT all together, 55
prioritizing requirements, 55–56
regulatory requirements, 53
reverse engineering, 54–55
technical requirements, 54
Business systems analyst (BSA), 484
C
Calendar dimensions, See Date dimensions
Cardinality, 181, 182t
Cascading style sheets (CSS), 360
Causal dimension, 262–263, 263f
Centers of excellence (COEs), 493
data-driven enterprise, 511–512
DI COE, 501–511
purpose of, 493
Central data warehouse (CDW), 109
Change data capture (CDC), 287, 292, 312–316
Chief information officer (CIO), 144, 451
Clean, consistent, conformed, current, and comprehensive (Five Cs), 12–14
Clustered configuration, 162
Column analysis, 319–320
Column-family, 164
Columnar databases, 160–161
Comparative analysis, 351
Compliance requirements, 46, 53
Conceptual data model, 174–175
Conforming dimensions, 220
Conforming facts, 220–221
Connectors, 312
Consolidated fact tables, 233–234, 233f
Contribution analysis, 352
Corporate information factory (CIF), 120, 121f
Corporate performance management (CPM), 139, 146
Correlation analysis, 352, 354
heat map, 355f
scatter plot, 356f
Cross-reference dimensions, 266b
Cross-table analysis, 320
Cubes, 159
CurrencyKey, 199
Custom data loaders, 312
Custom dimension groups, 271–272, 272f
Customer data integration (CDI), 104, 157, 506
Customer relationship management (CRM), 14, 68, 146, 502
Customer resource management, See Customer relationship management (CRM)
CustomerKey, 199
D
Dashboards, 150
Data, 3–4
architect, 437
auditability story, 131b
capture, 119, 167
cleansing, 89–90
conversion services, 320–321
data capture vs. information analysis, 10
BI operational system, 11
data warehousing, 11–12
operational BI, 11
deluge, 4–6
discovery, 151
federation, 154
5 Cs, 12–14
franchising, 91
data preparation vs., 95
franchising process, 133
need for, 91–93
process, 93–95, 94f
gathering, 409
information vs., 8–10
mining, 377
architecture for, 380
setting up, 377–378
techniques for, 381
preparation, 88, 89f
cleansing data, 90–91
consolidating data, 90–91
data franchising vs., 95
definitional work, 90
steps, 88–90
profiling services, 317
column analysis, 319–320
cross-table analysis, 320
primary key analysis, 320
relationship analysis, 320
source systems analysis, 318–320
source-to-target mapping, 320
table analysis, 319
and quality, 32–33
reporting and analysis, 410
requirements, 46, 51–52
restructuring process, 133
schemas, 125–126
scientists, 7, 377
source system, 68
requirements, 284–285
structure transformation services, 322
aggregation subservice, 323–324
dimension table subservice, 322–323
fact table subservice, 323, 323f
too much data, too little information, 8–10
too much information, 5f
transformation services, 320, 410
business transformation services, 321
data conversion services, 320–321
data integration workflow, 321f
data structure transformation services, 322–324
transport services, 332–333
variety, 4–6
velocity, 4–6
virtualization, 154
visualization, 359b, 401–402
volume, 4–6
workflow, 410f
Data Analysis eXpressions (DAX), 153
Data architecture, 68, 70f, 107–108, 469
See also Analytical data architecture (ADA); Information architecture; Technical architecture
choices, 118
data architecture selection, 119–122
data categories, 119
enterprise data bus architecture, 121f
Inmon vs. Kimball architecture comparison, 122t–126t
data integration workflow, 128–135
data modeling vs., 107b
data systems, 71f
data warehousing, 69–72
EDW, 68–69, 69f
history
in beginning, 108–109
BI accidental architecture, 115–116
data mart, 110–112, 111f
DW evolution, 108–110
EDW, 110f
federated DWs, 114–115, 115f
hub-and-spoke, 116–117, 117f
multiple data marts, 112–113, 112f
Data governance, 444–446
board, 446
business or IT, 445
organization, 446
task force, 446, 447t
on track, 447–448
working with COEs, 446, 447f
Data ingestion services, 312
audit columns, 313–314
database log or message queue scanners, 315
row difference comparisons, 315–316
table or event triggers, 315
timed extracts, 314–315
reference lookups, 316–317
SCD, 316, 316t
Data integration (DI), 14, 275, 410
architecture, 277–280
design, 285
conceptual data integration process, 285, 286f
designing data, 289–290
logical data integration modeling, 286–287, 287f
physical model, 287–288, 288f
source to target mapping, 288–289
efficient method, 277
ETL, 301, 302f
holistic approach, 275–276
incremental approach, 276–277
iterative approach, 276–277
jobs, 329
loading historical data, 295–296
historical data, 297–298
same as old, 296–297
manual coding vs. tool-based, 301–309
hand code, 302–304, 304f
tool selection, 305–308
using tools, 304–305
two tools, 309
productive method, 277
prototyping, 298
requirements, 280
business, 282–284
data sourcing system, 284–285
designing data models, 282f
prerequisites, 280–282
services, 309
access and delivery services, 312
breakdown of suite–services, 311f
data ingestion services, 312–317
data profiling services, 317–320
data quality services, 324–328
data transformation services, 320–324
data transport services, 332–333
extract, transform, load, and manage, 310f
operations management services, 331–332
process management services, 328–331
suite–services, 311f
standards, 290
development project standards, 291–292
development standards, 293–295
reusable components, 292–293
software development mindset, 290–291
swim lane, 489–490
testing, 298–299
workflow, 128–129, 128f, 321f
hub-and-spoke, 129, 129f
workflow, 278f–279f, 280t
Data Integration Centers of excellence (DI COE), 493, 501
See also Business Intelligence Centers of excellence (BI COE)
building business case, 504
data-integration investment portfolio, 505–507
enlist sponsorship, 504–505
businesses need, 502
data sources and silos exploding, 502
data-integration problems, 503–504
myriad integration approaches, 502–503
silos, 503
deliverables, 509–510
organizational model, 507
centralized services, 509
services, 508–509
sharing best practices, 507–508
technology, 508
skills, 510–511
Data integration framework (DIF), 86, 505
building blocks, 87t
information architecture, 86–100, 87f
BI and analytics, 95–97
data franchising, 91–95
data management, 98, 99f
data preparation, 88–91
metadata management, 98–100
Data management, 98, 99f
Data mart, 110–112, 111f, 125
Data Mining eXtensions (DMX), 153
Data model, 173–174
data modeling vs., 173–174
entity vs. dimensional, 178f
hybrid dimensional-normalized, 126, 128–130, 135, 144
levels, 174, 174f
conceptual data model, 175
logical data model, 175–176
physical data model, 176–177
usage, 178–179
Data modeling, 173–174
data architecture vs., 107b
data model vs., 173–174
ER modeling, 173, 179
attributes types, 180–181, 181f
building blocks, 179–180, 180f
cardinality, 181
entities types, 180–181, 181f
referential integrity, 188–189, 189f
types, 181–187
workflow, 177–178, 177f
Data quality, 132
metrics, 330
requirements, 51–52
services, 324
building, 327–328
implementation, 328
misconceptions, 325–327
Data shadow system, 54, 419f
benefits, 413–414
BI silos, 404f
damages, 412–413
evolution, 408
data workflow, 410f
filling in gap, 408–410
IT group response to, 411
rise and expansion, 411
misguided attempts to replace, 417–418
BI tools, 418
set of reports, 418
moving beyond, 414
business and IT, 415–416
changing approach, 416–417
changing culture, 417
choices assessment, 416t
stopping blame game, 415
organization, 405–406
problem, 403–405
renovation, 418–421
analytical process considerations, 421
balanced priorities, 421
considerations, 419–421
triage, 407, 408t
mild and moderate, 407
serious, 407–408
very serious, 408
types, 406, 407t
Data source system, See Systems of record (SOR)
Data warehousing (DW), 9, 24, 65, 144, 147, 308
applications, 173
BI and DI, 15f
industry terms, 16t–19t
technology and terms, 14–19
Data-and-database-task, 488
Data-driven enterprise, 511–512
Data-integration investment portfolio, 505–507
Database Administrator (DBA), 438
Database log, 315
Database management system (DBMS), 156–157
Date dimension, 219, 221, 223f, 239f
benefits, 224–225
in BI, 222
designing, 222–224
time dimension vs., 228–229
DateKey, 199
Degenerative dimensions, 230–232, 231f
Delivery services, 312
Developer unit tests, 372
Dimension, 203, 204f
hierarchy, 204–205, 205f
keys, 205, 205f
benefits, 208
not null values, 207–208
smart, 207
surrogate, 206
schemas, 208
multidimensional, 211–212, 212f
multifact star models, 212–213, 212f
snowflake, 210–211, 210f
table, 265
subservice, 322–323, 322f
Dimensional modeling, 216
See also Advanced dimensional modeling; Entity-relationship modeling (ER modeling)
achieving consistency, 220–221
consolidated fact tables, 233–234, 233f
date dimension, 221–225, 228–229
degenerative dimensions, 230–232, 231f
ER modeling vs., 213, 214f, 216f
comparing approaches, 213–215
structures, 215–216
event tables, 232–233, 232f
facts, 198–203
types, 202–203
fact tables, 218–219, 219t
measures, 201–202
granularity, 202f
high-level view, 198
logical design technique, 197
mapping
to business report, 217, 217f
to OLAP analysis, 217–218, 218f
role playing dimensions, 229–230, 230f
time dimension, 225–229
Distribution analysis, 357
Documentation, 277
Documenting requirements, 60–61
Dynamic hypertext markup language (DHTML), 360
E
8/80 rule, 467
End users, 380
Enterprise
application stack vendors, 166
data evolution, 144f
standard, 132
Enterprise application integration (EAI), 12, 153–154, 503
Enterprise data warehouse (EDW), 69f, 87–88, 110f, 148, 302, 338
rise of, 68–69
schemas, 125
Enterprise information integration (EII), 12, 154, 303, 503, 506
Enterprise message services (EMS), 154
Enterprise resource planning (ERP), 14, 68, 123, 175, 472–473, 494
Enterprise service bus (ESB), 82, 153–154, 312, 503
Entity, 179
Entity Relationship Diagrams (ERD), 437
Entity-relationship modeling (ER modeling), 173, 178–179
building blocks, 179–180, 180f
cardinality, 181
dimensional modeling vs., 213, 214f, 216f
comparing approaches, 213–215
structures, 215–216
entities and attribute types, 180–181, 181f
example, 185–187, 186f
referential integrity, 188–189, 189f
types, 181
identifying relationship, 182, 183f
many-to-many relationships, 184, 184f
nonidentifying mandatory relationship, 182–183, 183f
nonidentifying optional relationship, 183–184, 184f
recursive relationships, 185, 185f
Error handling services, 330–331
Event tables, 232–233, 232f
See also Fact tables
Extended project team, 440–441
Extensible markup language (XML), 153, 360
Extensible markup language for Analysis (XML/A), 153
Extract, load & transform (ELT), 154, 156f, 393
ETL vs., 155–157
Extract, transform, and load (ETL), 10, 66, 145
architecture, 156f
tools, 86, 301, 302f
workflows, 82
F
Fact table, 264
in dimensional modeling, 218–219, 219t
granularity, 202f
measures, 201–202
subservice, 323, 323f
Facts, 198–203
additive, 202–203
nonadditive, 203
semiadditive, 203
Fifth normal form (5NF), 190
First normal form (1NF), 190–191
Fiscal calendar, 239f
Fixed hierarchy, See Balanced hierarchy
Foreign key (FK), 188
Fourth normal form (4NF), 190
Full-fledged analytical application, 406
Functional decomposition, 46
G
Garbage dimension, See Junk dimension
Garbage in, garbage out (GIGO), 13, 382
Goldilocks syndrome, 45
“Green bar”, 74
Greenwich Mean Time (GMT), 229
H
Health care, 252, 263
Health Insurance Portability and Accountability Act (HIPAA), 53
High-tech titans, 166
Hot swappable dimension, 270–271, 271f
Hub-and-spoke architecture, 116–117, 117f
100-percent rule, 467
Hybrid BI project methodology, 477–478
Hybrid dimensional-normalized model, 126, 128–134, 135, 144
Hybrid online analytical processing (HOLAP), 159
Hypertext markup language (HTML), 360
I
Identifying relationship, 182, 183f
In-database analytics, 161–162
In-memory
columnar databases, 350–351
databases, 162
Incremental approach, 276–277
Information
backbone, 10
data capture vs. information analysis, 10
BI operational system, 11
data warehousing, 11–12
data vs., 8–10
Information architecture, 67–68, 85–86, 469
See also Analytical data architecture (ADA); Data architecture; Information architecture; Technical architecture
BI and analytics, 95–97
data franchising, 91–95
data management, 98, 99f
data preparation, 88–91
metadata management, 98–100
operational BI vs. analytical BI, 100
application-specific environment, 101–102
blend application-specific BI environment, 102–103
DW-based BI environment, 102–103
Information technology (IT), 144, 377
Infrastructure swim lane, 490–491
Integration Competency Center (ICC), 493
Integration testing, 299
Intellectual property (IP), 146–147
Internet of things (IoT), 4, 145
Iterative approach, 276–277
J
Java database connectivity (JDBC), 153, 312
JavaScript Object Notation (JSON), 154
Job control services, 328, 329f
Junk dimension, 265
alternatives, 266t
example, 267f
misguided attempts, 266
recommended solution, 266–267
Justification process, 23
approval obtainment, 40
BI road map creation, 35
building business case, 24–28
business
benefits documentation, 27, 28f
initiatives, 25
processes, 25–27
value determination, 27–28
pitfalls, 40–41
project
budget, 37–38
plan, 36–37
scope, 36
readiness assessment, 32
cultural change, 34
data and data quality, 32–33
expertise and experience, 33–34
financial commitment, 34
organization, 34
resource commitment, 34
ROI calculation and benefits, 39–40
sponsorship, 25–26
stakeholders, 26
technical case, 28
convincing business people, 30–31
convincing technologists, 31–32
product short lists, 28–30
K
Key performance indicators (KPIs), 12, 26–27, 49, 321, 338, 445, 487
Key-value, 164
L
Landing area, See Staging area
Logical data model, 175–176, 437
Logical data warehouse (LDW), 148
M
Many-to-many relationships, 184, 184f
Mappings, 289
Markets in Financial Instruments Directive (MiFID), 53
Massively parallel processing (MPP), 146–147, 162
Master data management (MDM), 33, 46, 68, 103, 146, 451–452
identification, 104
problem areas finding, 104–105
solutions, 105–106, 139
Mega-application vendor, 101
Message queue scanners, 315
Metadata management, 78–80, 98
categories, 98–99
value, 99–100
Mini-dimension approach, 254
multiple, 254–255
outrigger needed, 256–257
Mock-ups, 367, 368f
Multidimensional eXpressions (MDX), 153
Multidimensional online analytical processing (MOLAP), 159
Multidimensional schema, 211–212, 212f
Multifact star models, 212–213, 212f
Multiple approach—multiple mini-dimension, 255
Multiple data marts, 112–113, 112f
Multivalued dimension, 263–265
N
Natural key standard, 294f
Nonadditive facts, 203
Nonidentifying mandatory relationship, 182–183, 183f
Nonidentifying optional relationship, 183–184, 184f
Normalization, 189
See also Entity-relationship modeling (ER modeling)
ER model, 193–194
levels, 190
limits and purpose, 194–195
normalizing entity, 190
NoSQL database, 163–164
“Not Only” SQL database, See NoSQL database
NULL values, 207–208
O
One-off reports, 406
Online analytical processing (OLAP), 65, 94f, 159
analysis, 150
BI tool, 95
cubes, 91, 117, 148, 211–212
MDX language, 159–160
tools, 243
types, 159
Online transactional processing (OLTP), 213
Open database connectivity (ODBC), 153, 312
Open source software (OSS), 145
Operational data store (ODS), 65, 87–88, 113–114, 146
designs, 137–142
pragmatic approach, 142
rationale for, 138
data hub establishing for updates, 139–140
integrated reporting, 138–139
SOR for dimensions, 139
update data, 140
reexamining, 140
data hub establishing for updates, 141
integrated reporting, 140
SOR for dimensions, 140–141
update data, 141
Operational systems, 12, 178
bundled with reporting capabilities, 30
implementation in 3NF, 190
role, 11
trends, 123
Operations management services, 331–332
Organizational performance measure, See Fact
Outrigger tables, 244, 245f
P
Payment Card Industry (PCI), 53
People, process and politics (Three P’s), 425
building BI team, 431, 432f
extended project team, 440–441
project development team, 435–440
project management team, 434–435
project sponsorship and governance, 431–433
business and IT relationship, 427
in charge, 427–428
communication shortcomings, 428–429
users or customers, 427
under control, 426–427
data governance, 444–448
meeting expectations, 425–426
roles and responsibilities, 429
in back office, 430
in front office, 429–430
technology trap, 425–427
training, 441
business group, 442–443
IT group, 442
methods, 443–444
types of, 441–442
Performance management (PM), 82
Performance testing, 299
Periodic fact tables, 218
Phases, 474
Physical data model, 175–177, 438
PK, See Primary key
Portable network graphics (PNG), 360
Portfolio approach, 505–506
Power users, 342, 427
Predictive analytics, 151, 377
See also Advanced analytics; Big Data analytics
architecture for, 380
project methodology, 378f
resources and skills, 382
setting up, 377–378
tasks for, 378–380
techniques for, 381
tool selection, 380
Primary key (PK), 187
analysis, 320
Prioritizing requirements, 55–56
Process management services, 328
audit services, 329
data integration jobs, 329
data quality metrics, 330
error handling services, 330–331
table and row updates, 330
job control services, 328, 329f
Product, 143
architecture, 78
migration, 168–169
Product evaluation, 165, 167
BI product vendors, 165–166
dazed and confused, 166–167
product migration, 168–169
technology and product evaluation, 167–168
Product information management (PIM), 157
Productive method, 277
Productivity, 224
ProductKey, 199
Profile tables, See Hot swappable dimension
Program Management Office (PMO), 433
Project architecture, 470
Project development team, 435
BI
developer, 439
leader, 439
business analysis leader, 440
business analyst, 439–440
core functions, 435f
data architect, 437
data modeler or designer, 437–438
DBA, 438
DI developer, 438–439
DI leader, 439
principal architect, 436–437
source data analyst, 440
sub-teams, 436t
Project management
BI program, 450–460
enterprise business strategy and business initiatives, 450f
project methodologies, 473–478
role, 449–450
team, 434
BI/DW project advisor, 434–435
business advisor, 434
project development manager, 434
Project management office (PMO), 491
Project methodologies, 473–478
Project phases, 479–484, 479f
analysis and definition phase, 481–482
architect and design phase, 482
build, test, and refine phase, 482–483
deploy and roll-out phase, 484
implementation phase, 483
scope and plan phase, 480–481
PromotionKey, 199
Proofs of concept (POC), 88, 167, 455
Q
Query
processing, 160
speed, 224
types, 265
R
Radio Frequency Identification (RFID), 4
Ragged hierarchy, 240–241, 240f
Rapid application development (RAD), 455
Rapidly-changing dimension, 254
Real-time business intelligence, 11
Recursive
pointer, 242, 242f
relationships, 185, 185f
Reference lookups, 312, 316–317
Referential integrity, 132, 188–189, 189f
Reformat data, 89
Regulatory requirements, 46, 53
Relational database, 159
technology, 163
Relational online analytical processing (ROLAP), 75, 159
Relationship analysis, 320
Representational state transfer (REST), 312
Request for proposal (RFP), 480
Return on investment (ROI), 23, 74, 303, 341
calculation and benefits, 39–40
SOA benefit, 135
Reusing data, 277
Reverse engineering, 54–55, 419
Role playing dimensions, 229–230, 230f
S
Scalable vector graphics (SVG), 360
Scop creep, 481
Scope-priorities-budget task, 488
Scorecards, 150
Second normal form (2NF), 190–192
Self-referencing relationship, See Recursive relationships
Semiadditive facts, 203
Senior vice president (SVP), 452
Service level agreement (SLA), 327, 457
Service oriented architecture (SOA), 12, 145, 153–154, 303, 503
Shared dimensions, 212–213
“Shared everything” system, 162
“Shared nothing” system, 162
Single mini-dimension, 255
Sketches, 363, 363f
Slowly changing dimension (SCD), 237, 245–262, 283, 316
effective date standard, 295f
type 0 technique, 246
type 1 technique, 246–249
type 2 technique, 249–251
type 3 technique, 251–253
type 4 technique, 254–257
type 5 technique, 257–259
type 6 technique, 259–260
type 7 technique, 260–262
types, 316t
Small-to medium-size businesses (SMB), 301
Smart keys, 206f, 207
Snowflake schema, 210–211, 210f
Software prototyping, 369
Sorting loading utilities, 312
Source
data analyst, 440
source-to-target mapping, 320
systems analysis, 318–320, 319f
tables, 289
Spreadmart, See Data shadow system
Spreadsheets, 403
integration, 150–151
Sprints, 475
Staging area, 131–132
Stakeholders, 26
Standard calendar, 239f
Star schema, 208–209, 209f
Stepwise refinement, 46, 47f
Stored procedures (SP), 303
StoreKey, 199
Storyboards, 365, 366f
Subject matter expert (SME), 44, 49, 59, 281, 458, 498
Supply chain management (SCM), 6, 68, 146, 502
Surrogate keys, 206
Swim lanes, 479, 487–488
data and database, 488–489
data integration, 489–490
infrastructure, 490–491
program and project management, 491–492
Symmetric Multiprocessing (SMP), 162
System testing, 299
Systems development life cycle (SDLC), 473
Systems integrator (SI), 157
Systems of analytics (SOA), 71, 124
data workflow, 133
benefits, 135
need for, 134–135
Systems of integration (SOI), 71
data workflow, 130
data auditability story, 131b
data stores, 130f
splitting EDW, 132–133
staging area, 131–132
Systems of record (SOR), 51, 68, 71, 124, 338
data integration architecture, 277
dimension, 294f
SMEs, 498
T
Table analysis, 319
Target tables, 289
Technical architecture, 28, 72
analytical styles, 74f
data-related technology evolution, 77–78, 77f
DW and data stores, 75–76, 75f
convincing business people, 30–31
convincing technologists, 31–32
data integration, 76, 76f
product short lists, 28–30
source systems, 76–77, 77f
Technical metadata, 79
Technology architecture, 147, 148f, 470
BI analytics, 149–151, 149f
BI information access and data integration, 151–152
BI targets, 152–153
data access APIs, 153
data integration, 157
data integration suites, 154–155
ETL vs. ELT, 155–157
integration applications, 155
integration services, 153–154
databases, 158, 158f
BI appliances, 164–165
Big Data, 163–164
OLAP comparisons, 160t
relational, 159
relational alternatives for BI, 159–162
technology and product evaluation, 167–168
Technology evaluation, 165
BI product vendors, 165–166
dazed and confused, 166–167
product migration, 168–169
technology and product evaluation, 167–168
Third normal form (3NF), 122, 179b, 189–190, 192–193
database development, 190
no longer necessary, 122–123
Time boxing, 475
Time dimension, 225
date dimension vs., 228–229
time periods, 227–228
time-of-day
as dimension, 225–227, 226f
as fact, 225, 226f
Time zones, 229f
date vs. time dimension, 228–229
Time-series or trending analysis, 351–352
bar chart, 355f
line graph, 353f
“Top-down” approach, 452
Total cost of ownership (TCO), 34, 76, 135, 154–155, 305
Training, 441
See also Business intelligence (BI)
business group, 442–443
IT group, 442
methods, 443–444
types, 441–442
Transaction fact tables, 218
Transactional processing, See Operational systems
Transform data, 89
U
Unbalanced hierarchy, 241, 241f
Unified modeling language (UML), 477
Unit testing, 299
User acceptance testing (UAT), 455
User acceptance testing, 299
User interface (UI), 150–151
standards, 359–361
User unit tests, 374
V
Variable-depth hierarchy, 241–244
Velocity, 5
Vendor, 143
Volatile changing dimension, 254
Volume, 4
Volumes, varieties and velocity (3 Vs), 375
W
Waterfall methodology, 474–475
Wireframes, 363–365, 364f
Work breakdown structure (WBS), 450, 465, 467
See also Project management
for BI, 468
architecture WBS, 469–470
program WBS, 468
project WBS, 468–469
design principles, 467–468
X
XQuery, 153
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