Index
Page numbers followed by n indicate topics found in footnotes.
Numerics
3NF (third-normal form), 121
A
abduction, 114
abstract concepts
element coupling, 132
modeling, 106-107
providing definitions for, 107-113
abstract design patterns
canonical data models, 185
conceptual data models, 185
context, considering, 188-194
enterprise data models, 186-187
logical data models, 180-186
physical data models, 180-184, 188
abstraction, 178
life span of services and, 178
metamodels for, 178-181
in OSAPI, 158
provenance and, 194
software abstraction, 178n
views and, 194-201
abstraction layering, 31
data structure design and, 42-43
accountability, provenance and, 146
accuracy, 167n
in DQA, 86
action roles, oversight versus, 60
acronyms, specific to this book
Adam and Eve story, apple in, 1
Adam’s apple, 1
adaptability of viral data solutions, 177
adaptation to change, 20-43
best practices, 36-38
decisioning environment measurements, 33-37
eminent domain, 22-23
incentives and compensation, 32-33
metadata, 27-29
military, 29-30
quality measurement, 24-27
service-oriented architectures, 30-32, 38-43
viral data as symptom of, 24
adjudication in SÉANCE workflow, 96
adjusting data governance, 64-70
adoptability of insure controls, 57
advancement (moving data), 80
advocacy dial (data governance), 66, 69
aggregation hub, 205
Aldrich, Nelson, 9
alignment
of business and information technology, 15-20
CIA example, 16
multidivisional business structure, 16-20
viral data fostered by, 22
American Express, 21
American flag example (historical revisionism), 103
analogies, 2-3
analysis (in DQA), 86
Anderson, Laurie, 78
Apple Computer, 21
apple in Adam and Eve story, 1
appraisal (in DQA), 87
appropriation, 22-23
arbitrator communication style, 53
Ascential, xvii
Ashley, 122
assessments, xxxvi
data assessments
master data solutions, 205
multiple views for, 203-204
via redundancy, 204
scenarios for, 138
statistical sampling, 138
subjective and objective measures, 206
data quality assessment (DQA), 85-88
CIDER, 88-97
assure controls (data governance metamodel), 55-57
atomic metadata, 74
attributes
abstraction and, 188-194
naming, 182n
audience, considering in writing definitions, 112
audits, 58
authoritarian communication style, 54
B
Bacevich, Andrew, 11
Baltimore Orioles, 7
bandwidth, moving data, 72-73
baseball, 7
Basel II, 175n
basic tenets, in business, 46-47
BCNF (Boyce-Codd Normal Form), 121
Beckham, David, 6
behavior, rules of, 47
Beijerinck, Martinus, 4
best practices, 36-38
Bible translations, 1
big bang theory, 143n
blind overloading, 107-108
Bloomberg, Michael R., xxi
books, definition of, 11-12
Boulware, Lemuel, 175
bounded codified data, 131
Boyce-Codd Normal Form (BCNF), 121
Brown, Dan, 12
business
adaptation to change, 20-43
best practices, 36-38
decisioning environment measurements, 33-37
eminent domain, 22-23
incentives and compensation, 32-33
metadata, 27-29
military, 29-30
quality measurement, 24-27
service-oriented architectures, 30-32, 38-43
viral data as symptom of, 24
alignment with information technology, 15-20
CIA example, 16
multidivisional business structure, 16-20
viral data fostered by, 22
laws of, 46-47
business data
abstract concepts, providing definitions for, 107-113
blind overloading, 107-108
data models, 122-126
misinterpretation of, Social Security Number example, 104-106
modeling abstract concepts, 106-107
reasoning
case-based reasoning, 115
classification, reasoning through, 116-117
consistency-based reasoning, 115
generalization, reasoning through, 116
heuristic reasoning, 115
imprecise reasoning, 115
logical inference, 114
nonmonotonic reasoning, 116
parametric reasoning, 116
probabilistic reasoning, 115
semantic reasoning, 116
specialization, reasoning through, 117-122
types of, 113-114
semantic evaluations, 122
business metadata, 123
business needs, failure to identify, 29
business rules, data misalignment with, 126-127
business transactions, IT transactions versus, 176n
business-speak, 186
C
Callan, Erin, 174
Canby, George, 103
canonical data models, 185
Captain Scarlet, 76n
Metcalfe, Paul, 108
cardinality trait, evaluating, 133
Caruso-Cabrera, Michelle, 174
case-based reasoning, 115
Castillo, Alberto, 7
Central Intelligence Agency (CIA), 15
certification, 58
chain of custody, 145
change, adaptation to, 20-43
best practices, 36-38
decisioning environment measurements, 33-37
eminent domain, 22-23
incentives and compensation, 32-33
metadata, 27-29
military, 29-30
quality measurement, 24-27
service-oriented architectures, 30-32, 38-43
viral data as symptom of, 24
Charnetzky, Dennis and Daelyn, 12
chart of accounts in data governance, 64
Chekov, Anton, 5
Chicago White Sox, 7
CIA (Central Intelligence Agency), 15
circular definitions, 111
classification
data class taxonomy, 130-132
reasoning through, 116-117
codified data, 131
column traits, 132
cardinality, 133
domain, 133
format, 133
precision, 132
size, 132
communication
semantics and, 1-13
Adam and Eve story, 1
analogies, 2-3
books, definition of, 11-12
controlling viral data, 8
Federal Reserve System example, 8-10
information overload, 11-12
ISO 4217 currency standard, 13
political boundaries, 13
PowerPoint example, 10-11
property value mistake example, 12
sports statistics, 6-7
viral data as metaphor, 3-5
Shannon-Weaver communication model, 206
communication models
for data movement, 77-79
Shannon-Weaver, 206
communication traits of governance, 52-54
compensation plans, 32-33
complex metadata, 74
compliance, rules of, 47
composite metadata, 74
compression of data, 71-72
compulsory purchase, 22-23
concept-relationship-concept abstract metamodel, 178-181
conceptual data models, 185-186
concreteness, 178
conditioning in structural data design, 155-170
conflicts of interest, 49
consistency-based reasoning, 115
consolidation (moving data), 81
constraints as information loss, 28
construction (CIDER), 88
context, xxxvi-xxxvii, 209
in abstract design patterns, considering, 188-194
for column/element traits, 132
data assessment in, 201-203
continuity controls (data governance metamodel), 58-59
controlling viral data, 8
corporate governance, 46
course of action in SÉANCE workflow, 96
CRUD, 157n
cubism, 106
cultural name example (specialization reasoning), 117-122
D
The Da Vinci Code (Brown), 12
daedal data classes, 132
data
moving
ASPECT, 79-82
bandwidth considerations, 72-73
communication model, 77-79
data access, 76-77
flight analogy, 82-88
metadata, 73-76
role in data governance, 61
semistructured, 71, 73-76, 79, 93, 101, 131-132, 206
structured, 71, 73-74, 76, 79, 101, 131-132, 155-156, 206
unstructured, 28, 36, 71, 73, 76, 79, 101, 132, 206
data access, 76-77
data assessment
master data solutions, 205
multiple views for, 203-204
via redundancy, 204
scenarios for, 138
statistical sampling, 138
subjective and objective measures, 206
data chains, value chains versus, 144-145
data class taxonomy, 130-132
data compression, 71-72
data conditioning in structural data design, 155-170
data decay, 170-171
data governance, 46-48
data, role of, 61
data quality versus, 59
dialing (adjusting), 64-70
framework, 61-64
government regulations concerning, 60-61
metamodel, 54-59
oversight versus action roles, 60
provenance and, 147
role of, 175
data lineage, provenance and, 149-151
data misalignment
with business rules, 126-127
default entries, 129
mistaken entries, 126-129
data modeling example (governance), 49-51
data models, 122-126
canonical data models, 185
conceptual data models, 185
enterprise data models, 186-187
logical data models, 180-186
physical data models, 180-184, 188
data ownership, 129-130
data persistence, 71-72
abstraction and, 194
accountability, 146
data chains versus value chains, 144-145
data lineage and, 149-151
decisions concerning, 150-152
diplomatics, 146-147
late binding versus early binding, 148
statistics and, 7
Zachman Framework for Enterprise Architecture, 152-154
data quality. See also quality measurement
alignment and harmonization, 97-98
data governance versus, 59
matching, 98-100
merging, 100
survivorship, 100
data quality assessment (DQA), 85-88, 206
CIDER, 88-97
data structure design, effect on service-oriented architecture characteristics, 41-43
data synchronization, 171
Davison, Henry, 9
decayed data, 170-171
decision-making, 6
decisioning environment
defined, 33n
measurements, 33-37
decisions dial (data governance), 66-69
deductive reasoning, 114
default values, 129
definitions
for abstract concepts, providing, 107-113
qualities of, 108
scope of, 109-111
delta move (ASPECT), 81
Descartes, René, 139
design patterns, abstract
canonical data models, 185
conceptual data models, 185
context, considering, 188-194
enterprise data models, 186-187
logical data models, 180-186
physical data models, 180-184, 188
deviation, governance and, 45-46
dialing data governance, 64-70
differentia in definitions, 108
diplomatics, provenance and, 146-147
directives dial (data governance), 66-68
directives in data governance, 62-63
director communication style, 54
discovery (CIDER), 89
Dixon, Bernard, 5
domain trait, evaluating, 133
DQA3 (data quality [analysis][accuracy][appraisal]), 85-88, 206
CIDER, 88-97
Dupont, 16
E
early binding
metadata for, 149
provenance and, 148
Eastwood, Clint, 29
Electronic Data Systems Corporation, 21
element coupling, 132
element traits, 132
cardinality, 133
domain, 133
format, 133
precision, 132
size, 132
eminent domain, 22-23
Empire Today, 21
enforcement in data governance, 63
engagement in SÉANCE workflow, 96
enlightenment (CIDER), 89
enrichment (moving data), 81
ensure controls (data governance metamodel), 55-56
enterprise data models, 186-187
enterprise resource planning (ERP), 140n
envoy communication style, 54
ERP (enterprise resource planning), 140n
essential attributes in definitions, documenting, 110-111
ETL (extract, transform, and load), 82-83
evidentiary controls (data governance metamodel), 56
examination in SÉANCE workflow, 90-96
experiences dial (data governance), 66-67
expropriation, 22-23
extensibility, 31
data structure design and, 42
external governance, 49-52
F
facilitator communication style, 53-54
failure to identify business needs, reasons for, 29
false negatives, 99
false positives, 99
FARMADE technique (governance communication), 52-54, 206
Fastow, Andrew, 175
fat fingering, 126-129
Federal Reserve System, creation of, 8-10
feedback loops, 34
linear, 34
negative, 35
nonlinear, 35
positive, 34
viral data and, 35-37
Feynman, Richard, 11
field lengths in structural data design, 155-170
final move (ASPECT), 81
flat organizational structures, 17
flexibility of viral data solutions, 177
flight analogy (moving data), 82-88
in-flight, 82-88, 97, 101, 147, 179, 200
post flight, 82-88, 91, 97-98, 101, 147, 179, 200
preflight, 82-88, 97, 98n, 101, 147, 179, 200
FLWOR expressions, 198-199
Foreign Corrupt Practices Act of 1977, 60
forensic accounting, 174
format trait, evaluating, 133
Forrester, Jay, 140
framework for data governance, 61-64
Franks, Tommy, 10
fruit in Adam and Eve story, 1
functional dependencies, 121
G
General Motors, 21
generalization, reasoning through, 116
generations, 5
Genesis, apple in, 1
genus in definitions, 108
Gibbs, Linda, xxii
Glass-Steagall Act, 22
Goldman Sachs, 22
Google, 36
Googlebot, 36
governance
business laws, 46-47
communication traits, 52-54
conflicts of interest, 49
data governance, 48
data, role of, 61
data quality versus, 59
dialing (adjusting), 64-70
framework, 61-64
government regulations concerning, 60-61
metamodel, 54-59
oversight versus action roles, 60
data modeling example, 49-51
deviation and, 45-46
external governance, 49-52
intersituations, 52
intrasituations, 52
layers of, 46
management governance, 48
management versus, 52
punting, 51
rules of behavior, 47
rules of compliance, 47
self-governance, 47-48
government regulations, data governance and, 60-61
Gramm-Leach-Bliley Act of 1999, 60
granularity of provenance, 149-151
Grotius, Hugo, 22
guidelines in data governance, 63
H
Hadden, Briton, 11
harmonization (of data), 87n, 97-98
hasa (relationship term), 108
Health Insurance Portability and Accountability Act (HIPAA), 60
Heartbreak Ridge (film), 29
heuristic reasoning, 115
HHS-Connect, xxii-xxiii
hierarchical organizational structure, 17
HIPAA (Health Insurance Portability and Accountability Act), 60
historical revisionism, 103
Hobson’s choice, 30
Hopkinson, Francis, 103
Hoyle, Fred, 143
Hughes Aircraft, 21
I
identifiers, 130
imprecise reasoning, 115
improvement (CIDER), 88
incentive plans, 32-33
Federal Reserve System example, 8-10
latency and, 12
PowerPoint example, 10-11
independence of viral data solutions, 177
indicator data, 131
induction, 114
in-flight, 82-88, 97, 101, 147, 179, 200
information loss as constraint, 28
information overload, 11-12
information technology
adaptation to change, 20-43
best practices, 36-38
decisioning environment measurements, 33-37
eminent domain, 22-23
incentives and compensation, 32-33
metadata, 27-29
military, 29-30
quality measurement, 24-27
service-oriented architectures, 30-32, 38-43
viral data as symptom of, 24
alignment with business, 15-20
CIA example, 16
multidivisional business structure, 16-20
viral data fostered by, 22
initial seeding (ASPECT), 81
innuendos, 3
inspections, 58
insure controls (data governance metamodel), 55-57
integration forensics, 174
interfaced in OSAPI, 158
data structure design and, 42
interrogatives, 152
intersituations of governance, 52
intrasituations of governance, 52
IPO, 190n
Iraq war, miscommunication during, 10-11
isa (relationship term), 108
ISO 4217 currency standard, 13
IT transactions, business transactions versus, 176n
J–K
Java Message Service (JMS), 140n
Jekyll Island, 9-10
Jett, Joseph, 32
Jingle Heimer-Schmidt, John Jacob, 120-121
JMS (Java Message Service), 140n
judgment in SÉANCE workflow, 96
Kidder Peabody, 32
L
language translations
of Bible, 1
in definitions, 112
Lao-Tze, 24
late binding
metadata for, 149
provenance and, 148
latency, 5
misinformation and, 12
laws of business, 46-47
layering of abstraction, 31
data structure design and, 42-43
layers of governance, 46
Legislative Bill 1, 111
Legislative Bill 157, 110
length of fields in structural data design, 155-170
life events abstraction example, 189-193
life span
of data, 76
of services, 178
Lincoln, Abraham, 106-107
lineage, provenance and, 149-151
linear feedback loops, 34
Logan’s Run, 160n
logic, Wason test, 3
logical data models, 49-52, 67, 121, 182-188, 190, 192, 194
physical data models and, 180-184
logical inference, 114
Loro Piana, 145
loose coupling, 32
in three-tier architectures, 38-43
Luce, Henry, 11
M
major transformations, 84
Malaysia, 119
malum, 1
management, governance versus, 52
management governance, 48
mandates in data governance, 63
Marathon Oil, 21
Mars Climate Orbiter, 175
master data solutions, 205
matching (data), 98-100
matrix organizational structure, 17
measurement controls (data governance metamodel), 57
measurement points in assure controls (data governance metamodel), 57
measurements in decisioning environment, 33-37
measuring quality, 24-27
mediator communication style, 53-54
memory-resident databases, 72
merging (data), 100
message-service-message abstract metamodel, 178
metadata, 27-29
business metadata, 123
for early/late binding, 149
provenance and, 148
structural metadata, 123
structure of, 164-166
technical metadata, 123
types of, 73-76
metamodels
for abstraction, 178-181
of data governance, 54-59
metaphors, viral data as, 3-5
military, adaptation to change, 29-30
minor transformations, 84
“miracle on the Hudson,” 191
misalignment of data
with business rules, 126-127
default entries, 129
mistaken entries, 126-129
miscommunication, Iraq war example, 10-11
misinformation
Federal Reserve System example, 8-10
latency and, 12
PowerPoint example, 10-11
misinterpretation of business data, Social Security Number example, 104-106
Mizuho Securities, 127-128, 175
modeling
abstract concepts, 106-107
data, 122-126
canonical data models, 185
conceptual data models, 185
enterprise data models, 186-187
logical data models, 180-186
physical data models, 180-184, 188
Morgan Stanley, 22
Morgan, J. P., 9
moving data
ASPECT, 79-82
bandwidth considerations, 72-73
communication model, 77-79
data access, 76-77
flight analogy, 82-88
metadata, 73-76
Mudd, Daniel, 174
multidivisional business structure, 16-20
multiple views for data assessment, 203-204
N
names example (specialization reasoning), 117-122
naming attributes, 182n
narrowing data for parametric reasoning, 116
natural identifiers, 130
NBC Universal, 6
Nebraska safe-haven law example (documenting essential attributes), 110
negative feedback loops, 35
negative terminology in definitions, 111
network bandwidth, moving data, 72-73
New Testament translations, 1
Nikkei, 127
noise, 11n
in communication model, 78
nonlinear feedback loops, 35
nonmonotonic reasoning, 116
Norton, Charles, 9
null values, 133n
O
observation in SÉANCE workflow, 90-96
Old Testament translations, 1
operating controls (data governance metamodel), 55-56
Operation Urgent Fury, 30
operational metadata, 149
organic nature of insure controls, 57
organigrams, 17
organizational structures, types of, 17
overloading, blind, 107-108
oversight, action roles versus, 60
oversight dial (data governance), 66-68
ownership
of data, 129-130
in OSAPI, 158
P
pandemics, 4
parametric reasoning, 116
Paterson, David, 191n
pattern-matching evaluations, 122
perfect storm, 8n
performance in OSAPI, 158
performing controls (data governance metamodel), 56-57
persistence, 1n
of data, 71-72
of viral data in communication model, 78
persistence tier, 40
physical data models, 188
logical data models and, 180-184
pi, 132
Piatt Andrew Jr., Abram, 9
plausible reasoning, 115
political aspects of problem-solving, technical aspects versus, 140-142
political boundaries, 13
Ponzi, Charles, 173
Porter, Michael, 144
positive feedback loops, 34
positive terminology in definitions, 111
post flight, 82-88, 91, 97-98, 101, 147, 179, 200
PowerPoint in communication, 10-11
precision trait, evaluating, 132
preflight, 82-88, 97, 98n, 101, 147, 179, 200
presentation tier, 40
preservation (moving data), 80
proactive controls (data governance metamodel), 56
proactive data governance, 63
probabilistic comparison (of data), 98-100
probabilistic reasoning, 115
problem-solving
provenance in, 143-154
accountability, 146
data chains versus value chains, 144-145
data lineage and, 149-151
decisions concerning, 150-152
diplomatics, 146-147
late binding versus early binding, 148
Zachman Framework for Enterprise Architecture, 152-154
reactive traits for, 177-178
reductionism versus systems thinking, 139-140
technical versus political aspects, 140-142
procedural controls (data governance metamodel), 57
process tier, 41
propagation of viral data, 206-208
property value mistake example (latency), 12
abstraction and, 194
accountability, 146
data chains versus value chains, 144-145
data lineage and, 149-151
decisions concerning, 150-152
diplomatics, 146-147
late binding versus early binding, 148
statistics and, 7
Zachman Framework for Enterprise Architecture, 152-154
pseudo real time, 72n
punting governance opportunities, 51
Q
qualitative values, reporting viral data, 142
quality measurement, 24-27
quality of data
alignment and harmonization, 97-98
data governance versus, 59
matching, 98-100
merging, 100
survivorship, 100
quantitative values, reporting viral data, 142
quantity data, 131
QuIT CITeD (data class taxonomy), 130-132, 206
R
Radio frequency identification (RFID), 145
Rallying Point, 152-153
reaction to change, 20-43
best practices, 36-38
decisioning environment measurements, 33-37
eminent domain, 22-23
incentives and compensation, 32-33
metadata, 27-29
military, 29-30
quality measurement, 24-27
service-oriented architectures, 30-32, 38-43
viral data as symptom of, 24
reactive controls (data governance metamodel), 56
reactive data governance, 62-63
reactive traits (for problem-solving), 177-178
reasoning, types of, 113-114
case-based reasoning, 115
classification, reasoning through, 116-117
consistency-based reasoning, 115
generalization, reasoning through, 116
heuristic reasoning, 115
imprecise reasoning, 115
logical inference, 114
nonmonotonic reasoning, 116
parametric reasoning, 116
probabilistic reasoning, 115
semantic reasoning, 116
specialization, reasoning through, 117-122
reassure controls (data governance metamodel), 55, 58-59
reductionism
defined, 139
systems thinking versus, 139-140
redundancy, data assessment via, 204
refactoring, 123n
reference model, 100-101
data persistence, 71-72
data quality
alignment and harmonization, 97-98
matching, 98-100
merging, 100
survivorship, 100
data quality assessment (DQA), CIDER, 88-97
ASPECT, 79-82
bandwidth considerations, 72-73
communication model, 77-79
data access, 76-77
flight analogy, 82-88
metadata, 73-76
regulations, data governance and, 60-61
relationship terms in definitions, 108
remediation (CIDER), 89
reporting viral data, quantitative versus qualitative values, 142
representative communication style, 53
reusability, 31
data structure design and, 42
revenge theory, 5
revisionism
historical revisionism, 103
misinterpretation of business data, 104-106
RFID (Radio frequency identification), 145
rich metadata, 76
risk management, data governance and, 66
rogue applications, viral data from, 24
root-cause analysis, 105
Ross, Betsy, 103
rules of behavior, 47
rules of compliance, 47
S
Saarinen, Eliel, 188
Safeguarding Customer Information rule, 60
Safeway Stores, 21
sanctions dial (data governance), 66-68
Sarbanes-Oxley Act of 2002, 60, 175n
scenarios for data assessments, 138
Schwartz, Alan, 174
scope
of definitions, 109-111
in SÉANCE workflow, 89
Securities Exchange Act of 1934, 60
self-governance, 47-48
semantic disintegrity, 202
semantic evaluations, 122
semantic reasoning, 116
semantics, communication and, 1-13
Adam and Eve story, 1
analogies, 2-3
books, definition of, 11-12
controlling viral data, 8
Federal Reserve System example, 8-10
information overload, 11-12
ISO 4217 currency standard example, 13
political boundaries, 13
PowerPoint example, 10-11
property value mistake example, 12
sports statistics, 6-7
viral data as metaphor, 3-5
semistructured data, 71, 73-76, 79, 93, 101, 131-132, 206
Seinfeld, Jerry, 5
Senge, Peter, 140
service-oriented architectures
adaptation to change, 30-32
interoperability, 7
loose coupling in three-tier architectures, 38-43
technical characteristics of, 30-32
shades of governance, 48
Shannon, Claude, 77
Shannon-Weaver communication model, 77, 206
Shinseki, Eric, 24
Siekaczek, Reinhard, 173
silo-oriented organizational structures, 17-20
silos, 203
simple metadata, 74
size trait, evaluating, 132
Smith, Henry, 106
SOA, propagation of viral data, 206-208
Social Security Administration (SSA), 104
Social Security Number example (misinterpretation of business data), 104-106
software abstraction, 178n
solutions
provenance in, 143-154
accountability, 146
data chains versus value chains, 144-145
data lineage and, 149-151
decisions concerning, 150-152
diplomatics, 146-147
late binding versus early binding, 148
Zachman Framework for Enterprise Architecture, 152-154
reactive traits for, 177-178
reductionism versus systems thinking, 139-140
technical versus political aspects, 140-142
South Florida Sun-Sentinel (newspaper), 36
specialization, reasoning through, 117-122
sports statistics, 6-7
SSA (Social Security Administration), 104
stability in OSAPI, 158
staging areas, 95
standardization, 57
standards in data governance, 63
Stars and Stripes, 103
statistical sampling for data assessments, 138
statistics
provenance and, 7
in sports, 6-7
Streisand, Barbra, 175
Strong, Benjamin, 9
structural data design, data conditioning in, 155-170
structural metadata, 123
structure of metadata, 164-166
structured data, 71, 73-74, 76, 79, 101, 131-132, 155-156, 206
Sullenberger, Chelsey, 191
sunsetting, 178n
Sunstein, Cass, 2
surrogate values, 130
survivorship (data), 100
sustaining controls (data governance metamodel), 57
Swansea, 112
symbiosis (moving data), 80
synchronization of data, 171
systems thinking, 206
defined, 140
reductionism versus, 139-140
T
taxonomy of data classes, 130-132
technical aspects of problem-solving, political aspects versus, 140-142
temporal data, 131
Tenner, Edward, 5
Terra Gruppen, 13
TETLT (transform, extract, transform, load, and transform), 82-84
textual data classes, 131
thing modeling, 43n
third-normal form (3NF), 121
three-tier architectures, loose coupling in, 38-43
tight coupling, problems with, 39-43
time in data governance, 64
trans-enterprise, 145n
transformations in TETLT patterns, 83-84
transition (moving data), 81
translations in definitions, 112
Truman, Harry S, 118n
truth tables, 116
trustworthiness of data
master data solutions, 205
multiple views for, 203-204
via redundancy, 204
scenarios for, 138
statistical sampling, 138
subjective and objective measures, 206
Tufte, Edward, 10
U
U.S. Department of Homeland Security, hierarchical organizational structure, 17-20
unbounded codified data, 131
uncertainty, 99
United Airlines, 36
United States Steel, 21
unstructured data, 28, 36, 71, 73, 76, 79, 101, 132, 206
V
validity, 167n
value chains, 203
data chains versus, 144-145
Vanderlip, Frank, 9
vehicle identification number (VIN) example (structural data design), 158-169
views
abstraction and, 194-201
multiple views for data assessment, 203-204
VIN example (structural data design), 158-169
violet assessment example, xxxv-xxxvii, 209
viral data
in business and information technology alignment, 22
controlling, 8
defined, xxxiii
feedback loops and, 35-37
forms of, 175-176
host for, 176
as metaphor, 3-5
perfect storm for, 8
persistence in communication model, 78
propagation of, 206-208
quality measurement and, 24-27
reporting, quantitative versus qualitative values, 142
from rogue applications, 24
solutions, list of, 205
symptom of adaptation to change, 24
virus, origin of term, 4
W–Z
walkie-talkie, 128-129
Walsh, Mark, 33
Warburg, Paul, 9
Washington, George, 103
Wason test, 3
Wason, Peter, 3
Weaver, Warren, 77
Welsh, language, 112
Western Reminiscences (Smith), 106
Whitaker, Edmund, 45
Wilson, Woodrow, 9
WMI codes, 164
World Health Organization, 4
Wycliffe, John, 1
XM Radio, 21
Zachman Framework for Enterprise Architecture, 152-154