Enterprise data governance: reference & master data management, semantic modeling
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London [u.a.]
ISTE
2010
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | Adapted and updated from "Management des données l'enterprise" |
| Beschreibung: | XLIV, 288 S. |
| ISBN: | 9781848211827 |
Internformat
MARC
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| 100 | 1 | |a Bonnet, Pierre |e Verfasser |4 aut | |
| 240 | 1 | 0 | |a Management des données de l'enterprise |
| 245 | 1 | 0 | |a Enterprise data governance |b reference & master data management, semantic modeling |c Pierre Bonnet |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a London [u.a.] |b ISTE |c 2010 | |
| 300 | |a XLIV, 288 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Adapted and updated from "Management des données l'enterprise" | ||
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| 856 | 4 | 2 | |m Digitalisierung UB Bamberg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025917156&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-025917156 | |
Datensatz im Suchindex
| _version_ | 1809404287948685312 |
|---|---|
| adam_text |
Table
of
Contents
Testimonials from the
MDM
Alliance Group
. xiii
Foreword
. xxv
Preface
. xxix
Acknowledgements
. xxxix
Introduction to
MDM
. xli
Part one: The
MDM
Approach
.
l
Chapter
1.
A Company and its Data
. 3
1.1.
The importance of data and rules repositories
. 3
1.2.
Back to basics
. 6
1.2.1.
Past differences
. 9
1.2.2.
The rich data model
. 11
1.3.
Reference/Master data definition
. 12
1.3.1.
Data initialized before use
by transactional systems
. 14
1.3.2.
Duplicate data
. 14
1.3.3.
Data exchanged with third parties
. 19
1.4.
Searching for data quality
. 19
1.4.1.
Data quality
. 20
1.4.2.
The quality of data models
. 23
1.4.3.
The level of maturity of data quality
. 25
1.5.
Different types of data repositories
. 27
vi Enterprise Data
Governance
1.5.1.
Technical classification
. ^8
1.5.2.
Customer Data Integration
(CDI)
. 31
1.5.3.
Product Information Management (PIM) and
Product Life Management (PLM)
.
'л:і
1.5.4.
Lightweight Directory Access Protocol
(LDAP)
. . 35
Chapter
2.
Strategic Aspects
.
;i7
2.1.
Corporate governance
. ■''
2.1.1.
Forced against the wall by regulations
. 38
2.1.2.
The new scorecard
. 41
2.2.
The transformation stages of an IT system
. 42
2.2.1.
First stage: the data repository
. 43
2.2.2.
Second stage: the business rules repository
is added to the data repository
. 46
2.2.3.
Third Stage: adding the business
processes repository
. 49
2.3.
Sustainable IT Architecture
. 51
2.3.1.
The new management control
. 52
2.3.2.
Maintaining knowledge and the
strategic break
. 55
Chapter
3.
Taking Software Packages into Account
. . 57
3.1.
The dead end of locked repositories
. 57
3.2.
Criteria for choosing software packages
. 59
3.2.1.
Availability of the data model
. 61
3.2.2.
Repository updates
. 62
3.2.3.
Neutralization of a locked
MDM
. 62
3.3.
Impact for software vendors
. 63
3.4.
MDM
is also a software package
. 65
Chapter
4.
Return on Investment
. 69
4.1.
Financial gain from improved data quality
. 69
4.2.
The financial gain of data reliability
. 71
4.3.
The financial gain of mastering operational risks.
. . 74
4.3.1.
An all too often inefficient control system
. 74
4.3.2.
MDM
for the control of operational risks
. 76
4.4.
The financial gain of IS transformation
. 77
4.4.1.
The overlap of an Information System and IT.
. . 78
4.4.2.
Financial valuation of an Information System
. . 79
Table
of
Contents
vii
4.4.3.
The
MDM
as a springboard
for transformation of IS
. 81
4.5.
Summary of the return on investment of
MDM
. 83
Part two:
MDM
from a Business Perspective
. 87
Chapter
5.
MDM
Maturity Levels and
Model-driven
MDM
. 89
5.1.
Virtual
MDM
. 89
5.2.
Static
MDM
. 92
5.3.
Semantic
MDM
. 95
5.3.1.
Improved administration by business users
. 97
5.3.2.
A greater reliability in the data repository
. 97
5.3.3.
Preparation for
MDM
integration
with the rest of a system
. 98
5.4.
The
MDM
maturity model
. 100
5.5.
A Model-driven
MDM
system
. 103
5.5.1.
Variants
. 103
5.5.2.
Hiding join tables
. 104
Chapter
6.
Data Governance Functions
. 109
6.1.
Brief overview
. 109
6.2.
Ergonomics
.
Ill
6.3.
Version management
. 112
6.4.
The initialization and update of data
by use context
. 114
6.4.1.
The affiliation of contexts
. 115
6.4.2.
The automatic detection of shared data
. 117
6.5.
Time management
. 118
6.5.1.
Data history tracking
. 119
6.5.2.
Business transaction
. 120
6.5.3.
Validity period
. 121
6.6.
Data validation rules
. 122
6.6.1.
Facets
. 123
6.6.2.
Integrity constraints
. 124
6.6.3.
Business rules
. 126
6.7.
The data approval process
. 128
6.8.
Access rights management
. 129
6.9.
Data hierarchy management
. 130
6.10.
Conclusion
. 131
viii Enterprise
Data Governance
Chapter
7.
Organizational Aspects I·*·5
7.1.
Organization for semantic modeling
.
1·Μ
7.1.1.
The foundations of the organization
.
I·'*·1"'
7.1.2.
Data owners
.
l:i(i
7.1.3.
The Enterprise Data Office
. 1-47
7.1.4.
Does this organization involve risks?
. 1.49
7.2.
The definition of roles
.
146
7.2.1.
Data owner
. 146
7.2.2.
Data analyst
. 146
7.2.3.
Data architect
. 147
7.2.4.
Data cost accountant
. 148
7.2.5.
Data steward
. 148
7.3.
Synthesis of the organization required to
support the
MDM
. 148
Part three:
MDM
from the IT
Department Perspective
. 151
Chapter
8.
The Semantic Modeling Framework
. 153
8.1.
Establishing the framework of the method
. 153
8.1.1.
The objectives of semantic modeling
. 154
8.1.2.
The lifecy
cle
of semantic modeling
. 157
8.2.
Choosing the method
. 161
8.2.1.
The Praxeme method
. 161
8.2.2.
Choosing another method
. 170
8.3.
The components of Enterprise Data Architecture
. . . 172
8.3.1.
The business object
. 173
8.3.2.
The data category
. 175
8.3.3.
Business object domains
. 176
8.3.4.
Data repository architecture
. 176
8.4.
The drawbacks of semantic modeling
. 178
8.4.1.
The lack of return on investment
. 178
8.4.2.
Lack of competency
. 179
8.4.3.
The blank page effect
. 180
8.5.
Ready-to-use semantic models
. 180
8.5.1.
Software packages
. 181
8.5.2.
Industry specific models
. 182
8.5.3.
Generic data models
. 183
Table
of Contents
ix
Chapter
9.
Semantic Modeling Procedures
. 187
9.1.
A practical case of semantic modeling:
the address
. 187
9.1.1.
Non-compliant version of semantic modeling
. 188
9.1.2.
First draft of semantic modeling
. 191
9.1.3.
Modeling of the lifecycle of the address
. 192
9.1.4.
Complete semantic modeling of the address
. 197
9.2.
Example of Enterprise Data Architecture
. 199
9.3.
Semantic modeling procedures
. 202
9.3.1.
Extended business operation
. 202
9.3.2.
Elementary business operation
. 207
9.3.3.
Single-occurrence and multi-occurrence
business operations
. 208
9.3.4.
Fostering the upgradeability of data models
. 209
9.3.5.
Other principles
. 213
Chapter
10.
Logical Data Modeling
. 215
10.1.
The objectives of logical modeling
. 215
10.2.
The components of logical data modeling
. 216
10.3.
The principle of loose-coupling data
. 217
10.4.
The data architecture within categories
. 221
10.5.
Derivation procedures
. 221
10.5.1.
Derivation of the semantic classes
. 221
10.5.2.
Examples of derivation of semantic classes
. . . . 224
10.5.3.
Derivation of elementary business operations
. . 227
10.5.4.
Derivation of extended business operations.
. . . 228
10.5.5.
Derivation of inheritance
. 228
10.5.6.
Identifier management
. 229
10.5.7.
Calculated information
. 229
10.6.
Other logical modeling procedures
. 229
10.6.1.
Enumeration data type
. 229
10.6.2.
User message
. 230
10.6.3.
User interface components
. 230
10.6.4.
Data documentation
. 231
10.6.5.
Naming rules
. 231
Chapter
11.
Organization Modeling
. 233
11.1.
The components of pragmatic modeling
. 234
11.2.
Data approval processes
. 235
11.2.1.
Process example
. 235
x
Enterprise
Data Governance
11.2.2.
Synchronization of use cases with
processes
. . .
2.'J8
11.2.3.
The other processes
.
^·*9
11.3.
Use cases
.
'¿:i9
11.3.1.
Documentation of use cases
. 240
11.3.2.
Elementary use case
. ^42
11.3.3.
Extended use case
. 243
11.4.
Administrative objects
. 24.1
11.5.
The derivation of pragmatic models
to logical models
. 244
11.5.1.
Use cases
. 244
11.5.2.
Data approval process
. 245
11.5.3.
Administrative object
. 245
11.5.4.
Transaction
. 245
Chapter
12.
Technical Integration
of an
MDM
system
. 247
12.1.
Integration models
. 248
12.1.1.
Weak coupling
. 250
12.1.2.
Tight coupling
. 251
12.1.3.
Loose coupling
. 252
12.1.4.
Consequences of integration models
. 253
12.2.
Semantic integration
. 254
12.3.
Data synchronization
. 258
12.3.1.
Business event
. 260
12.3.2.
Organizational event
. 260
12.3.3.
Applicative event
. 260
12.4.
Integration with the BRMS
. 261
12.5.
Classification of databases and
software development types
. 263
Conclusion
. 267
Appendix. Semantic Modeling of Address
. 271
А.1.
The semantic model
. 272
A.2. Examples of screens generated by
Model-driven
MDM
. 277
A.2.1. Postal Code Patterns
.'.'.'.'.'. 277
A.2.2. Automatic calculation of postal code
. 279
A.2.3. Address input
. 281
A.3. Semantic modeling and data quality
. 282
Table
of
Contents
xi
A.4. Performance
. 282
A.5. Lifecycle
of the Address
business
object
. 282
A.6.
Insight into the XML schema
. 283
Bibliography
. 285
Index
. 287 |
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| id | DE-604.BV040938342 |
| illustrated | Not Illustrated |
| indexdate | 2024-09-06T00:26:37Z |
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| isbn | 9781848211827 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025917156 |
| oclc_num | 731405377 |
| open_access_boolean | |
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| physical | XLIV, 288 S. |
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| spelling | Bonnet, Pierre Verfasser aut Management des données de l'enterprise Enterprise data governance reference & master data management, semantic modeling Pierre Bonnet 1. publ. London [u.a.] ISTE 2010 XLIV, 288 S. txt rdacontent n rdamedia nc rdacarrier Adapted and updated from "Management des données l'enterprise" Datensicherung (DE-588)4011144-1 gnd rswk-swf Unternehmensarchitektur (DE-588)7856257-0 gnd rswk-swf Unternehmensarchitektur (DE-588)7856257-0 s Datensicherung (DE-588)4011144-1 s DE-604 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3520858&prov=M&dok_var=1&dok_ext=htm Inhaltstext Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025917156&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bonnet, Pierre Enterprise data governance reference & master data management, semantic modeling Datensicherung (DE-588)4011144-1 gnd Unternehmensarchitektur (DE-588)7856257-0 gnd |
| subject_GND | (DE-588)4011144-1 (DE-588)7856257-0 |
| title | Enterprise data governance reference & master data management, semantic modeling |
| title_alt | Management des données de l'enterprise |
| title_auth | Enterprise data governance reference & master data management, semantic modeling |
| title_exact_search | Enterprise data governance reference & master data management, semantic modeling |
| title_full | Enterprise data governance reference & master data management, semantic modeling Pierre Bonnet |
| title_fullStr | Enterprise data governance reference & master data management, semantic modeling Pierre Bonnet |
| title_full_unstemmed | Enterprise data governance reference & master data management, semantic modeling Pierre Bonnet |
| title_short | Enterprise data governance |
| title_sort | enterprise data governance reference master data management semantic modeling |
| title_sub | reference & master data management, semantic modeling |
| topic | Datensicherung (DE-588)4011144-1 gnd Unternehmensarchitektur (DE-588)7856257-0 gnd |
| topic_facet | Datensicherung Unternehmensarchitektur |
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