This document presents a comparative analysis of various data management maturity models, focusing on their applicability to small and medium-sized enterprises. It highlights key questions regarding data management capabilities, maturity measurement, and benchmarking against industry peers while providing insights into existing frameworks such as DAMA-DMBOK, DCAM, and others. The study aims to clarify the definitions and scopes of data management, assisting organizations in selecting the appropriate maturity model for their needs.

1. A COMPARATIVE STUDY OF
DATA
MANAGEMENT
MATURITY
MODELS
M a y 2 0 1 9

2. T H E A I M O F T H I S P R E S E N T A T I O N I S T O …
… S H A R E T H E R E S U L T S O F A C O M P A R A T I V E A N A L Y S I S O F L E A D I N G
D A T A M A N A G E M E N T / G O V E R N A N C E ( D M * ) I N D U S T R Y R E F E R E N C E
M O D E L S
… P R E S E N T A D M M A T U R I T Y M O D E L F O R S M A L L - A N D M E D I U M
S I Z E D C O M P A N I E S
* DM : data management and data governance, the abbreviation will be used throughout the presentation

3. Integrated
DM model for
medium-
sized
companies
Comparison
of the key DM
industry
maturity
models
Definition of
data
management
Introduction
S T R U C T U R E

4. “ A S A D A T A
M A N A G E M E N T
P R O F E S S I O N A L , Y O U
A R E F A C I N G A
C H A L L E N G E : T H E R E
A R E P L E N T Y O F
D I F F E R E N T
R E F E R E N C E G U I D E S ,
S T A N D A R D S A N D
M O D E L S , B U T T H E Y
A R E N O T I N
A G R E E M E N T W I T H
E A C H O T H E R . ”
V E R Y C O M M O N Q U E S T I O N S A R E :
• W h a t a r e t h e k e y d a t a m a n a g e m e n t
c a p a b i l i t i e s t h a t a r e f e a s i b l e a n d
a p p l i c a b l e f o r s m a l l a n d m e d i u m - s i z e d
c o m p a n i e s ?
• H o w c a n y o u m e a s u r e t h e m a t u r i t y o f
d a t a m a n a g e m e n t i n y o u r c o m p a n y ?
• H o w c a n y o u c o m p a r e t h e s t a t u s o f
d a t a m a n a g e m e n t i n y o u r c o m p a n y
w i t h p e e r s i n t h e i n d u s t r y ?
T H E R E A R E P L E N T Y O F R E F E R E N C E I N D U S T R Y D A T A
M A N A G E M E N T A N D M A T U R I T Y M O D E L S

5. F o r t h e a n a l y s i s
o f D M m o d e l s ,
w e w i l l t a k e 3
i n d u s t r y
r e f e r e n c e
g u i d e s :
D A M A - D M B O K 2 ,
D C A M , T O G A F
9 . 1 a n d 6
m a t u r i t y
m o d e l s * .
CMMI CERT-
RMM Data
management
maturity model
Stanford Data
Governance
Maturity Model
Gartner
Enterprise
Information
Management
Maturity Model
IBM Data
Governance
Council
Maturity Model
ISO 8000-61
ISO 8000-62
* In this presentation, we only used information available in sources open to public
W E L O O K E D F O R T H E A N S W E R S T O T H E S E
Q U E S T I O N S I N T H E F O L L O W I N G S O U R C E S

6. … B E C O M E F A M I L I A R W I T H C O M M O N A L I T I E S A N D D I F F E R E N C E S
O F E X I S T I N G D A T A M A N A G E M E N T M A T U R I T Y M O D E L S
… B E A B L E T O C H O O S E T H E R I G H T A P P R O A C H F O R Y O U R D M
P R A C T I C E S A N D B U I L D Y O U R O W N D M M A T U R I T Y M O D E L
T h a n k s i n a d v a n c e f o r l e a v i n g y o u r f e e d b a c k
o n o u r L i n k e d I n p a g e o r o u r w e b s i t e .
H O P E F U L L Y , B Y T H E E N D O F T H I S P R E S E N T A T I O N
Y O U W I L L :

7. T H E F I R S T Q U E S T I O N I S :
D O W E , D M
P R O F E S S I O N A L S , H A V E
A N A L I G N E D D E F I N I T I O N
A N D U N D E R S T A N D I N G
O F D A T A M A N A G E M E N T
A N D I T S S C O P E ?
I F Y O U R E A D S O M E O F
T H E S E D E F I N I T I O N S , Y O U
R E A L I Z E , T H A T T H E R E
A R E D I F F E R E N T
V I E W P O I N T S O N W H A T
D A T A M A N A G E M E N T
A C T U A L L Y I S .
D A M A - D M B O K
“Data management is the
development, execution and
supervision of plans, policies,
programs, and practices that
deliver, control, protect, and
enhance the value of data and
information assets throughout
their lifecycle.” 1
C O B I T 4 . 1
“Effective data management requires
identifying data requirements. The data
management process also includes the
establishment of effective procedures to
manage the media library, backup and
recovery of data and proper disposal of
media. Effective data management helps
ensure the quality, timeliness and
availability of business data.”2
D C A M
“[…] proper data
management is about
managing data as
‘meaning’.” 3
I n f o r m a t i c a
“Data management is the implementation of
policies and procedures that put organizations in
control of their business data regardless of where it
resides.”4
D a t a v e r s i t y
“Data Management is a comprehensive
collection of practices, concepts,
procedures, processes, and a wide range of
accompanying systems that allow for an
organization to gain control of its data
resources.”5
T e c h n o p e d i a
“Data management refers to an organization's
management of information and data for secure and
structured access and storage.
Data management tasks include the creation of data
governance policies, analysis and architecture;
database management system (DMS) integration;
data security and data source identification,
segregation and storage.”6
W H A T I S D A T A M A N A G E M E N T A L L A B O U T ?

8. T H E T W O B E S T K N O W N R E F E R E N C E G U I D E S I N D A T A M A N A G E M E N T :
The DAMA-DMBOK 2 Data
management framework (The DAMA
Wheel)9
DAMA Environmental Factors
Hexagon10
DCAM Framework11
1.0
Data Management Strategy
5.0
Data Architecture
2.0
Data Management Business
Case and Funding Model
6.0
Technology Architecture
3.0
Data Management Program
7.0
Data Quality Program
4.0
Data Governance
8.0
Data Control Environment
M A I N D M I N D U S T R Y R E F E R E N C E G U I D E S
D A M A - D M B O K 2 7
T H E D A T A M A N A G E M E N T B O D Y O F
K N O W L E D G E
b y D A M A I n t e r n a t i o n a l
D C A M 8
D A T A M A N A G E M E N T C A P A B I L I T Y
A S S E S S M E N T M O D E L
b y E D M C o u n c i l

9. Data
management
Enterprise
perspective
Data
management
professionals
perspective
B R O A D P E R S P E C T I V E :
F R O M T H E E N T E R P R I S E P O I N T
O F V I E W O N T H E L I F E C Y C L E
O F D A T A C I R C U L A T I N G I N A
C O M P A N Y
D A M A - D M B O K 2
N A R R O W P E R S P E C T I V E :
F R O M T H E V I E W P O I N T O F
T A S K S T O B E D O N E B Y D A T A
M A N A G E M E N T
P R O F E S S I O N A L S
D C A M
Two main perspectives on data management
T W O M A I N P E R S P E C T I V E S O N D A T A M A N A G E M E N T

10. F O R T H E P U R P O S E O F
C O M P A R I N G
D I F F E R E N T D A T A
M A N A G E M E N T /
G O V E R N A N C E
M A T U R I T Y M O D E L S ,
W E H A V E C R E A T E D A
M E T A M O D E L O F D A T A
M A N A G E M E N T .
T H E M O D E L
H I G H L I G H T S T H E K E Y
H I G H L E V E L D A T A
M A N A G E M E N T
C A P A B I L I T I E S F R O M
T H E B R O A D
( E N T E R P R I S E )
P E R S P E C T I V E . Metamodel of Data management capabilities
Data & Information
Enterprise architecture
Information Technology
Security
Data & information quality
describes, classifies, models, designs
describes, classifies, models, designs
designs
Data management framework/
Data governance
coordinates with
manages
enables security of
implements
enables
Lifecycle of
has
D A T A M A N A G E M E N T E X P L A I N E D

11. I N O R D E R T O B E A B L E T O
M A K E A C O M P A R A T I V E
A N A L Y S I S , W E H A V E
C R E A T E D A F R A M E W O R K
B A S E D O N T H E M A T U R I T Y
M O D E L S B Y C A R N E G I E
M E L L O N
U N I V E R S I T Y 1 2 A N D T H E
I N S T I T U T E O F I N T E R N A L
A U D I T O R S 1 3 .
T H E P R O P O S E D
F R A M E W O R K I N C L U D E S
4 K E Y C O M P O N E N T S
L e v e l o f m a t u r i t y : p r o g r e s s i o n s t a g e s i n d a t a
m a n a g e m e n t d e v e l o p m e n t .
S u b j e c t d o m a i n s a n d s u b - d o m a i n s : a l s o
c a l l e d ‘ B u s i n e s s c a p a b i l i t i e s ’ . A c c o r d i n g t o T h e
O p e n G r o u p d e f i n i t i o n , a c a p a b i l i t y i s ‘ a n
a b i l i t y [ . . ] t h a t a b u s i n e s s m a y p o s s e s s o r
e x c h a n g e t o a c h i e v e a s p e c i f i c p u r p o s e
o r o u t c o m e ’ a n d w h i c h i s c o n s t i t u t e d f r o m
r o l e s , p r o c e s s e s , i n f o r m a t i o n a n d t o o l s ’ 1 4 .
S u b j e c t d o m a i n d i m e n s i o n s : a s e t
c h a r a c t e r i s t i c s t h a t a r e u s e d a s a n a s s e s s m e n t
c r i t e r i a f o r e a c h d o m a i n o r s u b - d o m a i n .
A r t i f a c t : b e n c h m a r k s o r e x a m p l e s o f p r a c t i c e s
o r d e l i v e r a b l e s f o r d o m a i n d i m e n s i o n s t o
s p e c i f y a l e v e l o f D M m a t u r i t y .
A F R A M E W O R K F O R C O M P A R I N G
D M M A T U R I T Y M O D E L S

12. Model/
Key components
to compare
DAMA-DMBOK2 DCAM
CMMI CERT-
RMM
IBM Data
Governance Council
Maturity model
Stanford Data
Governance
Maturity Model
Gartner’s
Enterprise
Information
Management
Maturity Model
COBIT 4.1
ISO 8000-61
ISO 8000-62
Scope
Data
management
Data
management
Data
governance
Data
governance
Data
governance
Information
management
IT Governance Data Quality
Number of
maturity levels
6 6 Unknown 5 5 Unknown 6
DQ-6
Process-5
Number of
domains
11 8 6 4 2 7 NA* 4
Domain type Knowledge Area Capability Process Competency Process Unknown
Control
objectives
Process
Number of sub-
domains
>4 112 25 10 6 NA NA Subprocess-14
Number of
domain
dimensions
4 NA Unknown Unknown Unknown Unknown NA NA
Artifacts Not available Available Unknown Examples Available Unknown Available Unknown
*NA – not applicable
C O M P A R I S O N O F M A T U R I T Y M O D E L S :
G E N E R A L I N F O R M A T I O N

13. Model/
Maturity
levels
DAMA-
DMBOK2 15 DCAM16 CMMI CERT-
RMM
IBM Data
Governance
Council Maturity
model17
Stanford Data
Governance
Maturity
Model18
Gartner’s
Enterprise
Information
Management
Maturity
Model
COBIT 4.1
19 ISO 8000-6220
Data Quality
ISO 8000-6221
Process
0 No capability Non-existent Immature
1 Initial/ Ad-hoc Non-initiated Initial Initial Initial/Ad-hoc Basic Basic
2 Repeatable Conceptual Managed Managed
Repeatable but
intuitive
Managed Managed
3 Defined Developmental Defined Defined Defined Established Established
4 Managed Defined
Quantitively
Managed
Quantitively
Managed
Managed and
Measurable
Predictable Predictable
5 Optimized Achieved Optimizing Optimizing Optimized Innovating Innovating
6 Enhanced
Remarks Unknown Unknown
C O M P A R I S O N O F M A T U R I T Y M O D E L S :
M A T U R I T Y L E V E L S

14. Model/
Key components to
compare
DAMA-DMBOK2 22 DCAM23 CMMI CERT-RMM24
IBM Data Governance
Council Maturity
model
25
Stanford Data
Governance
Maturity Model26
Gartner’s Enterprise
Information
Management
Maturity Model27
COBIT 4.1
28
ISO 8000-6129
Domain type Knowledge Area Capability Process Competency Process Unknown Control objectives Process
Key domains/
Sub-domains
1.Data governance
2.Data architecture
3.Data modeling & design
4.Data storage and
operations
5.Data security
6.Data Integration &
Interoperability
7.Document & Content
management
8.Reference & Master
data
9.DHW&BI
10.Metadata
11.Data Quality
1.Data Management
Strategy
2.Data Management
Business Case
3.Data Management
Program
4.Data Governance
5.Data Architecture
6.Technology
Architecture
7.Data Quality
8.Data Control
Environment
1.Data Management
Strategy
2.Data Governance
3.Data Quality
4.Data Operations
5.Platform and
Architecture
6.Measurement and
Analysis
7.Process Management
8.Process Quality
Assurance
9.Risk Management
10.Configuration
Management
Supporting disciplines
1.Data Architecture
2.Classification and
Metadata
3.Audit Information
Logging and Reporting
Core disciplines
4.Data Quality
Management
5.Information Life-cycle
Management
6.Information Security
and Privacy
Enablers
7.Organizational
Structure and Awareness
8.Policy
9.Stewardship
Outcomes
10.Data Risk
Management and
Compliance
11.Value Creation
Foundation
1.Awareness
2.Formalization
3.Metadata
Project
4.Stewardship
5.Data Quality
6.Master Data
1.Vision
2.Strategy
3.Metrics
4.Information
Governance
5.Organizations and
Roles
6.Information Life Cycle
7.Enabling Infrastructure
1.Business requirements
for data management
2.Storage and Retention
Arrangements
3.Media Library
Management System
4.Disposal
5.Backup and Restoration
6.Security Requirements
for Data Management
1.Data Quality
2.Data Related Support:
2.1.Data Architecture
management
2.2.Data Transfer
Management
2.3.Data Operations
Management
2.4.Data Security
Management
3.Resource provision
3.1.Data Quality
Organization
Management
3.2.Human Resource
Management
Key domain’s
dimensions
1.Activity
2.Tools
3.Standards
4.People and resources
1.People
2.Policies
3.Capabilities
C O M P A R I S O N O F M A T U R I T Y M O D E L S :
K E Y D O M A I N S , S U B - D O M A I N S A N D D I M E N S I O N S

15. Data management framework
*Data Management
Technology
Information & Data
Security
Data quality
Related capabilities
Enterprise
architecture
1
Data governance DM* Strategy
DAMA-DMBOK2
2 DCAM
DM Business case
Data control
environment
1 2 2 2 2
1
Document &
Content
management
1
1 2
Technology
architecture
Application
architecture
Data architecture
Data modeling
and design
Business
architecture
11 22
Data storage and
operations
Data integration
& interoperability
DWH & BI
1
1
1
Data
Reference Master Transactional
1 1
Metadata
1
Structured Unstructured
2
3 TOGAF9.1
3 3 3 3 3
4 CMMI CERT-RMM
5 IBM DGC MM
6 Stanford DG MM
7 Gartner’s EI MMM
8 COBIT 4.1
9 ISO 8000-61, 62
4 4
4
4
Measurement &
Analysis
Process
Management
Process Quality
Assurance
Risk Assessment
Configuration
Management
Audit Inf. Logging
& Reporting
Data Risk Mgmt &
Compliance
Value Creation
4 4 4 4 4 5 5 5
Organizational
Structure
Awareness Policy FormalizationMetricsStewardship
4 4
5
Information Life
cycle mgmt
Enabling
infrastructure
5
5
5
5 5 5 5
5
6
6
6
66 6
7
7
77 7 77
8
9
9
9
9
A N I N T E G R A T E D V I E W O N D M C A P A B I L I T I E S
S P E C I F I E D I N D M M A T U R I T Y M O D E L S

16. 1 . T h e r e a r e s e v e r a l D a t a m a n a g e m e n t / g o v e r n a n c e m a t u r i t y
m o d e l s a v a i l a b l e , b u t t h e y c a n h a r d l y b e c o m p a r e d . T h e
d i f f e r e n c e s c a n b e f o u n d i n e a c h o f t h e f o u r m o d e l c o m p o n e n t s ,
w h i c h a r e : l e v e l s , s u b j e c t ( s u b ) - d o m a i n s , s u b j e c t d o m a i n
d i m e n s i o n s a n d a r t i f a c t s .
2 . T h e r e a r e 8 k e y s u b j e c t a r e a s , w h e r e s u b j e c t d o m a i n s a r e
l o c a t e d :
D a t a & I n f o r m a t i o n ( a s s e t s ) , E n t e r p r i s e A r c h i t e c t u r e ,
T e c h n o l o g y , D a t a G o v e r n a n c e , D a t a Q u a l i t y , S e c u r i t y , C o n t e n t
a n d D o c u m e n t m a n a g e m e n t , R e l a t e d c a p a b i l i t i e s .
3 . E a c h c o m p a n y t h a t w o u l d l i k e t o a s s e s s i t s m a t u r i t y s h o u l d
a l i g n t h e D M m o d e l t h e y u s e w i t h t h e D M m a t u r i t y m o d e l .
4 . T h e s i t u a t i o n w i t h m a t u r i t y m o d e l s h a r d l y a l l o w s u s t o r e a c h
o n e o f t h e k e y g o a l s o f t h e m a t u r i t y m o d e l s : c r e a t i n g b e n c h m a r k s
f o r c o m p a r i s o n b e t w e e n d i f f e r e n t c o m p a n i e s .
T H E L E S S O N S W E H A V E L E A R N E D

17. T H E A N A L Y S I S O F T H E S E M O D E L S T H A T W E H A V E D I S C U S S E D , H A S
L E A D T O T H E D E V E L O P M E N T O F
A N E W , R E V O L U T I O N A R Y I N T E G R A T E D M O D E L F O R D A T A
M A N A G E M E N T ,
W H I C H :
… A L I G N S T H E 3 M A I N D M M O D E L S : D M M E T A M O D E L , D M
C A P A B I L I T Y M O D E L A N D D M M A T U R I T Y M O D E L
… S P E C I F Y T H E K E Y D M C A P A B I L I T I E S I N T H E ‘ N A R R O W ’ A N D ‘ B R O A D ’
S E N S E V I E W P O I N T S O N D A T A M A N A G E M E N T
… A S S I S T I N I M P L E M E N T A T I O N O F D M F U N C T I O N W I T H I N A M I D -
S I Z E D C O M P A N Y A N D I N A S S E S S M E N T O F I T S M A T U R I T Y .
… A N D M O S T I M P O R T A N T L Y

18. D I D Y O U K N O W T H A T A N O R A N G E I S A H Y B R I D B E T W E E N P O M E L O A N D
M A N D A R I N ? 3 0
T H I S A N A L O G Y H A S I N S P I R E D T H E N A M E O F T H E M O D E L , A S I T P E R F E C T L Y
S Y M B O L I Z E D O U R A T T E M P T S T O C R O S S T H E ‘ P O M E L O S ’ O F D M M E T A M O D E L S
A N D ‘ M A N D A R I N S ’ O F D M M A T U R I T Y M O D E L S .
R E A D T H I S S T O R Y H E R E .
W E H A V E C A L L E D I T …
THE ORANGE MODEL
OF DATA MANAGEMENT

19. … S I M P L I F Y T H E I M P L E M E N T A T I O N O F T H E D A T A M A N A G E M E N T
F U N C T I O N B Y O F F E R I N G A U N I F I E D A P P R O A C H B A S E D O N
R E C O G N I Z E D I N D U S T R Y R E F E R E N C E G U I D E S A N D B E S T P R A C T I C E S .
… I N T E G R A T E D A T A M A N A G E M E N T C A P A B I L I T Y M O D E L W I T H
M A T U R I T Y M O D E L T H A T A I D S W I T H :
• A S S E S S M E N T O F T H E A S - I S A N D T H E T O B E S I T U A T I O N S W I T H D A T A
M A N A G E M E N T W I T H I N A C O M P A N Y
• D E V E L O P M E N T O F A D M R O A D M A P A N D S T R A T E G Y
• S P E C I F I C A T I O N O F D A T A M A N A G E M E N T B U S I N E S S C A P A B I L I T I E S B O T H I N
‘ N A R R O W ’ A N D ‘ B R O A D ’ S E N S E V I E W P O I N T S O N D M
• C O M P A R I S O N O F P R A C T I C E S I N D I F F E R E N T C O M P A N I E S
• D E V E L O P M E N T O F T H E M A T U R I T Y M O D E L A N D C O R R E S P O N D I N G T E S T S F O R
D I F F E R E N T T Y P E S O F D M S T A K E H O L D E R S
T H I S N E W I N T E G R A T E D M O D E L H E L P S C O M P A N I E S

20. B E F O R E A C O M P A N Y C A N S T A R T I M P L E M E N T I N G T H E ‘ O R A N G E ’
M O D E L , T H E Y N E E D T O H A V E A C L E A R I D E A O F T H E L E V E L O F
M A T U R I T Y O F T H E I R C U R R E N T D A T A M A N A G E M E N T .
W E H A V E D E V E L O P E D A S I M P L E D A T A M A N A G E M E N T M A T U R I T Y
S C A N T H A T C A N H E L P W I T H T H A T .
T H E D M S C A N I S A V A I L A B L E F O R F R E E O N
M A K E S U R E T O T R Y I T O U T , I T W I L L O N L Y T A K E 1 0 - 1 5 M I N U T E S
O F Y O U R T I M E !
W E H A V E D E V E L O P E D
A B R A N D N E W M A T U R I T Y M O D E L A S W E L L !
D A T A C R O S S R O A D S . N L / D M - M A T U R I T Y - S C A N /

21. L E V E L 1
U N C O N T R O L L E D
I f i t w e r e
h u m a n , i t
w o u l d b e a
P L A Y F U L
T O D D L E R
H O W M A T U R E I S D A T A M A N A G E M E N T
I N Y O U R C O M P A N Y ?
L E V E L 2
A D - H O C
I f i t w e r e
h u m a n , i t
w o u l d b e
C R E A T I V E
C H I L D
L E V E L 3
I N D E V E L O P M E N T
I f i t w e r e
h u m a n , i t
w o u l d b e
C U R I O U S
T E E N A G E R
L E V E L 4
C A P A B L E
I f i t w e r e
h u m a n , i t
w o u l d b e
A M B I T I O U S
A D U L T
L E V E L 5
E F F E C T I V E
I f i t w e r e
h u m a n , i t
w o u l d b e
W I S E S E N I O R
C I T I Z E N

22. I F Y O U W A N T T O L E A V E F E E D B A C K , O R J U S T H A V E
A C H A T …
Y O U C A N R E A D M O R E A B O U T U S O N
DATACROSSROADS.NL
S E N D A N E - M A I L T O
C O N T A C T @ D A T A C R O S S R O A D S . N L
O R C O N N E C T W I T H U S O N L I N K E D I N
L I N K E D I N . C O M / C O M P A N Y / D A T A C R O S S R O A D S

23. 1. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition.
Technics Publications, 2017., p.17
2. COBIT 4.1, p.141
3. cdn.ymaws.com/edmcouncil.org/resource/resmgr/featured_documents/dcam_model_int
ro_and_fwd.pdf, p.3
4. www.informatica.com/nl/services-and-training/glossary-of-terms/data-management-
definition.html#fbid=MC3vz19f3Tt
5. www.dataversity.net/what-is-data-management/
6. www.techopedia.com/definition/5422/data-management
7. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition.
Technics Publications, 2017.
8. cdn.ymaws.com/edmcouncil.org/resource/resmgr/featured_documents/dcam_model_int
ro_and_fwd.pdf
9. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition.
Technics Publications, 2017, p.36
10. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition.
Technics Publications, 2017, p.36
11. cdn.ymaws.com/edmcouncil.org/resource/resmgr/featured_documents/dcam_model_int
ro_and_fwd.pdf , p.3
12. resources.sei.cmu.edu/library/asset-view.cfm?assetid=58916
13. www.iia.nl/SiteFiles/IIA_leden/PG%20Maturity%20Models.pdf
14. The Open Group. Open Group Guide. Business Capabilities. Prepared by the Open Group
Architecture Forum Business Architecture Work Stream. The Open Group, March 2016,
p.2,3
15. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition.
Technics Publications, 2017, p.531
16. cdn.ymaws.com/edmcouncil.org/resource/resmgr/featured_documents/DCAM_Scoring_
Guide.pdf
17. www-
935.ibm.com/services/uk/cio/pdf/leverage_wp_data_gov_council_maturity_model.pdf
18. web.stanford.edu/dept/pres-provost/cgi-bin/dg/wordpress/wp-
content/uploads/2011/11/StanfordDataGovernanceMaturityModel.pdf
19. COBIT 4.1. ISACA, p.144
20. shop.standards.ie/store/PreviewDoc.aspx?saleItemID=3115275, p.iii
21. shop.standards.ie/store/PreviewDoc.aspx?saleItemID=3115275, p.iii
22. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition.
Technics Publications, 2017, p.36
23. cdn.ymaws.com/edmcouncil.org/resource/resmgr/featured_documents/dcam_model_int
ro_and_fwd.pdf, p.2
24. cmmiinstitute.com/data-management-maturity
25. www-
935.ibm.com/services/uk/cio/pdf/leverage_wp_data_gov_council_maturity_model.pdf
26. web.stanford.edu/dept/pres-provost/cgi-bin/dg/wordpress/wp-
content/uploads/2011/11/StanfordDataGovernanceMaturityModel.pdf
27. blogs.gartner.com/andrew_white/files/2016/10/On_site_poster.pdf
28. COBIT 4.1. ISACA, p.142
29. es.slideshare.net/dqteam/ponencia-ismael-caballero-desayuno-afsm-90030982, slide 31.
30. en.wikipedia.org/wiki/Orange_(fruit)
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