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A Comparative Study of Data Management Maturity Models

In this presentation we compare existing Data Management / Data Governance Maturity Models and discuss different approaches to viewing Data Management / Data Governance.
We also present a new model for Data Management which unifies various existing models and provides a fresh perspective on Data Management, its assessment and implementation.

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A Comparative Study of Data Management Maturity Models

  1. 1. A COMPARATIVE STUDY OF DATA MANAGEMENT MATURITY MODELS M a y 2 0 1 9
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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) R E F E R E N C E S

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