1.
Review of Data Management
Maturity Models
Alan McSweeney

2.
Objectives
•
Review existing data management maturity models to identify core
set of characteristics of an effective data maturity model
− DMBOK (Data Management Book of Knowledge) from DAMA (Data
Management Association) http://www.dama.org/i4a/pages/index.cfm?pageid=3345
− MIKE2.0 (Method for an Integrated Knowledge Environment) Information
Maturity Model (IMM) http://mike2.openmethodology.org/wiki/Information_Maturity_QuickScan
− IBM Data Governance Council Maturity Model http://www.infogovcommunity.com/resources
− Enterprise Data Management Council Data Management Maturity Model http://edmcouncil.org/downloads/20130425.DMM.Detail.Model.xlsx
•
Not intended to be comprehensive
October 23, 2013
2

3.
Maturity Models (Attempt To) Measure Maturity Of
Processes And Their Implementation and Operation
•
Processes breathe life into the organisation
•
Effective processes enable the organisation to operate
efficiently
•
Good processes enable efficiency and scalability
•
Processes must be effectively and pervasively
implemented
•
Processes should be optimising, always seeking
improvement where possible
October 23, 2013
3

4.
Basis for Maturity Models
•
Greater process maturity should mean greater business
benefit(s)
− Reduced cost
− Greater efficiency
− Reduced risk
October 23, 2013
4

5.
Proliferation of Maturity Models
•
Growth in informal and ad hoc maturity models
•
Lack rigour and detail
•
Lack detailed validation to justify their process structure
•
Not evidence based
•
Lack the detailed assessment structure to validate
maturity levels
•
Concept of a maturity model is becoming devalued
through overuse and wanton borrowing of concepts from
ISO/IEC 15504 without putting in the hard work
October 23, 2013
5

6.
Issues With Maturity Models
•
How to know you are at a given level?
•
How do you objectively quantify the maturity level scoring?
•
What are the business benefits of achieving a given maturity level?
•
What are the costs of achieving a given maturity level?
•
What work is needed to increase maturity?
•
Is the increment between maturity levels the same?
•
What is the cost of operationalising processes?
•
How do you measure process operation to ensure maturity is being
maintained?
•
Are the costs justified?
•
What is the real value of process maturity?
October 23, 2013
6

7.
ISO/IEC 15504 – Original Maturity Model - Structure
Part 1
Part 9
Concepts and Introductory
Guide
Vocabulary
Part 6
Part 7
Part 8
Guide to Qualification of
Assessors
Guide for Use in Process
Improvement
Guide for Determining
Supplier Process Capacity
Part 3
Performing an Assessment
Part 2
A Reference Model for
Processes and Process
Capability
October 23, 2013
Part 4
Guide to Performing
Assessments
Part 5
An Assessment Model and
Indicator Guidance
7

8.
ISO/IEC 15504 – Original Maturity Model
•
Originally based on Software process Improvement and
Capability Determination (SPICE)
•
Detailed and rigorously defined framework for software
process improvement
•
Validated
•
Defined and detailed assessment framework
October 23, 2013
8

9.
ISO/IEC 15504 - Relationship Between Reference
Model and Assessment Model
Capability Dimension
Process Dimension
Process Category
Processes
Indicators of Process
Performance
Reference
Model
Capability Levels
Process Attributes
Assessment
Indicators
Indicators of Process
Capability
Base Practices
Work Practices and
Characteristics
October 23, 2013
Management Practices
Indicators of
Practice
Performance
Attribute Indicators
9

10.
ISO/IEC 15504 - Relationship Between Reference
Model and Assessment Model
•
Parallel process reference model and assessment model
•
Correspondence between reference model and
assessment model for process categories, processes,
process purposes, process capability levels and process
attributes
October 23, 2013
10

11.
ISO/IEC 15504 - Indicator and Process Attribute
Relationships
Process Attribute Ratings
Based On
Evidence of Process Performance
Evidence of Process Capability
Provided By
Provided By
Indicators of Process Performance
Indicators of Process Capability
Consist Of
Consist Of
Best Practices
Management Practices
Assessed By
Assessed By
Work Product Characteristics
October 23, 2013
Practice
Performance
Characteristics
Resources and
Infrastructure
Characteristics
11

12.
ISO/IEC 15504 - Indicator and Process Attribute
Relationships
•
Two types of indicator
− Indicators of process performance
• Relate to base practices defined for the process dimension
− Indicators of process capability
• Relate to management practices defined for the capability dimension
•
Indicators are attributes whose existence that practices
are being performed
•
Collect evidence of indicators during assessments
October 23, 2013
12

13.
Structure of Maturity Model
Maturity Model
Maturity Level 1
Process Area 1
Process 1
Maturity Level 2
Maturity Level N
Process Area 2
Process N
Process 1
Process Area N
Process N
Process N
Generic Goals
Specific Goals
Generic Practices
Specific Practices
Generic Practice 1
Generic Practice N
Specific Practice 1
Specific Practice N
Sub-Practice 1.1
Sub-Practice N.1
Sub-Practice 1.M
October 23, 2013
Process N
Sub-Practice N.M
13

14.
Structure of Maturity Model
•
Set of maturity levels on an ascending scale
−
−
−
−
−
•
5 - Optimising process
4 - Predictable process
3 - Established process
2 - Managed process
1 - Initial process
Each maturity level has a number of process areas/categories/groupings
− Maturity is about embedding processes within an organisation
•
•
Each process area has a number of processes
Each process has generic and specific goals and practices
− Specific goals describes the unique features that must be present to satisfy the process
area
− Generic goals apply to multiple process areas
− Generic practices are applicable to multiple processes and represent the activities
needed to manage a process and improve its capability to perform
− Specific practices are activities that are contribute to the achievement of the specific
goals of a process area
October 23, 2013
14

15.
Approach to Improving Maturity Using Maturity
Models
•
•
Use sub-practices and practices to assess current state of key capabilities and
identify gaps
Allows effective decisions to be made on capabilities that need improvement
Sub-Practice(s)
Assess Current Status and
Assign Score
Practice(s)
Assess Current Status and
Assign Score
Implement Goals
Goal(s)
Assess Current Status and
Assign Score
Achieve Process
Competency
Processes
Assign Overall Capability
Status Score
Implement Sub-Practices
Implement Practices
October 23, 2013
15

16.
Hierarchy of Maturity Model Practices, Goals,
Processes and Maturity Levels
Maturity Level
Process Contributes To
Achievement Of
Maturity Level
Evolution
To Greater
Maturity
Processes
Defined Goals Must Be
Achieved to Ensure
Fulfilment of Process
Goal(s)
Practices Contribute to
the Achievement of
Goals
Practice(s)
Sub-Practice(s)
October 23, 2013
Implement Practices
Implement Sub-Practices
16

17.
Achieving a Maturity Level
Improvement
Maturity Level
Maturity Level
Process
Process
Process
Goal
Goal
Goal
Practice
Practice
Practice
Sub-Practice
October 23, 2013
Maturity Level
Sub-Practice
Sub-Practice
17

18.
Maturity Levels
•
Maturity levels are intended to be a way of defining a
means of evolving improvements in processes associated
with what is being measured
October 23, 2013
18

19.
Means of Improving and Measuring Improvements
•
Staged or continuous
− Staged method uses the maturity levels of the overall model to
characterise the state of an organisation’s processes
• Spans multiple process areas
• Focuses on overall improvement
• Measured by maturity levels
− Continuous method focuses on capability levels to characterise
the state of an organisation’s processes for process areas
• Looks at individual process areas
• Focuses on achieving specific capabilities
• Measured by capability levels
October 23, 2013
19

20.
Staged and Continuous Improvements
Level
Continuous Improvement
Capability Levels
Staged Improvement
Maturity Levels
Level 0
Incomplete
Level 1
Performed
Initial
Level 2
Managed
Managed
Level 3
Defined
Defined
Level 4
Level 5
October 23, 2013
Quantitatively Managed
Optimising
20

21.
Continuous Improvement Capability Levels
Level
Capability Levels
Key Characteristics
Level 0
Incomplete
Level 1
Performed
Level 2
Managed
Not performed or only partially performed
Specific goals of the process area not being satisfied
Process not embedded in the organisation
Process achieves the required work
Specific goals of the process area are satisfied
Planned and implemented according to policy
Operation is monitored, controlled and reviewed
Evaluated for adherence to process documentation
Those performing the process have required training, skills, resources and
responsibilities to generate controlled deliverables
Level 3
Defined
October 23, 2013
Process consistency maintained through specific process descriptions and
procedures being customised from set of common standard processes using
customisation standards to suit given requirements
Defined and documented in detail – roles, responsibilities, measures, inputs,
outputs, entry and exit criteria
Implementation and operational feedback compiled in process repository
Proactive process measurement and management
Process interrelationships defined
21

22.
Achieving Capability Levels For Process Areas
Common
Standards
Exist That
Are
Customised
Ensuring
Consistency
Policies Exist
For
Processes
Processes
Are
Performed
Level 0
Process Are
Planned And
Monitored
Level 1
Level 3
Level 2
Defined
Managed
Performed
Incomplete
October 23, 2013
22

23.
Staged Improvement Maturity Levels
Level
Maturity
Levels
Level 1 Initial
Level 2 Managed
Level 3 Defined
Key Characteristics
Ad hoc, inconsistent, unstable, disorganised, not repeatable
Any success achieved through individual effort
Planned and managed
Sufficient resources assigned, training provided, responsibilities allocated
Limited performance evaluation and checking of adherence to standards
Standardised set of process descriptions and procedures used for creating individual processes
Defined and documented in detail – roles, responsibilities, measures, inputs, outputs, entry
and exit criteria
Proactive process measurement and management
Process interrelationships defined
Level 4 Quantitatively
Managed
Quantitative objectives defined for quality and process performance
Performance and quality defined and managed throughout the life of the process
Process-specific measures defined
Performance is controlled and predictable
Level 5 Optimising
Emphasis on continual improvement based on understanding of organisation business
objectives and performance needs
Performance objectives are continually updated to reflect changing business objectives and
organisational performance
Focus on overall organisational performance and defined feedback loop between
measurement and process change
October 23, 2013
23

24.
Achieving Maturity Levels
Processes Are
Controlled
and
Predictable
Common
Standards
Exist That Are
Customised
Ensuring
Consistency
Level 1
Level 4
Level 3
Level 2
Continual SelfImprovement
Level 5
Standard
Approach To
Measurement
Disciplined
Approach
To
Processes
Process Link
to Overall
Organisation
Objectives
Optimising
Quantitatively
Managed
Defined
Managed
Initial
October 23, 2013
24

25.
Staged Improvement Measurement and
Representation
Maturity Model
Seeks to Gauge Overall
Organisation Maturity Across All
Process Areas
Maturity Level 1
Process Area 1
Process 1
Maturity Level 2
Process Area 2
Process N
Process 1
Process Area N
Process N
Process N
Generic Goals
Process N
Specific Goals
Generic Practices
Specific Practices
Generic Practice 1
Generic Practice
N
Specific Practice 1
Sub-Practice 1.1
October 23, 2013
Maturity Level N
Sub-Practice 1.M
Specific Practice
N
Sub-Practice N.1
Sub-Practice N.M
25

26.
Maturity Model
•
Maturity
Model
Maturity
Level 1
Maturity
Level 2
Maturity
Level 3
Maturity
Level 4
Maturity
Level 5
Process 2.1
Process 3.1
Process 4.1
Process 5.1
Process 2.2
Process 3.2
Process 4.2
Process 5.2
Process 2.3
Process 3.3
Process 4.3
Process 2.4
October 23, 2013
To be at Maturity
Level N means
that all processes
in previous
maturity levels
have been
implemented
Process 4.4
26

27.
Achieving Maturity Levels
Level 5
Optimising
Level 4
Quantitatively
Managed
Level 3
Initial
Process
Process
October 23, 2013
Process
Process
+
+
Process
+
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Level 1
Process
Process
Level 2
Process
Process
Process
Defined
Managed
Process
27

28.
Achieving Maturity Levels
What Are The Real Benefits of Achieving a Higher
Maturity Level?
Level 5
What Is The Real Cost of Achieving a Higher Maturity
Level?
Level 4
What Is The Real Cost of Maintaining The Higher
Maturity Level?
Quantitatively
Managed
Level 3
Initial
Process
October 23, 2013
Process
+
+
Process
+
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Level 2
Managed
Process
Process
Defined
Level 1
Optimising
28

29.
Continuous Improvement Measurement and
Representation
Seeks to Gauge
The Condition Of
One Or More
Individual
Process Areas
Process Area 1
Process 1
Maturity Model
Maturity Level 1
Maturity Level 2
Process Area 2
Process N
Process 1
Process Area N
Process N
Process N
Generic Goals
Process N
Specific Goals
Generic Practices
Specific Practices
Generic Practice
1
October 23, 2013
Maturity Level N
Generic Practice
N
Specific Practice
1
Specific Practice
N
29

30.
Generalised Information Management Lifecycle
Architect, Budget, Plan,
Design and Specify
Implement Underlying
Technology
De
fi
ne
,D
esi
gn
, Im
Get This Right and Your
Information Management
Maturity is High
Enter, Create, Acquire,
Derive, Update,
Integrate, Capture
ple
Secure, Store, Replicate
Ad men
and Distribute
mi t, M
nis e
ter asu
, S re,
Present, Report,
tan M
Analyse, Model
da an
ag
rds
, G e, M
ov on
Preserve, Protect and
ern it
or,
Recover
an
ce Co
, F nt
un rol
d
,S
Archive and Recall
taf
f, T
rai
na
nd
Delete/Remove
October 23, 2013
30

31.
Generalised Information Management Lifecycle
General set of information-related skills required of the IT
function to ensure effective information management and
use
• Transcends specific technical and technology skills and
trends
•
− Technology change is a constant
Data management maturity is about having the
overarching skills to handle change, perform research,
adopt suitable and appropriate new technologies and
deliver a service and value to the underlying business
• There is no point in talking about Big Data when your
organisation is no good at managing little data
•
October 23, 2013
31

32.
Generalised Information Management Lifecycle
Architect, Budget, Plan,
Design and Specify
Implement Underlying
Technology
De
fi
ne
,D
esi
gn
, Im
Enter, Create, Acquire,
Derive, Update,
Integrate, Capture
What Processes Are Needed
To Implement Effectively
the Stages in the
Information Lifecycle?
ple
Secure, Store, Replicate
Ad men
and Distribute
mi t, M
nis e
ter asu
, S re,
Present, Report,
tan M
Analyse, Model
da an
ag
rds
, G e, M
ov on
Preserve, Protect and
ern it
or,
Recover
an
ce Co
, F nt
un rol
d
,S
Archive and Recall
taf
f, T
rai
na
nd
Delete/Remove
October 23, 2013
32

33.
Dimensions of Information Management Lifecycle
Information Type Dimension
Operational
Analytic
Master and
Data
Data
Reference Data
Unstructured
Data
Architect, Budget, Plan, Design and Specify
Implement Underlying Technology
Lifecycle Dimension
Enter, Create, Acquire, Derive, Update,
Integrate, Capture
Secure, Store, Replicate and Distribute
Present, Report, Analyse, Model
Preserve, Protect and Recover
Archive and Recall
Delete/Remove
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Train and Administer,
Standards, Governance, Fund
October 23, 2013
33

34.
Dimensions of Information Management Lifecycle
•
Information lifecycle management needs to span different
types of data that are used and managed differently and
have different requirements
− Operational Data – associated with operational/real-time
applications
− Master and Reference Data – maintaining system of record or
reference for enterprise master data used commonly across the
organisation
− Analytic Data – data warehouse/business intelligence/analysisoriented applications
− Unstructured Data – documents and similar information
October 23, 2013
34

35.
Linking Generalised Information Management
Lifecycle to Assessment of Information Maturity
•
How well do you implement information management?
•
Where are the gaps and weaknesses?
•
Where do you need to improve?
•
Where are your structures and policies sufficient for your
needs?
October 23, 2013
35

36.
Dimensions of Data Maturity Models
MIKE2.0 Information
Maturity Model (IMM)
IBM Data Governance
Council Maturity Model
DAMA DMBOK
Enterprise Data
Management Council
Data Management
Maturity Model
People/Organisation
Data Governance
Data Management Goals
Policy
Organisational Structures &
Awareness
Stewardship
Corporate Culture
Technology
Compliance
Policy
Value Creation
Measurement
Data Risk Management &
Compliance
Information Security &
Privacy
Data Architecture
Management
Data Development
Data Operations
Management
Data Security Management
Process/Practice
Data Architecture
Data Quality Management
Classification & Metadata
Information Lifecycle
Management
Audit Information, Logging &
Reporting
October 23, 2013
Governance Model
Data Management Funding
Data Requirements Lifecycle
Reference and Master Data
Management
Standards and Procedures
Data Warehousing and
Business Intelligence
Management
Document and Content
Management
Metadata Management
Data Quality Management
Data Sourcing
Architectural Framework
Platform and Integration
Data Quality Framework
Data Quality Assurance
36

37.
Data Maturity Models
•
All very different
•
All contain gaps – none is complete
•
None links to an information management lifecycle
October 23, 2013
37

38.
Mapping IBM Data Governance Council Maturity
Model to Information Lifecycle
Organisational Structures & Awareness
Architect, Budget, Plan, Design and Specify
Stewardship
Implement Underlying Technology
Policy
Enter, Create, Acquire, Derive, Update,
Integrate, Capture
Value Creation
Secure, Store, Replicate and Distribute
Data Risk Management & Compliance
Present, Report, Analyse, Model
Information Security & Privacy
Preserve, Protect and Recover
Data Architecture
Archive and Recall
Data Quality Management
Delete/Remove
Classification & Metadata
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Train and Administer,
Standards, Governance, Fund
Information Lifecycle Management
Audit Information, Logging & Reporting
October 23, 2013
38

39.
IBM Data Governance Council Maturity Model–
Capability Areas
Organisational Stewardship
Structures &
Awareness
Process
Maturity
Policy
Organisational Process
Awareness
Value Creation Data Risk
Information
Management & Security &
Compliance
Privacy
Data
Architecture
Assets
Business
Process
Maturity
Data
Integration
Accountability Roles &
& Responsibility Structures
Roles &
Metrics
Responsibilities
Resource
Commitment
Measurement
Standards &
Disciplines
Quality
Communication Value Creation
Processes
Metrics &
Reporting
Reporting
Responsibility
Regulations,
standards, and
policies
Accountability Data asset and
risk
classification
Risk
Management
Management buy-in
Framework
Incident
Ownership &
Response
responsibility
Certification
Training and
accountability
Policies &
Standards
Tools
Design
requirements
Process and
technology
Access Control
Identity
Requirements
Integration
Metrics
Risk Status
Characteristic
Organisations
Data Models &
Metadata
Management
Analytics
Data Quality
Management
Classification & Information
Metadata
Lifecycle
Management
Process
Maturity
Semantic
Capabilities
Content
Process
Maturity
Organisational Content
Awareness
Audit
Information,
Logging &
Reporting
Quality
Security
Technology &
Infrastructure
Business Value Organisational Reporting
Awareness
Consistency
(Format &
Semantics)
Business Value Ownership
(Roles &
Responsibilities)
Collection
Automation
Reporting
Automation
Evaluation &
Measurement
Remediation &
Reporting
October 23, 2013
39

40.
Mapping MIKE2.0 Information Maturity Model to
Information Lifecycle
People/Organisation
Architect, Budget, Plan, Design and Specify
Policy
Implement Underlying Technology
Technology
Enter, Create, Acquire, Derive, Update,
Integrate, Capture
Compliance
Secure, Store, Replicate and Distribute
Measurement
Present, Report, Analyse, Model
Process/Practice
Preserve, Protect and Recover
Archive and Recall
Delete/Remove
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Training and Administer
October 23, 2013
40

41.
MIKE2.0 Information Maturity Model – Capability
Areas
People/
Organisation
Policy
Technology
Compliance
Measurement
Process/Practice
Audits
Benchmarking
Common Data Model
Communication Plan
B2B Data Integration
Cleansing
Audits
Metadata Management
Audits
Benchmarking
Common Data Services
Data Integration (ETL &
EAI)
Data Ownership
Data Quality Metrics
Common Data Model
Data Quality Metrics
Data Quality Metrics
Dashboard (Tracking /
Trending)
Data Analysis
Common Data Services
Data Analysis
Data Analysis
Security
Profiling / Measurement Common Data Model
Metadata Management Communication Plan
Data Quality Strategy
Data Capture
Issue Identification
Cleansing
Data Capture
Data Standardisation
Service Level Agreements B2B Data Integration
Data Ownership
Executive Sponsorship
Data Integration (ETL &
EAI)
Data Quality Metrics
Data Quality Metrics
Issue Identification
Data Standardisation
Communication Plan
Dashboard (Tracking /
Trending)
Data Analysis
Data Subject Area
Coverage
Data Quality Strategy
Master Data ManagementData Stewardship
Data Standardisation
Platform Standardisation Data Validation
Data Validation
Privacy
Master Data Management
Executive Sponsorship
Profiling / Measurement Metadata Management
Master Data ManagementRoot Cause Analysis
Platform Standardisation
Privacy
Security
Profiling / Measurement
Security
Security
October 23, 2013
Cleansing
Dashboard (Tracking /
Trending)
Data Analysis
Data Capture
Data Integration (ETL &
EAI)
Data Ownership
Data Quality Metrics
Data Standardisation
Data Stewardship
Executive Sponsorship
Issue Identification
Master Data Management
Metadata Management
Privacy
Profiling / Measurement
41

42.
Mapping DAMA DMBOK to Information Lifecycle
Data Governance
Architect, Budget, Plan, Design and Specify
Data Architecture Management
Implement Underlying Technology
Data Development
Enter, Create, Acquire, Derive, Update,
Integrate, Capture
Data Operations Management
Secure, Store, Replicate and Distribute
Data Security Management
Present, Report, Analyse, Model
Reference and Master Data Management
Preserve, Protect and Recover
Data Warehousing and Business Intelligence
Management
Archive and Recall
Document and Content Management
Delete/Remove
Metadata Management
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Training and Administer
Data Quality Management
October 23, 2013
42

43.
DAMA DMBOK Maturity Model – Capability Areas
Data
Governance
Document
Metadata
Data Quality
Data
Data
Data
Data Security Reference and Data
Architecture Development Operations
Management Master Data Warehousing and Content Management Management
and Business Management
Management
Management
(RMD)
Management Intelligence
Data
Management
Planning
Data
Management
Control
Enterprise
Information
Needs
Enterprise Data
Model
Data Modeling,
Analysis, and
Solution Design
Detailed Data
Design
Align With Other
Business Models
Database
Architecture
Data Model and
Design Quality
Data
Implementation
Data Integration
Architecture
Database Support Data Security and
Regulatory
Requirements
Data Technology Data Security
Policy
Management
Reference and
Master Data
Integration
Master and
Reference Data
Data Security
Data Integration
Standards
Architecture
RMD
Data Security
Management
Controls and
Procedures
Users, Passwords, Match Rules
and Groups
Business
Intelligence
Information
DW / BI
Architecture
Data Warehouses
and Data Marts
BI Tools and User
Interfaces
Documents /
Records
Management
Content
Management
Metadata
Requirements
DQ Awareness
Metadata
Architecture
DQ Requirements
Metadata
Standards
Managed
Metadata
Environment
Create and
Maintain
Metadata
Integrate
Metadata
Profile, Analyse,
and Assess DQ
DQ Metrics
DQ Business
Rules
Enterprise
Taxonomies
Data Access
Views and
Permissions
User Access
Behaviour
Process Data for
Business
Intelligence
Tune Data
Establish
“Golden” Records Warehousing
Processes
Hierarchies and BI Activity and
Affiliations
Performance
Metadata
Repositories
DQ Service Levels
Metadata
Architecture
Information
Confidentiality
Integration of
New Data
Distribute
Metadata
Continuously
Measure DQ
Audit Data
Security
Replicate and
Distribute RMD
Query, Report,
and Analyse
Metadata
Manage DQ
Issues
DW / BI
Architecture
Changes to RMD
October 23, 2013
DQ Requirements
Data Quality
Defects
Operational DQM
Procedures
Monitor DQM
Procedures
43

44.
Mapping Enterprise Data Management Council Data
Management Maturity Model to Information Lifecycle
Data Management Goals
Architect, Budget, Plan, Design and Specify
Corporate Culture
Implement Underlying Technology
Governance Model
Enter, Create, Acquire, Derive, Update,
Integrate, Capture
Data Management Funding
Secure, Store, Replicate and Distribute
Data Requirements Lifecycle
Present, Report, Analyse, Model
Standards and Procedures
Preserve, Protect and Recover
Data Sourcing
Archive and Recall
Architectural Framework
Delete/Remove
Platform and Integration
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Training and Administer
Data Quality Framework
Data Quality Assurance
October 23, 2013
44

45.
EDM Council Maturity Model – Capability Areas
Data
Corporate
Management Culture
Goals
DM Objectives Alignment
Data
Standards and Data Sourcing
Requirements Procedures
Lifecycle
Standards
Sourcing
Governance
Data
Structure
Requirements Areas
Requirements
Definition
DM Priorities Communicatio Organisational Business Case Operational Standards
Procurement
n Strategy
Model
Impact
Promulgation & Provider
Management
Scope of DM
Oversight
Funding
Data Lifecycle Business
Program
Model
Management Process and
Data Flows
Governance
Data
Implementatio
Depenedencie
n
s Lifecycle
Human Capital
Ontology and
Requirements
Business
Semantics
Measurement
Data Change
Management
October 23, 2013
Governance
Model
Data
Management
Funding
Total Cost of
Ownership
Architectural Platform and Data Quality Data Quality
Framework Integration Framework Assurance
Architectural DM Platform Data Quality
Standards
Strategy
Development
Architectural Application
Data Quality
Approach
Integration
Measurement
and Analysis
Release
Management
Historical Data
Data Profiling
Data Quality
Assessment
Data Quality
for Integration
Data Cleansing
45

46.
Differences in Data Maturity Models
•
Substantial differences in data maturity models indicate
lack of consensus about what comprises information
management maturity
•
There is a need for a consistent approach, perhaps linked
to an information lifecycle to ground any assessment of
maturity in the actual processes needed to manage
information effectively
October 23, 2013
46

47.
More Information
Alan McSweeney
http://ie.linkedin.com/in/alanmcsweeney
October 23, 2013
47