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Review of Data Management
Maturity Models
Alan McSweeney
October 23, 2013 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 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 4
Basis for Maturity Models
•
Greater process maturity should mean greater business
benefit(s)
−Reduced cost
−Greater efficiency
−Reduced risk
October 23, 2013 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 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 7
ISO/IEC 15504 – Original Maturity Model - Structure
Part 1
Concepts and Introductory
Guide
Part 9
Vocabulary
Part 6
Guide to Qualification of
Assessors
Part 7
Guide for Use in Process
Improvement
Part 8
Guide for Determining
Supplier Process Capacity
Part 3
Performing an Assessment
Part 4
Guide to Performing
Assessments
Part 2
A Reference Model for
Processes and Process
Capability
Part 5
An Assessment Model and
Indicator Guidance
October 23, 2013 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 9
ISO/IEC 15504 - Relationship Between Reference
Model and Assessment Model
Process Dimension Capability Dimension
Reference
Model
Process Category
Processes
Capability Levels
Process Attributes
Assessment
Indicators
Indicators of Process
Performance
Base Practices
Indicators of Process
Capability
Management Practices
Work Practices and
Characteristics Attribute Indicators
Indicators of
Practice
Performance
October 23, 2013 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 11
ISO/IEC 15504 - Indicator and Process Attribute
Relationships
Process Attribute Ratings
Evidence of Process Performance Evidence of Process Capability
Indicators of Process Performance Indicators of Process Capability
Best Practices Management Practices
Work Product Characteristics
Practice
Performance
Characteristics
Resources and
Infrastructure
Characteristics
Based On
Provided By Provided By
Consist Of Consist Of
Assessed By Assessed By
October 23, 2013 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 13
Structure of Maturity Model
Maturity Model
Maturity Level 1 Maturity Level 2 Maturity Level N
Process Area 1 Process Area 2 Process Area N
Process 1 Process N Process N Process NProcess 1 Process N
Generic Goals Specific Goals
Specific Practices
Generic Practices
Specific Practice 1 Specific Practice NGeneric Practice 1 Generic Practice N
Sub-Practice 1.1 Sub-Practice N.1
Sub-Practice N.MSub-Practice 1.M
October 23, 2013 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 15
Approach to Improving Maturity Using Maturity
Models
Goal(s)
Practice(s)
Processes
Sub-Practice(s)
Achieve Process
Competency
Implement Practices
Implement Sub-Practices
Implement Goals
•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
Assess Current Status and
Assign Score
Assess Current Status and
Assign Score
Assign Overall Capability
Status Score
Assess Current Status and
Assign Score
October 23, 2013 16
Hierarchy of Maturity Model Practices, Goals,
Processes and Maturity Levels
Goal(s)
Processes
Practice(s)
Maturity Level
Process Contributes To
Achievement Of
Maturity Level
Defined Goals Must Be
Achieved to Ensure
Fulfilment of Process
Practices Contribute to
the Achievement of
Goals
Implement Practices
Evolution
To Greater
Maturity
Sub-Practice(s) Implement Sub-Practices
October 23, 2013 17
Achieving a Maturity Level
Goal
Process
Practice
Maturity Level
Goal
Process
Practice
Maturity Level
Goal
Process
Practice
Maturity Level
Improvement
Sub-Practice Sub-Practice Sub-Practice
October 23, 2013 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 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 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 Quantitatively Managed
Level 5 Optimising
October 23, 2013 21
Continuous Improvement Capability Levels
Level Capability Levels Key Characteristics
Level 0 Incomplete
Not performed or only partially performed
Specific goals of the process area not being satisfied
Process not embedded in the organisation
Level 1 Performed
Process achieves the required work
Specific goals of the process area are satisfied
Level 2 Managed
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
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
October 23, 2013 22
Achieving Capability Levels For Process Areas
Level 0
Incomplete
Level 1
Performed
Level 2
Managed
Level 3
Defined
Processes
Are
Performed
Policies Exist
For
Processes
Process Are
Planned And
Monitored
Common
Standards
Exist That
Are
Customised
Ensuring
Consistency
October 23, 2013 23
Staged Improvement Maturity Levels
Level Maturity
Levels
Key Characteristics
Level 1 Initial Ad hoc, inconsistent, unstable, disorganised, not repeatable
Any success achieved through individual effort
Level 2 Managed Planned and managed
Sufficient resources assigned, training provided, responsibilities allocated
Limited performance evaluation and checking of adherence to standards
Level 3 Defined 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 24
Achieving Maturity Levels
Level 1
Initial
Level 2
Managed
Level 3
Defined
Level 4
Quantitat-
ively
Managed
Disciplined
Approach
To
Processes
Processes Are
Controlled
and
Predictable
Common
Standards
Exist That Are
Customised
Ensuring
Consistency
Standard
Approach To
Measurement
Level 5
Optimising
Process Link
to Overall
Organisation
Objectives
Continual Self-
Improvement
October 23, 2013 25
Staged Improvement Measurement and
Representation
Maturity Model
Maturity Level 1 Maturity Level 2 Maturity Level N
Process Area 1 Process Area 2 Process Area N
Process 1 Process N Process N Process NProcess 1 Process N
Generic Goals Specific Goals
Specific Practices
Generic Practices
Specific Practice 1 Specific Practice
N
Generic Practice 1 Generic Practice
N
Sub-Practice 1.1 Sub-Practice 1.M Sub-Practice N.1 Sub-Practice N.M
Seeks to Gauge Overall
Organisation Maturity Across All
Process Areas
October 23, 2013 26
Maturity Model
•
To be at Maturity
Level N means
that all processes
in previous
maturity levels
have been
implemented
Maturity
Model
Maturity
Level 1
Maturity
Level 2
Maturity
Level 3
Maturity
Level 4
Maturity
Level 5
Process 2.1
Process 2.2
Process 3.1
Process 3.2
Process 3.3Process 2.3
Process 4.1
Process 4.2
Process 4.3
Process 4.4Process 2.4
Process 5.1
Process 5.2
October 23, 2013 27
Achieving Maturity Levels
Level 1
Initial
Level 2
Managed
Level 3
Defined
Level 4
Quantitat-
ively
Managed
Level 5
Optimising
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process Process
Process Process
+++
Process Process
Process
Process
October 23, 2013 28
Achieving Maturity Levels
Level 1
Initial
Level 2
Managed
Level 3
Defined
Level 4
Quantitat-
ively
Managed
Level 5
Optimising
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process
Process Process
Process Process
+++
Process Process
Process
Process
What Are The Real Benefits of Achieving a Higher
Maturity Level?
What Is The Real Cost of Achieving a Higher Maturity
Level?
What Is The Real Cost of Maintaining The Higher
Maturity Level?
October 23, 2013 29
Continuous Improvement Measurement and
Representation
Maturity Model
Maturity Level 1 Maturity Level 2 Maturity Level N
Process Area 1 Process Area 2 Process Area N
Process 1 Process N Process N Process NProcess 1 Process N
Generic Goals Specific Goals
Specific Practices
Generic Practices
Specific Practice
1
Specific Practice
N
Generic Practice
1
Generic Practice
N
Seeks to Gauge
The Condition Of
One Or More
Individual
Process Areas
October 23, 2013 30
Generalised Information Management Lifecycle
Enter, Create, Acquire,
Derive, Update,
Integrate, Capture
Secure, Store, Replicate
and Distribute
Preserve, Protect and
Recover
Archive and Recall
Delete/Remove
Define, Design, Implement, Measure, Manage, Monitor, Control, Staff, Train and
Administer, Standards, Governance, Fund
Implement Underlying
Technology
Architect, Budget, Plan,
Design and Specify
Present, Report,
Analyse, Model
Get This Right and Your
Information Management
Maturity is High
October 23, 2013 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 32
Generalised Information Management Lifecycle
Enter, Create, Acquire,
Derive, Update,
Integrate, Capture
Secure, Store, Replicate
and Distribute
Preserve, Protect and
Recover
Archive and Recall
Delete/Remove
Define, Design, Implement, Measure, Manage, Monitor, Control, Staff, Train and
Administer, Standards, Governance, Fund
Implement Underlying
Technology
Architect, Budget, Plan,
Design and Specify
Present, Report,
Analyse, Model
What Processes Are Needed
To Implement Effectively
the Stages in the
Information Lifecycle?
October 23, 2013 33
Dimensions of Information Management Lifecycle
Architect, Budget, Plan, Design and Specify
Enter, Create, Acquire, Derive, Update,
Integrate, Capture
Secure, Store, Replicate and Distribute
Preserve, Protect and Recover
Archive and Recall
Delete/Remove
Implement Underlying Technology
Present, Report, Analyse, Model
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Train and Administer,
Standards, Governance, Fund
Operational
Data
Analytic
Data
Unstructured
Data
Master and
Reference Data
Lifecycle Dimension
Information Type Dimension
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/analysis-
oriented applications
−Unstructured Data – documents and similar information
October 23, 2013 34
October 23, 2013 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 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 Organisational Structures &
Awareness
Data Governance Data Management Goals
Policy Stewardship Data Architecture
Management
Corporate Culture
Technology Policy Data Development Governance Model
Compliance Value Creation Data Operations
Management
Data Management Funding
Measurement Data Risk Management &
Compliance
Data Security Management Data Requirements Lifecycle
Process/Practice Information Security &
Privacy
Reference and Master Data
Management
Standards and Procedures
Data Architecture Data Warehousing and
Business Intelligence
Management
Data Sourcing
Data Quality Management Document and Content
Management
Architectural Framework
Classification & Metadata Metadata Management Platform and Integration
Information Lifecycle
Management
Data Quality Management Data Quality Framework
Audit Information, Logging &
Reporting
Data Quality Assurance
October 23, 2013 37
Data Maturity Models
•
All very different
•
All contain gaps – none is complete
•
None links to an information management lifecycle
October 23, 2013 38
Mapping IBM Data Governance Council Maturity
Model to Information Lifecycle
Architect, Budget, Plan, Design and Specify
Enter, Create, Acquire, Derive, Update,
Integrate, Capture
Secure, Store, Replicate and Distribute
Preserve, Protect and Recover
Archive and Recall
Delete/Remove
Implement Underlying Technology
Present, Report, Analyse, Model
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Train and Administer,
Standards, Governance, Fund
Organisational Structures & Awareness
Policy
Value Creation
Information Security & Privacy
Data Architecture
Data Quality Management
Stewardship
Data Risk Management & Compliance
Classification & Metadata
Information Lifecycle Management
Audit Information, Logging & Reporting
October 23, 2013 39
IBM Data Governance Council Maturity Model–
Capability Areas
Organisational
Structures &
Awareness
Stewardship Policy Value Creation Data Risk
Management &
Compliance
Information
Security &
Privacy
Data
Architecture
Data Quality
Management
Classification &
Metadata
Information
Lifecycle
Management
Audit
Information,
Logging &
Reporting
Process
Maturity
Organisational
Awareness
Process Assets Responsibility Regulations,
standards, and
policies
Business
Process
Maturity
Process
Maturity
Semantic
Capabilities
Quality
Accountability
& Responsibility
Roles &
Structures
Roles &
Responsibilities
Metrics Accountability Data asset and
risk
classification
Data
Integration
Content Process
Maturity
Security
Resource
Commitment
Standards &
Disciplines
Measurement Quality Risk
Management
Framework
Management
buy-in
Data Models &
Metadata
Management
Organisational
Awareness
Content Technology &
Infrastructure
Communication Value Creation Processes Incident
Response
Ownership &
responsibility
Analytics Business Value Organisational
Awareness
Reporting
Consistency
(Format &
Semantics)
Metrics &
Reporting
Reporting Certification Training and
accountability
Business Value Ownership
(Roles &
Responsibilities)
Policies &
Standards
Design
requirements
Collection
Automation
Tools Process and
technology
Reporting
Automation
Metrics Access Control
Risk Status Identity
Requirements
Characteristic
Organisations
Integration
Evaluation &
Measurement
Remediation &
Reporting
October 23, 2013 40
Mapping MIKE2.0 Information Maturity Model to
Information Lifecycle
Architect, Budget, Plan, Design and Specify
Enter, Create, Acquire, Derive, Update,
Integrate, Capture
Secure, Store, Replicate and Distribute
Preserve, Protect and Recover
Archive and Recall
Delete/Remove
Implement Underlying Technology
Present, Report, Analyse, Model
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Training and Administer
People/Organisation
Technology
Compliance
Process/Practice
Policy
Measurement
October 23, 2013 41
MIKE2.0 Information Maturity Model – Capability
Areas
People/
Organisation
Policy Technology Compliance Measurement Process/Practice
Audits Common Data Model B2B Data Integration Audits Data Quality Metrics Audits
Benchmarking Communication Plan Cleansing Metadata Management Dashboard (Tracking /
Trending)
Benchmarking
Common Data Services Data Integration (ETL &
EAI)
Common Data Model Data Quality Metrics Data Analysis Cleansing
Communication Plan Data Ownership Common Data Services Data Analysis Profiling / Measurement Common Data Model
Dashboard (Tracking /
Trending)
Data Quality Metrics Data Analysis Security Metadata Management Communication Plan
Data Analysis Data Quality Strategy Data Capture Issue Identification Cleansing Dashboard (Tracking /
Trending)
Data Capture Data Standardisation Data Integration (ETL &
EAI)
Service Level Agreements B2B Data Integration Data Analysis
Data Ownership Executive Sponsorship Data Quality Metrics Data Subject Area
Coverage
Data Capture
Data Quality Metrics Issue Identification Data Standardisation Data Integration (ETL &
EAI)
Data Quality Strategy Master Data ManagementData Stewardship Data Ownership
Data Standardisation Platform Standardisation Data Validation Data Quality Metrics
Data Validation Privacy Master Data Management Data Standardisation
Executive Sponsorship Profiling / Measurement Metadata Management Data Stewardship
Master Data ManagementRoot Cause Analysis Platform Standardisation Executive Sponsorship
Privacy Security Profiling / Measurement Issue Identification
Security Security Master Data Management
Metadata Management
Privacy
Profiling / Measurement
October 23, 2013 42
Mapping DAMA DMBOK to Information Lifecycle
Architect, Budget, Plan, Design and Specify
Enter, Create, Acquire, Derive, Update,
Integrate, Capture
Secure, Store, Replicate and Distribute
Preserve, Protect and Recover
Archive and Recall
Delete/Remove
Implement Underlying Technology
Present, Report, Analyse, Model
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Training and Administer
Data Governance
Data Development
Data Operations Management
Reference and Master Data Management
Data Warehousing and Business Intelligence
Management
Document and Content Management
Data Architecture Management
Data Security Management
Metadata Management
Data Quality Management
October 23, 2013 43
DAMA DMBOK Maturity Model – Capability Areas
Data
Governance
Data
Architecture
Management
Data
Development
Data
Operations
Management
Data Security
Management
Reference and
Master Data
(RMD)
Management
Data
Warehousing
and Business
Intelligence
Document
and Content
Management
Metadata
Management
Data Quality
Management
Data
Management
Planning
Enterprise
Information
Needs
Data Modeling,
Analysis, and
Solution Design
Database Support Data Security and
Regulatory
Requirements
Reference and
Master Data
Integration
Business
Intelligence
Information
Documents /
Records
Management
Metadata
Requirements
DQ Awareness
Data
Management
Control
Enterprise Data
Model
Detailed Data
Design
Data Technology
Management
Data Security
Policy
Master and
Reference Data
DW / BI
Architecture
Content
Management
Metadata
Architecture
DQ Requirements
Align With Other
Business Models
Data Model and
Design Quality
Data Security
Standards
Data Integration
Architecture
Data Warehouses
and Data Marts
Metadata
Standards
Profile, Analyse,
and Assess DQ
Database
Architecture
Data
Implementation
Data Security
Controls and
Procedures
RMD
Management
BI Tools and User
Interfaces
Managed
Metadata
Environment
DQ Metrics
Data Integration
Architecture
Users, Passwords,
and Groups
Match Rules Process Data for
Business
Intelligence
Create and
Maintain
Metadata
DQ Business
Rules
DW / BI
Architecture
Data Access
Views and
Permissions
Establish
“Golden” Records
Tune Data
Warehousing
Processes
Integrate
Metadata
DQ Requirements
Enterprise
Taxonomies
User Access
Behaviour
Hierarchies and
Affiliations
BI Activity and
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
Changes to RMD Data Quality
Defects
Operational DQM
Procedures
Monitor DQM
Procedures
October 23, 2013 44
Mapping Enterprise Data Management Council Data
Management Maturity Model to Information Lifecycle
Architect, Budget, Plan, Design and Specify
Enter, Create, Acquire, Derive, Update,
Integrate, Capture
Secure, Store, Replicate and Distribute
Preserve, Protect and Recover
Archive and Recall
Delete/Remove
Implement Underlying Technology
Present, Report, Analyse, Model
Define, Design, Implement, Measure, Manage,
Monitor, Control, Staff, Training and Administer
Data Management Goals
Governance Model
Data Management Funding
Standards and Procedures
Data Sourcing
Architectural Framework
Corporate Culture
Data Requirements Lifecycle
Platform and Integration
Data Quality Framework
Data Quality Assurance
October 23, 2013 45
EDM Council Maturity Model – Capability Areas
Data
Management
Goals
Corporate
Culture
Governance
Model
Data
Management
Funding
Data
Requirements
Lifecycle
Standards and
Procedures
Data Sourcing Architectural
Framework
Platform and
Integration
Data Quality
Framework
Data Quality
Assurance
DM Objectives Alignment Governance
Structure
Total Cost of
Ownership
Data
Requirements
Definition
Standards
Areas
Sourcing
Requirements
Architectural
Standards
DM Platform Data Quality
Strategy
Development
Data Profiling
DM Priorities Communicatio
n Strategy
Organisational
Model
Business Case Operational
Impact
Standards
Promulgation
Procurement
& Provider
Management
Architectural
Approach
Application
Integration
Data Quality
Measurement
and Analysis
Data Quality
Assessment
Scope of DM
Program
Oversight Funding
Model
Data Lifecycle
Management
Business
Process and
Data Flows
Release
Management
Data Quality
for Integration
Governance
Implementatio
n
Data
Depenedencie
s Lifecycle
Historical Data Data Cleansing
Human Capital
Requirements
Ontology and
Business
Semantics
Measurement Data Change
Management
October 23, 2013 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 47
More Information
Alan McSweeney
http://ie.linkedin.com/in/alanmcsweeney
