SlideShare a Scribd company logo
Review of Data Management
Maturity Models

Alan McSweeney
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
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
Basis for Maturity Models
•

Greater process maturity should mean greater business
benefit(s)
− Reduced cost
− Greater efficiency
− Reduced risk

October 23, 2013

4
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Data Maturity Models
•

All very different

•

All contain gaps – none is complete

•

None links to an information management lifecycle

October 23, 2013

37
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
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
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
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
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
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
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
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
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
More Information
Alan McSweeney
http://ie.linkedin.com/in/alanmcsweeney

October 23, 2013

47

More Related Content

What's hot

Enterprise Data Management Framework Overview
Enterprise Data Management Framework OverviewEnterprise Data Management Framework Overview
Enterprise Data Management Framework Overview
John Bao Vuu
 
Introduction to Data Management Maturity Models
Introduction to Data Management Maturity ModelsIntroduction to Data Management Maturity Models
Introduction to Data Management Maturity Models
Kingland
 
Overcoming the Challenges of your Master Data Management Journey
Overcoming the Challenges of your Master Data Management JourneyOvercoming the Challenges of your Master Data Management Journey
Overcoming the Challenges of your Master Data Management Journey
Jean-Michel Franco
 
A Comparative Study of Data Management Maturity Models
A Comparative Study of Data Management Maturity ModelsA Comparative Study of Data Management Maturity Models
A Comparative Study of Data Management Maturity Models
Data Crossroads
 
Activate Data Governance Using the Data Catalog
Activate Data Governance Using the Data CatalogActivate Data Governance Using the Data Catalog
Activate Data Governance Using the Data Catalog
DATAVERSITY
 
Capability Model_Data Governance
Capability Model_Data GovernanceCapability Model_Data Governance
Capability Model_Data Governance
Steve Novak
 
Strategy For Data Quality
Strategy For Data QualityStrategy For Data Quality
Strategy For Data Quality
Database Answers Ltd.
 
Most Common Data Governance Challenges in the Digital Economy
Most Common Data Governance Challenges in the Digital EconomyMost Common Data Governance Challenges in the Digital Economy
Most Common Data Governance Challenges in the Digital Economy
Robyn Bollhorst
 
CDMP preparation workshop EDW2016
CDMP preparation workshop EDW2016CDMP preparation workshop EDW2016
CDMP preparation workshop EDW2016
Christopher Bradley
 
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...
DATAVERSITY
 
Building a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsBuilding a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business Goals
DATAVERSITY
 
Building a Data Governance Strategy
Building a Data Governance StrategyBuilding a Data Governance Strategy
Building a Data Governance Strategy
Analytics8
 
DMBOK and Data Governance
DMBOK and Data GovernanceDMBOK and Data Governance
DMBOK and Data Governance
Peter Vennel PMP,SCEA,CBIP,CDMP
 
Data at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceData at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and Governance
DATAVERSITY
 
Introduction to Data Governance
Introduction to Data GovernanceIntroduction to Data Governance
Introduction to Data Governance
John Bao Vuu
 
How to Realize Benefits from Data Management Maturity Models
How to Realize Benefits from Data Management Maturity ModelsHow to Realize Benefits from Data Management Maturity Models
How to Realize Benefits from Data Management Maturity Models
Kingland
 
Building a Data Quality Program from Scratch
Building a Data Quality Program from ScratchBuilding a Data Quality Program from Scratch
Building a Data Quality Program from Scratch
dmurph4
 
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...Introduction to DCAM, the Data Management Capability Assessment Model - Editi...
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...
Element22
 
Data Modeling, Data Governance, & Data Quality
Data Modeling, Data Governance, & Data QualityData Modeling, Data Governance, & Data Quality
Data Modeling, Data Governance, & Data Quality
DATAVERSITY
 
Data Quality Best Practices
Data Quality Best PracticesData Quality Best Practices
Data Quality Best Practices
DATAVERSITY
 

What's hot (20)

Enterprise Data Management Framework Overview
Enterprise Data Management Framework OverviewEnterprise Data Management Framework Overview
Enterprise Data Management Framework Overview
 
Introduction to Data Management Maturity Models
Introduction to Data Management Maturity ModelsIntroduction to Data Management Maturity Models
Introduction to Data Management Maturity Models
 
Overcoming the Challenges of your Master Data Management Journey
Overcoming the Challenges of your Master Data Management JourneyOvercoming the Challenges of your Master Data Management Journey
Overcoming the Challenges of your Master Data Management Journey
 
A Comparative Study of Data Management Maturity Models
A Comparative Study of Data Management Maturity ModelsA Comparative Study of Data Management Maturity Models
A Comparative Study of Data Management Maturity Models
 
Activate Data Governance Using the Data Catalog
Activate Data Governance Using the Data CatalogActivate Data Governance Using the Data Catalog
Activate Data Governance Using the Data Catalog
 
Capability Model_Data Governance
Capability Model_Data GovernanceCapability Model_Data Governance
Capability Model_Data Governance
 
Strategy For Data Quality
Strategy For Data QualityStrategy For Data Quality
Strategy For Data Quality
 
Most Common Data Governance Challenges in the Digital Economy
Most Common Data Governance Challenges in the Digital EconomyMost Common Data Governance Challenges in the Digital Economy
Most Common Data Governance Challenges in the Digital Economy
 
CDMP preparation workshop EDW2016
CDMP preparation workshop EDW2016CDMP preparation workshop EDW2016
CDMP preparation workshop EDW2016
 
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...
 
Building a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsBuilding a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business Goals
 
Building a Data Governance Strategy
Building a Data Governance StrategyBuilding a Data Governance Strategy
Building a Data Governance Strategy
 
DMBOK and Data Governance
DMBOK and Data GovernanceDMBOK and Data Governance
DMBOK and Data Governance
 
Data at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceData at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and Governance
 
Introduction to Data Governance
Introduction to Data GovernanceIntroduction to Data Governance
Introduction to Data Governance
 
How to Realize Benefits from Data Management Maturity Models
How to Realize Benefits from Data Management Maturity ModelsHow to Realize Benefits from Data Management Maturity Models
How to Realize Benefits from Data Management Maturity Models
 
Building a Data Quality Program from Scratch
Building a Data Quality Program from ScratchBuilding a Data Quality Program from Scratch
Building a Data Quality Program from Scratch
 
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...Introduction to DCAM, the Data Management Capability Assessment Model - Editi...
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...
 
Data Modeling, Data Governance, & Data Quality
Data Modeling, Data Governance, & Data QualityData Modeling, Data Governance, & Data Quality
Data Modeling, Data Governance, & Data Quality
 
Data Quality Best Practices
Data Quality Best PracticesData Quality Best Practices
Data Quality Best Practices
 

Similar to Review of Data Management Maturity Models

PECB Webinar: Aligning ISO 25000 and CMMI for Development
PECB Webinar: Aligning ISO 25000 and CMMI for DevelopmentPECB Webinar: Aligning ISO 25000 and CMMI for Development
PECB Webinar: Aligning ISO 25000 and CMMI for Development
PECB
 
Focus your investments in innovations
Focus your investments in innovationsFocus your investments in innovations
Focus your investments in innovations
Kobi Vider
 
continuous improvement in school management (4) .pdf
continuous improvement in school management (4) .pdfcontinuous improvement in school management (4) .pdf
continuous improvement in school management (4) .pdf
lynnmdasuki1
 
Structured NERC CIP Process Improvement Using Six Sigma
Structured NERC CIP Process Improvement Using Six SigmaStructured NERC CIP Process Improvement Using Six Sigma
Structured NERC CIP Process Improvement Using Six Sigma
EnergySec
 
PMP-Scope Management area
PMP-Scope Management areaPMP-Scope Management area
PMP-Scope Management area
Zaur Ahmadov, PMP
 
Ch28
Ch28Ch28
Ch28
phanleson
 
9.process improvement chapter 9
9.process improvement chapter 99.process improvement chapter 9
9.process improvement chapter 9
Warui Maina
 
Product quality management
Product quality managementProduct quality management
Product quality management
Sunil Meena
 
Lean Transformation
Lean TransformationLean Transformation
Lean Transformation
nikatmalik
 
eUnit 2 software process model
eUnit 2  software process modeleUnit 2  software process model
eUnit 2 software process model
Preeti Mishra
 
chapter04-120827115356-phpapp01.pdf
chapter04-120827115356-phpapp01.pdfchapter04-120827115356-phpapp01.pdf
chapter04-120827115356-phpapp01.pdf
AxmedMaxamuud6
 
Chapter 4 Requirements ModelInformation Technology Project Management - part ...
Chapter 4 Requirements ModelInformation Technology Project Management - part ...Chapter 4 Requirements ModelInformation Technology Project Management - part ...
Chapter 4 Requirements ModelInformation Technology Project Management - part ...
AxmedMaxamuudYoonis
 
Introduction to CMMI-DEV v1.3 - Day 4
Introduction to CMMI-DEV v1.3  - Day 4Introduction to CMMI-DEV v1.3  - Day 4
Introduction to CMMI-DEV v1.3 - Day 4
Sherif Salah, MBA, ITIL, CMMI, MCSA, TQM
 
SAI Global Webinar: Tips for Effective Internal Auditing
SAI Global Webinar: Tips for Effective Internal AuditingSAI Global Webinar: Tips for Effective Internal Auditing
SAI Global Webinar: Tips for Effective Internal Auditing
Switzerland09
 
Risk elimination and safety committee
Risk elimination and safety committeeRisk elimination and safety committee
Risk elimination and safety committee
Hpm India
 
Software process maturity+ framework
Software process maturity+ frameworkSoftware process maturity+ framework
Software process maturity+ framework
Vishnuvarthanan Moorthy
 
Process improvement & service oriented software engineering
Process improvement & service oriented software engineeringProcess improvement & service oriented software engineering
Process improvement & service oriented software engineering
Sweta Kumari Barnwal
 
IT 8076 Software Testing Unit1
IT 8076 Software Testing Unit1IT 8076 Software Testing Unit1
IT 8076 Software Testing Unit1
Roselin Mary S
 
Capability Maturity Model Integartion
Capability Maturity Model IntegartionCapability Maturity Model Integartion
Capability Maturity Model Integartion
Saqib Raza
 
08 projectqualitymanagement
08 projectqualitymanagement08 projectqualitymanagement
08 projectqualitymanagement
Dhamo daran
 

Similar to Review of Data Management Maturity Models (20)

PECB Webinar: Aligning ISO 25000 and CMMI for Development
PECB Webinar: Aligning ISO 25000 and CMMI for DevelopmentPECB Webinar: Aligning ISO 25000 and CMMI for Development
PECB Webinar: Aligning ISO 25000 and CMMI for Development
 
Focus your investments in innovations
Focus your investments in innovationsFocus your investments in innovations
Focus your investments in innovations
 
continuous improvement in school management (4) .pdf
continuous improvement in school management (4) .pdfcontinuous improvement in school management (4) .pdf
continuous improvement in school management (4) .pdf
 
Structured NERC CIP Process Improvement Using Six Sigma
Structured NERC CIP Process Improvement Using Six SigmaStructured NERC CIP Process Improvement Using Six Sigma
Structured NERC CIP Process Improvement Using Six Sigma
 
PMP-Scope Management area
PMP-Scope Management areaPMP-Scope Management area
PMP-Scope Management area
 
Ch28
Ch28Ch28
Ch28
 
9.process improvement chapter 9
9.process improvement chapter 99.process improvement chapter 9
9.process improvement chapter 9
 
Product quality management
Product quality managementProduct quality management
Product quality management
 
Lean Transformation
Lean TransformationLean Transformation
Lean Transformation
 
eUnit 2 software process model
eUnit 2  software process modeleUnit 2  software process model
eUnit 2 software process model
 
chapter04-120827115356-phpapp01.pdf
chapter04-120827115356-phpapp01.pdfchapter04-120827115356-phpapp01.pdf
chapter04-120827115356-phpapp01.pdf
 
Chapter 4 Requirements ModelInformation Technology Project Management - part ...
Chapter 4 Requirements ModelInformation Technology Project Management - part ...Chapter 4 Requirements ModelInformation Technology Project Management - part ...
Chapter 4 Requirements ModelInformation Technology Project Management - part ...
 
Introduction to CMMI-DEV v1.3 - Day 4
Introduction to CMMI-DEV v1.3  - Day 4Introduction to CMMI-DEV v1.3  - Day 4
Introduction to CMMI-DEV v1.3 - Day 4
 
SAI Global Webinar: Tips for Effective Internal Auditing
SAI Global Webinar: Tips for Effective Internal AuditingSAI Global Webinar: Tips for Effective Internal Auditing
SAI Global Webinar: Tips for Effective Internal Auditing
 
Risk elimination and safety committee
Risk elimination and safety committeeRisk elimination and safety committee
Risk elimination and safety committee
 
Software process maturity+ framework
Software process maturity+ frameworkSoftware process maturity+ framework
Software process maturity+ framework
 
Process improvement & service oriented software engineering
Process improvement & service oriented software engineeringProcess improvement & service oriented software engineering
Process improvement & service oriented software engineering
 
IT 8076 Software Testing Unit1
IT 8076 Software Testing Unit1IT 8076 Software Testing Unit1
IT 8076 Software Testing Unit1
 
Capability Maturity Model Integartion
Capability Maturity Model IntegartionCapability Maturity Model Integartion
Capability Maturity Model Integartion
 
08 projectqualitymanagement
08 projectqualitymanagement08 projectqualitymanagement
08 projectqualitymanagement
 

More from Alan McSweeney

Data Architecture for Solutions.pdf
Data Architecture for Solutions.pdfData Architecture for Solutions.pdf
Data Architecture for Solutions.pdf
Alan McSweeney
 
Solution Architecture and Solution Estimation.pdf
Solution Architecture and Solution Estimation.pdfSolution Architecture and Solution Estimation.pdf
Solution Architecture and Solution Estimation.pdf
Alan McSweeney
 
Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...
Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...
Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...
Alan McSweeney
 
Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...
Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...
Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...
Alan McSweeney
 
IT Architecture’s Role In Solving Technical Debt.pdf
IT Architecture’s Role In Solving Technical Debt.pdfIT Architecture’s Role In Solving Technical Debt.pdf
IT Architecture’s Role In Solving Technical Debt.pdf
Alan McSweeney
 
Solution Architecture And Solution Security
Solution Architecture And Solution SecuritySolution Architecture And Solution Security
Solution Architecture And Solution Security
Alan McSweeney
 
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...
Alan McSweeney
 
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...
Alan McSweeney
 
Solution Security Architecture
Solution Security ArchitectureSolution Security Architecture
Solution Security Architecture
Alan McSweeney
 
Solution Architecture And (Robotic) Process Automation Solutions
Solution Architecture And (Robotic) Process Automation SolutionsSolution Architecture And (Robotic) Process Automation Solutions
Solution Architecture And (Robotic) Process Automation Solutions
Alan McSweeney
 
Data Profiling, Data Catalogs and Metadata Harmonisation
Data Profiling, Data Catalogs and Metadata HarmonisationData Profiling, Data Catalogs and Metadata Harmonisation
Data Profiling, Data Catalogs and Metadata Harmonisation
Alan McSweeney
 
Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...
Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...
Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...
Alan McSweeney
 
Analysis of Decentralised, Distributed Decision-Making For Optimising Domesti...
Analysis of Decentralised, Distributed Decision-Making For Optimising Domesti...Analysis of Decentralised, Distributed Decision-Making For Optimising Domesti...
Analysis of Decentralised, Distributed Decision-Making For Optimising Domesti...
Alan McSweeney
 
Operational Risk Management Data Validation Architecture
Operational Risk Management Data Validation ArchitectureOperational Risk Management Data Validation Architecture
Operational Risk Management Data Validation Architecture
Alan McSweeney
 
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...
Alan McSweeney
 
Ireland 2019 and 2020 Compared - Individual Charts
Ireland   2019 and 2020 Compared - Individual ChartsIreland   2019 and 2020 Compared - Individual Charts
Ireland 2019 and 2020 Compared - Individual Charts
Alan McSweeney
 
Analysis of Irish Mortality Using Public Data Sources 2014-2020
Analysis of Irish Mortality Using Public Data Sources 2014-2020Analysis of Irish Mortality Using Public Data Sources 2014-2020
Analysis of Irish Mortality Using Public Data Sources 2014-2020
Alan McSweeney
 
Ireland – 2019 And 2020 Compared In Data
Ireland – 2019 And 2020 Compared In DataIreland – 2019 And 2020 Compared In Data
Ireland – 2019 And 2020 Compared In Data
Alan McSweeney
 
Review of Information Technology Function Critical Capability Models
Review of Information Technology Function Critical Capability ModelsReview of Information Technology Function Critical Capability Models
Review of Information Technology Function Critical Capability Models
Alan McSweeney
 
Critical Review of Open Group IT4IT Reference Architecture
Critical Review of Open Group IT4IT Reference ArchitectureCritical Review of Open Group IT4IT Reference Architecture
Critical Review of Open Group IT4IT Reference Architecture
Alan McSweeney
 

More from Alan McSweeney (20)

Data Architecture for Solutions.pdf
Data Architecture for Solutions.pdfData Architecture for Solutions.pdf
Data Architecture for Solutions.pdf
 
Solution Architecture and Solution Estimation.pdf
Solution Architecture and Solution Estimation.pdfSolution Architecture and Solution Estimation.pdf
Solution Architecture and Solution Estimation.pdf
 
Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...
Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...
Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...
 
Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...
Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...
Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...
 
IT Architecture’s Role In Solving Technical Debt.pdf
IT Architecture’s Role In Solving Technical Debt.pdfIT Architecture’s Role In Solving Technical Debt.pdf
IT Architecture’s Role In Solving Technical Debt.pdf
 
Solution Architecture And Solution Security
Solution Architecture And Solution SecuritySolution Architecture And Solution Security
Solution Architecture And Solution Security
 
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...
 
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...
 
Solution Security Architecture
Solution Security ArchitectureSolution Security Architecture
Solution Security Architecture
 
Solution Architecture And (Robotic) Process Automation Solutions
Solution Architecture And (Robotic) Process Automation SolutionsSolution Architecture And (Robotic) Process Automation Solutions
Solution Architecture And (Robotic) Process Automation Solutions
 
Data Profiling, Data Catalogs and Metadata Harmonisation
Data Profiling, Data Catalogs and Metadata HarmonisationData Profiling, Data Catalogs and Metadata Harmonisation
Data Profiling, Data Catalogs and Metadata Harmonisation
 
Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...
Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...
Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...
 
Analysis of Decentralised, Distributed Decision-Making For Optimising Domesti...
Analysis of Decentralised, Distributed Decision-Making For Optimising Domesti...Analysis of Decentralised, Distributed Decision-Making For Optimising Domesti...
Analysis of Decentralised, Distributed Decision-Making For Optimising Domesti...
 
Operational Risk Management Data Validation Architecture
Operational Risk Management Data Validation ArchitectureOperational Risk Management Data Validation Architecture
Operational Risk Management Data Validation Architecture
 
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...
 
Ireland 2019 and 2020 Compared - Individual Charts
Ireland   2019 and 2020 Compared - Individual ChartsIreland   2019 and 2020 Compared - Individual Charts
Ireland 2019 and 2020 Compared - Individual Charts
 
Analysis of Irish Mortality Using Public Data Sources 2014-2020
Analysis of Irish Mortality Using Public Data Sources 2014-2020Analysis of Irish Mortality Using Public Data Sources 2014-2020
Analysis of Irish Mortality Using Public Data Sources 2014-2020
 
Ireland – 2019 And 2020 Compared In Data
Ireland – 2019 And 2020 Compared In DataIreland – 2019 And 2020 Compared In Data
Ireland – 2019 And 2020 Compared In Data
 
Review of Information Technology Function Critical Capability Models
Review of Information Technology Function Critical Capability ModelsReview of Information Technology Function Critical Capability Models
Review of Information Technology Function Critical Capability Models
 
Critical Review of Open Group IT4IT Reference Architecture
Critical Review of Open Group IT4IT Reference ArchitectureCritical Review of Open Group IT4IT Reference Architecture
Critical Review of Open Group IT4IT Reference Architecture
 

Recently uploaded

Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and BioinformaticiansBiomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Neo4j
 
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and MilvusGenerating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Zilliz
 
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyFreshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
ScyllaDB
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
ssuserfac0301
 
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Tosin Akinosho
 
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Edge AI and Vision Alliance
 
Dandelion Hashtable: beyond billion requests per second on a commodity server
Dandelion Hashtable: beyond billion requests per second on a commodity serverDandelion Hashtable: beyond billion requests per second on a commodity server
Dandelion Hashtable: beyond billion requests per second on a commodity server
Antonios Katsarakis
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
Jason Yip
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Safe Software
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
panagenda
 
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframeDigital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Precisely
 
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and MilvusBuilding Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Zilliz
 
5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides
DanBrown980551
 
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERPBest 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Pixlogix Infotech
 
Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
Zilliz
 
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Tomaz Bratanic
 
"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota
Fwdays
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
DianaGray10
 
Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)
Jakub Marek
 
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
saastr
 

Recently uploaded (20)

Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and BioinformaticiansBiomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
 
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and MilvusGenerating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
 
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyFreshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
 
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
 
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
 
Dandelion Hashtable: beyond billion requests per second on a commodity server
Dandelion Hashtable: beyond billion requests per second on a commodity serverDandelion Hashtable: beyond billion requests per second on a commodity server
Dandelion Hashtable: beyond billion requests per second on a commodity server
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
 
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframeDigital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
 
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and MilvusBuilding Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
 
5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides
 
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERPBest 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
 
Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
 
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
 
"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
 
Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)
 
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
 

Review of Data Management Maturity Models

  • 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