Review of Data Management Maturity Models

Review of Data Management Maturity Models

Alan McSweeney

October 23, 2013 2

Objectives

• Review existing data management maturity models to identify coreset 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 beingmaintained?

• Are the costs justified?

• What is the real value of process maturity?

October 23, 2013 7

ISO/IEC 15504 – Original Maturity Model - Structure

Part 1Concepts and Introductory

Guide

Part 9Vocabulary

Part 6Guide to Qualification of

Assessors

Part 7Guide for Use in Process

Improvement

Part 8Guide for Determining

Supplier Process Capacity

Part 3Performing an Assessment

Part 4Guide to Performing

Assessments

Part 2A Reference Model for Processes and Process

Capability

Part 5An 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

ReferenceModel

Process Category Processes

Capability Levels Process Attributes

AssessmentIndicators

Indicators of Process Performance

Base Practices

Indicators of Process Capability

Management Practices

Work Practices and Characteristics

Attribute IndicatorsIndicators 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 CharacteristicsPractice

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 PracticesGeneric 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


Assign Overall Capability Status 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

DefinedProcesses

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

ManagedDisciplined 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






Specific Practice 1Specific Practice

NGeneric 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


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


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






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, M

anage, 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


• 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


Enter, Create, Acquire, Derive, Update,

Integrate, Capture



Archive and Recall

Delete/Remove

Define, Design, Implement, Measure, M

anage, Monitor, Control, Staff, Train and

Administer, Standards, Governance, Fund




What Processes Are Needed To Implement Effectively

the Stages in the Information Lifecycle?

October 23, 2013 33

Dimensions of Information Management Lifecycle


Enter, Create, Acquire, Derive, Update, Integrate, Capture



Archive and Recall

Delete/Remove



Define, Design, Implement, Measure, Manage, Monitor, Control, Staff, Train and Administer,

Standards, Governance, Fund

Operational Data

Analytic Data

Unstructured Data

Master and Reference Data

Lifecy

cle D

ime

nsio

n

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 ModelCompliance Value Creation Data Operations

ManagementData 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 IntegrationInformation 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





Archive and Recall

Delete/Remove



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





Archive and Recall

Delete/Remove



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





Archive and Recall

Delete/Remove




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





Archive and Recall

Delete/Remove




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 informationmanagement 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

Review of Data Management Maturity Models

Documents

Transcript of Review of Data Management Maturity Models