Mike2.0 Information Governance Overview

Information Governance Solution Offering
Overview

Introducing MIKE2.0 (Method for Integrated Knowledge Environment)

Sean McClowry
IM Solution Suite Architecture and Delivery Lead
BearingPoint
November 2007

Contents

This presentation covers the following
Information Management
─ Our View
─ Why Governance is Important

Information Governance
─ Where it fits in the Overall Model
─ Guiding Principles
─ Key Activities

Getting Started: Information Maturity (IM) QuickScan
Information Governance Organisational Models
Advanced Techniques: Networked Information Governance

2
MIKE2.0 Methodology A Methodology for Information Development

Our View: IM is a “Complexity Problem”

Exponential growth of raw data and
information
Complexity of data and information is
not appreciated; they are in constant
flux across the enterprise 24 hours a day
Efforts to increase visibility and access to
relevant data and information are
expensive, with insufficient ROI
Better standards and transparency are
needed to increase confidence and
enable opportunities
Federation is a significant factor in
complexity
True business insight still very hard to
attain and quality is a huge problem

3

Our View: The Solution Demands a Standard

Processes and standards for managing
and reporting data and information have
not kept pace – everyone has “their”
way
Many problems are solved through
informal networks – we need to link
formal structures to these networks
We want organisations to begin to
develop a competency for “Information
Development”
We aim to re-shape the industry by
creating the standard
An open and collaborative approach
is the key to delivering a standard for
such a complex problem

4

Our View: Time to Act

The problem has been growing for years. Here is why IM is now a
Mainstream Issue:
High Impact: What is a business without its customers, its products and
its employees?
Federation: Organisations are becoming increasingly federated and even
minor issues with data cause viral problems when propagated across the
enterprise.
Globalisation: multi-lingual and multi-character set issues, 24x7 data
availability, support for multi-channels
Compliance initiatives: the War on Terror and corporate scandals in the
US have put additional pressures on the enterprises.
It’s a big, complex problem: There is much to be gained for vendors
of applications, information/integration technology and systems
integrators.
Information is an Asset: Organisations increasingly see the importance
of Information Development. Its not just functions and infrastructure.

5

Our View: What is Information Governance?

What is Information Governance?
“Governancequot; is what information management is mostly all about. Information
management is the process by which those who set policy guide those who
follow policy. Governance concerns power, and applying an understanding of
the distribution and sharing of power to the management of information
technologies” [i]

What is the right way to apply it?
Governance can involve “centralised” power, but traditional push-down
models of architecture and standards only provide part of the solution.
Implemented the wrong way, governance can hamper innovation and
agility.
Some standards are needed or we cannot be agile or innovative – we’re
always fighting fires.
With a foundation of standards, we can distribute power and empower a
community to be far more productive.

[1] Strausmann, Paul A. Information, Information Management and Governance. (2001)

6

Our Approach: Integrated Solutions

Info Mgmt

Business Info Asset Access, Search Enterprise Data Enterprise Info Architecture,
Intelligence Management and Delivery Management Content Mgmt Strategy & Gov

Corp Performance Information Lifecycle Enterprise Portals Document
Data Warehousing Information Governance
Management Management & Info Delivery Management

Metric & Dashboard Master Records, Contracts, Service Oriented, EII &
Information Security Enterprise Search
Design Data Mgmt and IP Management Model Driven Architecture

Profitability, Value & Metadata, Taxonomy Customer Data ERP Document Mgmt Enterprise Data
Mobile Device Access
Pricing Mgmt Cataloging Integration Integration Management Strategy

Real Time Workflow Information Data Quality Enterprise Content
Digital Asset
Customer Decisioning Management Improvement Management Strategy
Management

Operational Access Monitoring Content Management- Enterprise Information
Data Migration
Performance Mgmt & Control Web Content Assessment

Balance Business Data Center
Collaboration Information Mgmt COE
Scorecard Management
Environments, COI Organisation and
Knowledge Capture Shared Service Model
HR Performance Mgmt Information System
Rewards Usability

Data Mining, Analytics
Modeling & Simulation

Business Activity
Monitoring

Composite Solution Offerings
Info Mgmt Data Driven Information Networked Info Agile Info
Enterprise 2.0
Strategy IT Transformation Sharing Governance Development

7

Our Approach: An Open Source Methodology

MIKE2.0 (Method for an Integrated Knowledge Environment)
MIKE2.0 (Method for an Integrated Knowledge Environment)
Information Management Framework
A comprehensive approach to Enterprise
Information Management
Much more than a classic methodology:
architecture, tools, code
Helping to shape new theories on Information
Management
Core methodology with formal release cycle
Governance council
Framework for any open method

Web / Enterprise 2.0
Developed as part of an open community
Can be integrated to internally held and shared
content
The goal is to develop “the standard” that
everyone can map to and help create

Open Source (software and content):
All content is freely available under the Create
Commons (Attribution) License
MediaWiki based
Have extended MediaWiki and contributed to the
community
Providing an organizing framework for
www.openmethodology.org development of open source IM technologies

8

Our Approach: Open Source + Internal Assets
Enterprise 2.0 Mashups
Open Methodology site

Assessment Tools
Integrated Approach

9

Our Approach: Collaborative Solutions

Information Management Solution Suite

Delivered through a Collaborative Approach

Enterprise Information Management

Commercial & Open Source
Core Solution Offerings by Solution Capabilities
Business Solutions

Product Solutions
Information Access, Search and
Business Intelligence
Asset Management Content Delivery

Enterprise Data Management Enterprise Content Management

Information Strategy, Architecture and Governance

Sets the new standard for Information Development through an Open Source Offering

10

Our Approach: Supported through a Foundation

Information Management Solution Suite

Delivered through a Collaborative Approach

Enterprise Information Management

Commercial & Open Source
Solution Capabilities that provide a foundation for Suite Delivery
Architecture Framework
Business Solutions

Governance Framework

Product Solutions
Overall Information Access, Search and
Business Intelligence Usage Model
ImplementationAsset Management
Guide Content Delivery

Enterprise Data Management Content Management
Enterprise
Foundational Solutions

Information Strategy, Architecture and Governance
Supporting Assets

Sets the new standard for Information Development through an Open Source Offering

11

Information Governance: Guiding Principles

Build an Information Centric Organisation
1. Accountability. Due the nature of information capture and how it flows
across the enterprise, everyone has a role to play in how it is governed.
Key roles are filled by senior executives such as the CIO, Information
Architects and Data and Content Stewards.

2. Efficient Operating Models. Common standards, methods,
architecture and collaborative techniques allow the Governance model to
be implemented in a physically central, virtual or offshore model.

3. Senior Leadership. Senior Leaders must align and work towards a
common goal of improved information, while appreciating Information
Management is still immature as a discipline and be ready for challenges.

12


Treat Information as an Asset
4. Historical Quantification. Common architectural models and tools-
based quantitative assessments of data and content are key aspects of
establishing a known baseline to move forward.
5. Information Value Assessment. Organizations should provide a
mechanism to assign an economic value to the information assets and
the resulting impacts of Information Governance practices.
6. A Common Methodology. An Information Governance programme
should include a common set of activities, tasks and deliverables to build
a competency
7. Standard Models A common definition of terms, domain values and
their relationships is one of the fundamental building blocks of
Information Governance.
8. Governance Tools. Measuring the effectiveness of an Information
Governance program requires tools to capture assets and performance.

13


Be Pragmatic in a Strategic Context
9. Strategic Approach. Improvements will typically be measured over
months and years, not days. This model must allow for tactical
improvements.
10.Comprehensive Scope. An Information Governance approach should
be comprehensive in its scope, covering structured data, unstructured
content and the whole lifecycle of information.
11.Architecture. An Information Management architecture should be
defined for the current-state, transition points and target vision.
12.Continuous Improvement. It is not always cost-effective to fix all
issues in a certain area, but to instead follow the “80/20 rule”. It should
re-factor a baseline through audits, monitoring, technology re-factoring
and personnel training.
13.Flexibility for Change. While an Information Governance program
involves putting standards in place, it must have an inbuilt pragmatism
and flexibility for change.
14

Networked Information Governance

Apply Web2.0/Enterprise.2.0 Principles for Better Governance
14. Collaborative Community. Collaborative technologies can streamline
communications to capture content in informal network as well as build the formal.
15. Organizing the Informal Network. Build a content model that is easily
populated through user-driven categorization, informal collaboration begins to take
on more formal structures.
16. Aggregation of Ideas. Not all good ideas have to come from the inside. Social
Computing techniques provide an easy way to bring linked content together.
17. Linking the Informal to Formal. The same principle of applying content
categories can be applied to formal governance processes.
18. Searching the Knowledge Network. Enterprise Search techniques should be
implemented to make this information easily accessible.
19. Collaborative Asset Management. The maturity of your business and technology
assets should be a known quantity and this information easily shared across the
organization.
20. Global Standards Bodies. Having an external perspective through a central
authority can help to balance competing interests and work to a similar approach.
15

The 5 Phases of MIKE2.0

Information Development through the 5 Phases of MIKE2.0

Continuous Implementation Phases
Strategic Programme
Blueprint is done once
2 3
1 ent ent
ent
crem Increm Increm
In

Design

Develop
Roadmap &
Phase 1 Phase 2
Foundation
Business Assessment Technology Assessment
Activities
Deploy

Improve
Begin
Next
Increment
Phase 3, 4, 5

Improved Governance and Operating Model

16

Key Governance Activities
The MIKE2.0 approach for improving Data Governance goes across all 5 phases of the methodology. The most
critical activities for improving Data Governance are as follows:
Activity 1.4 Organisational QuickScan
Activity 1.6 Information Governance Sponsorship and Scope
Activity 1.7 Initial Information Governance Organisation
Activity 2.7 Information Governance Policies
Activity 2.8 Information Standards
Activity 3.5 Business Scope for Improved Information Governance
Activity 3.6 Enterprise Information Architecture
Activity 3.7 Root Cause Analysis on Information Governance Issues
Activity 3.8 Data Governance Metrics
Activity 3.11 Data Profiling
Activity 3.12 Data Re-Engineering
Activity 5.11 Continuous Improvement - Compliance Auditing
Activity 5.12 Continuous Improvement - Standards, Policies and Processes
Activity 5.13 Continuous Improvement - Data Quality
Activity 5.14 Continuous Improvement - Infrastructure
Activity 5.15 Continuous Improvement - Information Development Organization
Activity 5.16 Continuous Improvement – MIKE2.0 Methodology
Other MIKE2.0 Activities are also relevant, but these are particularly focused on Data Governance

17

Phase 1. Business Assessment and Strategy Definition Blueprint
Quickly Understand Issues Establish Leadership Establish Team

Organisational IG Sponsorship Initial IG
QuickScan and Scope Organisation

• Conduct Information Maturity • Confirm scope of Data Governance • Establishment Data Governance
Assessment Program Council

• Build Inventory of Information • Confirm in-scope data subject • Assignment of roles and
Assets areas responsibilities

• Determine Economic Value of • Assign Data Stewards to each • Definition of communications model
Information subject area and tracking mechanism

• Assess organizational structure, • Re-alignment of Business and
people and their skills Technology Strategy

An initial gap analysis is developed by assessing the organisation’s current-state issues and vision for the future-
state. Data Governance scope driven by high-level information requirements and complemented by the
definition of a strategic conceptual architecture.

18

Phase 2. Technology Assessment and Selection Blueprint
Deliver Policy Framework Standards for Implementation Metadata Management

Info Governance Info Governance Initiate Metadata-
Policies Standards Driven Approach

• Definition of Information • Info Specification Standards Metadata Management goes across
Governance Policy Requirements multiple activities in MIKE2, through a
• Info Modelling Standards metadata-driven architecture
• Definition of Information
Governance Policies • Info Capture Standards
• Get some form of repository and
base meta-model in place from the
• Approval and Distribution of • Info Security Standards
onset
Information Governance Policies
• Info Reporting Standards
• Metadata management for improved
DG is more than a data dictionary

• The goal is Active Metadata
Integration

Driven by information management guiding principles, a Policy Framework and common set of Data Standards
are created that will be used throughout the implementation program. MIKE2 starts with a reference model for
metadata management

19

Phase 3. Roadmap and Foundation Activities
Determine Key Data Elements Overall KDE Architecture Determine Process Issues

Business Scope
Enterprise Root Cause
for Improved
Information Analysis of DG
Information
Architecture Issues
Governance

• Prevent Issues related to Source
• Define Business Process Scope for • Overlay System Architecture on
Increment Enterprise Data Model System Edits

• Determine KDEs and Prioritize by • Define Master Data Management • Prevent Issues related to Business
Business Impact Architecture Process
• Capture Recommend Business • Define BusinessTime Model for • Prevent Issues related to
Process Changes KDEs Technology Architecture
• Define Data Definitions and • Summarize Root Cause Issues and
Business Rules Recommend Changes

The MIKE2.0 governance approach focused around Key Data Elements (KDEs). These are the subset of data
elements that are used to make the most critical business decisions. The Enterprise Information Architecture is
built out over time using these KDEs to define a framework for Master Data Management.

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Phase 3. Roadmap and Foundation Activities (continued)
Assess issues with KDEs Quantitatively Understand DQ Iteratively fix DQ issues

Data Governance Data Re-
Data Profiling
Metrics Engineering

• Define Metric Categories and • Prepare for Assessment • Prepare for Re-Engineering
Measurement Techniques
• Perform Column Profiling • Perform Data Standardization
• Gather Current-State Metrics on
• Perform Table Profiling • Perform Data Correction
each KDE
• Perform Multi-Table Profiling • Perform Data Matching and
• Define Target Metrics on each KDE
Consolidation
• Finalize Data Quality Report
• Perform Data Enrichment

• Finalize Business Summary of Data
Quality Impacts

Metrics are defined for how data will be measured initially as well as target measures. Data Profiling is used for
quantitative estimates and data is re-engineered in an iterative fashion. Artifacts stored in a metadata model.

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Phase 5. Develop, Test, Deploy and Improve
Continuous Improvement Continuous Improvement Continuous Improvement

Standards,
Compliance
Policies and Data Quality
Auditing
Processes

• Attain Sponsorship of Data • Review and Revise Data • Conduct Ongoing Data Quality
Governance Policies Monitoring
Governance Board

• Review and Revise Data • Associate Data Quality Issues with
• Define Compliance Auditing
Governance Metrics Root Causes
Processes

• Review and Revise Data • Execute Issue Prevention Process
• Train Staff on Compliance
Governance Standards
Standards

• Review and Revise Data
• Conduct Auditing Processes
Governance Processes
•Present Auditing Results and
• Implement Changes as Required
Recommendations

The MIKE2.0 Methodology is based around the Continuous Improvement. That means that we are continually re-
factoring towards the strategic vision and there are planned activities to revisit the existing implementation.

22

Phase 5. Develop, Test, Deploy and Improve (continued)
Continuous Improvement Continuous Improvement Continuous Improvement

Information Contribute to
Infrastructure Development Open MIKE2.0
Organization Methodology

• Re-factor Integration • Move to a Central Architecture and Help improve the overall approach to
Infrastructure Delivery Model Data Governance used by our
community:
• Progressively Automate Processes • Develop Staff and their Skills
• Help complete wanted assets
• Review and Recommend Physical • Implement Data Governance
Infrastructure Changes Incentives • Assist with Peer reviews

• Move to a Metadata-Driven • Review and Revise • Propose new core supporting assets
Architecture Communications Model
• Recommend extensions to overall
methodology

Be an active collaborator

Users of MIKE2.0 are encouraged to be part of an active community. The collaborative environment for MIKE2
allows the core method to be improved over time, whilst within a release cycle and product roadmap for stability.

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Getting Started: QuickScan Assessment
Information Development through the 5 Phases of MIKE2.0

Continuous Implementation Phases
Strategic Programme
Responsible Status
Activity 1.4 Organisational QuickScan for Information
Blueprint is done once
Development
nt 2 nt 3
nt 1
eme In creme In creme
In cr
1.4.1 Assess Current-State Application Portfolio

1.4.2 Assess Information Maturity
Design

1.4.3 Assess Economic Value of Information
Develop
Roadmap &
Phase 1 Phase 2
Foundation
1.4.4 Assess Infrastructure Maturity
Business Assessment Technology Assessment
Activities
Deploy

1.4.5 Assess Key Current-State Information
Improve
Processes
Begin Next
Increment 1.4.6 Define Current-State Conceptual
Phase 3, 4, 5
Architecture
Improved Governance and Operating Model
1.4.7 Assess Current-State People Skills

1.4.8 Assess Current-State Organisational
Structure
Phase 1 – Business Assessment and Strategy Definition Blueprint
1.4.9 Assemble Findings on People, Organization
1.1Strategic 1.2 Enterprise 1.3 Overall Business and its Capabilities
Mobilisation Information Strategy for
Management Information
Awareness Development

1.4 Organisational 1.5 Future State 1.6 Data Governance
QuickScan for Vision for Sponsorship and
Information Information Scope
Development Management

1.7 Initial Data 1.9 Programme
1.8 Business
Governance Review
Blueprint Completion
Organisation

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Task 1.4.2 is used to conduct an object Information Governance Assessment
Task 1.4.2 is used to conduct an object Information Governance Assessment

25


Information Maturity Model: Measure Your Data Governance Maturity Level
Information Maturity Model: Measure Your Data Governance Maturity Level

META Group developed a 5-level Information Maturity
Model (IMM) to use as an information maturity
guideline. We have extended this model as part of
MIKE2.0.
High Level 5
It is similar to the Software Capability Maturity Model
Optimised
(CMM) and focuses initially on data quality.
Information Development maturity

The key criteria for assessing information maturity is
Level 4
being able to measure it.
Information Development is
Managed a strategic initiative, issues
are either prevented or
Level 3 corrected at the source,
and best-in-class solution
Proactive Information managed as
architecture is
enterprise asset
implemented. Focus is on
Level 2 and well-developed
engineering processes and continuous improvement.
Reactive Information
organization structure
Development is part of
exists.
the IT charter and
Level 1
enterprise management
Aware Awareness and processes & exist.
action occur in
response to issues.
Action is either
There is awareness system- or
that problems exist department-specific. MIKE2.0 uses an objective assessment of your current
but the organization
and desired information maturity levels to construct a
has taken little action
program for improving Data Governance.
regarding how data is
managed.
Low
Information Accuracy & Organizational Confidence
High
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Level 1 Data Governance Organisation – Aware. An Aware Data Governance Organisation knows that the organisation has
issues around Data Governance but is doing little to respond to these issues. Awareness has typically come as the result of some
major issues that have occurred that have been Data Governance-related. An organisation may also be at the Aware state if they
are going through the process of moving to state where they can effectively address issues, but are only in the early stages of
the programme.

Level 2 Data Governance Organisation – Reactive. A Reactive Data Governance Organisation is able to address some of its
issues, but not until some time after they have occurred. The organisation is not able to address root causes or predict when
they are likely to occur. quot;Heroesquot; are often needed to address complex data quality issues and the impact of fixes done on a
system-by-system level are often poorly understood.

Level 3 Data Governance Organisation – Proactive. A Proactive Data Governance Organisation can stop issues before they
occur as they are empowered to address root cause problems. At this level, the organisation also conducts ongoing monitoring of
data quality to issues that do occur can be resolved quickly.

Level 4 Data Governance Organisation – Managed. A Managed Data Governance Organisation has a mature set of
information management practices. This organisation is not only able to proactively identify issues and address them, but defines
its strategic technology direction in a manner focused on Information Development.

Level 5 Data Governance Organisation – Optimal. An Optimal Data Governance Organisation is also referred to as the
Information Development Centre of Excellence. In this model, Information Development is treated as a core competency across
strategy, people, process, organisation and technology. a

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Data Governance Maturity
Moving Up the Maturity Model

To formulate, communicate, pilot and deploy a centralised organisation for
Information Development is a significant undertaking. The following artifacts
from MIKE2.0 can be used to assist in this effort:
A comprehensive Role Inventory across aspects of the organisation with associated
competencies and metrics
A set of Position Descriptions based upon the Role Inventory
Organisational Structures populated with these Position Descriptions
Create assessment material to support manager and staff assessment of individual
competencies
Formulate a Gap Analysis based on target Organizational Structure and Role
competencies vs. current capabilities
To validate the processes and structures of the organization via a pilot script
A Training profile for staff
A Recruiting profile recommending to fill typical recruiting needs
An Organisational Transition Plan across the Data Governance Maturity Model

28

Data Governance Organisational Model
Level 2 Data Governance Team (FS Institution Example)

There is a minimum team structure that should be used for data governance on any project. The
example model shows this data governance structure for a Data Warehouse implementation, where
the core focus is for risk management.

Executive
Sponsor

Program
Manager

Source Data
Risk Modeling
IT
System Warehouse
Coordinator Team Rep.
Managers Delivery
Manager

Data
Quality
Data Quality
Manager
Working Group

29

Level 2 Data Governance Team – Roles and Responsibilities

Role Responsibility

Executive Sponsor Strategic oversight of program and related data issues
Stakeholders
Governance

Sponsorship of business cases for remediation efforts

Ownership of legacy system-specific issue resolution
Legacy System Manager
Provision of system SMEs for issue remediation

Management of issue escalations to business executives and source system owners
program Manager
Provision of resources for issue verification and remediation

IT Coordinator Overall guidance for technical issue resolution
Ensures remediation efforts align with overall data asset architecture
Management of internal trouble ticket process for source system remediation
Governance Working Group

Data Modeling Team Rep Overall guidance for issue prioritisation and functional resolution
Provision of risk modeling SMEs for data issue management

Management level oversight of data environment, data cleansing activities and deployment
Data Asset Delivery Manager Provision of technical data resources
Management responsibility for technical deliverables

Data Quality Manager Definition of the overall approach for short and long term DQ activities
Identification and management of critical DQ issues
Coordination of DQ resources
Oversight of the execution of DQ testing and reporting

30


Focused on Data Investigation and Re-Engineering
Focused on Data Investigation and Re-Engineering

Executive
Data Governance Council Sponsor Enterprise Data Warehouse
Steering Committee

Data Quality Leader
Executive Steering
Data Strategy & Queue
Technical Analysts Overall Coordination of DQM
Management (DSQ) Committee Strategy Program

Department 5 Function 1
Department 1 Department 4
Department 3
Department 2
(IBD) (eg. Risk)
(eg. Equities) (MCD)
(IMD)
(eg. FID)

Data Stewards (End-to-end Responsibility for these Subject Areas)

Technical Analysts DQ Analysts
Business
Analysts
Define Standards Compliance Auditing
Define Standards Compliance Auditing
• Specification • Data Standard
Source Data Collaboration • Specification • Data Standard
Source Data Collaboration • Data Capture • Business Rule
• Data Capture
• Source Analysis • Business Rule
• Reporting
• Source Analysis • Data Management Process
• Reporting • Data Management Process
• Target Analysis
• Target Analysis
Define Business Rules Establish Metrics
Define Business Rules Establish Metrics
Data Modelling • Define • Metric Categories
Data Modelling • Define • Metric Categories
Collaboration • Test Compliance • Target Ratings
Collaboration • Test Compliance • Target Ratings
• Source to Logical
• Source to Logical
Business Process Definition
• Volume and performance Issue Management
Business Process Definition
• Volume and performance Issue Management
• Document & Model • Monitor & Report
• Document & Model • Monitor & Report
Physical Design
Physical Design
Collaboration
Definitions
Collaboration Profile & Measure
Definitions Profile & Measure
• Entities • Track Results
• Performance • Entities • Track Results
• Performance
Characteristics • Attributes • Facilitate Root Cause Analysis
Characteristics • Attributes • Facilitate Root Cause Analysis

31


Role Description Time Commitment

Full time
Executive Sponsor The Executive Sponsor sets initial direction and goals for the program. In an ongoing basis,
the Executive Sponsor approves information policy and tracks the progress of quality
initiatives compared to target plan.
Full time
Data Strategy & Queue The DSQ has responsibility for developing Data Quality strategy and policies, as well as
Management (DSQ) leadership and supervision for the overall program. Additional responsibilities include
approval of identified business process improvements and the communication plan.
Full time
Data Quality Leader (DQL) The DQL provides day to day leadership over the DQM program. The DQL has significant
DQM expertise and is deeply involved in all aspects of the program while also participating
in the DQM Executive Steering Committee (which includes considerably approval
responsibility). The DQL is also responsible for managing business process improvement
and the communication plan.
Full time
Data Steward Data stewards act as the conduit between IT and the business and accept accountability for
data definition, data management process definition, and information quality levels for
specific data subject areas. Data stewardship involves taking responsibility for data
elements for their end-to-end usage across the enterprise.
Full time
Technical Analyst Technical Analysts are members of existing project teams that are assigned to the DQM
project when specific activities in their project areas are impacted. They provide the
technical expertise required to implement new tools or to improve existing systems.
Full time
Business Analyst Business Analysts are members of the existing Business Units that are assigned to the DQM
project when specific activities in their business areas are impacted. They provide the
business expertise required to define the usage of key data elements and to improve
business processes.
Full time
Data Quality Analyst Data Quality Analysts are fully dedicated to the DQM project. Their responsibility is to
provide expertise on quality improvement best practices and to perform auditing to ensure
projects are complying with data quality management processes and standards.
Full time
Data Owner Data Owners are responsibility for the accuracy of the data in their area of responsibility.
For credit-related data, the Account Officers are the data owners. Ideally the data owners
would have a single interface into the source systems where key data elements reside.

32

View Focused on Data Stewardship and Ownership, other teams would include technology and Roles from Level 3 Org
View Focused on Data Stewardship and Ownership, other teams would include technology and Roles from Level 3 Org

Executive Sponsor DG Steering Committee (Finance, Credit,
Enterprise Data Architect, Audit, Retail,
C-Level
Wholesale, etc.

DATA GOVERNANCE
COUNCIL Chief Architect

XBR Program Manager

MDM Enterprise Data Warehouse SYSTEM & PROCESS SYSTEM OF RECORD OWNERS
OWNERS

BUS DATA CONCEPT IT
OWNERS DATA STEWARDS
MDM BUS: tbd BUS: tbd BUS: tbd
BUS: tbd
Business IT: tbd IT: tbd IT: tbd
IT: tbd
Owner
Classification Product
Classification Product PRMS CRS
New Position New Position BUS: tbd
tbd tbd BUS: tbd BUS: tbd BUS: tbd
#4 #4
IT: tbd
MDM Business IT: tbd IT: tbd IT: tbd
Analyst
Involved Party Hierarchy
Involved Party Hierarchy
New Position New Position BUS: tbd BUS: tbd BUS: tbd BUS: tbd
tbd tbd
#5 #4 IT: tbd IT: tbd IT: tbd IT: tbd
Business
Analyst – Arrangement Resource Item Arrangement
Credit Reports Resource Item
New Position New Position tbd BUS: tbd BUS: tbd BUS: tbd
BUS: tbd
#5 #5
IT: tbd IT: tbd IT: tbd
IT: tbd

IT Steward Event Event
New Position (To Be
BUS:tbd BUS: tbd BUS: tbd
#5 Assigned)
IT: tbd IT: tbd IT: tbd

Data Quality Lead
Business and Technical Analysts (Pool of Data Quality Analysts
Business and Technical Analysts (Pool of
resources to be assigned) (Pool of resources to be assigned)
resources to be assigned)

33


Role Responsibilities Time Commitment

– The Executive Sponsor will set the initial direction and goals for the program. On an ongoing basis, the
Executive – <5%
Executive Sponsor approves budgets, establishes highest level policies, and monitors information policy
Sponsor
setting and tracks progress of quality initiatives compared to target plan.

– Develop and monitor an overall strategic plan for data quality improvement encompassing all affected
Data – Quarterly
systems. Plan to include linkage and convergence of existing data warehouse’s and data marts.
Governance
– Adhoc
– Sponsor and champion for data quality initiatives for all systems, LOBs and functions. Ensure scheduling
Council
meetings as
and resource allocation across LOBs
needed
– Provide data quality feedback and progress across all LOBs, systems and functions
– Provide approval, prioritization, sign-off of major data quality initiatives.
– Communicate with business segments to ensure expectations for data quality initiatives are in-line with
what can be delivered.
– Oversight of business planning and requirements process to ensure data quality needs are appropriately
addressing the needs of the users.
– Resolution of escalated issues.

– Responsible for developing Data Governance strategy and policies, as well as leadership and supervision
Data – Monthly
for the overall program.
Governance initially
– Active working committee of the Data Governance board. Accountability for executing Board
Steering
– Move to
responsibilities.
Committee
quarterly
– Provide periodic data quality updates to the ITEC and policy committee
basis for the
– Definition and signoff of project scope, requirements and test results.
future
– Estimates high level funding needs, requests budget from the executive sponsor.
– Approval of identified data quality improvement initiatives.
– Will include members of the Lines of Business (Wholesale, Mortgage, Retail, PCS), Finance, IT, Credit
Risk Mgt, Company Quality Mgt, Audit, and the Enterprise Data Architect.

34



Data Quality – Provides day to day leadership over the data quality program. – Full time
Program – Focal point for coordinating System of Record (SOR) owners. – Staff support
Manager will be
– Guide and support requirements and testing of data quality initiatives
needed as
– Owner of scorecard process and execution. Provide scorecard feedback to all involved parties including
data
SOR owners, data concept owners, data stewards and to the Board
governance
– Ensures execution of policies and strategies of the Data Governance Board and Steering Committee.
grows
– Review and prioritizes projects, determine funding needs and requests funding approval from the Data
Quality Steering Committee
– Coordinate the release management program with LOBs and scheduling of data quality and technical
projects.
– Facilitates the development and training of best practice data quality policies, procedures and
methodologies.
– Monitors enterprise data quality milestones and performance measures to ensure enterprise-level data
quality. Provides feedback to ITEC and all LOBs

Enterprise Data – Provides single point of architectural coordination for all Enterprise Data Warehouse related approved – Full time
Architect initiatives
– Focuses on planning for infrastructure efficiencies, and linkage, cleansing and usage of data, ensures
implementation of remediation and the priority of issues
– Ensures the compliance and execution of the data governance program policies, processes and
procedures across data stewards
– Reconciliation, re-creation, metadata design and maintenance

Enterprise Data – Ensures the Enterprise Data Warehouse collectively meets the requirements of the business – Full time
Warehouse – Coordinates the resolution of issues identified by data concept owners and data stewards.
System and – Identifies new funding requirements, assists in prioritizing requests and submits to the data
Process Owner governance board for approval
– Coordinates on-going data integrity and linkage/usage with source system changes
– Coordinates efficient infrastructure investments

35



– The data concept owners initially will be senior credit risk management representatives responsible for enforcement of
Data Concept – Full time
common, enterprise wide business concepts for credit risk data.
Owner
– Provide business side leadership of data quality improvement initiatives.
(Business)
– Responsible for business concept definition, requirements definition and sign off, and testing review and sign off.
– They are responsible for prioritizing data quality projects and the appropriate use of data elements.
– Facilitate coordination required to resolve cross LOB naming and definition issues.
– Focuses on administering data policies, defining business rules, defining procedures for the data processes
– Responsible for on-going settlement of the Enterprise Data Warehouse with the SOR data.
– Oversight of one or more areas of an organization’s information models
Data Steward – 50%
– Will focus on a particular subject areas
(IT)
– Provide leadership on the IT side of data quality improvement initiatives by leading combined teams of technical,
business and quality analysts
– Participate, influence and sign off on data requirements and design of data quality related projects and applications.
– Determine how data will be managed
– Executes data quality scorecard for data subject areas across affected systems
– Provides technology direction for DQ improvement initiatives
– Documents and maintains data quality definitions and usage at the concept and data element level on Enterprise Data
Warehouse
– Accountable to the Data Governance Program Manager for planning and implementing data quality policies, strategies
System of – No changes
and initiatives at the application level
Record (SOR) required to
– Shapes, defines, manages and implements initiatives to improve data quality based upon data quality feedback
Owners existing
– Builds data quality projects into application strategic plan and LOB project funding plans commitment
– Provides business analysts and technical analysts to support data quality analysis and implementation levels
– Coordinates source system changes
– Responsible to exert influence and oversee input processes that feed system ensure consistent inputs in compliance
with standards and policies
– Partner with Enterprise Data Warehouse System and Process Owner to perform on-going reconciliations of their
systems with the Enterprise Data Warehouse

36



CDM Business – Responsible for assessment of data quality, remediation requirements and implementation of CDM – Full time
Owner – Provides requirements for extensions of Enterprise Data Warehouse data concepts and additional definitions
– Identify data quality issues and interacts with the Data Governance Lead for resolution
– Assessing the needs of end-users and to ensure the data is collected, aggregated, & reported accurately
– Coordinates prioritization of projects for self assessment gaps with Basel Steering Committee
– Responsible for on-going settlement of the cubes to the Enterprise Data Warehouse
– Initially: 100%
CDM IT – Improve and maintain the quality, accessibility and reusability of data and information
Steward – Focuses on administering data policies, defining business rules, defining procedures for the data processes
– Participate, influence and sign off on data requirements and project design on data quality related projects and
application, Executes data quality scorecard for data subject areas across affected systems
Data Quality – Manage the data quality analysts and coordinates the tasks for the business and technical analysts. – Full time
Lead – Point of contact to the data stewards/owners and the system owners. Will identify the data quality, business and
technical analysts needed to execute the data quality policies, processes, etc.
– Act as point of contact to the CDM, Enterprise Data Warehouse Stewards, and Systems of Record for small and
everyday changes required. Provide expertise on quality improvement best practices and to perform auditing to
ensure projects are complying with data quality management processes and standards.
Business – Business Analysts are members of the existing LOBs that are assigned to the Governance team when specific activities – As requested
Analysts in their business areas are impacted.
– Articulate the usage of data elements based on definitions and guidelines by data concept owners
– Validate and maintain business rules with the appropriate lines of business
– Define data field names, definitions, standards, will be assigned to work with the Data Stewards as necessary.
Accountable to the concept owners and/or the system owners
Technical – Technical Analysts are members of existing project teams that are assigned to the Governance team when specific – As requested
Analysts activities in their project areas are impacted.
– Understand data structure
– Provide technical expertise required to implement new tools and improve existing systems

37

Roles of Data Stewards and Data Owners

Issue Escalation
DG Steering
Issue Escalation Committee

DATA CONCEPT OWNERS AND
Input and STEWARDS
coordination (TRAFFIC COPS)
with LOB’s on
precise data
Feedback to
definitions
Data Concept System Owners
Business
Owners
Close
COMPANY Business-IT
SOR Owners
coordination
LOBs
on data
definitions,
quality and
standards
DQ Lead
Data Concept IT
Stewards

DG
Improvement
Opportunities
New Opportunity
Definition

38

Level 5 - Information Development Centre of Excellence

Organisation Framework: Balance of Power

In moving to the centralized model for information and
infrastructure development, Leadership, Architecture and
Delivery must represented on the team.
Leadership

The key team members across the areas must actively
collaborate through formal and informal reporting relationships
Architecture Delivery
to guide a strategic idea to its realization. It is an organizational
model that provides a “balance of power” whilst providing an
enabler to:

• Align Business and Technology Strategy
• Align Strategic and Tactical Objectives
• Technology procurement efficiencies
• Justify spend based on business case
• Balance risk with speed of delivery
• A common set of technology standards and policies
• Reuse at an enterprise level

This has shown to be a very successful model for contemporary
IT organizations and complements a centralized approach for the
Technology Backplane. It is a model focused on providing
solutions for the Business, driven by the needs of the Business.

39

Mike2.0 Information Governance Overview

Mike2.0 Information Governance Overview

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