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

2A Methodology for Information Development MIKE2.0 Methodology

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


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


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 approachis the key to delivering a standard for such a complex problem


Our View: Time to Act

The problem has been growing for years. Here is why IM is now aMainstream 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.


What is Information Governance?

“Governance" 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)

Our View: What is Information Governance?


Our Approach: Integrated Solutions

Composite Solution Offerings

Access, Search and Delivery

Enterprise DataManagement

EnterpriseContent Mgmt

Info Architecture, Strategy & Gov

Info Mgmt

Business Intelligence

Info Asset Management

Corp PerformanceManagement

Metric & Dashboard Design

Profitability, Value &Pricing Mgmt

Real TimeCustomer Decisioning

Operational Performance Mgmt

Information LifecycleManagement

Information Security

Metadata, TaxonomyCataloging

Workflow InformationManagement

Enterprise Portals& Info Delivery

Enterprise Search

Mobile Device Access

Data Warehousing

Master Data Mgmt

Customer DataIntegration

Data QualityImprovement

Document Management

Records, Contracts,and IP Management

ERP Document MgmtIntegration

Information Governance

Service Oriented, EII &Model Driven Architecture

Enterprise Data Management Strategy

Enterprise Content Management Strategy

Enterprise InformationAssessment

Balance Business Scorecard

HR Performance MgmtRewards

Data Mining, AnalyticsModeling & Simulation

Business Activity Monitoring

Access Monitoring & Control

Data Center Management

Information SystemUsability

Data Migration

Digital Asset Management

Content Management-Web Content

Collaboration Environments, COIKnowledge Capture

Information Mgmt COE Organisation and

Shared Service Model

InformationSharing

Enterprise 2.0Networked Info Governance

Agile Info Development

Info Mgmt Strategy

Data Driven IT Transformation


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 development of open source IM technologieswww.openmethodology.org

Our Approach: An Open Source Methodology

MIKE2.0 (Method for an Integrated Knowledge Environment)MIKE2.0 (Method for an Integrated Knowledge Environment)


Our Approach: Open Source + Internal Assets

Open Methodology site Enterprise 2.0 Mashups

Assessment Tools Integrated Approach


Information Management Solution Suite

Delivered through a Collaborative Approach

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

Enterprise Information Management

Core Solution Offerings by Solution Capabilities

Co

mm

erci

al &

Op

en S

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P

rod

uct

So

luti

on

sAccess, Search and Content Delivery

Business Intelligence InformationAsset Management

Bu

sin

ess

So

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on

s

Enterprise Data Management Enterprise Content Management

Information Strategy, Architecture and Governance

Our Approach: Collaborative Solutions


Information Management Solution Suite


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

Enterprise Information Management

Co

mm

erci

al &

Op

en S

ou

rce

P

rod

uct

So

luti

on

sAccess, Search and Content Delivery

Business Intelligence InformationAsset Management

Bu

sin

ess

So

luti

on

s

Enterprise Data ManagementEnterprise Content Management

Information Strategy, Architecture and Governance

Solution Capabilities that provide a foundation for Suite Delivery

Supporting Assets Go

vern

ance

Fra

mew

ork

Arc

hit

ectu

re F

ram

ewo

rk

Usage ModelOverall Implementation Guide

Foundational Solutions

Our Approach: Supported through a Foundation


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.

Information Governance: Guiding Principles


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 ofInformation Governance.

8. Governance Tools. Measuring the effectiveness of an Information Governance program requires tools to capture assets and performance.



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.



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.

Networked Information Governance


Information Development through the 5 Phases of MIKE2.0

Improved Governance and Operating Model

Phase 2Technology Assessment

Phase 1Business Assessment

Phase 3, 4, 5

Develop

Deploy

Design

Improve

Increment

1

Increment

2

Increment

3

Roadmap & Foundation Activities

Begin Next Increment

Strategic Programme Blueprint is done once

Continuous Implementation Phases

The 5 Phases of MIKE2.0


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

Key Governance Activities


Phase 1. Business Assessment and Strategy Definition Blueprint

Quickly Understand Issues

Organisational QuickScan

IG Sponsorship and Scope

Establish Leadership

• Conduct Information Maturity Assessment

• Build Inventory of Information Assets

• Determine Economic Value of Information

• Assess organizational structure, people and their skills

• Confirm scope of Data Governance Program

• Confirm in-scope data subject areas

• Assign Data Stewards to each subject area

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.

Initial IG Organisation

Establish Team

• Establishment Data Governance Council

• Assignment of roles and responsibilities

• Definition of communications model and tracking mechanism

• Re-alignment of Business and Technology Strategy



Phase 2. Technology Assessment and Selection Blueprint

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

Deliver Policy Framework

Info Governance Policies

Info Governance Standards

Standards for Implementation Metadata Management

• Definition of Information Governance Policy Requirements

• Definition of Information Governance Policies

• Approval and Distribution of Information Governance Policies

• Info Specification Standards

• Info Modelling Standards

• Info Capture Standards

• Info Security Standards

• Info Reporting Standards

Initiate Metadata-Driven Approach

Metadata Management goes across multiple activities in MIKE2, through a metadata-driven architecture

• Get some form of repository and base meta-model in place from the onset

• Metadata management for improved DG is more than a data dictionary

• The goal is Active Metadata Integration



Phase 3. Roadmap and Foundation Activities

Determine Key Data Elements

Business Scope for Improved Information Governance

Enterprise Information Architecture

Root Cause Analysis of DG

Issues

Overall KDE Architecture Determine Process Issues

• Define Business Process Scope for Increment

• Determine KDEs and Prioritize by Business Impact

• Capture Recommend Business Process Changes

• Overlay System Architecture on Enterprise Data Model

• Define Master Data Management Architecture

• Define BusinessTime Model for KDEs

• Define Data Definitions and Business Rules

• Prevent Issues related to Source System Edits

• Prevent Issues related to Business Process

• Prevent Issues related to Technology Architecture

• Summarize Root Cause Issues and 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.



Phase 3. Roadmap and Foundation Activities (continued)

Assess issues with KDEs

Data GovernanceMetrics

Data ProfilingData Re-

Engineering

Quantitatively Understand DQ Iteratively fix DQ issues

• Define Metric Categories and Measurement Techniques

• Gather Current-State Metrics on each KDE

• Define Target Metrics on each KDE

• Prepare for Assessment

• Perform Column Profiling

• Perform Table Profiling

• Perform Multi-Table Profiling

• Finalize Data Quality Report

• Prepare for Re-Engineering

• Perform Data Standardization

• Perform Data Correction

• Perform Data Matching and Consolidation

• 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.



Phase 5. Develop, Test, Deploy and Improve

Continuous Improvement

Compliance Auditing

Standards, Policies and Processes

Data Quality

Continuous Improvement Continuous Improvement

• Attain Sponsorship of Data Governance Board

• Define Compliance Auditing Processes

• Train Staff on Compliance Standards

• Conduct Auditing Processes

•Present Auditing Results and Recommendations

• Review and Revise Data Governance Policies

• Review and Revise Data Governance Metrics

• Review and Revise Data Governance Standards

• Review and Revise Data Governance Processes

• Implement Changes as Required

• Conduct Ongoing Data Quality Monitoring

• Associate Data Quality Issues with Root Causes

• Execute Issue Prevention Process

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.



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

Continuous Improvement

InfrastructureInformation Development Organization

Contribute to Open MIKE2.0 Methodology

Continuous Improvement Continuous Improvement

• Re-factor Integration Infrastructure

• Progressively Automate Processes

• Review and Recommend Physical Infrastructure Changes

• Move to a Metadata-Driven Architecture

• Move to a Central Architecture and Delivery Model

• Develop Staff and their Skills

• Implement Data Governance Incentives

• Review and Revise Communications Model

Help improve the overall approach to Data Governance used by our community:

• Help complete wanted assets

• Assist with Peer reviews

• Propose new core supporting assets

• 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.



Information Development through the 5 Phases of MIKE2.0

Improved Governance and Operating Model

Phase 2Technology Assessment

Phase 1Business Assessment

Phase 3, 4, 5

Develop

Deploy

Design

Improve

Increment 1

Increment 2

Increment 3

Roadmap & Foundation Activities

Begin Next Increment

Strategic Programme Blueprint is done once

Phase 1 – Business Assessment and Strategy Definition Blueprint

1.1Strategic Mobilisation

1.2 Enterprise Information Management Awareness

1.3 Overall Business Strategy for Information Development

1.4 Organisational QuickScan for Information Development

1.5 Future State Vision for Information Management

1.6 Data Governance Sponsorship and Scope

1.7 Initial Data Governance Organisation

1.8 Business Blueprint Completion

1.9 Programme Review

Continuous Implementation Phases

1.4.7 Assess Current-State People Skills

1.4.8 Assess Current-State Organisational

Structure

1.4.9 Assemble Findings on People, Organization

and its Capabilities

1.4.4 Assess Infrastructure Maturity

1.4.3 Assess Economic Value of Information

Responsible

1.4.6 Define Current-State Conceptual

Architecture

1.4.5 Assess Key Current-State Information

Processes

1.4.2 Assess Information Maturity

1.4.1 Assess Current-State Application Portfolio

Activity 1.4 Organisational QuickScan for Information

Development

Status

Getting Started: QuickScan Assessment



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


Information Accuracy & Organizational Confidence

Low

High

Information Development maturity

High

Reactive

Level 2

Aware

Level 1

Proactive

Level 3

Managed

Level 4

Optimised

Level 5

Information Development is a strategic initiative, issues are either prevented or corrected at the source, and best-in-class solution architecture is implemented. Focus is on continuous improvement.

Information managed as enterprise asset and well-developed engineering processes and organization structure exists.

Awareness and action occur in response to issues. Action is either system- or department-specific.

Information Development is part of the IT charter and enterprise management processes & exist.

There is awareness that problems exist but the organization has taken little action regarding how data is managed.

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.

It is similar to the Software Capability Maturity Model (CMM) and focuses initially on data quality.

The key criteria for assessing information maturity is being able to measure it.

MIKE2.0 uses an objective assessment of your current and desired information maturity levels to construct a

program for improving Data Governance.

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



�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 somemajor 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. "Heroes" 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



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

Data Governance Maturity

Moving Up the Maturity Model


Executive

Sponsor

Program

Manager

Risk Modeling

Team Rep.

IT

Coordinator

Source

System

Managers

Data

Warehouse

Delivery

Manager

Data

Quality

Manager Data Quality

Working Group

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.

Data Governance Organisational Model

Level 2 Data Governance Team (FS Institution Example)


Govern

ance W

ork

ing G

roup

Govern

ance

Sta

keholders

Overall guidance for issue prioritisation and functional resolution

Provision of risk modeling SMEs for data issue management

Data Modeling Team Rep

Management level oversight of data environment, data cleansing activities and deployment

Provision of technical data resources

Management responsibility for technical deliverables

Data Asset Delivery Manager

Strategic oversight of program and related data issues

Sponsorship of business cases for remediation efforts

Executive Sponsor

Ownership of legacy system-specific issue resolution

Provision of system SMEs for issue remediation Legacy System Manager

Management of issue escalations to business executives and source system owners

Provision of resources for issue verification and remediation

program Manager

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

IT Coordinator

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

Data Quality Manager

ResponsibilityRole


Level 2 Data Governance Team – Roles and Responsibilities


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

Compliance Auditing

• Data Standard

• Business Rule

• Data Management Process

Establish Metrics

• Metric Categories

• Target Ratings

Issue Management

• Monitor & Report

Profile & Measure

• Track Results

• Facilitate Root Cause Analysis

Compliance Auditing

• Data Standard

• Business Rule

• Data Management Process

Establish Metrics

• Metric Categories

• Target Ratings

Issue Management

• Monitor & Report

Profile & Measure

• Track Results

• Facilitate Root Cause Analysis

Define Standards

• Specification

• Data Capture

• Reporting

Define Business Rules

• Define

• Test Compliance

Business Process Definition

• Document & Model

Definitions

• Entities

• Attributes

Define Standards

• Specification

• Data Capture

• Reporting

Define Business Rules

• Define

• Test Compliance

Business Process Definition

• Document & Model

Definitions

• Entities

• Attributes

Source Data Collaboration

• Source Analysis

• Target Analysis

Data Modelling

Collaboration

• Source to Logical

• Volume and performance

Physical Design Collaboration

• Performance Characteristics

Source Data Collaboration

• Source Analysis

• Target Analysis

Data Modelling

Collaboration

• Source to Logical

• Volume and performance

Physical Design Collaboration

• Performance Characteristics

Technical Analysts

ExecutiveSponsor

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

Department 1(eg. Equities)

Department 2

(eg. FID)

Department 3 (IMD)

Department 5(IBD)

Function 1 (eg. Risk)

Data Governance Council

Executive Steering Committee

Data Quality Leader

Overall Coordination of DQM Strategy Program

Data Strategy & Queue

Management (DSQ)

Department 4(MCD)

Enterprise Data Warehouse Steering Committee

Technical Analysts DQ AnalystsBusiness Analysts




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.

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.

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.

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.

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.

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.

The DSQ has responsibility for developing Data Quality strategy and policies, as well as

leadership and supervision for the overall program. Additional responsibilities include

approval of identified business process improvements and the communication plan.

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.

Description

Full timeData Owner

Full timeData Quality Analyst

Full timeBusiness Analyst

Full timeTechnical Analyst

Full timeData Steward

Full timeData Quality Leader (DQL)

Full timeData Strategy & Queue

Management (DSQ)

Full timeExecutive Sponsor

Time CommitmentRole




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

DATA GOVERNANCE COUNCIL

Data Quality Lead

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

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

Executive Sponsor

C-Level

Data Quality Analysts (Pool of resources to be assigned)

MDM

ClassificationNew Position

#4

ProductNew Position

#4

Involved PartyNew Position

#5

HierarchyNew Position

#4

ArrangementNew Position

#5

EventNew Position

#5

Resource ItemNew Position

#5

Classificationtbd

Producttbd

Involved Partytbd

Arrangementtbd

Event(To Be

Assigned)

Hierarchytbd

Resource Item

BUS DATA CONCEPT OWNERS

ITDATA STEWARDS

SYSTEM OF RECORD OWNERS

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

CRSBUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

PRMSBUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS:tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

MDMBusiness Owner

MDM Business Analyst

Business Analyst –

Credit Reports

IT Steward

XBR Program Manager

Chief Architect

DG Steering Committee (Finance, Credit, Enterprise Data Architect, Audit, Retail,

Wholesale, etc.

Enterprise Data Warehouse SYSTEM & PROCESS OWNERS




– Monthly

initially

– Move to

quarterly

basis for the

future

– Responsible for developing Data Governance strategy and policies, as well as leadership and supervision for the overall program.

– Active working committee of the Data Governance board. Accountability for executing Board responsibilities.

– Provide periodic data quality updates to the ITEC and policy committee

– Definition and signoff of project scope, requirements and test results.

– 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.

Data

Governance

Steering

Committee

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

Executive

Sponsor

– Quarterly

– Adhoc

meetings as

needed

– Develop and monitor an overall strategic plan for data quality improvement encompassing all affected systems. Plan to include linkage and convergence of existing data warehouse’s and data marts.

– Sponsor and champion for data quality initiatives for all systems, LOBs and functions. Ensure scheduling and resource allocation across LOBs

– 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.

Data

Governance

Council

Responsibilities Time CommitmentRole




– Full time– Ensures the Enterprise Data Warehouse collectively meets the requirements of the business– Coordinates the resolution of issues identified by data concept owners and data stewards.– Identifies new funding requirements, assists in prioritizing requests and submits to the data

governance board for approval– Coordinates on-going data integrity and linkage/usage with source system changes– Coordinates efficient infrastructure investments

Enterprise Data Warehouse System and Process Owner

– Full time– Provides single point of architectural coordination for all Enterprise Data Warehouse related approved 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 Architect

– Full time – Staff support

will be needed as data governance grows

– Provides day to day leadership over the data quality program.– Focal point for coordinating System of Record (SOR) owners.– Guide and support requirements and testing of data quality initiatives– Owner of scorecard process and execution. Provide scorecard feedback to all involved parties including

SOR owners, data concept owners, data stewards and to the Board– Ensures execution of policies and strategies of the Data Governance Board and Steering Committee.– 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

Data Quality Program Manager





– No changes

required to

existing

commitment

levels

– Accountable to the Data Governance Program Manager for planning and implementing data quality policies, strategies and initiatives at the application level

– Shapes, defines, manages and implements initiatives to improve data quality based upon data quality feedback

– Builds data quality projects into application strategic plan and LOB project funding plans

– Provides business analysts and technical analysts to support data quality analysis and implementation

– 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

System of

Record (SOR)

Owners

– 50%– Oversight of one or more areas of an organization’s information models

– Will focus on a particular subject areas

– 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

Data Steward

(IT)

– Full time– The data concept owners initially will be senior credit risk management representatives responsible for enforcement of common, enterprise wide business concepts for credit risk data.

– Provide business side leadership of data quality improvement initiatives.

– 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.

Data Concept

Owner

(Business)





– As requested– Technical Analysts are members of existing project teams that are assigned to the Governance team when specific activities in their project areas are impacted.

– Understand data structure– Provide technical expertise required to implement new tools and improve existing systems

Technical Analysts

– As requested– Business Analysts are members of the existing LOBs that are assigned to the Governance team when specific activities 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

Business Analysts

– Full time– Manage the data quality analysts and coordinates the tasks for the business and technical analysts. – 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.

Data Quality Lead

– Initially: 100%– Improve and maintain the quality, accessibility and reusability of data and information– 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

CDM IT Steward

– Full time– Responsible for assessment of data quality, remediation requirements and implementation of CDM– 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

CDM Business Owner






Roles of Data Stewards and Data Owners

COMPANYLOBs

DATA CONCEPT OWNERS AND STEWARDS

(TRAFFIC COPS)

Data Concept Business Owners

Data Concept IT Stewards

Close Business-IT coordination on data definitions, quality and standards

DG Steering Committee

SOR Owners

DG Improvement Opportunities

Issue Escalation

Issue Escalation

Feedback to System Owners

DQ Lead

Input and coordination with LOB’s on precise data definitions

New Opportunity Definition



Level 5 - Information Development Centre of Excellence

Architecture Delivery

Leadership

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

The key team members across the areas must actively collaborate through formal and informal reporting relationships 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.

Organisation Framework: Balance of Power


INFORMATION DEVELOPMENTLeadership Team

Executive Sponsor s

C-Level

XBR Program Manager

Chief Architect

Information Development Steering Committee (Representatives from Business

and Technology)

CIO

CIO Reporting and Communication Structure

Data Stewardship and OwnershipData Stewardship and Ownership

Delivery TeamDelivery Team

Information Integration Standards

Manager

Information Repository Development

Manager

Information Process Development

Manager

MIS Business Development

Manager

Information Quality Development

Manager

Data Quality Lead

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

(Pool of resources to be assigned)

MDM

ClassificationNew Position #4

ProductNew Position #4

Involved PartyNew Position #5

HierarchyNew Position #4

ArrangementNew Position #5

EventNew Position #5

Resource ItemNew Position #5

Classificationtbd

Producttbd

Involved Partytbd

Arrangementtbd

Event(To Be Assigned)

Hierarchytbd

Resource Item

BUS DATA CONCEPT OWNERS ITDATA STEWARDS

SYSTEM OF RECORD OWNERSS

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

CRSBUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

PRMSBUS: tbdIT: tbd

BUS: tbdIT: tbd

BUS:tbdIT: tbd

BUS: tbdIT: tbd

BUS: tbdIT: tbd

MDMBusiness Owner

MDM Business Analyst

Business Analyst – Credit Reports

IT Steward

Enterprise Data Warehouse SYSTEM & PROCESS OWNERWNERS

Architecture TeamArchitecture Team

Technology Backplane


Information Architect

Business Domains

Business Domains

Business Architects

Chief Architect

Business ArchitectsBusiness

Architects Infrastructure Architect

Enterprise ArchitectureEnterprise Architecture

Data Governance Organizational Model



INFORMATION DEVELOPMENTLeadership Team

Executive Sponsor s

C-Level

XBR Program Manager

Chief Architect

Information Development Steering Committee (Representatives from Business

and Technology)

CIO

CIO Reporting and Communication Structure

Delivery TeamDelivery Team

Information Integration Standards

Manager

Information Repository Development

Manager

Information Process Development

Manager

MIS Business Development

Manager

Information Quality Development

Manager

Architecture TeamArchitecture Team



Information Architect

Business Domains

Business Domains

Business Architects

Chief Architect

Business ArchitectsBusiness

Architects Infrastructure Architect

Enterprise ArchitectureEnterprise Architecture

Information Integration StandardsInformation Integration Standards

Information Integration / Standards Manager

Metadata Development and Management

Technical Modelling

Common Information Standards

Business Modelling

Data Governance Organizational Model



Governance is a critical aspect of the framework used to solve IM issues

A Networked Governance Model:

� Provides a collaborative framework for the informal network

� Makes it easier to work together across the Organisation

� Enhances the informal network and brings it together into the formal approach


Information GovernanceStrategy

Technology &Architecture

Investigation and Monitoring

Information Governance Organisation

Information Governance Processes


Policies

Information GovernanceStrategy

Technology &Architecture

Investigation and Monitoring

Information Governance Organisation

Information Governance Processes


Policies

Networked Information Governance: Information Governance + Enterprise 2.0Networked Information Governance: Information Governance + Enterprise 2.0

http://mike2.openmethodology.org/index.php/Networked_Information_Governance_Solution_Offering



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.Organising 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.



A collaborative community across the organisationA collaborative community across the organisation

Open Content – Web 2.0

Integrated Approach


Mashup Content – Enterprise 2.0

Social Networking


Tag cloudThe bigger the tag, the more

articles in the category

Categories are on the bottom of each article in the wiki

If you click on the category the relevant articles

appear


Organising the Informal Network: Categories link articles together and to mike2.0Organising the Informal Network: Categories link articles together and to mike2.0


Organising the Informal Network: Categories link articles together and to mike2.0Organising the Informal Network: Categories link articles together and to mike2.0

You can write in any tags you want

You can use categories as

tags

You can select from popular tags



Open Assets (OM wiki, i.e. mike2.0 wiki)

Shared Assets(OM bookmarks)

Private Assets(IM wiki,

IM bookmarks)

Aggregation of Ideas: Solutions are “Mashups” of open, shared and private assets Aggregation of Ideas: Solutions are “Mashups” of open, shared and private assets



MIKE2.0 content on key activities for a project

MIKE2.0 Supporting Assets on specific techniques

Best practices from the web

Aggregation of Ideas: Solutions are “Mashups” of open, shared and private assets Aggregation of Ideas: Solutions are “Mashups” of open, shared and private assets



Link the Formally Developed Content to Informal NetworksLink the Formally Developed Content to Informal Networks


The same principle of applying content categories to the informal network can be applied to more traditional governance processes

This approach is used to link formal and informal assets together.

Formal Information Governance processes are used to define this taxonomy.


Search the Knowledge Network. Looks for assets classified against the standard.Search the Knowledge Network. Looks for assets classified against the standard.


Open Assets (OM wiki, i.e. mike2.0 wiki)

Shared Assets(OM bookmarks)

Private Assets(IM wiki,

IM bookmarks)


Common Categories are Key

Collaborative Asset Management: Asset Maturity is Known and AssessedCollaborative Asset Management: Asset Maturity is Known and Assessed

Then you find the assets

Asset Maturity classified Formally Asset Maturity classified Informally


MIKE2.0 is an Open and Collaborative Global Standards Body for the IndustryMIKE2.0 is an Open and Collaborative Global Standards Body for the Industry
