Operationalizing Data Governance - An Effective ... Data Governance Components •Vision & Mission •Objectives & Goals •Alignment with Corporate Objectives •Alignment with Business

The First Step in Information Management

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Operationalizing Data Governance - An Effective, Accountability Based Approach for

the Enterprise

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Agenda

Understanding Stakeholders

Building an Organizational Operating Model

Creating Accountability

Implementing an Execution Operating Model

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Change Management

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Change Management

Stakeholder Management

Commun-ication

Account-ability


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Purpose: Increase Stakeholder Engagement


Using this framework enables clear gaps in stakeholder engagement to be identified and subsequent change strategies to be put in place to enable the gaps to be closed

T I M E Status Quo Vision

CO

MM

ITM

EN

T /

EN

TH

USIA

SM

High

Contact I’ve heard about this program/project

Low

I know the concepts

Awareness

I understand how Program/project positively impacts

and benefits me and the organization

Positive Perception

This is how we do business

Institutionalization

Understanding I understand what this means to me and the organization as a whole

Adoption I am willing to work hard to make this a success

Internalization I’ve made this my own and will constantly create innovative ways to use it

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• Engagement Strategy: • Focused effort must be given

to high priority groups

• Provide sufficient level of information to less influential groups to ensure buy-in

• Move people and or groups to the right by trying to increase their level of interest

• Forms the foundation of your engagement / communication strategy

Stakeholder Engagement Strategy


Meet

Their Needs

Key

Player

Least

Important

Show

Consideration

Stakeholder Influence

Stak

eho

lde

r In

flu

ence

Stakeholder Interest

4

Organization


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Data Governance Components

• Vision & Mission

• Objectives & Goals

• Alignment with Corporate

Objectives

• Alignment with Business

Strategy

• Guiding Principles

• Statistics and Analysis

• Tracking of progress

• Monitoring of issues

• Continuous Improvement

• Score-carding

• Policies & Rules

• Processes

• Controls

• Data Standards & Definitions

• Metadata, Taxonomy,

Cataloging, and Classification • Operating Model

• Arbiters & Escalation points

• Data Governance

Organization Members

• Roles and Responsibilities

• Data Ownership &

Accountability

• Collaboration & Information

Life Cycle Tools

• Data Mastering & Sharing

• Data Architecture & Security

• Data Quality & Stewardship

Workflow

• Metadata Repository

• Communication Plan

• Mass Communication

• Individual Updates

• Mechanisms

• Training Strategy

• Business Impact & Readiness

• IT Operations & Readiness

• Training & Awareness

• Stakeholder Management & Communication

• Defining Ownership & Accountability

Change

Management


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Process

• How are decisions made?

• Who makes them?

• How are Committee’s used?

Culture

• Centralized

• Decentralized

• Hybrid

Operating Model • Data Governance

Owner

• SME’s

• Leadership

People


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Operating Model

Outlines how Data Governance will operate

Forms basis for the Data Governance organizational structure – but isn’t an org chart

Ensures proper oversight, escalation and decision making

Ensures the right people are involved in determining standards, usage and integration of data across projects, subject areas and lines of business

Creates the infrastructure for accountability and ownership


Wikipedia: An Operating Model describes the necessary level of business process integration and data standardization in the business and among trading partners and guides the underlying Business and Technical Architecture to effectively and efficiently realize its Business Model. The process of Operating Model design is also part of business strategy.

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Style: Centralized

Pros:

• Formal Data Governance executive position

• Data Governance Steering Committee reports directly to executive

• Data Czar/Lead – one person at the top; easier decision making

• One place to stop and shop

• Easier to manage by data type

Cons:

• Large Organizational Impact

• New roles will most likely require Human Resources approval

• Formal separation of business and technical architectural roles

Bus / LOBs

Operating Model - Centralized


DG Executive Sponsor

DG Steering

Committee

Center of Excellence (COE)

Data Governance Lead

Technical Support

Data

Architecture

Group

Technical Data

Analysis

Group

Business Support

Business Analysis Group

Data Management

Group

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LOB/BU Data Governance Steering Committee

LOB/BU Data Governance Working Group

Operating Model - Decentralized


Data Stewards Application

Architects

Business

Analysts Data Analysts

Style: Decentralized

Pros:

• Relatively flat organization

• Informal Data Governance bodies

• Relatively quick to establish and implement

Cons:

• Consensus discussions tend to take longer than centralized edicts

• Many participants compromise governance bodies

• May be difficult to sustain over time

• Provides least value

• Difficult coordination

• Business as usual

• Issues around co-owners of data and accountability

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Operating Model - Hybrid


Style: Hybrid

Pros:

• Centralized structure for establishing appropriate direction and tone at the top

• Formal Data Governance Lead role serving as a single point of contact and accountability

• Data Governance Lead position is a full time, dedicated role – DG gets the attention it deserves

• Working groups with broad membership for facilitating collaboration and consensus building

• Potentially an easier model to implement initially and sustain over time

• Pushes down decision making

• Ability to focus on specific data entities

• Issues resolution without pulling in the whole team

Cons:

• Data Governance Lead position is a full time, dedicated role

• Working groups dynamics may require prioritization of conflicting business requirements

• Too many layers

Data Governance Steering Committee

Data Governance Office

Data Governance Working Group

Business Stakeholders IT Enablement

Data Governance Organization

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Data Governance Leadership Team

Sample Multi-Domain Operating Model

Program Oversight & Direction

Executive Sponsor

Program Management

DG Working Group Data Governance Program Management Team

DG Program Manager

DG Coordinator

Program Execution

IT Manager

Data Domain Owners

Business Data Leads

Data Acquisition

Data Stewardship

IT Enablement

Supply Chain International Sales HR Finance IT Marketing

Customer Product Employee Vendor Supplier

DG Data Quality Manager


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Sample Operating Model

EIM Team

Governance Council Governance

Council Governance

Council (Virtual)

Enterprise Risk and

Other Operational

Groups

Governance Council Governance

Council

Projects, Lines of

Business and Horizontal

Teams

Accountable to

Collaborates to operationalize EIM, direct involvement in work if needed

Oversight and assurance of work being done

EIM

Tea

m

Data Governance

Office

Content Governance

Office

Architecture Governance

Office

Supporting Resources

Drives Data Governance Council

Drives Information Governance Council

Drives Architecture Governance Council

Resources to help on projects or particular tasks (should be minimal)


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Operating Model

Operating Model

Organizational Structure


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Implementing Accountability


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Iterative Approach

Driven by DG Team • Consistent DG frameworks • Data management across the enterprise • Progress metrics, accountability

Driven by DG • Disciplined review, alignment and agreement • Prioritize across tracks by DG with managed backlog • Analyze metrics and continuously assess progress • Continuous improvement to achieve overall goals, including

regular assessment of approach

Driven by Data Domain or Process Area • Enabled by DG • Ownership of execution per Data Governance Roadmap

• Guidelines • Processes • DQ • Master Data • Meta Data • …

• Accountable via metrics and participation in DG

DG Management

Track 1

Track N

Iteration Management

Bac

klo

g


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Roadmap

Reporting

Data Quality

Alignment & Iteration Management

Data Governance Leadership

2 week iterations

Iteration 1 Iteration 2 Iteration 3 Iteration 4 Iteration N Iteration N+1 Iteration N+M Sustain —>

Iteration Plan

and Evaluation

Criteria

Knowledge Sharing (SharePoint)

Meeting Management

Release Exit - Formal reassessment of DG

effectiveness, operating model, metrics,

process, etc.

Communication

Define Scope Draft

Engagement

Process with

SDLC/EMPO

Draft Data

Classification

Guideline &

Process

Iteration Evaluation

Master Data Management To Be Confirmed

Draft Issue

Resolution

Process for DQ

Draft New

Element

Guideline &

Process

Draft Data

Guidelines

(Overall Policy)

Clarify Roles /

Responsibilities Create Meeting

Protocols

Create Decision

Making Process Draft Guiding

Principles

Deliverable Review


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Metrics

Common Data Domain Metrics (Progress Metrics)

− Number of requests following documented process

− Number of requests not completed

− Average time to request completion

Specific Data Domain Metrics (Impact Metrics):

− Data Quality

− Process Efficiency

DG Metrics

− Size of the backlog

− Number of escalations (and number being worked on, vs. number put into

backlog)

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Maturity Model

Simple measurement across 5

categories

− Objective as much as possible

− Score from 0-5

− Clear descriptions of what each

score means

Reviewed every iteration

Don’t necessarily have to achieve a 5

in everything

Allows organization to visualize where

we are and where we need to get to

− DG helps clearly identify steps to

get to desired state


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Common Iteration Evaluation Checklist

Data Sharing - Are there any data sharing requirements that should be added to backlog?

Review of priorities and prioritization for the next iteration

Review of Schedule and if schedule of previous iteration was incorrect, assessment of cause

Are we using the right process?

Are the right common metrics being measured and are the right DG metrics being measured?

Review of the actual metrics measurement (for example DG backlog size)

DG site review (operating model accuracy, updated dashboard and metrics, other materials current and correctly versioned, etc.)

Review accuracy of operating model

If this is end of iteration N, Iteration N+1 should be very well defined (scope locked down), N+2 should be largely defined (scope locked, except for impact of iteration N+1 evaluation), Iteration N+3 should have proposed scope

Assessment for each track as to what DG enablement means for upcoming iterations

Version assessment of all current requestor packages, in particular documented processes


Kelle O’Neal

[email protected]

415-425-9661

@1stsanfrancisco

Thank You!


Documents

Operationalizing Data Governance - An Effective ... Data Governance Components •Vision & Mission •Objectives & Goals •Alignment with Corporate Objectives •Alignment with Business