The First Step in Information Management
www.firstsanfranciscopartners.com
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
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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
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Meet
Their Needs
Key
Player
Least
Important
Show
Consideration
Stakeholder Influence
Stak
eho
lde
r In
flu
ence
Stakeholder Interest
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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
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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
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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
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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
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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
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Kelle O’Neal
415-425-9661
@1stsanfrancisco
Thank You!
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