1. Data Governance:
Business First,
Govern Always
Melvin Cheong | Sales Engineer

2. The Need for Business-First Governance
of governance
initiatives fail to
deliver expected
outcomes
80%
Source: Gartner
Unrealistic
Expectations
Lack of
Leadership
and
Ownership
Overlooking
Cultural
Factors
Regulatory
and
Compliance
Challenges
Insufficient
Resources
and
Support
Poor
Training
and
Education
Resistance
to
Change
Inadequate
Communication
and
Engagement
Lack of
Continuous
Monitoring
and
Improvement
Lack of
Data Quality
and
Management

3. How to Build a Data Governance
Program That Lasts

4. Governance as a “painkiller” and “vitamin”
Goal DG Objective DG Capabilities
Improve
personalization of
customer products
and services
• Establish trusted view
of customer data
assets
• Data Catalog
• Data Lineage
• Approval Workflow
• Data Integrity rules
Accurate and
timely credit-risk
analysis
• Underwriting
• Loan office
• Finance
• •10% reduction in
expected loss
• •20% lower
Probability of Default
Increase user
productivity by
improving time-to-
insights
• Launch data literacy
campaign across
business data SMEs
• Data lineage
• Data Catalog
• Automated workflow
Mitigate risk and
facilitate regulatory
compliance and
reporting
• Establish risk and
control framework for
regulatory drivers
• PII detection
• Data monitoring
• Access control
Centralized collection
of customer data
elements used for
marketing and
promotion
Data profile providing
additional context on
volume, counts,
location, and contents
Data lineage flow of
upstream/downstream
relationships
Impact analysis to
business processes,
metrics, and analytics
Approved governance
ownership indicating
data is certified for
access and use
Automated approval
workflow to grant
access to data at
source
Data integrity metrics
to indicate data that is
accurate, consistent,
and trusted
Quality monitoring to
trigger notifications
below acceptable
values
P A I N K I L L E R
“ M u s t H a v e s ”
V I T A M I N
“ B o n u s ”

5. Business-First
Data Governance
Prioritize What Matters Most
CRITICAL
DATA
Data
Selection of data maintained at the
system level (tables and fields)
Information
Information required to run the business
and conduct daily operations
KPIs / Performance Measures /
Analytics
Measuring process effectiveness &
enabling sound business decisions
Actionable Insights & Business Value
Strategic enterprise and organizational
business value drivers

6. Business-First
Data Governance
Build Engagement Across 3 Levels
Operational
Strategic
Tactical

7. Key Components to a Sustainable Program
Decision Tree to identify
critical data
Business Accountability for
data with ‘fit for purpose’
operating models/processes
Data Integrity Framework
that ensures the availability,
usability, integrity, and
sustainability of our most
critical data
Data Performance
Measures that organizes
critical data quality and
governance metrics
Successful
Data
Programs

8. Data Integrity Framework
Data Integrity
Data
Integrity
Policies, Processes, &
Standards
Structure
Strategy
Technology
Metrics
Communication

9. Operating Models
Governance operating models
typically established for …
• Data governance artifacts
(business glossary, data dictionary,
data standards, business rules)
• Data profiling/analysis
• Data cleansing/remediation
• KPI’s and business metrics

10. Data Governance Metrics Model
Business impact
• Analytics enablement
• Process enablement
• KPI’s / PPI’s
• Project acceleration
Performance and value
• Data Quality (e.g. accuracy)
• # of touches
• Data error % (Rework %)
• Cycle time vs SLA’s
• Timeliness / availability
Efficiency & effectiveness
• Volume / counts
• Cycle time (Timeliness)
• Completeness
• Accessibility
• Scale (# Systems managed)
Ideal data quality metrics
Metrics are organised and managed in
three (3) main levels or categories
Every metric has a purpose (tells us a story)
• What are we doing?
• How are we doing?
• What’s changing (trends)?
• Are we making an impact?
Metrics are dimensionalized for
proper analysis and action

11. Decision Tree Drives Prioritization
What do we
govern?
How should we
govern?
Governance
Model
Who should
govern?
Where should
we govern?
Identify
Repeatable
Structured
Evaluate
Decision Tree Critical Data Elements
Governance Strategy, Method, Ownership Model

12. Data Catalog
Scavenger Hunt
Increased platform
adoption by 36%
Explainer Videos
Improved DG Council
attendance by 52%
Steward
Gamification
Increased workflow
speed by 18%
Craig

13. Key Components to a Sustainable Program
Decision Tree to identify
critical data
Business Accountability for
data with ‘fit for purpose’
operating models/processes
Data Integrity Framework
that ensures the availability,
usability, integrity, and
sustainability of our most
critical data
Data Performance
Measures that organizes
critical data quality and
governance metrics
Successful
Data
Programs

14. Cloud / VPC / On-Premises
Data
Integration
Data
Observability
Data
Quality
Geo
Addressing
Spatial
Analytics
Data
Governance
Data
Enrichment
APIs and SDKs
Enterprise Business
Systems
• Enterprise apps
• Analytics tools
• Precisely industry
apps
• BI dashboards
• AI/ML
Enterprise Data
Sources
• Business Intelligence
• CRM
• Workforce mgmt.
• Data warehouse
• ERP
• Billing
Data Integrity Services
Data Integrity Foundation Data catalog Intelligence Agents

15. Questions?
precisely.com/solution/data-governance-solutions