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How to Build Data
Governance
Programs That Last
A Business-First Approach
Esther Lim | Sales Engineering Manager | Precisely
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
“We need to
govern our data!”
A Typical Governance Story
LEADERSHIP
DATA
GOVERNANCE
TEAM
BUSINESS
USERS
DATA
GOVERNANCE
TEAM
BUSINESS
USERS
LEADERSHIP
INCITING
EVENT
Governance
spends more
time fighting
data fires.
Business
quickly loses
interest; stops
attending
meetings
Program
investment is
deprioritized
Asked to
help with
definitions,
approvals, and
ownership.
Team is
tasked with
putting
program in
place
Exec calls for
a data
governance
program
“We need to get the
business involved!”
“How does this help
me do my job?”
“We’re spending a lot more
time fighting data fires.
We need more meetings…”
“These meetings are
a waste of time!”
“I’m not seeing
the ROI”
Successful programs link
Data Governance to organisational goals
Organisational Goals Inform Your Steps
Data to
minimize risk
Data to
make decisions
Data to
run the business
REPORTING & COMPLIANCE ANALYTICS & INSIGHTS OPERATIONAL EXCELLENCE
Data protection
Risk and fraud
Privacy
Safety
Regulatory compliance
Internal reporting
Serviceability
Personalised Communication
Forecasting
Customer 360° view
Optimize working capital
Enhance customer care
Lower operating expenses
Improve citizen engagement
Data to
minimize risk
Data to
make decisions
Data to
run the business
REPORTING & COMPLIANCE ANALYTICS & INSIGHTS OPERATIONAL EXCELLENCE
Data protection
Risk and fraud
Privacy
Safety
Regulatory compliance
Internal reporting
Serviceability
Personalised Communication
Forecasting
Customer 360° view
Optimize working capital
Enhance customer care
Lower operating expenses
Improve citizen engagement
Organisational Goals Inform Your Steps
Mapping Data Governance Business Value
Goal Org Stakeholders Expected Outcomes DG Objective DG Capabilities
Improve
personalization of
services
Marketing
Finance
Service
• Increase NPS by
5%
• 10% increase in
service uptake
• Establish a common
view of trusted
customer data assets
• Data Catalog
• Data Lineage
• Approval
Workflow
• Data Integrity rules
Increase user
productivity by
improving time-
to-insights
Business Analytics
Data Office
Service
• Reduce time-to-
insight by 45%
• Establish stage
gates, rules, policies,
and quality measures
for end-to-end
processes
• DQ rules
• Business process
monitoring
• Data quality
metrics
Reduce costs
associated with
errors in reporting
Business Analytics
IT
Data Office
• Reduce manual
reporting costs by
15%
• Improve decision-
accuracy by 22%
• Launch data literacy
campaign across
business data SMEs
• Data lineage
• Data Catalog
• Automated
workflow
Governance as a “Painkiller” and “Vitamin”
Goal DG Objective DG Capabilities
Improve
personalization of
services
• Establish trusted view
of customer data
assets
• Data Catalog
• Data Lineage
• Approval Workflow
• Data Integrity rules
Increase user
productivity by
improving time-to-
insights
• Establish stage gates,
rules, policies, and
quality measures for
end-to-end processes
• DQ rules
• Business process
monitoring
• Data quality metrics
Reduce costs
associated with
errors in reporting
• Launch data literacy
campaign across
business data SMEs
• Data lineage
• Data Catalog
• Automated workflow
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 ”
Successful programs
prioritise the data that matters
Focusing on what matters (critical data adding value)
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
CRITICAL DATA
Prioritising What Matters
Goal Org Stakeholders Expected Results DG Objective DG Capabilities
Improve
personalization
of services
Marketing
Finance
Service
• Increase NPS by
5%
• 10% increase in
service uptake
• Establish a
common view of
trusted customer
data
• Data Catalog
• Data Lineage
• Approval
Workflow
• Data Integrity
rules
“We need to
personalize our
outreach to
improve services.”
Successful programs
build value across three levels
Value metrics across three levels
Strategic
• Business Transformation Lead
• CDO / Data & Analytics Lead
• CIO
Value Metrics: Business Impact / ROI
• Process enablement
• KPI’s / PPI’s
Value Metrics: Performance Improvement
• Data Quality
(e.g. Accuracy)
• # of touches
Value Metrics: Efficiency & Effectiveness
• Volume / counts
• Completeness
• Accessibility
• Curation times
• Scale (# Systems managed)
• Data Error % (Rework %)
• Cycle time vs SLA’s
• Timeliness / availability
• Customer sentiment
• Project acceleration
Operational
• Business Process Lead
• Data Governance Lead
• Data Management Lead
• Information Architect
Tactical
• Business Data SME
• Data Analyst / Scientist
• Data Steward
• Data Maintenance & Quality
• Data Engineer
The Value Story
• Catalog assets
• Terms defined
• Quality rules developed
• Data owners identified
• Issue requests
Tactical Value Metrics (Inputs)
• FTE Productivity
• Data Literacy index
• Adoption / NPS
• Cycle time
• Data sharing
Strategic Value Metrics (Outcomes)
• We identified 35% more underserved
citizens out of our total population…
• We increased program participation by
25%…
• And we’ve increased funding by 25% to
match participation levels…
• We’ve catalogued 10,000 data assets…
• Defined the top 50 critical business terms …
• Aligned on key rules and policies…
• And our data quality is showing 90+% accuracy
and consistency for reporting data…
Value metrics come together at each level to tell a complete story that resonates.
As a result…
Lead to
Proper Data Governance Removes Friction
Why aren’t people
coming to my monthly
governance meetings?
• Meetings
• Surveys
• Approvals
• Procedures
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
Session Takeaways
• Link data governance to
business goals and outcomes
• Communicate across three
stakeholder levels – Strategic,
Operational, and Tactical
• Prioritise the data and
capabilities that matter the
most. Focus on “painkillers”.
• Quantify business impact with
the value metrics that resonate
across each level
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
precisely.com/solution/data-governance-solutions

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How to Build Data Governance Programs That Last - A Business-First Approach.pdf

  • 1. How to Build Data Governance Programs That Last A Business-First Approach Esther Lim | Sales Engineering Manager | Precisely
  • 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. “We need to govern our data!” A Typical Governance Story LEADERSHIP DATA GOVERNANCE TEAM BUSINESS USERS DATA GOVERNANCE TEAM BUSINESS USERS LEADERSHIP INCITING EVENT Governance spends more time fighting data fires. Business quickly loses interest; stops attending meetings Program investment is deprioritized Asked to help with definitions, approvals, and ownership. Team is tasked with putting program in place Exec calls for a data governance program “We need to get the business involved!” “How does this help me do my job?” “We’re spending a lot more time fighting data fires. We need more meetings…” “These meetings are a waste of time!” “I’m not seeing the ROI”
  • 4. Successful programs link Data Governance to organisational goals
  • 5. Organisational Goals Inform Your Steps Data to minimize risk Data to make decisions Data to run the business REPORTING & COMPLIANCE ANALYTICS & INSIGHTS OPERATIONAL EXCELLENCE Data protection Risk and fraud Privacy Safety Regulatory compliance Internal reporting Serviceability Personalised Communication Forecasting Customer 360° view Optimize working capital Enhance customer care Lower operating expenses Improve citizen engagement
  • 6. Data to minimize risk Data to make decisions Data to run the business REPORTING & COMPLIANCE ANALYTICS & INSIGHTS OPERATIONAL EXCELLENCE Data protection Risk and fraud Privacy Safety Regulatory compliance Internal reporting Serviceability Personalised Communication Forecasting Customer 360° view Optimize working capital Enhance customer care Lower operating expenses Improve citizen engagement Organisational Goals Inform Your Steps
  • 7. Mapping Data Governance Business Value Goal Org Stakeholders Expected Outcomes DG Objective DG Capabilities Improve personalization of services Marketing Finance Service • Increase NPS by 5% • 10% increase in service uptake • Establish a common view of trusted customer data assets • Data Catalog • Data Lineage • Approval Workflow • Data Integrity rules Increase user productivity by improving time- to-insights Business Analytics Data Office Service • Reduce time-to- insight by 45% • Establish stage gates, rules, policies, and quality measures for end-to-end processes • DQ rules • Business process monitoring • Data quality metrics Reduce costs associated with errors in reporting Business Analytics IT Data Office • Reduce manual reporting costs by 15% • Improve decision- accuracy by 22% • Launch data literacy campaign across business data SMEs • Data lineage • Data Catalog • Automated workflow
  • 8. Governance as a “Painkiller” and “Vitamin” Goal DG Objective DG Capabilities Improve personalization of services • Establish trusted view of customer data assets • Data Catalog • Data Lineage • Approval Workflow • Data Integrity rules Increase user productivity by improving time-to- insights • Establish stage gates, rules, policies, and quality measures for end-to-end processes • DQ rules • Business process monitoring • Data quality metrics Reduce costs associated with errors in reporting • Launch data literacy campaign across business data SMEs • Data lineage • Data Catalog • Automated workflow 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 ”
  • 10. Focusing on what matters (critical data adding value) 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 CRITICAL DATA
  • 11. Prioritising What Matters Goal Org Stakeholders Expected Results DG Objective DG Capabilities Improve personalization of services Marketing Finance Service • Increase NPS by 5% • 10% increase in service uptake • Establish a common view of trusted customer data • Data Catalog • Data Lineage • Approval Workflow • Data Integrity rules “We need to personalize our outreach to improve services.”
  • 12. Successful programs build value across three levels
  • 13. Value metrics across three levels Strategic • Business Transformation Lead • CDO / Data & Analytics Lead • CIO Value Metrics: Business Impact / ROI • Process enablement • KPI’s / PPI’s Value Metrics: Performance Improvement • Data Quality (e.g. Accuracy) • # of touches Value Metrics: Efficiency & Effectiveness • Volume / counts • Completeness • Accessibility • Curation times • Scale (# Systems managed) • Data Error % (Rework %) • Cycle time vs SLA’s • Timeliness / availability • Customer sentiment • Project acceleration Operational • Business Process Lead • Data Governance Lead • Data Management Lead • Information Architect Tactical • Business Data SME • Data Analyst / Scientist • Data Steward • Data Maintenance & Quality • Data Engineer
  • 14. The Value Story • Catalog assets • Terms defined • Quality rules developed • Data owners identified • Issue requests Tactical Value Metrics (Inputs) • FTE Productivity • Data Literacy index • Adoption / NPS • Cycle time • Data sharing Strategic Value Metrics (Outcomes) • We identified 35% more underserved citizens out of our total population… • We increased program participation by 25%… • And we’ve increased funding by 25% to match participation levels… • We’ve catalogued 10,000 data assets… • Defined the top 50 critical business terms … • Aligned on key rules and policies… • And our data quality is showing 90+% accuracy and consistency for reporting data… Value metrics come together at each level to tell a complete story that resonates. As a result… Lead to
  • 15. Proper Data Governance Removes Friction Why aren’t people coming to my monthly governance meetings? • Meetings • Surveys • Approvals • Procedures
  • 16. 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
  • 17. Session Takeaways • Link data governance to business goals and outcomes • Communicate across three stakeholder levels – Strategic, Operational, and Tactical • Prioritise the data and capabilities that matter the most. Focus on “painkillers”. • Quantify business impact with the value metrics that resonate across each level
  • 18. 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