How to Make a Data Governance Program that Lasts

Business-first
Data Governance
2022
Cam Ogden | VP of Product Management
Precisely

65%
of data citizens
don’t know how
data governance
impacts their role
The Need for Business-First Governance
80%
of governance
initiatives fail to
deliver expected
outcomes
74%
of data leaders struggle to
calculate the ROI of data
governance projects
Gartner Forbes
HBR

“We need to
govern our data!”
3
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”

How to Build “Business-First”
Data Governance

Successful programs
link Data Governance to value drivers

Business 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
Net Promoter Score
Website traffic
Targeted marketing
Customer retention
Buying patterns
Customer 360° view
Optimize working capital
Enhance customer care
Improve product traceability
Reduce attrition
Lower operating expenses
Facilitate M&A

How data drives the business
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
Net Promoter Score
Website traffic
Targeted marketing
Customer retention
Buying patterns
Customer 360° view
Optimize working capital
Enhance customer care
Improve product traceability
Reduce attrition
Lower operating expenses
Facilitate M&A

Mapping data governance business value
Goal Org Stakeholders Expected Outcomes DG Objective DG Capabilities
Improve
personalization of
customer goods
and services
Marketing
Sales
Finance
• Increase NPS by 5%
• 17%+ repeat
customer purchases
• 11% reduced churn
• Establish a common
view of trusted
customer data
assets
• Data Catalog
• Data Lineage
• Approval
Workflow
• Data Integrity rules
Increase sales
and revenue
through faster
speed-to-market
Marketing
R&D
Finance
• $15M+ top-line
revenue
• 25% increased
deployment speed
• Establish stage
gates, rules,
policies, and quality
measures from
Ideation through
Commercialization
• DQ rules
• Business process
monitoring
• Data quality
metrics
Increase user
productivity by
improving time-
to-insights
Business Analytics
IT
Data Office
• Improve decision-
accuracy by 22%
• Reduce time-to-
insight by 45%
• Launch data
literacy campaign
across business
data SMEs
• Data lineage
• Data Catalog
• Automated
workflow
Reduce supply
chain costs
associated with
errors in orders
Vendor Management
Finance
Supply Chain
• Reduce COGS
by 4%
• Improve OTIF
by 15%
• Establish common
semantics view
across order
fulfillment data
• Impact analysis
• DQ rules
monitoring

Governance as a “painkiller” and “vitamin”
Goal DG Objective DG Capabilities
Improve
personalization of
customer goods
and services
• Establish trusted view
of customer data
assets
• Data Catalog
• Data Lineage
• Approval Workflow
• Data Integrity rules
Increase sales
through faster
speed-to-market
• Establish stage gates,
rules, policies, and
quality measures for
Commercialization
process
• DQ rules
monitoring
• Data quality metrics
Increase user
productivity by
improving time-to-
insights
• Launch data literacy
campaign across
business data SMEs
• Data lineage
• Data Catalog
• Automated workflow
Reduce supply
chain costs
associated with
errors in orders
• Establish common
semantics view
across order
fulfillment data
• Impact analysis
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
prioritize 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

Prioritizing what matters
Goal Org Stakeholders Expected Results DG Objective DG Capabilities
Improve
personalization
of customer
goods and
services
Marketing
Sales
Finance
• Increase NPS by
5%
• 17%+ repeat
customer
purchases
• 11% reduced churn
• Establish a
common view of
trusted customer
data
• Data Catalog
• Data Lineage
• Approval
Workflow
• Data Integrity
rules
“We need to
personalize our
outreach to
reduce churn.”

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

Building the Business Case
Operational
Data error %
Calculation
• 10,000 Customer records
• Total cost per error is $50
(2 people * 10 minutes * $2.50/minute)
• Assume 25% of errors can be avoided
with a governance program = 2,500
• 2500 * $50/savings = $125,000
Data remediation variables
Each data error takes
two people to fix
Each error takes each person
10 minutes to fix
Each person costs $2.50 per
minute (fully loaded cost)

• Master data degrades 2-7% per month (Gartner)
• Every data error has a 6X impact on business
processes
• Avg. cost to fix a data error is $20
• Avg. cost to remediate a data error that impacts
a business process is $16,500
• 17% higher data accuracy for organizations that
have a global data governance operating model
• 13% higher data accuracy for organizations that
have established data governance automation
• 37% lower “IT projects delivered over budget” for
organizations with Data Stewards
Presentation name
16
Choosing benchmarks that resonate
$XXX MM
$XX MM
$XX MM
Value tied to your specific
governance strategy
Benchmark data
Conservative estimate
of benchmark data

Value metrics across three levels
Strategic
• Business Transformation Lead
• CDO / Data & Analytics Lead
• CIO
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
$182M
$14M
$28M
Full Realization
Expected Target
Minimum Target
Increased Revenue from
New Products
$2.4M
$04M
$1.1MM
Full Realization
Expected Target
Minimum Target
Cost of Delayed
Product Information
$3.7MM
$1.1M
$1.7M
Full Realization
Expected Target
Minimum Target
Cost of Inaccurate
Product Data

Use Case
• 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)
• Our supplier data setup process has
decreased by 25%...
• We’re able to identify top 20 vendors
33% faster for contract renegotiations…
• And we’ve increased FTE productivity
by 20% due to data self-service …
• We’ve catalogued 10,000 supplier data assets…
• Defined the top 50 critical supplier terms …
• Aligned on key rules and policies for each…
• And our data quality is showing 90+% accuracy
and consistency for supplier spend data…
Value metrics come together at each level to tell a complete story that resonates.
As a result…
Lead to

Business-first
Data Governance
Link to business goals
Goal
P A I N K I L L E R S
V I T A M I N S

Business-first
Data Governance
Prioritize what matters
~95%
~5%

Business-first
Data Governance
Build value across 3 levels
Operational
Strategic
Tactical

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How to Make a Data Governance Program that Lasts

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