The financial services sector is investing heavily in data governance solutions to find, understand and trust customer data, while also managing compliance risk around an ever-evolving regulatory landscape more effectively.  But do you still find it difficult to get management support for data governance budgets? Do you have the tools you need to determine the “business cost of data” accurately? Can you show the CFO an ROI projection he can count on? Are you able to answer, “Will I see results on the top line or the bottom line?” Are your business line leaders able to identify areas that are losing money due to data problems? If you answered no to any of these questions, join Precisely in our upcoming webinar that will focus on how Financial Services companies can monetize the return on investment for data governance and how to relate it to business results that every senior leader understands. Join this on-demand webinar to learn about: - How to select data initiatives based on corporate goals and strategy - How to connect the dots from data challenges (quality, availability, accuracy, currency) to specific business metrics around - How to quantify the data contribution to improving business performance around - How to leverage metadata and linage to get a 360-degree understanding of your data - How to evaluate data assets by assigning measures and defining scores. - How to assign accountability to assets and processes - How to define and execute the workflows needed to implement corrective actions - How to highlight the benefits of data governance

1. Data Governance That
Drives the Bottom Line
Focusing data governance to improve
financial results

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Recording and slides
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3. “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”

4. Data Governance drives value creation
• Gartner “Data governance is the specification of decision rights and an accountability
framework to ensure the appropriate behavior in the valuation, creation, consumption and
control of data and analytics.”
• DAMA International “Data governance is the exercise of authority and control (planning,
monitoring, and enforcement) over the management of data assets.”
• Data governance Institute “Data governance is a system of decision rights and
accountabilities for information-related processes, executed according to agreed-upon
models which describe who can take what actions with what information, and when, under
what circumstances, using what methods.”
• Data governance 2.0 , just enough data governance, data governance framework, etc.
4

5. Benefits of a
business-first
approach
Accelerate program
roll-out by 18-40%
Increase likelihood of
reinvestment by over 75%
Generate 2-7x greater ROI

6. Mapping data governance to business value
Goal Org Stakeholders Expected Outcomes DG Objective DG Capabilities
Improve
personalization of
customer products
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
Accurate and timely
credit-risk analysis
• Underwriting
• Loan office
• Finance
• 10% reduction in
expected loss
• 20% lower Probability
of Default
• Establish stage gates,
rules, policies, and
quality measures
across credit risk
analysis process
• Analytics governance
• Model analysis
• 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
Mitigate risk and
facilitate regulatory
compliance and
reporting
• Compliance Office
• Finance
• IT
• 10% improvement to
Reputation Index
• 15% reduction in
regulatory fines and
settlements
• Establish risk and
control framework
for regulatory
drivers
• PII detection
• Data monitoring
• Access control

7. 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 ”

8. Prioritizing what matters
Goal Org Stakeholders Expected Results DG Objective DG Capabilities
Improve
personalization
of customer
goods and
services
Marketing
Sales
Finance
• Increase referrals
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.”

9. Operational
Bridging the gap between business & IT
Strategic
Tactical
e.g., KPIs / metrics,
strategic programs,
data privacy & protection
e.g., product development,
planning, sourcing,
manufacturing
e.g., data migrations, system
implementations, data
science & engineering
Critical data that drives
business processes
and operations
Grow the Business
Critical data assets that have
operational, compliance and
analytical business impacts
Run the Business
Critical information driving
business goals, objectives,
KPIs, and metrics
Transform the Business

10. Connecting Critical Data
10
Business / Program Goals
(e.g., Growing cross-sell, digital enablement)
Objectives and Metrics
(e.g., Data Sharing)
Governance Framework
& Operating Model
(e.g., Data Ownership, data stewardship)
Information (business terminology)
(e.g., P&C glossary for self-service)
Data
(e.g., Horace Mann critical data)

11. 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)
• Our customer onboarding process has
decreased by 25%...
• We’re able to identify 33% more customers
to cross-sell of lending products…
• 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 customer data assets …
• Aligned on key rules and policies for each…
• And our data quality is showing 90+% accuracy
and consistency for customer objects…
Value metrics come together at each level to tell a complete story that resonates.
As a result…
Lead to

12. How Precisely Data360 can help

13. Four Must-Haves for Data
Governance Success Recap
• Link data governance program initiatives
to higher-level business goals, stakeholders,
and business outcomes
• Deploy data governance capabilities that
directly serve as both painkillers and
vitamins to protect and grow the business
• Communicate Governance Value across
three levels – Strategic, Operational, and
Tactical
• Quantify business impact with value
metrics that resonate across each level

14. Precisely Data360 for financial services
14
Third party
data
validation
Policy
enforcement
Billing and
payment
reconciliation
Portfolio
reconciliation
Regulatory
risk
management
Validate data
transformation
Business-ready
data for
financial services
View operational data to mitigate
risk across the enterprise
Ensure books of record are
synchronized and accurate across
positions, trades, cash and third-
party sources
Reconcile invoices and payments to
ensure accuracy of results
Ensure critical 3rd party data is
timely, accurate and reconciles with
internal data
Establish and monitor adherence to
policies including CECL, IFRS9, SOX,
privacy, etc.
Validate data moving to the cloud
or data lake or system conversion
and consolidation

15. DG Capabilities: Painkillers & Vitamins
15
Provide ownership and
accountability of data assets via
roles and responsibilities
Data
stewardship
Obtain general statistics to learn
more about a field
Visualization
Visually connect impact analysis,
data lineage and business
processes with related data
assets
3D data
lineage
Utilize AI techniques to
automatically tag data for
categorization or to relate data
together
Machine
learning
Aggregate data quality results
and present data governance
scores by asset
Metrics &
scoring
Understand your data with
definitions, context and
crowdsource updates
Business
glossary
Customize your operating model
for reporting issues, questions or
approvals
Workflow
Harvest metadata and allow
business and technical metadata
to be searchable
Data catalog
Document policies and standards
and their relationships to data
Data policy
management

16. Link DG to higher-level goals
16
e.g., Inventory management, customer
onboarding, new product introduction,
financial reconciliation, etc.
e.g., SAP S/4 implementation(s), data
remediation system migrations, data
science & engineering, etc.
e.g., Enterprise KPIs / metrics, data privacy
& protection, strategic business drivers, etc.
Bottom
up
Middle
out
Top
down
Critical data that drives
business processes
and operations
Middle out
Critical data assets that have
operational, compliance and
analytical business impacts
Bottom up
Critical information driving
business goals, objectives,
KPIs, and metrics
Top down

17. Financial Services Precisely Data360 Demo
17
Demo Scenario
Investment Use Case
• User needs to create a report on
Positions
• Find the data they are looking
for
• Understand what other assets
are related to that data
• What is the Quality of the data
• Request access to the data.
• Data Stewards review request
and grant access.
Where to Start What You Will See
• Business Friendly UI
• Ability to search for both
business and technical
information
• Connecting business context to
physical data
• Collaboration across teams
• Data Stewardship
• Use CUSIPs to identify the
securities that make up the
position.
– CUSIPs are unique North
American security identifier
Role Based
• Business Analyst Point of View
– Focus on high-level strategic
policies and processes
• Data Steward Point of View
– Focus on tactical coordination
and implementation of data
usage and policies

18. Precisely Data360 demo

19. Key takeaways
1. Understand what data governance is
2. Understand how key financial measures
are impacted by data governance
3. Be able to connect business goals,
objectives & value with measured impacts
& risks
4. Be able to achieve 2 & 3 above with ease
5. Be able to identify the accountable party
and take corrective actions

20. Q&A