The document discusses building successful data governance programs that take a business-first approach. It recommends linking data governance to business goals, prioritizing critical data that drives key business metrics and outcomes, building engagement across operational, tactical, and strategic levels, and clearing a path for success by removing friction for stakeholders. Taking this approach can accelerate program roll-out by 18-40%, increase reinvestment likelihood by over 75%, and generate 2-7x greater ROI.
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3. ACCURACY CONSISTENCY CONTEXT
On-premises, Hybrid Cloud & Cloud
Insurance
Cloud Data Warehousing
Retail
Banking &
Financial
IT Operations Solutions Customer 360
INTEROPERABLE | INTELLIGENT | DYNAMIC
Telco Government Healthcare
Compliance
Precisely Data Integrity Suite
3
4. Connect today’s
infrastructure with
tomorrow’s technology
to unlock the potential of
all your enterprise data
Integrate
Understand your data and
ensure it is accurate,
consistent and complete
for confident
business decisions
Verify
Analyze location data for
enhanced and
actionable business
insights that drive
superior outcomes
Locate
Power enhanced decision
making with expertly
curated, up-to-date
business, location, and
consumer data
Enrich
The four elements of data integrity
D A T A I N T E G R I T Y
5. LEADER
Customer Comms
Management 2020
LEADER
Location Intelligence Platforms
Forrester Wave 2020
LEADER
Data Quality
Magic Quadrant 2021
The Forrester Wave™
2021 Data Governance
CHALLENGER
Data Integration
Magic Quadrant 2020
Precisely has earned industry recognition
DATA INTEGRITY
DATA INTEGRITY
IN ACTION
Integrate Verify Locate Enrich Engage
“Precisely now provides well-integrated
solutions for use cases needing a
combination of data integration
and data quality capabilities.”
“Precisely’s record for innovation in areas such as
data products and geodata enrichment are a key
strength…ideal for commercial enterprises servicing
a range of location intelligence use cases.”
“Precisely is the only
vendor to offer native
real-time personalized
and interactive video.”
6. Governance drives organizational
value in complex data environments
6
• Data integration
• Data cleansing
(data quality)
• Data supplementation
(Reference data)
• Data enrichment
(geocoding)
* Individual could be a profile, non-
profile or corporate customer
• Products and services
purchased
• Household relationships
• Organizational
relationships
• Social network
• Location relationships
• Single view of individual
• Relationships
• All interactions
• Transactional information
with business applications
• Sales, billing,
customer center,
support, etc
• Interactions information
from social media
• Anonymous web & mobile
interactions
• Applying analytical
capability to create insight
• Explore data
• Predict
• Optimize interactions
7. Governance drives organizational
value in complex data environments
7
Organizational
value
Data complexity
Spread sheets
Analytics/reporting
Single domain master data management
“Enterprise” data warehouse
“Multi-domain” master data management
8. “We need to
govern our data!”
8
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”
9. Business-first approach
• Link Data Governance to
business goals
• Prioritize the data that matters
• Build stakeholder engagement
across three levels
• Clear the path for success
12. Business goals inform your steps
REPORTING & COMPLIANCE ANALYTICS & INSIGHTS OPERATIONAL EXCELLENCE
Insured Data Protection
Risk and fraud in claims
Privacy of Insured and Agents
Record Management
Financial Modelling
Internal IT Controls
Net Promoter Score
Timely Claims Processing
Targeted marketing
Insured retention (renewals)
Enhance the Insured Portfolio
Insured 360° View
Reduce Expenses
Enhance Insured Satisfaction
Agency/Agent Management
Data Governance / Data Quality
Metrics
Insured Lifecycle Management
Business Modernization
13. How data drives the business
REPORTING & COMPLIANCE ANALYTICS & INSIGHTS OPERATIONAL EXCELLENCE
Insured Data Protection
Risk and fraud in claims
Privacy of Insured and Agents
Record Management
Financial Modelling
Internal IT Controls
Net Promoter Score
Timely Claims Processing
Targeted marketing
Insured retention (renewals)
Enhance the Insured Portfolio
Insured 360° View
Reduce Expenses
Enhance Insured Satisfaction
Agency/Agent Management
Data Governance / Data Quality
Metrics
Insured Lifecycle Management
Business Modernization
14. Mapping data governance business value
Goal Org Stakeholders Expected Outcomes DG Objective DG Capabilities
Improve
personalization of
offerings and
services
Marketing
Sales
Finance
• Increase new
product intro by 5%
• 17%+ repeat
customer renewals
• 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 errors in
correcting policies
associated with
errors in data
collection
Policy Management
Agencies / Agents
Claims Processing
• Reduce COGS by
4%
• Improve OTIF
by 15%
• Establish common
semantics view
across policies
fulfillment data
• Impact analysis
• DQ rules
• Business process
monitoring
15. Governance as a “painkiller” and “vitamin”
Goal DG Objective DG Capabilities
Improve
personalization of
customer offerings
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
• Business process
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 ”
16. Takeaways
• 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
18. Not all data is created equal
95% of
business
results
5% results
Data Governance
programs that
prioritize critical
data have 5x faster
time-to-value
~5% critical data
95% all other data
19. 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
20. Prioritizing what matters
Goal Org Stakeholders Expected Results DG Objective DG Capabilities
Improve
personalization
of customer
offerings 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.”
21. Prioritizing what matters
“We were able
to accelerate our
program roll-out
30% ahead of
schedule.”
- Director of Data Governance
Fortune 100 Financial Services company
Personalization
22. Takeaways
• Reduce time-to-value by
prioritizing the essential data
that delivers business goals,
metrics, and insights
• Use prioritization frameworks
that make it simple to identify
high value / low effort initiatives
24. 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
25. 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
26. 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 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…
Lead to
27. Takeaways
• Communicate governance value
across three levels – Strategic,
Operational, and Tactical
• Quantify business impact with
value metrics that resonate across
each level
29. Adding value or adding baggage?
• Meetings
• Surveys
• Approvals
• Procedures
Why aren’t people
coming to my monthly
governance meetings?
30. Bowling alley
framework
30
• Remove friction from stakeholders
achieving their desired outcome
• Make it easy for business teams to
contribute
• Keeps teams engaged and
wanting more
32. Takeaways
• Clear a path to your stakeholder’s
desired outcomes
• Use “bumpers” to make it easy on
your stakeholders to contribute to a
strong data culture
This diagram shows at a high level what I mean when I saw the suite is modular and interoperable.
In the center of the diagram, you can see the core competencies of the suite – data integration, data quality, location intelligence, and data enrichment. Each of those offers unique capabilities that solve data challenges – capabilities like data profiling, spatial visualization, integration of data into a cloud data warehouse, and more. You can choose just the capabilities you need to solve your challenges.
Beneath those capabilities you see a foundation that integrate the suite’s capabilities when they are implemented together. As future releases of the suite are delivered, you’ll see additional work done on the foundation to enhance interoperability, expand the way it applies machine learning to tough data integrity challenges, and dynamically response to its environment – taking the suite’s ability to deliver data integrity to the next level.
The suite integrates at all levels with today’s leading data technology providers. Our certified integrations and strategic relationships with partners like Collibra, Databricks, and Snowflake ensure more than just coexistence, but technical and workflow integration with the market-leading technology your business runs on today. And the suite offers APIs that enable developers to expand on its capabilities. So, your in-house team can build solutions to your unique requirements.
But let’s talk a bit more about the suite’s core capabilities.
Analysts recognize us as a LEADER in Data Quality...in Location Intelligence....in Data Enrichment AND in Customer Engagement
...and in 2020, Gartner increased our position in Data Integration from Niche to Challenger…
This lead us to consider the anatomy of the customer graph. We determined it would go beyond just the core customer data to include relationships (to products, households, personal networks, organisations, locations), interactions and insights attached to that customer.
This led us down the path of our noSQL platform for customer information management.