1.
How Data
Democratization and
AI Drive the Scope
for Data Governance
Ken Beutler, Sr. Director of Product Management, Precisely
Guest Speaker: Achim Granzen, Principal Analyst, Forrester

2.
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3.
Today’s speaker
Achim Granzen
Guest Speaker
Principal Analyst, Forrester
Ken Beutler
Senior Director, Product
Management, Precisely

4.
Stepping Up
Data Governance
Data
Democratization
gives everyone a
voice
Data Integrity is the
foundation of data
activities
Data Drives
insights
Risk & compliance
safeguard
innovation
Data collaboration
leverages your
assets
7

5.
LOCATION
QUALITY
GOVERNANCE
ENRICHMENT INTEGRATION
Your unique data
integrity journey
will reflect your
business
needs
Data
Integrity
Data integrity
is a journey
8
• Every journey to data integrity is unique and driven
by business initiatives
• Market trends are accelerating the need for data
integrity
• Precisely addresses needs across the data integrity
journey
The Precisely Data Integrity Suite
unites the steps to data integrity
that unlock incremental value

6.
Data
Integration
Data
Observability
Data
Quality
Geo
Addressing
Spatial
Analytics
Data
Governance
Data
Enrichment
Consistency
Accuracy Context
Data Integrity
Foundation MONITORING ORCHESTRATION SECURITY AUTHENTICATION ENTITLEMENTS
9
APIs
AGENTS DATA CATALOG

7.
Precisely Data Governance
module core capabilities
Provide ownership and
accountability of data assets via
roles and responsibilities
Data
stewardship
Link data assets with business
goals, KPI’s, and metrics
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
No code configuration to enable
collaboration & adoption
Flexible
metamodel
10

8.
Leading governance programs
11
Business accountability for master
data and an operating model for
data maintenance and data
governance
Business rules and standards for
data, accessible by those providing
it, and consistent across relevant
business processes
Well-defined data processes to
ensure that master data is captured
correctly, on time, by the right
person in support of the business
processes they support
Tools to capture, monitor and
enforce data standards and
business rules that are appropriate
and ‘fit for purpose’
Successful
data governance
programs

9.
Data governance framework
12
Policies, processes, standards
• Operating model
• Roles & responsibilities
• Data governance team
• Ownership
• Escalation structure
Structure
• Operating model
• Roles & responsibilities
• Data governance team
• Ownership
• Escalation structure
Strategy
• Vision statement
• Objectives & goals
• Building business case
• Building high level roadmap
• Alignment to data strategy
Technology
• Glossaries
• Metadata repository
• Business & technical lineage
• Workflows
• Enable collaboration
Metrics
• Statistics & analysis
• Progress tracking
• Issues monitoring
• Data governance scores
• Data quality scores
Communication
• Rollout Plan
• Communication Plan
• Training Plan
• Onboarding Data Stewards Plan
• Program Management
Data Governance

10.
Business-driven data governance methodology
13
Identify business
assets
Define business-impacting
characteristics
Implement & measure Data governance
initiated!
• Critical fields
• Business glossary
• Business lineage
• Ownership
• Data quality standards
There are many interconnected data assets across the organization. By prioritizing and focusing on specific business
data sets, the data governance program will have achievable goals to demonstrate continuously over time.
• KPI’s & business objectives
• Transformation metrics
• Data quality standards & metrics
• Cycle times/Curation times
• Volumes/counts

11.
Precisely delivers business-ready data
14
Data that is trusted
Data that is easy to
find and understand
Data that’s ready to
deliver outcomes
Precisely

12.
3 methods to connect data to business value
15
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

13.
Connect across all 3 business levels
16
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
“We don’t know where to start; we have so
much data…"
“We struggle with getting business ownership
and interest”
“We don’t have an approach for how we
need to govern our data”
“We don’t know how to measure what ‘good’
looks like…”
“The business rejected other tools because
they were too technical”
Pain Points

14.
The Precisely advantage
Line of sight from important
business initiatives to
critical data assets
Business value visibility
Data assets in simple business
language that is easy to
understand
Easy to understand
More meaningful context about
the data including origination,
value, usage, and
transformations
Deeper understanding
Single place to find data assets
and be confident that they are
accurate, consistent, and
contextualized
Democratized repository

15.
Q&A