Data is arguably your company’s greatest asset, and a thoughtful data governance strategy, along with robust tools like Collibra Data Governance Center (DGC), is essential to getting the most value from that data. However, even the best data governance programs will falter without data quality. Data governance systems provide a framework for the policies, processes, rules, roles and responsibilities that help you manage your enterprise data. But they don’t give you insight into the characteristics and quality of that data – such as errors, outliers and issues – nor how the data changes over time. During this webinar, we discuss how seamlessly integrating Trillium DQ with Collibra DGC creates a complete data governance solution that delivers rapid insights into the health of your data, ensuring trust and compliance with organizational policies and plans. We demonstrate how data is automatically exchanged between the tools so users can: • Quickly establish the rules needed to support policies • Evaluate their data against those rules on an ongoing basis • Identify problems or improvements with their data quality to take action

1. Unlocking Greater Insights
with Integrated Data
Quality for Collibra
Harald Smith, Dir. Product Marketing
Mike Sisolak, Sr. Sales Engineer

2. Housekeeping
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Recording and slides
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3. Speakers
Harald Smith
• Director Product Marketing, Syncsort Trillium
• 20+ years in Information Management incl. data quality, integration, and governance
• Co-author of Patterns of Information Management
• Author of two Redbooks on Information Governance and Data Integration
Michael Sisolak
• Pre-Sales Consultant for Syncsort
• Specializes in Data Quality, Data Governance, Data Integration and Big Data.
• 20+ years data management experience
• Trillium-Collibra integration expert

4. Terminology
Data Governance
The set of policies, processes, rules,
roles and responsibilities that help
organisations manage data as a
corporate asset.
Ensures the availability, usability,
integrity, accuracy, compliance and
security of data.
Data Quality
The processes and rules that help
ensure that data is “fit for use” in its
intended operational and decision-
making contexts.
Covers the accuracy, completeness,
consistency, relevance, timeliness and
validity of data.
4

5. 5
Areas of common interest
Data Availability
Data Compliance
Defining Key Data
Elements
Assigning Data Stewards
& Council
Glossaries &
Dictionaries
Data Consistency
& Standardization
Monitoring
Analytics
Policies & Rules
Metrics
Data Lineage
Reporting
Cleansing
Enrichment
Parsing
Discovery & Profiling
Matching, Suppression &
Deduplication
Data Quality
ACCURACY
COMPLETENESS
CONSISTENCY
RELEVANCE
TIMELINESS
VALIDITY
Data Governance
PEOPLE
PROCESSES
POLICIES
RULES
STANDARDS
DOCUMENTATION
SECURITY

6. Relevant
Rules &
Policies
Data Quality needs appropriate Data Governance tools to ensure the data is
cleaned and maintained within an appropriate data framework which is relevant
and pertinent to the business needs
Symbiotic relationship between DQ & DG
High
Quality
Data
Data Governance needs appropriate Data Quality tools to not-only clean the raw
data, but to illustrate data errors, peculiarities and issues, in order to help
compile the best standards and monitor the data quality over time
DQDG
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7. Comes down to three main facts of Data Governance
There is a need to know:
Why Trillium and Collibra?
Who owns the data?
Where can I get the data?
Can I trust the data?
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8. Comes down to three main facts of Data Governance
There is a need to know:
Why Trillium and Collibra?
Who owns the data?
Where can I get the data?
Can I trust the data?
Data Quality provides that trust!
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9. One common use case connecting Data Governance (policies) and
Data Quality (validation) is Report Certification
Use case:
• Focus on CDE's (Critical Data Elements) on a few key reports and have
Trillium supply the quality metrics to support those CDE’s
• This can typically be quickly accomplished with a small number of business
rules that ensure the quality of the data in Collibra for those CDE’s
• This allows for the ability to ensure that the data that is put on a regulatory
or compliance report is of the highest quality
Use case example
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10. 10
Business rule created in Collibra

11. Same rule ‘pushed’ to Trillium
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12. Rule detail in Trillium before created by Data Steward
Description from Collibra
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13. Rule detail in Trillium after created by Data Steward
Expression to evaluate rule against the data
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14. Setting acceptable standards by Data Steward
Threshold is adjustable as required
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15. Rule results captured in Trillium
Failing rows available to assist with root cause
analysis and Business Process adjustment
15

16. Updated rulebook in Collibra after ‘push back’
Data Quality Metric was created for rule results
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17. Data quality rule and metric with data from Trillium
Predicate = Expression from Trillium
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18. Data quality metrics for a business term
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19. Data quality metrics over time
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20. 20
Connection to/from Collibra is straightforward
Connect
Application
• Out-of-the-box packaged workflow with Trillium Discovery
• Automatically connect and deliver content through Collibra
Connect Hub via REST API’s
• Collibra Connect Hub provides a single self-service API which
facilitates connecting integrations to Collibra DGC

21. Integrated data duality for Collibra
Collibra Data Governance Center
• Enables non-technical users to define
business policies and data quality rules in
plain language
• Makes data quality performance available
to all users
Trillium Discovery
• Imports DGC business rules so technical user
can convert to executable data quality rules
• Constantly runs data quality metrics on near
real-time basis, passes results back to Collibra
dashboards
Rulebooks to Rules
Quality test Results
Bi-directional connectivity Constant sync
Metric falling below
thresholds can
trigger workflow in
Collibra Issue
Management
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22. • It is challenging for organizations to respond to data governance
policies and requirements in a timely manner even with tools like
Collibra DGC.
• Data typically comes from multiple disparate systems & sources
• The number of touchpoints for policies and rules has grown
dramatically.
• There is a higher demand and expectation for seeing data quality in
context.
• Regardless of the data governance policy, the simple fact is that
they all require metrics and insight based on actual data and
executed rules.
• Integrating data quality directly into data governance is more
critical than ever!
Summary
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23. Questions