1. FROM COMPLIANCE TO CUSTOMER 360:
WINNING WITH DATA QUALITY AND
DATA GOVERNANCE

2. 2
SPEAKER & COMPANY INTRODUCTIONS
Harald Smith
Director of Product Management
Trillium Software
• 20 years in Information Management incl.
data quality, integration, and governance
– Consulting, product management,
software & solution development
• Co-author of Patterns of Information
Management, as well as two Redbooks on
Information Governance and Data Integration
Ian Rowlands
Product Marketing Manager
ASG Technologies
Ian is responsible for the communication of ASG's
metadata-management based solutions. He was
previously VP of Metadata Development and VP for
Product Management. Before ASG, Rowlands was
Director of Indirect Channels for Viasoft, a leading
EAM vendor (acquired by ASG), owning relationships
with partners outside North America. Rowlands has
worked extensively in metadata management and IT
systems and financial management

3. DO YOU HAVE CONFIDENCE IN YOUR DATA?
“Diagnosing Key Data Management Challenges,.”
FIMA Benchmark Report, 2017
have deployed data lineage to
shed light on the
transformations their data
undergoes from a point of
origination.
of organizations employ Data
Quality Analysis for Data
Intelligence
But Only 34%
65%

4. POLL QUESTION 1
Which of these use cases of Data Quality/Data Governance are most
important to your organization?
1. Compliance (e.g. GDPR, CCAR, KYC)
2. Risk Mitigation
3. Business Value (e.g. data monetization)
4. Cost Reduction (e.g. process optimization)

5. Volume and Complexity Is
Growing
Compliance Demands Broader
and Deeper
Trust and Confidence in Data Is
Decreasing

6. Can I trust this
data to support
our new business
initiative?
Are we compliant
with Federal
Reporting
Regulations?
Do we have PII
or PCI exposed
in our data lake?
How many
places have we
stored the same
data?

7. SIGNIFICANT CHALLENGES FOR DATA
DRIVEN ORGANIZATIONS
▪ EU GDPR, CCAR, KYC,
AML
• Protect personal data
• Provide for “right to be
forgotten” and data
portability
• Demonstrate “privacy
by design”
• Document data
accuracy, privacy and
security
▪ Public Health Service
Act Section 340B
• Ensure price
transparency
• Enable covered
entities to stretch
Federal resources
• Document pricing,
strength, dosage,
delivery mechanisms
• Report to Health
Resources and
Services
Administration
▪ Data Monetization
• Test ability to
generate new
revenue stream from
available data
• Ensure highest
quality data delivery
to 3rd parties
• Self Service shopping
cart with quality data
sets
• Modernization of
data estate
• Better quality insights
and analytics
• Trusted, traceable
data
Compliance Risk Mitigation Business Value Cost
▪ Claims Processing
• Cost in rejected claims
• Manual rework cost to
fix errors
• Delays in adjusting
claims
• Customer
dissatisfaction
• Impacts and
corrections to
downstream reports

8. POLL QUESTION 2
How integrated, automated and available is Data Quality
for your users today?
1. Very much integrated and available
2. Somewhat integrated and available
3. Not at all integrated or available
4. Integrated but not very available
5. Available but not integrated

9. Risk Data Aggregation: Banks and other financial institutions must show prudent management
of risk.
The Ask: Reliably demonstrate, on demand, knowledge of how risk is calculated and
aggregated.
CHALLENGE
SOLUTION
BENEFIT
Financial institutions must access multiple sources for risk related data such as loan
balances, geographic risk and collateral. This calls into question whether banks know the
lineage, quality, completeness, and accuracy of the data.
With Enterprise Data Intelligence and Trillium data quality, financial institutions can
identify key data elements, use Zero-Gap Data lineage to accurately trace the data
through applications and data flows and judge quality at each stage.
Avoid increased intensity of supervision, be ready for audits, limit cost of expanded
capital “buffers” and other limits on risk-taking and growth opportunities required when
confidence in reliability and quality of compliance metrics is low.
TRILLIUM + ASG INTEGRATION USE CASES

10. JOINT SOLUTION

11. ENTERPRISE DATA INTELLIGENCE (& QUALITY)
SOLUTION OVERVIEW
Comprehensive Metadata
Repository
DI Platform – Deploy On-Premise or Partner Hosted
Policy Based Data Governance Data Quality Insight For Any User
Depth & Breadth: 220 ScannersZero-Gap Data Lineage Transparent Data Flow Visualization

12. DATA LINEAGE DELIVERS CONFIDENCE
Report Audit – Traceability of Process and Data
• Where does this data come from?
• What calculations are applied?
• What records are selected?
• How recent is the report?
• Can you trust this report and all of its data fields?
• Can I trace my data quality?
Knowing the flow of data proves its worth
Source Data ETL Data Warehouses
Data Lakes
Business
Objects
Business
Reports
How
Confident Is
Your Decision?

13. POLL QUESTION 3
Which of the following is most important to you?
1. Quality of critical data elements
2. Gain insight into data quality gaps
3. Quality being performed near source
4. Know where quality is compromised
5. Know if changes affect data/quality
6. View data quality trends over time

14. DATA QUALITY APPLICATION LEVEL
HIGH LEVEL RESULTS

15. DATA QUALITY APPLICATION LEVEL
HIGH LEVEL RESULTS

16. DATA QUALITY APPLICATION LEVEL
HIGH LEVEL RESULTS

17. PATENT PENDING ZERO GAP LINEAGE
DYNAMIC VIEWS OF CROSS PLATFORM TRACING
COBOL
IMS/DBD
COBOL BMS Map
Copybook
JAVA
MAINFRAME
Business Objects Universe
ETL
Oracle EDW
{PLSQL}
EDW to BI
Data Quality
Indicators
Transformation
Indicator
Transformation Detail

18. INCREASED VALUE FROM DATA LINEAGE AND
DATA QUALITY ACROSS YOUR ENTERPRISE
1 Associate quality measures to your critical data elements.
Validity
Sun 05/01/2016 12:00:00 PM MDT
Threshold: 96
Pass: 100
Dimensions: Accuracy
Completeness
Sun 05/01/2016 12:00:00 PM MDT
Threshold: 98
Pass: 99
Dimensions: Completeness

19. 2 Ensure data quality is being performed near the source.
INCREASED VALUE FROM DATA LINEAGE AND
DATA QUALITY ACROSS YOUR ENTERPRISE

20. INCREASED VALUE FROM DATA LINEAGE AND
DATA QUALITY ACROSS YOUR ENTERPRISE
3 Gain insight into where data quality might be compromised by data transformations and why.
4 Understand any changes that may impact critical data elements and data quality.

21. 5 View data quality measures over time (aka, trend analysis).
INCREASED VALUE FROM DATA LINEAGE AND
DATA QUALITY ACROSS YOUR ENTERPRISE

22. 6 View data quality summary or details associated with specified results
DRILL INTO IDENTIFIED DATA QUALITY ISSUES

23. DELIVERING VALUE THROUGH DATA LINEAGE
AND DATA QUALITY INTEGRATION
▪ Associate quality measures to your critical data elements.
▪ Gain insight into where data quality gaps exist.
▪ Ensure data quality is being performed near the source.
▪ Gain insight into where data quality might be compromised by data transformations and why.
▪ Understand any changes that may impact critical data elements and data quality.
▪ View data quality measures over time (aka, trend analysis).

24. TRUSTED DATA FOR BETTER BUSINESS DECISIONS
Data Lineage Data Quality

25. Check out the website at:
www.asg.com
Or reach out to:
Jill Anderson
Jill.Anderson@asg.com
Check out the website at :
www.trilliumsoftware.com
Or reach out to:
Don Marcotte
dmarcotte@syncsort.com
CONTACT ASG CONTACT TRILLIUM
DISCOVER MORE
Download the new Information Management whitepaper, Discover the Value of Data Quality for Data
Governance Success
http://response.trilliumsoftware.com/WP-Info-Management-Discover-Value Data-Quality-For-Data-Gov-
Success-2017