The document discusses the evolving landscape of data governance and the impact of social media on data practices in various industries. It highlights the increasing regulatory requirements for data management and the need for institutions to adopt robust data governance frameworks to ensure compliance and mitigate risks. Additionally, it underscores the importance of leveraging diverse data sources while managing consumer expectations and enhancing data quality.

1. Data Governance
What is it and
does social media
change the field?

2. Regulatory Guidance
Roles, goals and responsibilities surrounding data
Institutions are applying more resources to the use, protection and
governance of data – the regulators are not slowing down
OCC BULLETIN 2011–12
Issued jointly by the Office of the Comptroller
of Currency (OCC) and the Federal Reserve
System, supervisory guidance to ensure
sound practices in:
Data and attribute governance,
• Model validation, development,
implementation and use governance
• Controls, strategies and operations
ECOA / REG B
Assessment of disparate impact by the
CFPB under Regulation B to ensure models
and attributes used in consumer credit
decisions do not unfairly restrict access to
credit

3. The Federal Reserve Bank now requires the largest U.S. banks with assets of
$10B or more to undergo routine stress tests that gauge capital adequacy
COMPREHENSIVE CAPITAL ANALYSIS
AND REVIEW (CCAR)
DODD-FRANK ACT, STRESS TESTING
(DFAST)
Also - Increasing regulatory mandates on forecasting
New data insights are showing value
Under the Dodd-Frank ACT, bank holding
companies with assets of $10B or more are
required to conduct separate annual stress
tests using economic scenarios known as
“company run tests”
The Federal Reserve requires banks with
assets of $50Bn or more to submit to an
annual Comprehensive Capital Analysis and
Review (CCAR) centered on a supervisory
stress test to gauge capital adequacy

4. What is Data Governance?
Data governance is the management of the
data employed in an enterprise:
The nature of the predictive data has changed – it used to be an institutions
internal data and your CRA’s. Now – it is everywhere.
AVAILABILITY USABILITY
A sound data governance program includes:
A GOVERNING BODY OR COUNCIL
A DEFINED SET OF PROCEDURES
A PLAN TO EXECUTE THOSE
PROCEDURES
INTEGRITY SECURITY

5. What is driving lenders, retailers and service
providers to new data sources…
We don’t know what
we don’t know –
Where is the data to
inform?
We already have dozens of scorecards –
How to keep control among
the rising complexity?
Systems features are
becoming ubiquitous -
How do I
differentiate?
Transparency and education are
paramount -
How do I anticipate
needs better?
Consumer
behaviors and
perceptions of
value are
changing rapidly
Consumer expectations are rising -
Control and speed
drives investment

6. Experian view
Key drivers of the future regarding the fraud &
identity landscape
FRAUD DETECTION IS
NOT THE NUMBER
ONE PRIORITY
DEMAND FOR
GLOBAL
CAPABILITIES
INCREASING MOBILE
/ ONLINE
ADOPTION
CONSUMER
DEMAND FOR A
SINGLE CREDENTIAL
ALTERNATIVE TECHNOLOGIES
ARE EXPANDING AND
BECOMING MAINSTREAM
IDENTITY
RELATIONSHIP
MANAGEMENT
MULTI-CHANNEL FRAUD
DETECTION
CONTEXTUAL
AUTHENTICATION
KEY
DRIVERS

7. 7
Frictionless customer interaction & authentication
through the customer lifecycle is now a
competitive requirement
 Identity & credentials authentication
 Device & geo-location risk
 Malware detection
 Out-of-pattern behavior detection
 Identity & credential challenge /
re-authentication
 Device & geo-location risk
 Identity & credentials
authentication
 Identity authentication
 Device risk
 Compliance (KYC)

8. Source: - Google – mobile and digital usage across society (global figures)
… the nature and possible insights of the data generated by this activity grows also
Consumer use and reliance upon social media
devices and applications grows…
84%
Digital and mobile is central to
how people communicate
Of people with SmartPhones
use them to browse the Internet
59%
55%
Of people are on their
Smartphones / iPads while
watching TV
Of people with SmartPhones,
use them to make payments
31%
51%
Of all phones sold globally are
now Smartphones

9. Social media data - implications
Potential to provide an institution
with rich new insights into:
 Customer interests
 Values
 Capacity
 Lifestyle preferences – aspirational
indicators
 Buying SKU linkages
 Timing to next related transaction
 Fraud probability insights
 and of course - Repayment risk

10. Social media data - limitations
This medium is new, adoption is uneven and
usage is evolving‒ there are reasonable
concerns regarding:
 Definition, consistency across sources
 Data, persistence over time
 Performance, outcome consistency
These issues limit usage considerations:
 ‘Disparate impact’ implications are unknown.
 Risk insights seem not yet sufficiently
mature
 Usage within a marketing or acquisition
targeting stage may be a current opportunity.
Need to keep up with changing usage
patterns.

11. But – the consumer has a love/fear relationship with new
technologies and their participation may be fickle
Government
data accumulation –
and leaks
37% employers use social
media to screen applicants
Retailers, lenders and
service providers suffer
consumer confidence with
frequent data breaches
Computer virus
proliferation
Personal
devices
easily
hacked

12. Is there another view on the accumulation of data?
“Big data’s approach of collecting as much data as you can, even if it seems irrelevant,
because it may reveal a previously unknown correlation, also collides with the “data
minimization” principles of data privacy laws, which say that you only collect the data you
need to do the job.”
ZDNet’s Stiligherrian

13. More data from an ever expanding number
of sources will play a bigger role - everywhere
Lenders &
service
providers
Explore the value of new data
sources as they appear
Consumers
Demand more openness and
information on how data is used
Regulators
Broaden their mandate as behaviors
change and data is available
Data users
Navigate compliance challenges while
seeking greater insights into behaviors
to establish a sound financial position
balanced with maximum profitability

14. Data Governance ecosystem will have the same
set of goals
DATA
GOVERNANCE
To identify
inconsistencies in
deployment
To provide clear documentation for data
received via third party or internal sources
To achieve improved
compliance and avert
reputation risk
To deliver gap
mediation
To ensure compliance
with all applicable
regulatory requirements
For improvement in
scores, policies and
strategies
To reduce operational risk
associated with the use of
third party sourced data

15. Data quality lifecycle management
PROFILE: Find, catalog, discover unknown unknowns
ASSESS: Measure data quality, analyze root cause of
any deficiencies
QUANTIFY: Assign business impact and prioritize
TRANSFORM: Cleanse, consolidate and standardize
ENRICH: Integrate reference data as possible
PROTOTYPE: Dynamically design and validate
improvements
DEPLOY: Implement business data quality rules
REPORT: Measure business KPIs
ASSURE: Monitor data quality over time
New data sources will only make this virtuous cycle all the more important
ANALYZE
IMPROVE
CONTROL
ENTERPRISE
DATA
ASSETS

16. Typical findings
These findings will require a gap remediation plan to:
 Scope the extent of the gap and to provide guidance on business impact
 Identify issues in conflict with regulatory guidance
 Provide a rank ordering of the issues for the business to address
Variations from industry standard, best practice and regulatory compliance
Inconsistencies and variations in definitions across attributes
Inconsistencies in definitions across credit bureaus and other providers
Missing or inaccurate fields / values

17. Data governance improvement roadmap
DISCOVERY
DOCUMENTATION, GAP
REMEDIATION AND
VALIDATION
MONITORING
AND
REPORTING
ONGOING AUDIT,
REPORTING AND
DOCUMENTATION
DISCOVERY
• Gain understanding of existing processes and documentation through:
- Discovery and SME interviews
- Detailed information review at attribute level
• Gap analysis and roadmap execution creation
ATTRIBUTE DOCUMENTATION, GAP
REMEDIATION AND VALIDATION
• Document recommendations to best practice
• Perform impact simulation and ranking
• Augment documentation
MONITORING, NEW ATTRIBUTE DEV.
AND IMPLEMENTATION SUPPORT
• Develop ongoing monitoring MIS and
quality reporting.
• Develop and document protocols for new
attribute mgmt.
ONGOING AUDIT AND
MAINTENANCE
• Schedule ongoing audits and reports
• Monitoring third party data providers format/data
changes
• Assign responsibility for management of ongoing
action plan and documentation

18. Conclusion
 Consumers behaviors will continue to
change
 These changes and the business
insights will be revealed in a
continuously changing set of data
sources
 Regulatory and business MI demands
can only be met with a high intensity
data integrity ecosystem
 It requires an investment – but it is
worth it.
Hmmmm…How to
keep the wolves at
bay…

19. Receive help with data
governance by visiting
Experian’s global
consulting practice site
to help your business.