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Data Governance in the age of Social Media

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Data is key to all of us. Regardless if you are a banker, retailer, marketer or underwriter, we all strive to know the most about our prospects and customers. We need to know their likes, wants, pain points and a foresight into their interest. And we need to know it before the prospect or customer does. Given the never-ending need for further insights, many of us continually look for new data sources to provide this competitive edge. This is just good business. But there is a need to understand both the predictability and persistence of the data and the insights it provides.

This presentation explores:
The regulatory landscape
The new data sources being tested and used
The implications upon your data governance infrastructure
The path to ensuring your use of the data does not become more of a burden than a benefit

Published in: Business
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Data Governance in the age of Social Media

  1. 1. Data Governance What is it and does social media change the field?
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 19. Receive help with data governance by visiting Experian’s global consulting practice site to help your business.

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