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Changing Data Standards from Wall Street to DC & Beyond PowerPoint Presentation
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Changing Data Standards from Wall Street to DC & Beyond

Changing Data Standards from Wall Street to DC & Beyond

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Changing Data Standards from Wall Street to DC & Beyond

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  1. Changing Data Standardsfrom Wall Street to DC & Beyond John Mulholland Vice President for Enterprise Data Fannie Mae February 29, 2012

  2. Agenda Impetus for Change Technology Maturity Comparison Current State Future State Roadmap to Success The Balance Challenges & Opportunities Changing the Industry – Fannie Mae Leading Change

  3. Impetus for Change Turmoil in the financial industry has created a need for greater transparency 2007-Investment Banks, Bear Sterns & Lehman Brothers Collapse 2008-Goldman Sachs & Morgan Stanley abandon their status as investment banks 2010-Dodd-Frank Wall Street Reform and Consumer Protection Act 2008-Banks received $700B TARP funds 2008 2009 2010 2007 2011 Early 2010, Fannie Mae launches enterprise-wide data management program Fannie Mae deploys new capabilities in data controls & begins streamlining data infrastructure On September 7, 2008, James Lockhart, director of the Federal Housing Finance Agency (FHFA), announced that Fannie Mae and Freddie Mac were being placed into conservatorship of the FHFA. Wall Street to DC Digitization Data Mining Innovation Semantics Business Intelligence Industry Standards Proactive Data Quality The push to manage enterprise data is often a result of external forces

  4. Maturity of Mortgage Industry – a Comparison Airline Industry: near real-time tracking of all flights Credit Card Industry: American Express can detect fraudulent activity based upon your spending habits in near real-time, often denying charges on the spot The mortgage industry lags other industries in technology innovation

  5. Maturity of Mortgage Industry – a Comparison (cont’d) Other industries can track data near real-time, but our partners in the mortgage industry have difficulty tracking the status of their loans in real-time Secondary Mortgage Market • Buried under paper • Manual processes • Minimal automation The mortgage industry lags other industries in technology innovation

  6. Current State Current State infrastructure is complex and lacks automation…. Legacy point-to-point interfaces create unnecessary complexity

  7. Future State Future State infrastructure enables straight-through processing and offers operational efficiencies…. Trusted Sources of Data Data should be trusted as it flows with the proper data management controls

  8. Roadmap for Success Multi-year planning and funding required for execution • Constant focus on business value and innovation Continually Improve • Data Management practices become a part of the “fabric” of the company • Continue to build and refine target state enterprise capabilities • Continuous improvement and future readiness • Focus on innovative technology solutions • Establishes business accountability • Focuses on critical data needed to be managed at enterprise level Execute & Integrate • Integrate data management practices into development process • Focus on greatest business value • Adapt solution and reduce technology footprint • Embed target state enterprise capabilities in business Build Foundation • Implement data management tools to focus on data quality, metadata, and data security • Build enterprise-wide data governance processes • Integrate data governance, data quality, metadata, and data security practices • Defines plans for enterprise Define & Design • Define Enterprise Data Management strategy • Design enterprise data architecture Iterative execution must be tied to business value

  9. The Balance The triangle of people, process, and technology is fundamental and requires equal investment for success People Managing people and culture change Data--->Information Creating and Integrating Processes Enabling the business and innovation Technology Process Managing the challenges across people, process, and technology is critical for change

  10. Challenges: People Opportunity Challenge Invest in a strong Data Governance program Lack of accountability Put the “right” people in the “right” seats Lack of skills Data “hoarding” Data is an enterprise asset Resistance to change Executive level support Changing behavior requires a broad change management approach

  11. Changing Organizational Structures…. Cathryne Clay Doss of Capital One was appointed Chief Data Officer in 2003 Wikipedia Dr. Usama Fayyad, Chief Data Officer and Sr. Vice President of Yahoo!, was one of the first people known to officially hold this job title Wikipedia Citi was the first in the finance industry to name a Chief Data Officer 2007 “The role of Chief Data Officer emerges…it’s crucial to have a C-level person who is responsible for crafting and implementing data strategies, standards, procedures, and accountability policies at the enterprise level.” Information Management 2008 Bank of America Names John Bottega Chief Data Officer December 2011

  12. Challenges: Process Opportunity Challenge Enforce enterprise-wide data standards Lack of data standards Enterprise-level process integration Siloed processes Integrate within software development and architecture review processes No integration with development process The implementation and integration of enterprise-wide processes requires constant focus and attention from top executives

  13. Challenges: Technology Opportunity Challenge Consolidated trusted sources Data silos Data volumes and velocity Data optimization and scalability Simplify data architecture Complex data architectures Services-based architecture Real-time enterprise requirements Lack of straight-through processing Automated controls and monitoring Structured and unstructured data (email, video, logs, system events etc) Leverage “Big Data” solutions The mortgage industry needs to focus on technology innovation

  14. Technology for enterprise-wide data mgmt

  15. Origination Delivery Servicing Loss Mitigation How we are changing the industry….. Industry Standards Servicing Alignment Initiative Uniform Loan Delivery Dataset Primary Market Secondary Market Enhanced Analytics Predictive Models Fannie Mae Proactive Data Quality EarlyCheckTM Mortgage Industry Fannie Mae is improving our internal practices while moving the industry forward