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

2© 2012 Fannie Mae 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© 2012 Fannie Mae Impetus for Change Investment Banks, Bear Sterns & Lehman Brothers Collapse 2008-Goldman Sachs & Morgan Stanley abandon their status as investment banks 2008-Banks received $700B TARP funds 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. Early 2010, Fannie Mae launches enterprise-wide data management program 2010-Dodd-Frank Wall Street Reform and Consumer Protection Act Wall Street to DC Digitization Industry Standards Innovation Data Mining Business Intelligence Proactive Data Quality Semantics 2011 Fannie Mae deploys new capabilities in data controls & begins streamlining data infrastructure The push to manage enterprise data is often a result of external forces Turmoil in the financial industry has created a need for greater transparency

4© 2012 Fannie Mae Maturity of Mortgage Industry – a Comparison Credit Card Industry: American Express can detect fraudulent activity based upon your spending habits in near real-time, often denying charges on the spot Airline Industry: near real-time tracking of all flights The mortgage industry lags other industries in technology innovation

5© 2012 Fannie Mae Maturity of Mortgage Industry – a Comparison (cont’d) The mortgage industry lags other industries in technology innovation Secondary Mortgage Market ■ Buried under paper ■ Manual processes ■ Minimal automation 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

6© 2012 Fannie Mae Current State Legacy point-to-point interfaces create unnecessary complexity Current State infrastructure is complex and lacks automation….

7© 2012 Fannie Mae Data should be trusted as it flows with the proper data management controls Future State Future State infrastructure enables straight-through processing and offers operational efficiencies…. Trusted Sources of Data

8© 2012 Fannie Mae Iterative execution must be tied to business value Roadmap for Success Multi-year planning and funding required for execution Define Enterprise Data Management strategy Design enterprise data architecture 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 Continue to build and refine target state enterprise capabilities Continuous improvement and future readiness Focus on innovative technology solutions 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 Define & Design Build Foundation Execute & Integrate Continually Improve Defines plans for enterprise Establishes business accountability Focuses on critical data needed to be managed at enterprise level Data Management practices become a part of the “fabric” of the company Constant focus on business value and innovation

9© 2012 Fannie Mae The Balance People Process Technology Data--->Information Managing the challenges across people, process, and technology is critical for change The triangle of people, process, and technology is fundamental and requires equal investment for success Managing people and culture change Creating and Integrating Processes Enabling the business and innovation

10© 2012 Fannie Mae Challenges: People Changing behavior requires a broad change management approach Data “hoarding” Lack of accountability Lack of skills Data is an enterprise asset Invest in a strong Data Governance program Put the “right” people in the “right” seats Challenge Opportunity Resistance to change Executive level support

11© 2012 Fannie Mae Changing Organizational Structures…. “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 Citi was the first in the finance industry to name a Chief Data Officer 2007 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 Bank of America Names John Bottega Chief Data Officer December 2011

12© 2012 Fannie Mae Challenges: Process The implementation and integration of enterprise-wide processes requires constant focus and attention from top executives No integration with development process Lack of data standards Siloed processes Integrate within software development and architecture review processes Enforce enterprise-wide data standards Enterprise-level process integration Challenge Opportunity

13© 2012 Fannie Mae Challenges: Technology The mortgage industry needs to focus on technology innovation Data silos Data volumes and velocity Complex data architectures Real-time enterprise requirements Lack of straight-through processing Structured and unstructured data ( , video, logs, system events etc) Structured and unstructured data ( , video, logs, system events etc) Consolidated trusted sources Data optimization and scalability Simplify data architecture Services-based architecture Automated controls and monitoring Leverage “Big Data” solutions Challenge Opportunity

14© 2012 Fannie Mae Technology for enterprise-wide data mgmt

15© 2012 Fannie Mae Secondary Market Primary Market How we are changing the industry….. Origination Delivery Servicing Loss Mitigation Industry Standards Proactive Data Quality Enhanced Analytics EarlyCheck TM Uniform Loan Delivery Dataset Servicing Alignment Initiative Predictive Models Mortgage Industry Fannie Mae Fannie Mae is improving our internal practices while moving the industry forward