changing data standards from wall street to dc & beyond
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Changing Data Standards from Wall Street to DC & Beyond. John Mulholland Vice President for Enterprise Data Fannie Mae February 29, 2012. Agenda. Impetus for Change Technology Maturity Comparison Current State Future State Roadmap to Success The Balance Challenges & Opportunities - PowerPoint PPT PresentationTRANSCRIPT
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Changing Data Standardsfrom 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
2007-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
2010200920082007
Wall Street to DCDigitization
Industry Standards
Innovation
Data Mining
Business Intelligence
Proactive Data Quality
Semantics
2011Fannie 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
Securitization
CustomerEngagement
Underwriting and Pricing
Asset Liability
Management
Credit LossManagement
Securitization
CustomerEngagement
Underwriting and Pricing
Asset Liability
Management
Credit LossManagement
CustomerEngagement
Underwriting and Pricing
Asset Liability
Management
Credit LossManagement
Business Process Management
Business Rules
End-to-End automated reconciliation
Data Quality at Point of Entry
Straight-through Processing
Data Q
uality
Near Real-Time Infrastructure
Simplified Shared Computing Technology
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 SuccessMulti-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” Data “hoarding”
Lack of accountabilityLack of accountability
Lack of skillsLack of skills
Data is an enterprise assetData is an enterprise asset
Invest in a strong Data Governance programInvest in a strong Data Governance program
Put the “right” people in the “right” seatsPut the “right” people in the “right” seats
Challenge Opportunity
Resistance to changeResistance to change Executive level support 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 Officer2007
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 OfficerDecember 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
No integration with development process
Lack of data standardsLack of data standards
Siloed processesSiloed processes
Integrate within software development and architecture review processes
Integrate within software development and architecture review processes
Enforce enterprise-wide data standardsEnforce enterprise-wide data standards
Enterprise-level process integrationEnterprise-level process integration
Challenge Opportunity
13© 2012 Fannie Mae
Challenges: Technology
The mortgage industry needs to focus on technology innovation
Data silosData silos
Data volumes and velocityData volumes and velocity
Complex data architecturesComplex data architectures
Real-time enterprise requirementsReal-time enterprise requirements
Lack of straight-through processingLack of straight-through processing
Structured and unstructured data
(email, video, logs, system events etc)
Structured and unstructured data
(email, video, logs, system events etc)
Consolidated trusted sourcesConsolidated trusted sources
Data optimization and scalabilityData optimization and scalability
Simplify data architectureSimplify data architecture
Services-based architectureServices-based architecture
Automated controls and monitoringAutomated controls and monitoring
Leverage “Big Data” solutionsLeverage “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 ServicingLoss
Mitigation
Industry Standards
Proactive Data Quality
Enhanced
Analytics
EarlyCheckTM
Uniform Loan Delivery DatasetServicing Alignment Initiative
Predictive
Models
Mortgage Industry
Fannie Mae
Fannie Mae is improving our internal practices while moving the industry forward