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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

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Changing Data Standards from Wall Street to DC & Beyond


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Presentation Transcript
slide1

Changing Data Standardsfrom Wall Street to DC & Beyond

John Mulholland

Vice President for Enterprise Data

Fannie Mae

February 29, 2012

agenda
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

impetus for change
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

maturity of mortgage industry a comparison
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

slide5

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

current state
Current State

Current State infrastructure is complex and lacks automation….

Legacy point-to-point interfaces create unnecessary complexity

future state
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

slide8

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

the balance
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

challenges people
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

changing organizational structures
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

challenges process
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

challenges technology
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

how we are changing the industry

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