AN OVERVIEW OF THE SALLIE MAE DATA GOVERNANCE PROGRAM

DAMA Day

Washington, D.C.

September 19, 2011

A

N

O

VERVIEW OF THE

S

ALLIE

M

AE

S

ALLIE

M

AE

B

ACKGROUND

►

Sallie Mae is the nation’s leading provider of saving,

planning and paying for education programs

►

Since its founding more than 35 years ago, the

company has invested in more than 31 million people to

help them realize their dreams of higher education

►

Sallie Mae manages $236 billion in education loans and

serves 11 million student and parent customers

►

Through its Upromise affiliates, the company also

manages more than $27 billion in 529 college-savings

plans, and is a major, private source of college funding

contributions in America with more than $575 million in

member rewards

►

Sallie Mae is a Fortune 500 company with 8,000

D

ATA

G

OVERNANCE AND

D

ATA

Q

UALITY

:

T

HE

F

OUNDATION FOR

B

UILDING

S

ALLIE

M

AE

B

USINESS

Operational Alignment Initiatives

E

NTERPRISE

D

ATA

M

ANAGEMENT

S

TRATEGY AT

S

ALLIE

M

AE

Data Governance

Data Ownership

Data Stewardship

Data Quality

Metadata Management

Repository

Standards

Processes

Data Architecture & Design

Business

Context Model

Data Model

Conceptual

Logical Data

Model

Physical Data

Model



Provides the creation of management structure for

policies and rules governing enterprise data



Implement necessary tools to automate process



Provides documentation of all aspects of the business

and technology components of enterprise data



Includes repository building, defining standards,

architecture, maintenance process, tool selection

& implementation



Design, development and maintenance of data

models at the business context, conceptual, logical

and physical level



Represents the data entities, their relationships,

attributes, structure and usage



Provides capabilities to support comprehensive EDM

services

Data Management Services Definition

Mapping&Conversion Synchronization Exception Handling Performance Mgmt

Replatforming Integration Movement Matching Consolidation Quality Analysis Transformation

Enterprise Data Definition

Data

Architecture

Data

Governance

L

AYING THE

F

OUNDATION FOR

D

ATA

G

OVERNANCE

EDD Project

March - July

2006

E

NTERPRISE

D

ATA

D

EFINITION

– A

PPROACH

Both

“

top-down

”

and

“

bottom-up

”

approaches

to leverage

existing

information

Physical Database Structure

(Detailed Level)

E.G., Borrower table & associated columns

Top

Do

wn

Bo

_tt

om

Up

Conceptual Data Model (Abstract Level)

E.G., Borrower, Organization, Loan

Logical Data Model (Business Level)

E.G., Borrower Address, Borrower Phone,

EDD – C

ONCLUSION

Entities

Attributes

►

4 Month Effort (March through July 2006)

►

Why so quick?



Centralized Data Management team

C

HANGE IN

M

ARKETING

S

TRATEGY

2006

Moving from institution based

DG/DQ P

ROGRAM

T

IMELINE

Pilot Project:

7 DE

EDD Project

March - July

2006

August – October

2006

B

USINESS

Q

UESTIONNAIRE

-- S

AMPLE

Course of Study

Our Pain

When our group accesses these fields using the following system

(e.g., CLASS, Eagle II, CDDB)

we experience the following problems

which we assume are a result of the following root cause

Our Input

Our group updates this data using the following channels

many

times/day once/day once/week once/month less frequently

(website, call center, etc.)

We review the quality of this data

many

times/day once/day once/week once/month less frequently

using the following

methods

Our Understanding of Issues

disagree disagree not sure agree

strongly agree

We don't know whether active capture of course of study is occurring

D

EMONSTRATED

B

USINESS

V

ALUE

F

ROM

D

AY

O

NE

►

DG Pilot project



Increased revenue by

$2.4M for the first two

years based on an

estimated increase of

$50M in loan volume



Eliminated costs of $4.8M

spent on letters/postage

that were replaced by

email campaigns

►

Don’t be a solution

waiting for a problem,

find the problem and be

the solution to it!

DG/DQ P

ROGRAM

T

IMELINE

DG Program

Implemented

DG Program

Design

Pilot Project:

7 DE

EDD Project

March - July

2006

November 2006–

March 2007

August – October

C

OORDINATION AND

C

OOPERATION

►

The ability to get the right people together to

make decisions and agree on effective action

regarding Sallie Mae’s data is never easy

In a complex

environment

it can feel…

IMPOSSIBLE

M

AKING

D

ECISIONS AND

T

AKING

A

CTION

►

Fortunately, for data-related issues, Sallie Mae has in

place:



A process to

•

Make decisions

•

With appropriate representation (from LOBs, teams, etc.)

•

And knowledge (access to subject matter experts in business,

data, and IT)



So they can

•

Resolve issues

•

Implement effective changes

•

Avoid unexpected consequences

•

Communicate actions

D

ATA

G

OVERNANCE AT

S

ALLIE

M

AE

►

Data Governance is a discipline, a program, and a

key component of the Sallie Mae Enterprise Data

Strategy

►

Data Governance occurs where Business, IT, and

data intersect and includes proactive, reactive, and

ongoing efforts

D

ATA

G

OVERNANCE

C

OOKBOOK

►

The Data Governance Program

is defined in a

Data Governance Cookbook

with an introduction and nine

modules



Policy



Organization



Process



Office Administration



Organizational Alignment



Communications



Data Quality



DG and SMPAL

G

OVERNANCE

M

ATURITY

L

EVELS

FOR

S

ALLIE

M

AE

D

ATA

►

Sallie Mae adopted a Governance Maturity Model to

describe the levels of maturity for its enterprise

data

►

This model began with best practices from the Data

Governance Institute, then was customized to the

unique Sallie Mae environment

►

This model describes data that is:



Level 0

-

Ungoverned Data



Level 1

-

Modeled Data



Level 2

-

Repository Data



Level 3

-

Standardized Data



Level 4

-

Standardized with Known Issues Data



Level 5

-

Matured Data

S

ALLIE

M

AE

DG/DQ S

ERVICES

W

HO

’

S

W

HO

?

Business and IT Senior Management

IT Sponsor

Business Sponsor

Enterprise Data Management (EDM) Strategy

Data Governance Council

Barbara

Deemer and

other LOB

representatives

Data Quality Services (DQS)

DQ Core Team

Splits into

working groups

Data Governance Office

(DGO)

Michele Koch

Subject Matter

Experts (SMEs)

Business

Subject Matter

Experts (SMEs)

Data

Subject Matter

Experts (SMEs)

IT

H

OW

DG W

ORKS

Identify Issues

Research

_Issues

_{and Take Action}

Make Decisions

•

Track and Communicate Progress

DG

Council

Business

IT

Project

Teams

DGO

Management

DQS

Other Subject

Matter Experts

(SMEs)

(Business,

Data, and IT)

DGO

DGO

DG

Council

Project

Team

Data Modelers

Data

Architecture

Data

Stewards

Data Governance

Office (DGO)

Update

watch

list

Provide status

to

stakeholders

Trigger

Add

project

to watch

list

Send FYI to

Stewards

and

Modelers,

ask PM to

include

DGO on

stakeholder

and

participation

lists

Trigger

Trigger

Trigger

1. Project

Initiation

Invoke Data

Governance in

response to

triggers

:

• EA

Assessment

• Knowledge

that

Enterprise

Data will

2. Technical Design

Invoke Data Governance during or before

Update Metadata

Repository if needed

Perform

modeling

On

watch-list?

Update

DGO

Modeling

issues?

resolved?

resolved?

Help

resolve

3. Issue

Resolution

Use Data

Governance

to escalate

and resolve

issues

Determine how the Data Governance

Program fits into your SDLC

I

DENTIFIED

H

OW

G

OVERNANCE

I

NTEGRATED

T

HE

P

ERFECT

S

TORM

2007-2008

_{Sale of Sallie Mae}

S

OME

B

UMPS

A

LONG THE

W

AY

►

Communication is key when you encounter

R

OAD

T

O

R

ECOVERY

Progress continues

but at a slower pace

without additional

staff

Leveraged new

enterprise

initiatives to show

the value of Data

Governance

Used an audit to

re-seed the DG council

and gain support for

additional funding

H

EALTH

C

ARE

R

EFORM

2010

►

FFELP student loan program is abolished



80% of our ability to originate new assets is lost!

►

Time for some tough decisions



While competitors closed their doors, we aggressively

C

OMPANY

T

RANSFORMATION

►

Executives had

confidence in the data

needed to make

decisions to move the

company forward as a

result of strong Sallie

Mae data

management and DG

programs

S

TRONG

B

USINESS

/IT P

ARTNERSHIP

►

Data Governance Office =

3

►

Data Quality Services Team =

3

►

Data Governance Council



Lines of Business =

18



Business Data Stewards =

23

►

Business SMEs =

18

►

IT SMEs =

25

►

Data SMEs =

2

G

REAT

S

TAKEHOLDER

C

ARE

►

Serve in a “Trusted Broker” position in all

dealings with stakeholders

►

Ensure that members of senior management

and the DG Council are made aware of

potential impacts of decisions put before them

►

Arrange for mentoring or coaching of

Copyright © 2011 Sallie Mae, Inc. All rights reserved. Copyright © 2011 Sallie Mae, Inc. All rights reserved.

M

ETRICS

Capture as you go!

29

Pilot project metrics:

• Industry standards and publications

• Interviewed business areas

DG Program metrics:

•

Determine business value

• Define reporting categories

W

HAT A

DG/DQ P

ROGRAM

D

OES FOR

S

ALLIE

M

AE

Increase Revenue

Facilitate Private

Credit products

speed to market

Increase volume

available for the PUT

process and trusts

Improve servicing

performance for Dept

of Ed contracts =

increased Sallie Mae

volume percentage

awarded

Manage Cost and Complexity

Eliminate data

reconciliation efforts

and workarounds

Reduce operational

servicing costs

Implement enterprise

architecture

improvements (e.g.

SOA, person

matching)

Reduce Risk and Support Corporate Compliance

Improved risk

management and

corp. compliance

through DQ and

standardization

Reduce audit

findings due to

inaccurate or

inconsistent data

Improve

identification and

documentation of

identity fraud

Categories for DG Program metrics

Data

Standardization

33%

Data

Quality

37%

Metadata

Support

5%

Project

Support

11%

MDM

14%

D

OCUMENT

B

USINESS

V

ALUE

M

ETRICS

►

Implemented

resolutions for 77% of

the data issues since

DG Program inception

►

Utilized proven

techniques for

quantifying business

value

►

Linked business value

calculations to financial

statement categories

Goal:

Focus on solving business

problems while always keeping

your eye on delivering a first rate

DG Program

C

OMMUNICATION

Key is

regularly

and

consistently

!

►

Easy to focus on the day-to-day activities, must

1

2

3

M

ETHODS OF

C

OMMUNICATION



Decision makers for boundary

spanning data issues



Meets every other Tuesday



Meeting agenda and supporting

documentation posted on web

and sent prior to the meeting



Calendar of meeting dates



Meeting agenda and minutes



Participant list



DG issue reports



Documents and presentations



Meeting archives, DG news and



Use to send information on new

data issues, pose questions,

comments, and concerns



Mailbox goes directly to:

DGO Program Director

Chief Data Steward

DGO Assistant

T

YPES OF

C

OMMUNICATION

►

Mission and value statements

►

Elevator speech

►

Slogan/Logo

►

Status Reports

►

Dashboards

DG/DQ P

ROGRAM

T

IMELINE

DG Program

Implemented

DG Program

Design

DQ Program

Design

Pilot Project:

7 DE

EDD Project

DQ Program

& Pilot

Implementation

Monitor data

Focus on

root cause &

prevention

March - July

2006

November 2006–

March 2007

October-December

2009

July 2010 -

Present

August – October

Data Quality Services

The DQ Program:

•

Develops DQ management as a

core competency

•

Improves DQ throughout Sallie

The DG Program:

•

Provides guidance,

prioritization, and

decisions for DQ

activities

•

Oversees resolution

of DQ issues

K

EY

DG/DQ E

NGAGEMENTS

►

Reconciliation projects for Finance

►

Loan Acquisition Conversions

►

EDW Support

►

MDM Support

►

New project support

►

Metadata support

►

Training on DQ tools

►

Consulting to teams

H

OW THE

DQ P

ROGRAM

S

HOWS

V

ALUE AND

P

ROGRESS

►

Three categories of metrics are reported



State of Data Quality



Business Value from Data Quality



Data Quality Program Performance

►

For each category, a dashboard

level status is summarized

from detailed reports

►

Drilldown information is

included as appropriate for

the specific metric

Dashboard

Drilldown

N

EXT

S

TEPS

►

DG program must evolve and

grow as Sallie Mae continues

to redefine itself and expand

into new product areas

►

Continue to move from

reactive to pro-active

►

DG/DQ become integral to

our core business processes

as the environment gets

L

OOKING

A

HEAD

►

Need to maintain data governance vigilance in

order to ensure customer satisfaction and data

quality

►

For further information, please contact

Michele Koch

[email protected]

703-984-6601

Q

UESTIONS

?

Data Governance

Data Governance

Solving boundary-spanning issues

by pulling together the pieces of the data puzzle.

Data Governance

Data Governance

Solving boundary-spanning issues