TDWI Presentation explaining how Enterprise Data Governance can be utilized to support Business Strategy.

1. Using Data Governance to
Support Business Strategy
Rob Lux
CTO, GMAC ResCap
August 19, 2008

2. Agenda
• GMAC Background
• Why Data Governance?
• Strategic Data Initiative (SDI)
• Data Governance at GMAC
• Lessons Learned
• Questions
2

3. GMAC background
 In November 2006 a Cerberus led consortium acquired 51% of GMAC
 GMAC Financial Services began to integrate its business units
3

4. GMAC background
4

5. GMAC background
• 2006 GMAC ResCap was formed
• GMAC’s Residential Mortgage business
• Merger of two like-sized companies:
– GMAC Residential Funding Corporation (GMAC-RFC)
– GMAC Residential Mortgage
5

6. GMAC background
• Merger necessitated the integration of two like-sized,
independent entities
• Different people, processes, and technology
• Each company had its own separate and distinct systems:
– Lending
– Servicing
– Capital markets
– General Ledger
– HR
– Data Warehouses
– Etc.
• There was a need to integrate the data of the two organizations
– Our Data Services organization was created to address this need
6

7. Why Data Governance?
• Gartner estimates that organizations spend at least
70% percent of their BI budgets to resolve issues
related to people, process, and governance
• "Due to a lack of a cohesive strategy, many
organizations have created multiple, uncoordinated
and tactical BI implementations, which has resulted in
silos of technology, skills, processes and people."
– Betsy Burton, VP and distinguished analyst at Gartner
7

8. Importance of Data to Financial
Services
• Two sustaining elements for a Financial Services
company:
1. Information
2. Access to Capital
• GMAC rated Data Integrity as Top Priority in an
Executive Survey
8

9. Is this the “Axis of Evil”…
9

10. …or is this the “Access of Evil?”
10

11. Dramatic consequences
June 03, 2003 TORONTO (Reuters) - Fannie Mae, which finances home mortgages,
TransAlta Corp. said on Tuesday it will take a stated in a news release of third-quarter
$24 million charge to earnings after a bidding financials that it had discovered a $1.136
snafu landed it more U.S. power transmission billion error in total shareholder equity. Jayne
hedging contracts than it bargained for, at Shontell, Fannie Mae senior vice president for
higher prices than it wanted to pay. investor relations, explained in a written
[...] the company's computer spreadsheet statement, "There were honest mistakes made
contained mismatched bids for the contracts, it in a spreadsheet used in the implementation of
said. "It was literally a cut-and-paste error in an a new accounting standard."
Excel spreadsheet that we did not detect when —From PC World
we did our final sorting and ranking bids prior
to submission," TransAlta chief executive Steve
Snyder said in a conference call. "I am clearly
disappointed over this event. The important
thing is to learn from it, which we've done."
11

12. Data Issues get worse during an M&A
#53
Homecomings / #24 - IMS-R DW Data Finance
#4 & 50 - 1st & HE E-Commerce
Retail NC Loan Info Master #25 Valuation
ADI #4 & 50 1st Servicing #33 RVA/RIF
MortgageFlex #34
Master
& HE NC
Loan Info Other Servicing
Correspondent #1 - 1st & HE #28 Apps
Loan Info IMS-R HIP
#20 - 1st & HE Data
Café 4.0
Servicing Data
Café 2.2 #35 Café 2.2 Data
IMS-R
Capital Markets #27
AssetWise #2 & 16 - 1st & HE #26 Servicing
Servicing Data RFC
#14 & 15 – IMS-R SBO SBO #30
#42 - 1st Loan Info
DRAFT
Café 2.2 Data
(specific products ) #3 & 49 - 1st & HE
May go through ADI IMS-R #31
NC Loan Info
Café 4.0
#44 - IMS-R Data
Data Warehouse /
Institutional ODS/Vision #29
#1 - 1st & HE Loan Info Automated Pooling
Café 4.0
Finance
#18 Café 2.2 st
#48
IMS-R #2 & 16 - 1 & HE Commitment #37 Manual
Conforming Gate
Loan Info Servicing Info Interface
AssetWise Data #32
(Manual) #22
Commitment
Homecomings / Management
Broker Finance #23
Asset Lock #51
MortgageFlex #19 - 1st & HE Conforming Loan #11
Bid Commit PeopleSoft
1st & HE Servicing Data #52 #43
Middleware /Business App #36
#42 - 1st & HE Loan Info
st
1 & HE Servicing Data Common Loan Interface #6 - 1st & HE
#54 (CLI) Servicing
General Data #13 - Summary
Ledger Entries
#47 Ledger
1st & HE #9 - 1st & HE
Correspondent Loan Loan Info GLS
Direct/Ditech #5 - 1st & HE Loan Info Info
#21- Finance
WALT 1st & HE Servicing Data #8 - Loan Updates Detailed
Eclipse Engenious Ledger SmartStream
Engenious Middleware Entry
Capital Markets #46 Contract ID
Sales & File
Resi Lookup
Switch #10 - 1st & HE Switch Service
Retail Loan Info CMS
#41 - HE Loan Info
CoPilot #7 - Daily
Back #45- Contract
Retail Interface ID Lookup
#40 - 1st & HE Loan Info Request
Pilot
Lendscape st
#39 - 1 & HE Servicing Data Servicing
#12
#38 - HE Servicing Data MortgageServ (LOIS, NELI)
12

13. GMAC ResCap Data Program – July 2006
Residential Finance Group: Importance versus Effectiveness Gap -
5.0 Jul
Key Strengths y
High Priorities
20
06
Strategy and Planning
Survey concluded
that Data is of high
Enterprise Architecture
Availability Management
Security Policies and Stds
Data and Knowledge Mgmt importance and that
it was ineffectively
Importance
Portfolio Management
4.0 Project Mgmt and Execution
IT Staff Development Value Demonstration
managed.
Application Design Leadership Development
Business Case Discipline
Risk Management Disaster Recovery and BCP
Requirements Definition Process Digitization
Performance Management IT-Enabled Collaboration
Technology Innovation
Performance Reporting Life-Cycle Cost Efficiency
Maint. Cost Containment
Cost Transparency
Vendor Perf Oversight
Potentially Over Opportunistic
Allocated Improvement
Vendor Segmentation
3.0
0.00 1.00
Effectiveness Gap = Importance - Effectiveness
Governance Performance Measurement and Value Demonstration
Security and Business Continuity Planning Infrastructure Delivery and Management ----- Importance Ave: 3.82
Applications Delivery and Management Vendor Management ----- Company Gap Ave: 0.67
Talent Management Business Enablement
13

14. GMAC ResCap Data Program – July 2007
Residential Finance Group: Importance versus Effectiveness Gap - July 2007
7.0
Key Strengths High Priorities
6.5
Availability Management
Strategy and Planning
6.0 Business Continuity Planning
Business Responsiveness Project Delivery
Partner Requirements Definition
End-User Support Business Liaison
Importance Financial Impact
Security Technical Skills
Technology Provisioning Skills Adaptation
Leadership Skills
5.5 Risk Management Business Case Achievement
Data and Knowledge Management System Adoption
Value Demonstration
Business Skills
Prioritization Discipline Business Functionality
5.0 Business Case Discipline Communication
Project Skills Cost Transparency
Technology Innovation
Vendor Alignment
Opportunistic
User Training
Low ROI Improvement
4.5
(0.8) (0.3) 0.3 0.8
ResCap-RFG Average
Effectiveness Gap = Business Partner Importance - Business Partner Effectiveness
Benchmark Average
14

15. GMAC ResCap - Strategic Data
Initiative
15

16. Strategic Data Initiative - Approach
Step #1 – Get sponsorship from the top
It’s easier to get everyone marching in the same direction when it
comes from the top
Try for the CEO – if that doesn’t work the CFO and COO are your best
bets
16

17. Strategic Data Initiative - Approach
Step #2 – Focus on Culture during an M&A
Collaborated with a team of Business and IT stakeholders to build SDI
Performed a cultural assessment:
- Human Synergistics OCI
- Competing Value’s Framework
17

18. Strategic Data Initiative - Approach
Step #3 – We took a “Best of Both Worlds” (or Reese’s) approach
- Assessed components of both the RFC and RESI data programs
- Used strengths from each one and sought to enhance them
- Where neither was strong brought in outside help
- Your situation may vary – it may make more sense to take an acquisition
approach
18

19. Strategic Data Initiative - Mission
“The people, process, standards, tools, and
procedures that develop a long-term
organizational framework and foundation
enabling ResCap to manage data as a
strategic asset, that will be used as a
trusted source of information across
the Enterprise.”
19

20. Strategic Data Initiative -
Deliverables
• SDI had three major deliverables:
– Establish an Enterprise Data Governance organization
– Establish an Enterprise Data Stewardship organization
– Establish an IT Data Services organization
Data
Steering Governance
Committee
Working
Group
Minimum
Data
Data
Quality
Standards
Meta-Data
Management
Enterprise Enterprise
Stewardship Architecture
Business Unit SDI Data
Stewardship Services
Data
Data Sharing Data
Stewardship Architecture
20

21. SDI – IT Data Services Org
Data
• Data Governance Steering Governance
Committee
• Data Stewardship Working
Group
• Data Architecture Data
Minimum
Data
Quality
• Data Reporting Meta-Data
Standards
Management
• Data Integration Enterprise Enterprise
Stewardship Architecture
• Database Administration Business Unit SDI Data
Stewardship Services
• Project Management Data Data
Sharing Data
Stewardship Architecture
• Consulting
• Training
• Vendor Management
21

22. SDI – Data Architecture
• Data Architecture
– Consulting
– Data Modeling
– Data Analysis
– Data Quality processes &
standards
– Data Security
– Data Standards
– Tool Standards
– External standards bodies
(MISMO, XBRL, HL7, etc.)
22

23. SDI – Data Stewardship Model
Data
Steering Governance
Committee
DATA GOVERNANCE
Working
Data Governance Steering Committee (DGSC) Group
Data
Governance Minimum
Data
Roles Data
Data Governance Working Group (DGWG) Quality
Standards
Meta-Data
Management
Enterprise Enterprise
Enterprise Data Stewardship Office (EDSO) Stewardship Architecture
Enterprise
Data Stewardship Business Unit SDI Data
Roles Program Manager Program Staff
Stewardship Services
Data
Data Sharing Data
Stewardship Architecture
Business Units Data Stewards
(BUDS)
Business Unit
Business Unit Data Steward Manager
Business Unit Data Steward Manager
Business Unit
Data Stewardship Data Steward Manager
Roles
Definer Producer User
Definer Producer User
Definer Producer User
Note: Business Units may choose to assign one or more associates to fulfill the different data
stewardship roles within the business unit
.
23

24. Data Governance
Data Governance at GMAC ResCap
– Executes and enforces authority over the management of data assets through
Data Quality, Stewardship, and Standards initiatives
– Empowers an organization to define guiding principles, policies, processes,
standards and technologies
– Ensures the quality, consistency, accuracy, availability, accessibility, and audit-
ability of GMAC’ s data
In order to:
– Support sustainable growth Data
– Improve investor and client satisfaction Steering Governance
Committee
– Provide disciplined leadership Working
Group
– Manage and reduce risk
Minimum
– Streamline operations and improve time to market Data
Quality
Data
Standards
Meta-Data
Management
Enterprise Enterprise
Stewardship Architecture
Business Unit SDI Data
Stewardship Services
Data
Data Sharing Data
Stewardship Architecture
24

25. Data Governance Defined
25

26. Data Governance Purpose
Improve productivity and lower cost of operations by:
– Approves, sponsors, and prioritizes all Enterprise Data projects
– Managing data so that it is available, complete, timely, and accurate
– Defining and enforcing data quality and data integrity standards
– Identifying and promoting standard tools and data quality standards
Improve risk posture by:
– Establishing data stewardship throughout the organization
– Implementing an effective process for escalating, prioritizing, tracking, solving and reporting on
enterprise data risk issues
– Establishing rules governing the lifecycle of data
– Identifying and utilizing standard tools and access policies to allow for authorized and verified
access to data
Improve organizational effectiveness through
– Measuring the effectiveness of Data Governance and its alignment to corporate goals
– Assumes ownership of all Enterprise Data
– Owns the Enterprise Data Warehouse and Enterprise Data Repository
– Resolves disputes regarding data issues
– Manages data quality
26

27. Data Governance Organization
Steering Committee
– Made up of Senior Business leaders
– Maintains ultimate accountability for all facets of Data Governance
– Establishes the Working Group to achieve the Data Governance goals and
objectives
– Reviews results of the Working Group on a regular basis
– Meets monthly
Working Group
– Two or more business data SME’ s from each business area
– Appointed by the Steering Committee member to achieve the Data
Governance goals and objectives
– Strives to build consensus across organizational boundaries
– Escalates issues to Steering Committee when appropriate
– Meets weekly or more frequently if necessary
27

28. Data Governance Representation
• Asset Management • GMAC Corporate
• Banking / Ancillary • Human Resources
• Broker • Information
• Capital Markets Technology
• Consumer Lending • Institutional
• Correspondent • Legal
• Credit Risk • Operations Risk
• Finance • Servicing
• Warehouse Lending
28

29. Organization Membership
Steering Committee
– One Chairperson
– One senior manager from each business group in ResCap
– Chairperson for the committee is appointed by the Executive Committee and
position is reviewed annually
– IT only has one seat – the CIO; all others are business people
Working Group
– Facilitator plus one or more representatives for each Steering Committee
member
– Facilitator for the Working Group is appointed by the Steering Committee
– Representatives appointed by Steering Committee Member for their business
group
– Recognized as experts or SMEs in their line of business
– Many are also Data Stewards for their business area
29

30. Roles and Responsibilities
Steering Committee Chair
– Establishes agendas, leads meetings and records results
– Facilitates votes on business before the Committee
Steering Committee Member
– Ensures effective utilization of the program throughout ResCap
– Votes on business before the Committee, either in person or via proxy
– Appoints Working Group representative(s)
– Works with Working Group representatives and other Steering Committee
Members to gauge progress and resolve issues related to Data Governance
goals and objectives
30

31. Roles and Responsibilities
Working Group Facilitator
– Establishes agendas, leads meetings and records results
– Works to build consensus and arbitrate disputes
– Manages voting process
– Escalates issues to the Steering Committee when appropriate
Working Group Member
– Effectively represents views of their business or support unit as well as
understands the views and needs of the enterprise
– Implements programs and participates in projects to achieve the Data
Governance goals and objectives
– Directs metadata requirements
31

32. Working Group Member Profile
• Effectively represent the views of their business or support unit
• Communicate the policies, standards and decisions of the Data Governance
Organization to their organization
• Implement programs and participate in projects to achieve the Data
Governance goals and objectives
• Work to define data in the best interest of the organization,
• Act as an advocate for Data Governance and effective corporate-wide data
management
• Exercise authority for making decisions regarding data and related policies.
32

33. Working Group Member Attributes
• Understanding of the Mortgage Business in general and a strong
understanding of their Business/Support unit
• Understanding of the scope and location of the data within their business
area, and relationships to other business areas
• Strong knowledge of data attributes, their source, usage, and definition
• Knowledgeable of the strengths and weaknesses of data as it exists within
the business unit
• Demonstrated ability to work on a team
33

34. Working Group Member Workload
• Workload – 2 to 3 hours per week
• Communications and Execution – WG representatives are the Steering
Committee member’s link to the Working Group
• Coverage – Provide adequate representation for your organization
(more than one representative allowed)
• Teamwork – A business area must work as a unit
• Attendance – Primaries and backups should be assigned. Attendance
is tracked and published.
• Performance – Individuals are responsible for active participation in the
Working Group, and must have performance goals for Data
Governance activities.
34

35. Decision-making
The Steering Committee operates by simple majority vote of full
membership
– At least 75% representation (through attendance or proxy) is required for
quorum
– Voting can only take place if quorum is achieved
– Chairperson has voting and veto privileges
– Decisions can result in approval, conditional approval, rejection, rejection
with request for follow-up, or refer to Executive Committee
– Decisions can be appealed by the Steering Committee Member to their
Executive Committee representative, who can choose to bring the matter to
the Executive Committee for consideration
35

36. Decision-making
The Working Group operates by consensus – 100% concurrence
is required for approval
– Each organization has one vote, regardless of the number of representatives
– Facilitator has no voting privileges
– The group works to define the problem so the decision can result in
approved by consensus, rejected with a request to return with additional
information, rejected as presented, or escalated to the Steering Committee
36

37. Data Governance
Accomplishments
• Enterprise Data Model
– Modified a generic Industry data model to accurately represent our business
• Data Quality
– Identified issues with certain calculations in a source system; reviewed with Credit
Policy & Capital Markets; clarified business rules for calcs; source system modified
to conform to business rules.
– Initiated a pilot of the Larry English TIQM data quality methodology.
• Data Survivorship
– Determined the correct System of Record for 572 data elements in the EDR that
could be sourced from either the Origination or Servicing system. In some
instances both records were stored for historical purposes.
• Data Security
– Classified the GMAC Proprietary data elements in the EDR. These are stored in
the Metadata tool and reports which contain these data elements contain a “GMAC
Proprietary” footer.
• Data Mart project reviews
– Reviewed designs of multiple data mart projects
37

38. Data Governance
Accomplishments
• MISMO support
– Ensure that Enterprise data conforms to MISMO XML standards
– Actively participate in MISMO Governance
• GMAC ResCap Integration Project
– Documented the current state data stores and data flows for the Enterprise
– Identified the data requirements for all the Data Consumers – ~7,000 data
elements
– Consolidated these data requirements – eliminating dupes and conforming names
- ~3,500 data elements
– Reviewed the data needs among the Data Producers to optimize builds of
interfaces
– Developed a scorecard (13 questions) to determine what data is strategic
– Strategic data to be hosted in Enterprise Data Repository
• Enterprise Data Repository (EDR)
– Single Source of Truth for our Enterprise Data
– Used to build functional data marts
– Owned and maintained by Data Governance group
38

39. Developed Data Architecture
Rules
• Enterprise Data Architecture Rules
Data is owned by the Data is adjudicated by
corporation Data Governance
Data is managed by Data is structured and
data stewardship stored based on its
behavior and usage
Data is shared and Data is not duplicated
accessed using unless duplication is
common methods necessary
Data is secured Meta data is maintained
Data is modeled using Data is managed using
naming conventions approved standards and
and standards tools
39 39

40. Consolidated Business Data
Requirements
• Output
– Normalized business data requirements from
~7000 elements to ~3500 elements
• Benefits
– Provided data producers a de-duped listing from
which to work
– Provided data producers a single list of consumer
data needs so they can determine how to expand
their platforms
40 40

41. Scored Enterprise Strategic Data
• What
– Score the consolidated list using criteria
developed by the Data Governance Working
Group
• Why
– Define candidate list of data elements for EDR
– Develop one drop-off point for sharing data with other business units rather
than developing many point-to-point ones between them
– Eliminate any subsequent work for producers to address needs for new
consumers
– Sharing data in this way follows many of the enterprise data architecture
rules defined by the Data Governance Working Group
41 41

42. Enterprise Data Repository (EDR)
Lending Data (current data)
LendScape CFP
• Ten data sources (NetOxygen)
Retail
• Target is Enterprise Data (Pilot)
Repository (EDR) – all data elements Retail HEQ
(Co-Pilot)
will be conformed & cleansed. Ditech / Direct ETL Processing
(Eclipse/LPM)
• Single version of the truth for our Wholesale EDAP
(WALT) Enterprise Data Repository
Enterprise data Business Specific
Data Marts
• Data marts will be built from EDR
Lending Data EDR
(historical loads) - Extraction
- Transformation
Retail - Loading into ODS
• Enterprise Data Model used to Customer / Borrower Business Lending LendScape
(Pilot Archive) - Data cleansing
- Meta data Product / Loan
design EDR
Property
Retail
Servicing
(Co-Pilot)
Risk Management ECR
• 3NF Ditech
(Eclipse)
• Data Governance “owns” EDR Wholesale
(WALT / EDAP)
• 808 data elements to start
Servicing Data
• ~800 more being added for NC MortgageServ
Other Data
Credit
Excelis
(historical)
Shaw
(historical) Business Objects
SAS Reports
Reports
42

43. Developed charge-back model
2007 ISCO BU Name Total Allocation Total Percent
Admin Overhead and Other Ops $ 772.50 0.70%
Automated Decisioning $ 25.43 0.02%
CFO Office $ 8,100.41 7.33%
Construction Lending $ 84.75 0.08%
Consumer Lending Admin $ 11,975.62 10.84%
Corporate Real Estate $ 101.70 0.09%
Correspondent Funding $ 18,002.86 16.30%
Ditech $ 14,656.39 13.27%
EDAP Services ESDO
ISCO $ 1,249.78 1.13%
ESG Fee Based Servicing $ 2,203.61 1.99%
Strategic Business Unit Consumer Lending Admin ESG Owned Servicing $ 30,696.58 27.79%
Reporting Period April, 2007 Financial Services $ 3,992.11 3.61%
GHS Mortgage $ 118.66 0.11%
GHS Other - Admin $ 101.70 0.09%
Metric % of Total $ Allocation
GHS RE Co-Owned $ 2,911.31 2.64%
Data Mart Hosting (MB) 127,146,944 12.25% $ 708.63
GHS RE Franchise $ 16.95 0.02%
Business Objects Usage (# Users) 23 0.60% $ GHS Relocation
69.27 $ 3,043.21 2.75%
Home Connects $ 853.78 0.77%
Business Objects Hosting (MB) 78 0.61% $ 73.55
Home Solutions Svg Cross Sell $ 42.38 0.04%
DataStage Usage (Seconds) 2,115,824 17.63% $ 2,284.21
Human Resources $ 16.95 0.02%
DataStage Hosting (MB) 324,604 10.19% $ Investment Banking - Cap Markets $
1,119.85 1,792.14 1.62%
IT Lendscape $ 1,658.23 1.50%
Enterprise Allocation $ 1,530.63
Operational Risk Management $ 668.90 0.61%
Base Support (Hours) 79 10.84% $ 6,189.48
Retail Network Summary $ 6,941.79 6.28%
Total 10.84% $ Retention
11,975.62 $ 305.11 0.28%
Strategic Sourcing $ 127.13 0.12%
Voice of the Customer $ 8.48 0.01%
Warehouse and Finance Solutions $ - 0.00%
Services: ECR Data Mart, Business Objects Universe, Business Objects Accounts $ 110,468.46 100.00%
43

44. Invoice for Data Management
services
44

45. Lessons Learned
1. Obtain Senior Executive (CEO if possible)
sponsorship for Data Governance
2. Can not underestimate the importance of Culture
3. Choose an approach to merging your Data
programs
4. Need a clearly defined strategic mission and
program to transform the way you manage data
5. Consolidate Data Architecture & Delivery services
– create a single point of accountability for IT Data
Delivery in your organization
45

46. Potential pit-falls
1. Changes to Executive staff during M&A can derail
Data Governance continuity
2. Management Consulting companies don’t know
your company as well as you do
3. Data Governance can be perceived as
bureaucratic
46

47. Where to go for more information
• The Data Warehousing Institute (TDWI)
– http://www.tdwi.org/index.aspx
• Data Management Association (DAMA)
– http://dama.org/
• DM Review magazine
– http://www.dmreview.com/
• MDM Institute
– http://www.tcdii.com/index.html
• The Data Administration Newsletter (TDAN)
– http://www.tdan.com/
47

48. Questions
48

49. Contact Information
• If you have further questions or comments:
Rob Lux
CTO, GMAC ResCap
rob.lux@gmacrescap.com
215-734-4205
www.mortgagecto.org
49