![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](https://image.slidesharecdn.com/tdwiexecsum08-124759355682-phpapp01/75/Data-Governance-11-2048.jpg)
Uploaded byRob Lux
Data Governance
The document discusses the establishment of data governance at GMAC ResCap following its merger. It outlines the need for a cohesive data strategy to integrate various business units, enhance data quality, and manage data as a strategic asset, ultimately improving operational effectiveness and reducing risks. Key initiatives include forming a data governance organization, developing data stewardship roles, and integrating data to support business strategies.
Data Governance
- 1. Using Data Governanceto 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.
- 5. GMAC background • 2006GMAC 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 • Mergernecessitated 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 Datato 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 thisthe “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 getworse 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 DataProgram – 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 DataProgram – 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 – ITData 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 – DataArchitecture • 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 – DataStewardship 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 Governanceat 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.
- 26. Data Governance Purpose Improveproductivity 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 SteeringCommittee – 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 SteeringCommittee 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 WorkingGroup 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 MemberProfile • 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 MemberAttributes • 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 MemberWorkload • 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 SteeringCommittee 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 WorkingGroup 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 StrategicData • 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 DataManagement services 44
- 45. Lessons Learned 1. ObtainSenior 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 gofor 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 • Ifyou have further questions or comments: Rob Lux CTO, GMAC ResCap rob.lux@gmacrescap.com 215-734-4205 www.mortgagecto.org 49