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2017 DATA MANAGEMENTINDUSTRY BENCHMARK SUMMARY

Confidential Copyright © 2017 EDM Council Inc. InPartnershipwith Poweredby

DATA MANAGEMENT ASSESSMENT 2017

The 2017 benchmarking study consists of 22 DCAM-derived questions that highlight the most essential concepts in data management. The results include comparison to the previous study performed in 2015.

Participants included the full spectrum of financial services companies (universal, buy, sell, asset servicing, insurance) with an emphasis on G-SIBs and Tier 1 firms. We hand selected a “control group” of companies that we knew had demonstrated an organizational commitment to data management as the baseline for evaluating overall industry progress.

The good news is that the financial industry has made steady progress in establishing data management programs and implementing foundational governance. Data management is a “known objective” and the strategies and organizational structures are advancing. But beyond regulatory compliance and BCBS 239 adherence, questions still remain about the “why” of data management.

We acknowledge the industry’s efforts in working to establish data management as a core operational requirement. We have created a functional collaboration with IT and with the other control functions. The industry has made headway on governance-related mechanisms (i.e. setting up the ODM, implementing policies, establishing stewardship functions) and all are hard at work mapping lineage, reconciling glossaries, identifying CDEs and documenting data flows.

The results are not so encouraging from a “fix the data” perspective. Many firms are making some progress using labor-intensive (short-term)

approaches that fill immediate needs by pulling data from standard file paths that unfortunately break because data is not aligned to meaning … it resides in multiple repositories (often with little documentation) … and it is held together by rigid legacy systems with fragile business logic. The result is that we are collectively adding more strings to the Gordian Knot - and we are hesitant to change these vertically aligned systems for fear of creating operational chaos.

Many firms are still mired in tactical mapping of data from physical repositories to applications and reports – and we have not yet escaped from the tyranny of manual reconciliation. Trust and confidence remains the elusive goal of data management – and are still the essential prerequisites for both reporting and business analytics. Without adopting a trusted and harmonized content infrastructure, there is little point to implementing data policy and governance.

There are clearly some bright spots for the practice of data management. We have made progress in overcoming the inertia of organizational change management. But the underlying truth remains. We can’t respond to regulatory pressure, achieve automation or put data to work until we fix the underlying data challenges. The EDM Council believes these obstacles can be addressed.

Michael Atkin, Managing Director EDM Council, June 2017


BENCHMARKING PROFILE (NEW)

ProfileSummary

• More consolidatedresponses:betterreflectionoffirm-widecapability

• ControlGroup(organizationalcommitment)andbuy-side(learnfromindustryexperience)showconsistentprogress

• G-SIBs= scoresdownoverall(gradingwithmorerigorbasedonexperience)

• AssetServicing=continuetoleadasthedatamanagementstarsoftheindustry

• Overall understanding ofdatamanagementlowerthanexpected(outsideofrisk)

• Globalcoveragesupportsfederatedgovernance(empoweringregionaldatateams)

• Enterprise-widescopeisapositiveindicatorthatdataisasharedresourceacrossthefulllifecycle(althoughdatamanagementisstillsilo-based)

7%

21%

72%

No Formal Program LOB, Group, DivisionEnterprise-Wide

86%

14%

76%

24%

79%

21%

Global Regional

2%

7%

10%

12%

24%

45%

InsuranceAsset Serving

Buy-SideRetail/Commerical …

Sell-SideUniverisal Bank

Control

General

Industry

ProgramScope ProgramRegion

2%

22%

76%

9%

20%

71%

5%

6%

13%

19%

19%

38%

Insurance

Asset Serving

Sell-Side

Retail/Commerical Bank

Buy-Side

Univerisal Bank

5%

8%

10%

21%

22%

34%

Asset Serving

Insurance

Sell-Side

Retail/Commerical Bank

Buy-Side

Univerisal Bank


BENCHMARKING PROFILE (CONTINUED)

Control

General

Industry

3%

15%

51%

31%

NotActive

Planning

Formed

FullyOperational

3%

17%

52%

28%

NotActive

Planning

Formed

FullyOperational

0%

9%

45%

46%

NotActive

Planning

Formed

FullyOperational

GovernanceImplemented

9% 22%

19% 5%

10% 35%

0%

Risk

Other

Operations

Marketing

LineofBusiness

InformationTechnology

Finance

14% 10%

32% 1% 0%

31% 6%

Risk

Other

Operations

Marketing

LineofBusiness


Finance

13% 14%

28% 2%

7% 32%

4%

Risk

Other

Operations

Marketing

LineofBusiness


Finance

WheretheCDOResides

73%

27%

FundingModel

Annual Multi-Annual

54%

49%

50%

21%

25%

24%

25%

26%

26%

Industry

General

Control

ProgramFunding

CorporateFunding

LOBCont

Project-Based

14%

21%

19%

67%

53%

58%

14%

22%

19%

Industry

General

Control

CDOLengthofTime

<1Year

1-3Years

>3Years

14%

21%

19%

67%

53%

58%

14%

22%

19%

5%

4%

4%

Industry

General

Control

ODMYearsofOperation

<1Year

1-3Years

>3Years

5+Years

100%

81%

86%

19%

14%

Industry

General

Control

OfficeOfDataManagement

Yes

No

23%16%

Control Industry

GlossaryOperation

70%ofthe

IndustrygrouphasaChiefDataOfficer

90%ofthe

ControlgrouphasaChiefDataOfficer


DATA MANAGEMENT DRIVERS

DataManagementDrivers(citedasaprimarydriver) Control General Industry1. RiskManagement(BCBS239/FRTB)BCBS 69% 65% 66%2. TrustinData(fit-for-purpose)BCBS 60% 58% 59%3. OtherRegulatory(GDPR/KYC) 36% 38% 38%4. OperationalEfficiency(costcontainment/automation) 26% 43% 38%5. BusinessInsight(analytics) 40% 36% 37%6. StressTesting(CCAR,BaselIII) 41% 36% 37%7. Transparency(derivatives,CATS,MiFID)BCBS 17% 24% 22%

• Data Quality: trusted and fit-for-purpose data is the primary objective of data management• Analytics: If you have to fix your data for regulatory purposes, do it right and get business value• Efficiency: Cost containment is a by-product of good data management, not a driver• Regulation: most regulatory goals have the same data management prerequisites (precise

identification, shared language based on obligation, ability to unravel aggregates, ability to analyze interdependencies, creation of links across processes)

Systemic Risk à Stress Testing à Exposure à Concentration à Collateral Flow à Best Execution à Real-Time Valuation à Audit Trails à Privacy


DATA MANAGEMENT PRIORITIES

DataManagementPriorities(highpriority) Control General Industry

1. DefiningCDEsBCBS(complexanddependentonapplication) 71% 75% 74%2. ImprovingDataQualityBCBS(manualreconciliationisstillaproblem) 69% 72% 71%3. GovernanceImplementationBCBS(precipiceofimplementation) 64% 74% 71%4. Roles&ResponsibilitiesBCBS(needcompetenceholdingthereins) 59% 63% 62%5. EnterpriseGlossaryBCBS(fundamental(apriori)pre-requisite) 67% 59% 61%6. ImplementingPolicyBCBS(datamanagementenforcedbypolicy) 50% 62% 58%7. DocumentingLineageBCBS(untanglingtheGordianKnotissignificant) 71% 52% 57%8. Metrics(essentialforsustainablecommitment) 55% 46% 48%9. CommitmentfromStakeholders(commitmentindangerofwaning) 50% 47% 48%10. ImplementingStewardshipBCBS(datamanagementperformedbyLOB) 40% 50% 47%11. ResolvingPainPoints 45% 44% 44%12. EcosystemCollaboration 38% 44% 42%13. TechnicalIntegrationBCBS 36% 36% 36%14. Messaging(LOBbuy-in) 40% 33% 35%15. VerifyingRequirementsBCBS 22% 34% 31%16. ReduceManualReconciliationBCBS 33% 23% 25%17. SustainableFundingBCBS 28% 23% 24%18. HiringDataManagementStaff 21% 25% 23%19. Adaptability(scenario-basedanalysis)BCBS 12% 14% 13%

Regulation

OPS

Sustainability


BENCHMARK SCORING CRITERIA

Score Category Description

1 Not Initiated Ad hoc (performed by heroes)

2 Conceptual Initial Planning (white board sessions)

3 DevelopmentalEngagement Underway (stakeholder recruitment; project-based activity)

4 Defined

Performed and Verified (roles and responsibilities structured; policy and standards implemented; content infrastructure established; sustainable funding)

5 AchievedAdopted and Enforced (executive management sanctioned; activity coordinated; adherence audited; strategic funding)

6 Enhanced Integrated (continual improvement)

Scoring Criteria

EngagementRight people at the right levels with organizational influence involved in the

data program

ProcessEstablished, structured,

standardized and repeatable

EvidenceCapabilities supported by

auditable evidence of adherence

Line of Demarcation• 3.75+ (well on way)• 3.40↓ (work to do)


SIX AREAS OF OPERATIONAL ACTIVITY

Data Management Program

Stakeholder Commitment Governance Implementation

Content Infrastructure Data Quality Control

Eco-System

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LOW

Medium

High

3.75

2.0

3.0

4.0

5.0

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q8 Q21 Q22

2015-Industry 2015-Control 2015-Buy-Side 2015-SellSide 2015-G-SIB



ResultsandObservationsDataManagementProgram

Setting Up Program (Questions 1,2,3):

Thefinancialindustryismakingsteadyprogress.Datamanagementisa“knownobjective”andthestrategiesarebeingdefined.Itisstillunclearwhetherthestrategiesarefullyunderstoodandfunctionallyendorsedbystakeholders.TheauthorityoftheODMtoimplementthedataprogramisstillbeingdefined.Datamanagement(asacorepartofthewayfinancialinstitutionsoperate)isgrowingbutstillnotfullyentrenched.


Q1:DATA MANAGEMENT STRATEGYQuestion1:Ourorganizationhasadefinedandendorseddatamanagementstrategy

Implications: Definestheobjectivesofthedatamanagementprogramincludinghowitwillbeimplementedandgoverned.Thegoalisconfidenceinprogramobjectives,approachandimplementation

KeyObservations

• Flatprogressondatamanagementstrategy.Atthispointinthecyclewewouldexpectasignificantincrease

• Datamanagementisarecognizedconceptwithsignificantimprovementfromthebuy-side(footholdestablished)

• G-SIBscoresrecalibrated(downward)basedonimplementationexperience;regulatorytriageisatacticalactivityandbyitselfisnotadatamanagementstrategy

BCBSRequirement:Strategyisacoreobjective.Strategydefineshowthefirmisapproachingandimplementingdatamanagement(defined,shared,aligned,enforceable)

Opinion/Trend Achieved Capability

18%+2%

Industry Tier1 Control Buy-Side SellSide G-SIB2015 2017 2015 2017 2015 2017 2015 2017 2015 2017 2015 2017

NotInitiated(1) 4% 5% 2% 4% 3% 2% 7% 3% 4% 5% 3% 6%Planning(2) 12% 10% 11% 10% 10% 5% 19% 10% 10% 8% 11% 14%Underway(3) 29% 30% 31% 29% 30% 24% 29% 40% 29% 31% 22% 17%Performed(4) 39% 37% 37% 41% 40% 38% 36% 25% 40% 38% 43% 47%Achieved(5,6) 16% 18% 19% 16% 17% 31% 10% 23% 16% 18% 21% 17%

High

Low

Medium3.5 3.6

3.93.6 3.6 3.6

3.5 3.6 3.6 3.2 3.5 3.72.0

3.0

4.0

5.0

55%

30%

15%

69%

24%

7%

17%

64%

20%

57% 48%56%

29% 40% 31%

14% 13% 13%


Question2:Thegoals,objectivesandauthoritiesofthedatamanagementprogramarewellcommunicated

Implications: Communicationsisnotasidelineactivity.Theobjectiveisenhancedawarenessandalignmentbystakeholdersonthegoals,scope,prioritiesandpoliciesforthedataprogram

KeyObservations

• CommunicationaboutthegoalsandauthorityoftheDMPisstilldevelopingandishamperedbyorganizationaldynamicsandinternalpolitics

• RiskandtopmanagementunderstandtheDMPobjectivebecauseofregulatorypressure(executivesintheLOBlag)

• Controlgroupcommunicationfirmlyestablished(authorityincreasedandroleformalized),buy-sidetakesabigstepforwardandG-SIBsscoresfallslightly


20%+5%



High

Low

Medium3.4 3.5

4.03.5 3.5 3.4

3.3 3.4 3.3 2.9 3.4 3.62.0

3.0

4.0

5.0

74%

48% 47%

32%

17%

35%

19%8%

19%

Q2:STRATEGY COMMUNICATED

52%

30%

19%

53%

28%

20%

48%

36%

16%


Question3:Thedatamanagementprogramisestablishedandhastheauthoritytoenforceadherence

Implications: TheOfficeofDataManagementneedstheformalandofficialauthoritytoimplementthedatamanagementprogram.Thegoalistomakesurethestakeholdersunderstandthatadherenceismandatory

KeyObservations

• AuthorityoftheODMisstillbeingdefined(stakeholderrequirementsunderdebate)

• Datamanagementasanorganizationalconstructnotfullyentrenched• Noticeableshift(downward)from“achieved”to“defined”forG-SIBs• Controlgrouppassesthe“lineofdemarcation”

BCBSRequirement:AuthorityofODMismandatorytoensureadherence


20%-2%



High

Low

Medium3.5 3.6 3.9

3.23.5 3.5

3.4 3.6 3.5 3.2 3.5 3.72.0

3.0

4.0

5.0

50%

19%

31%

67%

22%

10%

58%

26%

17%

Q3:AUTHORITY

15%25%

16%

31%

40%

31%

54%46%

53%


ResultsandObservationsStakeholderCommitment




Eco-System

ALIG

NM

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COM

MUN

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AUTH

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ALIG

NMEN

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FUN

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MET

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RESO

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S

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SYST

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ANIZ

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N

STEW

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POLI

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ADHE

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MEA

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CDE

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AGE

PRO

FILI

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CON

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CAPA

BILIT

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TECH

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L ARC

H

LOW

Medium

High

3.75

2.0

3.0

4.0

5.0




Commitment from Stakeholders (Questions 4,5,6,7):

Theindustryhasmadelittlemeasurableprogressinfullyunderstandingthe“why”ofdatamanagement.Thereisgoodnewshowever.Creatingatruedatamanagementcultureisdifficult,butitcanbeachievedoncetheprogramtakeshold.Thebuy-sidecontinuestotakeboldstepsforward.


Q4:STAKEHOLDERS UNDERSTAND

Question4:Stakeholdersunderstand(andbuyinto)theneedforthedatamanagementprogram

Implications: Theconceptofdatacontentaspartoftheoperationalinfrastructureoftheorganizationisnewtomanystakeholders.Theindustrystillfaceschallengesadoptingadatamanagementculture.

KeyObservations

• Stakeholderunderstanding(andbuy-in)ontheneedfordatamanagementisstable

• Some“whyarewedoingdatamanagement”questionsremainamongG-SIBs

• LOBexecutivesarestillnotcompletelyonboardwiththegoalsandimportance

• UnderstandingofBCBS239objectiveisnotthesameasunderstandingthedatamanagementmandate


21%+1%



High

Low

Medium3.5 3.6

3.9 3.6 3.5 3.6

3.5 3.6 3.7 3.3 3.6 3.82.0

3.0

4.0

5.0

54%

29%

69%

17%

22%

9%

59%

27%

14%

57%

28%

16%

51%

40%

10%

54%

29%

17%


Q5:FUNDING MODEL

Question5:Thefundingmodelforthedatamanagementprogramisestablishedandsanctioned

Implications: Fundingforthedatamanagementprogramcan’tbeoptional.Thereisadifferencebetween“seedfunding”and“sustainablefunding”forthedataprogram

KeyObservations

• Industrycontinuestoimprovewiththecontrolgroupandassetservicingmovingtowardsustainablemulti-yearfunding

• Buy-sidesurpassesindustryaverageand“ridingthecurve”offundingtoestablishtheOfficeofDataManagement

• Euphoriaof“seedfunding”iswearingoffamongG-SIBswithadropinscores(warningsignandfearthatwithoutsustainablefunding,datamanagementwillwane)

BCBSRequirement:Fundingisessentialtodrivedatamanagementprograms(defined,authorizedandenforced)


30%+1%



High

Low

Medium3.6 3.6

4.13.7 3.6 3.6

3.4 3.6 3.5 3.1 3.6 3.92.0

3.0

4.0

5.0

58%

19%

24%

69%

19%

12%

60%

15%

24%

59%

19%

22%

63%

15%

23%

55%

23%

22%


Q6:METRICS ON COSTS AND BENEFITS

Question6:Thecostsof(andbenefitsassociatedwith)thedatamanagementprogramarebeingmeasured

Implications: Youcan’tmanagewhatyoucan’tmeasure.AstandardmethodologyforcapturingexpensesandbenefitsbeyondtraditionalROIisnecessaryforlong-termorganizationalbuy-in.

KeyObservations

• Thelowestofthescoresfromlastyeardroppedevenlower• Metricsaboutdatamanagement(whichareneededtobecomeestablishedasanoperationalactivity)arestillelusive

• Acrosstheboard– wearestillonlybarelyoutoftheconceptualrealm


7%-0.5%



High

Low

Medium 2.7 2.8 3.02.7 2.7 2.7

2.8 2.9 2.8 2.7 2.8 3.12.0

3.0

4.0

5.0

23%

34%

43%

33%

36%

31%

26%

49%

26%

26%

33%

41%

21%

38%

43%

24%

34%

42%


Q7:SUFFICIENT RESOURCESQuestion7:Thedatamanagementprogramissufficientlyresourced

Implications: Datamanagementrequirescommitmentfromawidevarietyofstakeholders.Thegoalisalignmentofobjectiveswithprojectdelivery,funds,staffcapabilityandtimeallocation

KeyObservations

• Scoresrisefor“achieved”butdropoverallacrosstheindustryincludingamongTier1andG-SIBs

• Resourcesarederivedbasedondefineddeliverables,stakeholdercommitmentandfundingalignmenttoroadmapsandworkstreams

• Governanceimplementationwascitedasoneofthetopprioritiesfortheindustry

BCBSRequirement:Stakeholdersneedtobeaccountablewithresourcesandoperationalfunding


19%+6%



High

Low

Medium 3.2 3.3 3.53.1 3.2 3.3

3.3 3.4 3.4 2.9 3.4 3.52.0

3.0

4.0

5.0

39%

34%

27%

49%

29%

22%

40%

27%

33%

43%

31%

26%

30%

38%

33%

38%

37%

24%


ResultsandObservationsGovernanceImplementation




Eco-System

ALIG

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TECH

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L ARC

H

LOW

Medium

High

3.75

2.0

3.0

4.0

5.0




Governance Implementation (Questions 9,10,11,12):

Progressontheestablishmentoffoundationalgovernanceissubstantial.Theorganizationalstructuresaredefined.Stewardshipisbeingassigned.Thedevelopmentofpolicyisprogressing.Practicalimplementationishighonthelistofindustrypriorities.Signsarepositive– althoughwerecognizethatchangingthewayorganizationsoperateisnotsimple.


Q9:GOVERNANCE STRUCTURE IMPLEMENTED

Question9: Datagovernancestructureandauthorityisimplementedandcommunicated

Implications: Turnsdatamanagementobjectivesintooperationalreality.HireCDO…establishPMO…definegovernanceroutines…establishprojectreviewprocedures…makefundingoperational

KeyObservations

• Wehaveestablishedfoundationalgovernanceandthecapabilityismaturing.

• Theentireindustryiswellonthewaytoachievement(inchingabovethelineofdemarcation)

• Buy-sidelagsslightlyreflectiveofthelackofmaturityoftheOfficeofDataManagement

BCBSRequirement:ODMisneededtoimplementthedatamanagementprogram


25%+7%



High

Low

Medium3.6 3.7

4.3

3.43.7 3.9

3.4 3.6 3.5 3.0 3.4 3.72.0

3.0

4.0

5.0

56%

28%

16%

83%

10%7%

67%

24%

9%

59%

28%

14%

46%

40%

16%

60%

27%

13%


Q10:OWNERS AND STEWARDS IN PLACE

Question10: Governance“owners”and“stewards”areinplacewithclearlydefinedrolesandresponsibilities

Implications: Appointmentofindividualswithinthelinesofbusinessandcontrolfunctionswithdirectresponsibilityfordatamanagement(requirementscapture,qualitycontrol,datameaning,transformationprocesses,dataflow,coordinationacrossfunctions)

KeyObservations

• Strongpositivescores(milestoneachievement)acrosstheindustry• Lineofbusinessgovernanceisapproachingthedefined,communicatedandstaffedlevel

• DatastewardsinG-SIBsalignedwithriskdatamanagementobjectives

BCBSRequirement:Operationalgovernanceishowdatamanagementbecomesareality


23%+7%



High

Low

Medium3.5 3.7 4.0

3.5 3.6 3.7

3.2 3.3 3.4 3.0 3.2 3.42.0

3.0

4.0

5.0

50%

30%

20%

67%

22%

11%

55%

30%

16%18% 21% 17%

26%33% 31%

56%48% 52%


Q11:POLICIES AND STANDARDS IMPLEMENTED

Question11: Datapoliciesandstandardsaredocumented,implementedandenforced

Implications: Datamanagementisgovernedviapolicyandimplementedbasedonorganizationalstandards.Implementationofthe“rulesoftheroad”withconsequencesandagreementfromstakeholdersisapre-requisiteforeffectivedatamanagement.

KeyObservations

• ThehighestscoresacrossthefullbenchmarkingstudyforbothG-SIBsandtheControlgroup

• Entireindustryisnearingthelineofdemarcation(majormilestoneachievement)

• Dramaticimprovementsillustratethecommitmentoftheindustrytomeetingregulatoryrequirements(adoptionofpolicyisaBCBS239requirement)

BCBSRequirement:“Controlenvironment”isenforcedbycorporatepolicy.Essentialcomponent.


26%+10%



High

Low

Medium3.6 3.7

4.13.4

3.8 4.0

3.3 3.4 3.3 2.7 3.4 3.82.0

3.0

4.0

5.0

52%

30%

18%

74%

17%

9%

65%

23%

12%15% 18% 15%

29%

43%

27%

57%40%

58%


Q12:ADHERENCE TO STANDARDSQuestion12: Theendusercommunityisadheringtothedatagovernancepolicyandstandards

Implications: Adherencefollowsadoption.Adoptionfrequentlyrequiresa“burn-in”periodtoenablestakeholderstoadjustandaddressgapsindatamanagementcapability.Adherencealsorequiresenforcementmechanismsandthepresenceofcontrolprocesses(i.e.checkpoints,formalreviewprocesses,auditengagement)

KeyObservations

• Industryhasmadeconsiderableprogressmovingadherencealongthecapabilitycurve

• Adherencetopolicyusuallylagsimplementationandisafinalstagemetric

• Fillingthegapfromlow“developmental”to“achieved”isnoteasy

BCBSRequirement:Accountabilitytopolicyistheauditstandard.


9%+4%



High

Low

Medium 3.0 3.1 3.32.9 3.1 3.1

2.7 2.8 2.8 2.4 2.8 2.82.0

3.0

4.0

5.0

32%

36%

32%

41%

36%

23%

38%

32%

31%30%41%

28%

34%33%

37%

35%28% 35%


ResultsandObservationsContentInfrastructure




Eco-System

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L ARC

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LOW

Medium

High

3.75

2.0

3.0

4.0

5.0




Content Infrastructure (Questions 13,14,15,16,17):

ThisisaboutPrinciple2ofBCBS239– thebuildingblockforachievingadatacontrolenvironment.Withoutatrustedandharmonizedcontentinfrastructure,thereislittlepointtoimplementingdatapolicyandgovernance.Workisunderwayonlineage,CDEdesignationandglossaryreconciliation,butthetasksaresignificantandprogressisslow.


Q13:DATA HARMONIZATION ACROSS REPOSITORIESQuestion13: Thebusinessmeaningofdataisdefined,harmonizedacrossrepositoriesandgoverned

Implications: Oneoftheprincipleobjectivesofdatamanagement.Withoutachievingthiscapability,theindustrywillnotbeabletounravelinterconnections,managecomplexity,automateprocessesormanagelinkedriskacrossthefinancialsystem

KeyObservations

• SomeprogressamongControlandG-SIBsduetoBCBS239• Scoresremaininthe“conceptual”tolow“developmental”level.• Workisunderwaybutglossaryreconciliationisamoresignificanttaskthananticipated.

• Thechallengethatstartswithdefinitionswillgetmorecomplicatedastermsandrelationshipsaremappedfromphysicalrepositoriesandlinkedtologicaldatamodels.

BCBSRequirement:Thisistheprimedirectiveforharmonization,comparabilityandscenario-basedanalysis


8%+1%



High

Low

Medium 3.0 3.0 3.3 3.1 3.0 3.2

2.9 2.9 2.9 2.7 3.0 3.02.0

3.0

4.0

5.0

30%

40%

30%

38%

43%

19%

35%

38%

27%30% 30% 29%

39% 40% 41%

31% 30% 30%


Q14:CRITICAL DATA ELEMENTS

Question14: Criticaldataelementsareidentifiedandmanaged

Implications: :Unravelingthedataattributesthathaveamaterialimpactonbusinessfunctionsisanessentialcomponentofthedatamanagementequation.Ithelpstoestablishprioritiesanddeterminewheretofocuslimitedresources.CDEsarederivedfromlineageandfrequentlyhavetobereverseengineeredfromcalculatedandaggregationprocesses.

KeyObservations

• Progressfromallsegmentsoftheindustry(recognitionoftheimportance)

• DeterminationofCDEcriteriaisthenumber1priorityoftheindustry• Muchoftheindustryprogressisfocusedoncriticaldatainputsforriskreporting

• Estimationofthiscapabilityislikelyoverstatedwhenputintooverallcontext.

BCBSRequirement:Datathatsupportscriticalbusinessfunctionsmustbedefinedandmanaged


14%+2%



High

Low

Medium3.3 3.4

3.73.3 3.4 3.5

3.2 3.4 3.2 3.0 3.2 3.32.0

3.0

4.0

5.0

41%

39%

20%

52%

43%

5%

47%

33%

20%18% 23% 18%

40% 35% 38%

43% 43% 43%


Q15:LOGICAL DATA DOMAINS

Question15: Logicaldatadomainshavebeendeclared,prioritizedandsanctioned

Implications: :Dataneedstobecharacterizedandorganizedasanearlystepinthedatamanagementprogram.Managingthisacrossacomplexenterpriseisadifficultpropositionandisbasedonanunderstandingofhowthebusinessfunctionsandhowdataismanufacturedacrosslinkedprocesses.

KeyObservations

• ProgressisrelativelyflatapartfromtheControlgroup• Logicaldomainsareeasytounderstand,buthardtoimplement• Implementationoflogicaldomainsviaauthorizedprovisioningpointsandchangingdataaccessmechanismsexacerbatesthechallenges

BCBSRequirement:Anothercorecomponentofthe“controlenvironment”


11%-3%



High

Low

Medium 3.2 3.23.6 3.4 3.2 3.4

3.3 3.3 3.4 3.4 3.2 3.42.0

3.0

4.0

5.0

37%

39%

24%

54%

34%

12%

44%

42%

14%23% 18% 23%

38% 38%41%

41% 45%35%


Q16:END-TO-END LINEAGEQuestion16: End-to-enddatalineagehasbeendefinedacrosstheentiredatalifecycle

Implications: DocumentinglineagestartswithdatadiscoveryandincludesreverseengineeringcriticalmeasuresandunravelingtheflowofdataacrossverticallyalignedITenvironments.Itisanessentialpartofthedataquality(andrequirements)verificationprocess– butwiththousandsofapplicationsandhundredsofdatamodels– theprocessisdaunting.

KeyObservations

• UnravelingtheGordianKnotremainsasignificantchallenge• Theindustryisstillgenerallyhoveringinthelate“conceptual”stageofthiscapability

• Dataforensicsisnotasimpletaskandisthesecondhighestdatamanagementpriorityfortheindustry

BCBSRequirement:Mustunderstandhowdataflowsacrosslinkedprocessesbasedonreverseengineeringofapplicationsthroughcalculationtosystemsofrecord


6%+1%



High

Low

Medium 2.8 2.83.3

2.6 2.9 2.9

2.7 2.8 2.8 2.4 2.8 2.92.0

3.0

4.0

5.0

23%

36%

41%

40%

38%

22%

33%

27%

40%39%48%

38%

34%

35%

35%

27% 18% 27%


Q17:TECHNICAL ARCHITECTUREQuestion17: Technicalarchitectureisdefinedandintegrated

Implications: Translationofdatamanagementobjectivesintothepathwayfortechnicalimplementation.Thisisacollaborativeactivitytodefinethedatabasestrategies,analyticsplatforms,middlewaresolutionsandprocesscontrolconsiderationsneededtosupportthedataobjectivesoftheorganization

KeyObservations

• Industryisintheprocessofdefiningtheintegratedarchitecturestrategy

• RealityisaverticallyalignedITinfrastructurethatwasindependentlydeveloped

• Theimportanceofanenterprisetechnologygovernancetoprovideauthoritativecontentisrisingasapriority


14%NEW

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

100%

Industry Tier1 Control Buy-Side SellSide G-SIB

2017- Low. 2017- Medium. 2017- High.

Industry Tier1 Control Buy-Side SellSide G-SIB2017 2017 2017 2017 2017 2017

NotInitiated(1) 8% 8% 5% 8% 6% 8%Planning(2) 17% 16% 17% 10% 20% 21%Underway(3) 34% 33% 31% 48% 32% 20%Performed(4) 26% 29% 33% 15% 27% 30%Achieved(5,6) 14% 14% 14% 20% 14% 21%

High

Low

Medium 3.2 3.3 3.3 3.3 3.2 3.4

2.0

3.0

4.0

5.02017

40%

34%

25%

47%

31%

22%

51%

20%

29%

New Question

24% 18% 26%

33% 48% 32%

43%35% 41%


ResultsandObservationsDataQualityData Quality (Questions 18,19,20):

Wehaven’tescapedthetyrannyofmanualreconciliation.Overalltheindustryisstillmiredinthetacticalmappingofdatafromphysicalrepositoriestoapplicationsandreports.Trustandconfidenceremainstheelusivegoalofdatamanagement– andistheessentialprerequisiteforbothreportingandbusinessanalytics.Thereisastronglinkbetweenenhancingqualityandimplementingthecontentinfrastructure.




Eco-System

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LOW

Medium

High

3.75

2.0

3.0

4.0

5.0





Q18:DATA PROFILINGQuestion18: AlldataundertheauthorityoftheDataManagementProgramisprofiled,analyzedandgraded

Implications: Profilingcreatesaqualitybenchmarkfortheorganizationtosupportthedeliveryoffit-for-purposedataacrossalldataqualitydimensions.Thisincludesthedefinitionofbusinessrulesandthecaptureofprofilingresultsasmetadata

KeyObservations

• Stillamongthelowestcompositescoresacrosstheentirebenchmark

• Notmuchprogresswithdataqualitydimensionsstillbeingdefinedandprioritized

• Controlgrouptakesasmallstepforward

BCBSRequirement:Evaluationofqualityagainstalldimensionsofquality(drivesremediation)


5%+1%



High

Low

Medium2.6 2.7 3.0

2.6 2.7 2.72.7 2.7 2.7 2.4 2.7 2.9

2.0

3.0

4.0

5.0

21%

39%

40%

32%

38%

30%

26%

44%

31%

39% 43% 39%

38%40%

37%

24% 18% 24%


Q19:DATA QUALITY PROCESSESQuestion19: Proceduresformanagingdataqualityaredefined,implementedandmeasured

Implications: Demonstratesthepresenceofadataqualitystrategyincludingtheidentificationofaccountableparties,assignmentofDQresponsibilitiesandtheimplementationofadataqualitycontrolprocess.

KeyObservations

• Controlmechanismsandcheckpointsaredefinedandbeingimplementedacrosstheindustry

• Implementationisunevenandbifurcatedatbothearlyandadvancedcapability

• Dataqualityimprovementislinkedtogovernanceimplementationandwhiletherearesomebrightspotsthisremainsapriority

BCBSRequirement:Managementofchainofsupplyandcorecomponentof“controlenvironment”


13%+3%



High

Low

Medium 3.1 3.2 3.4 3.1 3.2 3.3

3.0 3.1 3.1 2.8 3.0 3.32.0

3.0

4.0

5.0

37%

33%

30%

43%

34%

23%

41%

32%

28%28% 31% 27%

33% 33% 34%

39% 38% 39%


Q20:ROOT CAUSE ANALYSISQuestion20: Rootcauseanalysisisperformedandcorrectivemeasuresarebeingimplemented

Implications: Tacticalapproachestofixingmisalignedorcorrupteddatadonotidentifyoraddresssystemicdataqualityissues.Addressingrootcausereducestheneedforongoingremediationandimprovesconfidenceasdataflowsacrossprocessesandamongcounterparties

KeyObservations

• Notmuchprogressinidentifyingandaddressingtherootcauseofdataqualityproblems

• Industryisstillengagedindefiningtheapproachtodeterminingthecauseoferrors

• Managingrootcauseremediationrequiresstrongcollaborationacrossthesupplychain

BCBSRequirement:Mustfixtheunderlyingproblemofinconsistentdataorremainmiredinreconciliation


14%+2%



High

Low

Medium 3.1 3.2 3.3 3.1 3.2 3.3

3.0 3.1 3.0 3.1 3.0 3.32.0

3.0

4.0

5.0

38%

31%

31%

43%

26%

31%

44%

30%

26%30% 31% 25%

28% 35%31%

42% 35% 43%


ResultsandObservationsEcosystemCollaboration




Eco-System

ALIG

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AUTH

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ALIG

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CON

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BILIT

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L ARC

H

LOW

Medium

High

3.75

2.0

3.0

4.0

5.0




Ecosystem Collaboration (Questions 8,21,22):

Abrightspot.DataisasharedresourceandcollaborationacrossITandtheothercontrolfunctionscontinuestomoveforward.ThisisatributetoorganizationalcohesionwithinthefinancialindustryandthematurityofIT.


Q8:COLLABORATION WITH CONTROL FUNCTIONS

Question8:Datamanagementoperatescollaborativelywithexistingenterprisecontrolfunctions

Implications: EffectivedatamanagementisbuiltoncollaborationacrossIT,operations,controlfunctionsandlinesofbusiness

KeyObservations

• Abrightspotwithscoresrisingsignificantlyacrosstheindustry• Datamanagementisacollaborativeactivityandtheothercontrolfunctionsaredependentontrustindata

• Anothertipofthehattotheregulatorydriver(GDPR,KYC,informationsecurity)


18%+7%



High

Low

Medium3.3 3.4 3.7

3.2 3.3 3.4

3.0 3.1 2.9 2.8 2.9 3.22.0

3.0

4.0

5.0

43%

32%

25%

24%

61%

28%

44%

33%

23%23%15%

33%22%

31% 34%

47%40% 44%


Q21:TECHNOLOGY STANDARDS AND GOVERNANCEQuestion21: Technologystandardsandgovernanceareinplacetosupportdatamanagementobjectives

Implications: Technologyinfrastructuregovernanceguaranteesadherencetoplatformandtoolstandards.ThegoalistoensurealignmentofITwiththedataneededforapplicationsalongwiththeabilitytocomplywithSLAsanddatacontentrequirements.

KeyObservations

• NoteworthyprogressinthealignmentofITgovernancewithdatamanagementstrategy(ITisnotthebarrier)

• CapabilityenhancementsobservedforboththeControlgroupandtheG-SIBs

• CollaborationunderwaytoensureITstandardsandgovernanceprocessesarealignedwiththegoalsofthedatamanagementprogram


12%+3%



High

Low

Medium 3.2 3.2 3.53.1 3.2 3.4

3.1 3.1 3.1 2.9 3.0 3.12.0

3.0

4.0

5.0

30%

40%

30%

49%

33%

19%

47%

27%

26%29% 30% 28%

30% 38%28%

42%33%

44%


Q22:CAPABILITY ALIGNMENT

Question22: Thedatamanagementprogramisalignedwithinternaltechnicalandoperationalcapabilities

Implications: EnsuresthattheobjectivesstatedinthedatamanagementstrategyareconsistentwithandachievablegiventheresourcesandcapabilitiesoftheITarchitecture(includingoperationalimplementationrequirements)

KeyObservations

• ImplementationconceptsarebeingalignedandverifiedbyITandoperationalstakeholders

• DoubledigitcapabilityenhancementsobservedforboththeControlgroupandtheG-SIBs

• PartnershipbetweenITanddatamanagementsolidifying


13%+4%



High

Low

Medium 3.2 3.2 3.53.1 3.2 3.4

3.1 3.1 3.2 3.0 3.1 3.12.0

3.0

4.0

5.0

38%

35%

27%

49%

31%

21%

47%

33%

20%25% 30%24%

38%43%

35%

37%28%

41%


BENCHMARK ADVISORY TEAM

MichaelAtkinCEO

EDMCouncil

Mike is a professional facilitator and has beena financial information industry advocate forover 25 years. He is an active participant inindustry initiatives, provides consultation toglobal market authorities on the dataimplications of financial regulatory reform andis a subject matter expert on reference datastrategy, governance, data quality andstandards.

JohnBottegaSeniorAdvisor,

ChiefDataOfficerForumEDMCouncil

John is a senior strategy and data managementexecutive with more than 30 years of experiencein the industry. Over his career, John has heldvarious positions in supporting a firm’s datamanagement functions. Since 2006, John hasheld the title of Chief Data Office in both theprivate and public sectors, serving as CDO forCiti and Bank of America, and holding the post ofCDO for the Federal Reserve Bank of New York.Today, John is the Principal and ManagingMember of his own consulting firm, DataManagement Advisory Services, LLC, and issupporting the Enterprise Data ManagementCouncil as Senior Advisor and Consultant,responsible for the Council’s CDO Forum andData Management Practices.

PredragDizdarevicManagingPartner

Pellustro

Predrag is an established entrepreneur withextensive experience in managing anddeveloping services and softwareorganizations within financial services andinformation technology industries. Predragadvises financial services companies on datamanagement and technology strategies, andprivate equity firms on investment selectionand the creation of new businesses within thefinancial technology and data space. He wasone of the stakeholders in the design andpositioning of the Pellustro platform.

GavinKaimowitzGlobalDataPracticeLead

SapientConsulting

Gavin leads Sapient Consulting’s DataManagement Practice across the capital andcommodity sectors, comprising of more than500 active consultants. Gavin is responsible fordelivery quality and oversight, collation andgeneration of best practices, thought leadershipand strategy for data management-relatedinitiatives. He has a proven track record in thefinancial services data domain of solutiondesign, delivery, business case definition,strategic roadmap design and in building datamanagement-related products. He also leadsthe Sapient Synapse product offering.


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