2025年数据与AI雷达：掌握数据与人工智能转型的10大挑战研究报告英文版-Wavestone

2025DATA&AIRADAR

10challenges

tomasteryourData

&AItransformation

in2025

2

Summary

CHAPTER1

TheriseofAI:timetoscaleupandindustrializegovernance

1.IndustrializingAI

2.GenAIatscale:movingbeyondPoCtorealizetheopportunities

offeredbygenerativeAI

3.AIgovernance:increasedcomplexitytoaddressalltheunderlying

issues

4.Data/AIacculturationonalargescale,toaccelerateinnovation

andprepareforthefuture

CHAPTER2

UnlockthefullpotentialofData

5.AfederatedorganizationtounifyData-relatedroles,standards,and

practices

6.DemocratizetheuseofDataforasmanypeopleaspossible

7.Datastorytelling,ortheartofmakingDataspeakforitself

CHAPTER3

Datagovernanceandqualityremainkeyconcerns

8.SuccessfullycombiningDataQualityandDataObservability

9.GovernanceofunstructuredData:agrowingproblemfor

organizations

10.SystematizethemeasurementofthevaluegeneratedbyData

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andwhat'snext?Preparingforthefuturebyputting27

peopleattheheartoftransformation

Data&AI:whatcanweexpect

in2025?

Theyear2024hasbeenachallengingoneforChiefDataOfficers/AIleaders,manyofwhomnowalsoholdthetitleofChiefData&AIOfficer,alongwiththeirteams.

TheriseofAI,particularlygenerativeAI,increasedinterestfromexecutivecommittees(ExCom)inData&AI,andevolvingregulationshaveputChiefDataOfficersunderpressuretotacklemultiplechallenges.

They’vehadtoaddresstheseemergingprioritieswhileexcellingintheirtraditionalrole:ensuringthecompanymaximizesthevalueofitsdataassetstoenhanceperformance,decision-making,and

competitiveness.

Tohelpyouprepareyourstrategiesandroadmapsfor2025(andbeyond),WavestonehasanalyzedthemajortrendsthatareshapingthedailyresponsibilitiesofChiefDataOfficersandthecore

challengestheywillfaceintheyearsahead.

WavestonesupportstheCDOs/AIleadersofmajorcompaniesandpublicinstitutionsonallgeographicalregions.Thisradarreflectstheassignmentscarriedoutduringtheyearandthechangesobservedbyallthefirm'sData&AIexperts.Wehavethusidentified

the10hottopicsfortheperiodahead.

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TheriseofAI:

timetoscaleupandindustrialize

governance

CHAPTER1

1.IndustrializingAI

Despitetheriseofartificialintelligenceinthe

businessworld,around85%ofAIprojectsstillfailtoreachproduction.Thisfiguredecreasesslightlyfromyeartoyear,butremainsrelativelyhigh,

andonlythemostmatureorganizationsmanagetobringitdowndrastically.

TotakeAIprojectsallthewaytoproductionandintegratethemattheheartofbusinessprocesses,companiesarerethinkingtheiroperatingmodel.Thestakesaretwofold:

→Defineanorganization,rolesand

responsibilitiesthatprovidealltheskills

neededtoautonomouslydeliveraproductthroughoutitslifecycle;

→Definepracticestoensureproper

implementationofAI,fromdesignto

maintenanceinoperationalconditions.

Todate,thefirstchallengehasmostlybeen

addressedbysettingupAIFactory-type

organizations.Thesearespecifictoeach

organization,butwecanoutlineastate-of-the-artAIFactoryin3mainfunctions:

→ThefirstaimstoaddresstheData/AI

ecosystems,buildrelationshipswithpublishersandderivemaximumvaluefromthe

opportunitiesofferedbythemarket;

→Thesecondistodeliverusecases,bothforexperimentationandindustrialization;

→ThethirdaimstosteertheFactory'sactivities,bothupstream(managingbusinessdemand,drivingproposalstoaddressnewusecases)anddownstream(facilitatingadoption,drivingchangeandpromotingAIanditspotential

withintheorganization).

Thesecondchallengeliesindeliverypractices.AnMLmodelisnotenoughtodelivervalue;it

needstobedesignedfordeploymentin

production.Alltoooften,modelcreationis

decoupledfromdeployment,anddesignedfor

experimentation.Thereisnodesignauthority,nodevelopmentframework,noautomatedtasks,noversioning.MovingtowardsAIindustrialization

meansdeployingpartiallyautomatedmodelandinfrastructuremanagementpracticesthatare

designedforproductionuse.MLOpsaimstoaddresstheseissues.

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CHAPTER1

Illustrationgénériqued'uneAIFactoryàl'étatdel'Arten2025

MLOps:thekeytomanagingAImodelsinproduction

FollowingonfromDevOps,MLOpsisasetofpracticesaimedatunifyingdevelopment

activities(Dev)andoperations(Ops),to

continuouslydeliverandmanageAImodels,fromdevelopmenttomonitoring,via

acceptanceanddeployment.Whilecertain

teamsarenowincreasinglyproficientininitialmodelproduction,thenextchallengestobeaddressedwillbeadvanced,automatic

performancemonitoring,driftdetectionandorchestrationofcontinuousretrainingto

maintainmodelrelevance.

ThechallengeforDataScientistsisnolongertoconfinethemselvestomodeldevelopment,buttoacquireMLEngineeringskillstoensuretheimplementationofthesepractices.Andfororganizations,thechallengeistodefinea

coherent,sharedframeworkacrosstheorganization,basedofcourseonbest

practicessharedbythemarket,andtodrivechangetoensureitsapplication.

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CHAPTER1

2.GenAIatscale:movingbeyondPoC

torealizetheopportunitiesofferedby

generativeAI

2024sawtheriseofGenAI.OurstudyshowedinMay2024that

74%oforganizationshadalreadybegunworkonimplementinggenerativeAI.PoCshavebeenlaunchedsince2023andhaveledtoconvincingresults.Nevertheless,wenotethatmany

companiesarestillstuckinthePoCphaseandhavenotyetbeenabletomoveontotheindustrializationstage.SomeprojectsarestillPoCsmorethanayearaftertheirlaunch!

“GenerativeAIhasmade

decision-makersPoC-phobic."

Toavoidthissituation,werecommend:

→DefinesimplerulesforallPoCslaunchedwithinanorganization.Forexample,PoCsmustbe

limitedintimeandbudget,undergoaninitialarbitrationattheendofthefirstmilestone,andcanpotentiallybeextendedonceiftheinitialresultsareconvincing.Iftheexpectedresultstakealongtimetobedemonstrated,it'slikelythattheprojectwillhavetobeterminated,andthefocusshiftedtootherusecases;

→ManageasingleportfolioofGenAIPoCs(evenifdeliveryisdecentralized),andcollectively

arbitratewhichPoCsdeservetogofrom

deliverytoscale(demonstratedvalue,optimalreturnoninvestment,etc.).Thesemustbe

limitedinnumber,andacollectiveeffortmustbemadetodelivertheseprojectsas

effectivelyaspossible.

Atechnologicalstrategytobecarefullythoughtout

It'stemptingtowanttomovefasttotake

advantageofGenAIandgainacompetitiveedgequickly.

Somecompanieshaveentrusteddatascientistswiththekeystothehouse,inordertopromote

innovationandtime-to-value.However,this

strategycanbefraughtwithrisks:thefactthat

usecasesaredeliveredlocally,conceivedasa

singleunit,withrelativelylittleflexibilityand

scalability.AllthisatatimewhentheentireGenAIecosystemisevolvingveryrapidly,and

innovationsabound(newmodels,new

capabilities,etc.).How,forexample,canwe

ensurethatwewillbeabletomakethemostofanewmodelavailableonthemarketthatoffers

improvedperformance,withouthavingtorebuildeverything?

Worsestill,somecompanies,outofasenseof

havingfallenbehindtheircompetitors,quicklyforgedpartnershipswithcertaintechgiantstoreassuredecision-makers.Thesechoiceshave

sometimesledtovendorlockingsituations,in

whichanorganizationfindsitselflimitedinits

abilitytoinnovate,explorealternativesand

optimizeitsorientationsforspecificusecases.Inadditiontoflexibility,thefinancialequationcanalsobedegradedinthelongrun.

Companiesthathavethoughtthroughtheir

medium-tolong-termstrategyhavethustakentheirdesireforrobustnessandtechnology

diversificationintotheirownhands.Forexample,manyarebuildingmodel-agnosticGenAI

platformsandarchitectures.Suchaplatformmakesitpossibletohost,trainandmonitora

varietyofLLMs,enablingtheLLMbestsuitedtoeachsituationtobeusedtoderiveoptimum

value.Flexibilityisguaranteed,asisadaptabilityandtheabilitytoaccommodateafreshly

publishedmodel.

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CHAPTER1

MaterializationintheformofAgents

AnAIAgentisasystemdesignedtoreasonwithcomplexproblems,createactionplans,and

executetheseplansusingaseriesoftools.Unliketraditionalcomputerapplications,theseagentshaveadvancedreasoning,memorization,and

taskexecutioncapabilities.Theseagentscan,forexample,solvecomplexproblems(e.g.,generateprojectplans,writecode...),performself-criticismbyanalyzingtheirownoutputs,useexistingtools

andIS,andevenperforminter-agentcollaboration.

TheseAgentsaremadeupofseveralelements:

1.ACoreAgent:thecentralelementintegratingallprocessingfunctionalities.

2.Amemorymodule:storesandretrieves

informationtomaintaincontextandcontinuityovertime.

3.Asetoftools:externalresourcesandAPIsthattheagentcanusetoperformspecifictasks.

4.Aplanningmodule:analyzesproblemsanddevelopsstrategiestosolvethem.

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CHAPTER1

3.AIgovernance:increased

complexitytoaddressalltheunderlyingissues

TheriseofAIisalsopromptingdecision-makerstostrengthen

governance,bygraspingalltheissuesatstake.Thesearemany:riskmanagement,compliance,sovereignty,ethics,carbon

footprint,arejustsomeoftheissuestobeaddressedthroughthisgovernance.Anumberofissuesneedtobetackledatthesame

time,inordertoestablishasustainableapproachtoimplementingartificialintelligence.

AnorganizationadaptedtoaddressAI-relatedchallengesholistically

Organizationshavebeguntosetupan

organizationandrolestogovernAI-relatedtopics.Allofthemarefacingachallenge:managingtostriketherightbalancebetweenhavingaglobal,centrally-controlledview,whilenotbridlinglocalinitiativesandinnovativebusinesslineinitiatives.

Thekeyconcernisthereforeto"manage

decentralization"oftheIAinitiativeportfolio.Toachievethis,weneedto:

→Defineclearrolesandresponsibilitiesbetweencentralandlocallevels;

→Forcross-functionalfunctions(CDO,DPO,AIFactory,etc.),defineeachperson's

responsibilitiesandspecifyboundariesandinteractions.

→Implementmulti-levelgovernance:

•Astrategicbody,involvingmembersoftheexecutivecommittee,toapprehendallthesubjectsinherentinAI,particularlythe

impactsonHR,partners,customers...;

•Anoperationalbody,tomanagetheportfolio,highlightlocalinitiativesandencouragethedisseminationofsuccessfulinitiatives

throughouttheorganization.Thisbody

enablesustomaintainanexhaustiveviewoftheportfolio,soastobettermanagerisks

andensurecompliancewiththeAIAct(seebelow).

AgrowingneedfortrustedAI

TrustedAIreferstoartificialintelligencedesignedanddeployedinsuchawayastoguaranteehighlevelsoftransparency,safety,fairness,and

respectforhumanrightsandethicalvalues.ThisimpliesthatAIisdevelopedincompliancewith

rigorousstandardstoavoiduncontrolledbias,

protectuserprivacy,andensurerobustnessinthefaceoferrorsorcyber-attacks.TrustedAIisalsoexplainable,enablinguserstounderstandhow

andwhyitmakescertaindecisions.Finally,itincludesresponsiblegovernance,where

designersandoperatorsassumeresponsibilityfor

itsimpacts,whileintegratingcontroland

supervisionmechanismstopreventabuseormalicioususe.

SettinguptrustedAIsrequirestheinvolvementofavarietyofplayers:

→EthicsandCSRteams,todefineanAIpolicythatembodiesclearprinciplesandisalignedwiththeorganization'svalues;

→Engineersdevelopingthemodels,inordertoprovideexplainableanddocumentedmodels

→HRteams,tohelpsetuptrainingmodulessothatteamsapplytheseinstructions;

→BusinessesandDatascientists,toanalyzebiasesandmonitorresults;

→CISOandDPO,toguaranteesecurity,testvulnerabilitiesandcarryoutcontrols;

→ChiefData&AIOfficers,tosteertheoverallapproach.

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CHAPTER1

TheAIAct:Gettingyourorganization

startedoncompliance

TheAIActwasenactedandcameintoforcein

August2024.TheActaimstoensurethatartificialintelligencesystemsandmodelsmarketedwithintheEuropeanUnionareusedethically,safelyandinawaythatrespectsEUfundamentalrights.

TheAIActthuscreatesregulationsapplicabletoartificialintelligencesystemsandmodelsbeingcommercializedandmarketed.Research

activitieswithnocommercialobjectivearenot

affected.Allsuppliers,distributorsordeployersofAIsystemsandmodels,legalentities(companies,foundations,associations,researchlaboratories,etc.),headquarteredintheEuropeanUnion,or

whenheadquarteredoutsidetheEuropeanUnion,whomarkettheirAIsystemormodelinthe

EuropeanUnion.

ThelevelofregulationandassociatedobligationsdependonthelevelofriskpresentedbytheAI

systemormodel.Thereare4levelsofrisk,and4levelsofcompliance:

→AIwithunacceptablerisk:AIsystemsand

modelswithunacceptableriskareprohibitedandmaynotbemarketedintheEuropean

Unionorusedforexport;

→High-riskAI:high-riskAIsystemsandmodelsmustbeCEmarkedtobemarketed;

→Low-riskAI:low-riskAIsystemsandmodelsmustbesubjecttoinformationand

transparencyobligationsvis-à-visusers;

→Minimal-riskAI:minimal-riskAIsystemsandmodelscancomplywithconductmeasures.

SpecialobligationsapplytogenerativeAIandtothedevelopmentofgeneral-purposeAImodels*(e.g.LLMs),withdifferentregulationsdependingonwhetherthebasicmodelisaccessibleornot,andonothersubsidiarycriteria(computing

power,numberofusers,etc.).

AIwillbeimplementedgraduallyoverthecomingyears:

→February2,2025:Article5takeseffect,

banningAIsystemswithunacceptablerisks;

→August2,2025:regulationsongeneral-

purposeAImodelswillbegin.TheEUAIOffice,alreadyestablished,willoverseegovernanceandregulatoryprocedures.Sanctionsfornon-compliancewillalsobegintoapply;

→August2,2026:generalapplicationoftheAIActbegins,withtheexceptionofarticle6

paragraph1onhigh-risksystems;

→August2,2027:appliedruleswillbeextendedtohigh-risksystems.TheEuropean

Commissionwillpublishpracticalguidelinesandexamplesofhigh-riskAIsystemsby

February1,2026.

Howtogetstartedoncompliance?StartwithanassessmentoftheAIsystemsinplaceandintheprocessofbeingsetup.

Asareminder,Wavestonehas

published,withFranceDigitaleandGide,

apracticalguide

toenable

companiestounderstandandapplyEuropeanAIlaw.

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CHAPTER1

Risksandcybersecurity,alltoooften

forgotteninprojects

Withallthebuzzaroundartificialintelligence(AI),organizationsarefacingunprecedentedthreatsthatgototheveryheartofthesemodels.New

attacksaretakingshape,suchaspoisoning

(modifyingtrainingdatatotrickit),oracle

(hijackingAIstomakethemrevealthingsthey

shouldn't),orillusion(makingAIsbelievethingsthatarefalsebutinvisibletohumans).Newriskassessmentandprotectionmeasuresneedtobeputinplace.

Intheshortterm,thepriorityisthereforetosecurebusinessprojectsusingAI,particularlyinthe

followingstages:

→ClassificationofAIusecasesaccordingto

regulatorycriteria(refertothefutureEuropeanAIAct)ortheNIST(NationalInstituteof

StandardsandTechnology)AIriskmanagementframework;

→Definitionoftheresponsibilitymatrixandgovernanceforvalidatingusecases,takingintoaccountcybersecurity,transparency,privacy,biasandethics;

→Implementspecificmeasureswhen

necessary,eitherbyintegratingthemdirectlyintotheprojectdesign,orbyimplementing

newAIsecurityproductsthatarestartingtoappear.

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CHAPTER1

4.Data/AIacculturationonalarge

scale,toaccelerateinnovationandprepareforthefuture

Oneofthemainobstaclestotheadoptionofinnovationsremainshumanresistance.AIisnoexception,anditsadoptionremains

amajorchallenge,notleastbecauseofthefearsand

misunderstandingsitarouses.Itisthereforebecomingcrucial

forcompaniestoacculturatetheirteamstoAI,bydemystifyingthistechnologyandexplainingitsapplicationsinconcreteterms.

and(and

DemystifyingAI:Reassuringinformingaboutwhatitcancannot)do

ThefirstchallengeofacculturationtoAIisto

dispelthemisunderstandingsandfearsthat

surroundit.Formany,AIisstillperceivedasa

mysterious,eventhreateningtechnology,capableofreplacinghumansormakinguncontrollable

decisions.ItisthereforeessentialtodemystifyAIbyexplainingwhatitcando,butalsoits

limitations.Forexample,AIisextremelypowerfulwhenitcomestoprocessinglargequantitiesofdataandautomatingrepetitivetasks,butitlackshumanawarenessandintuition.Reassuring

teamsonthesepointshelpspreparethemto

collaboratewiththesetechnologies,ratherthanfearingthem.

Daringtoexploretechnicalconcepts:thechallengeofmakingapplicationsconcrete

Beyondthisdemystification,it'simportanttodaretogofurtherinpedagogybyexplainingto

businessteamswhatthetechnologiesunderlyingAIareinconcreteterms.AImanagers:dareto

explaintoyourbusinessteamsandexecutives

whatOCR,LLMandclusteringare.Byrepeating

theseexplanationsandtakingastepback,you'llbeabletomaketheapplicationofAIinbusinessprocessestangible.Employeeswillthenbeabletounderstandhowthesetechnologiescanbe

integratedintotheirday-to-daywork,identifytheprocessesthatcouldbetransformedandthe

pocketsofvaluetobeexploitedforthecompany.

"NoData,noAI!«

Finally,acculturationmustinsistthatmagicalAIdoesn'texist,andthatitrequirestrainingmodelsonqualityData.AndtoinvolvebusinessunitsinDatamanagement.

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Unlockthefull

potentialofData

CHAPTER2

5.Afederatedorganizationtounify

Data-relatedroles,standards,andpractices

EveryonenowagreesthatDataisakeyassetforthesuccessoforganizations.Yetitspotential

oftenremainsunder-exploited,dueto

organizationalsilos,knowledge,datacontrol,

accessibility,andinteroperability.Heterogeneouspracticesalsomakecollaborationbetween

differentteamsdifficult.

Tomeetthesechallenges,Datagovernance

remainsthewatchword.Anoperationalmodel

coveringorganization,roles,andresponsibilities,aswellasoperatingmodes,remainsamust-

haveforallorganizations.Andbeyondtheory,

thinkingaboutimplementationthroughconcretepracticesthatcanbeunderstoodbyallplayersisastrongdifferentiator,wheremanyorganizationsstillconfinethemselvestodescriptionwithout

tangibleapplicationoftheelementsdefined.Thisoperationalmodelcoversseveralkey

themes:

ThemostmatureorganizationshavetakenstepstogivethekeystotheirDataassetsbacktothe

business.ThismeansinvolvingbusinessfunctionsinDatamanagementandmakingthem

accountablefortheseactivities.Forexample:

→Businesses(re)becomeaccountableforDatamanagementwithintheirscope(Data

mapping,documentation,qualitymonitoringandimplementationofcorrectiveactions,

etc.);

→TheDataOfficeplaystheroleoforchestra

conductor,definingtheframework,tools,

policies,practices,andstandards.Itisalso

responsibleforsupportingthebusinessunitsintheirdevelopment,throughtrainingand

coaching.

1.Aneworganization,federatedaroundDatadomains,inwhichbusinesses

regaincontroloftheirData

Foralongtime,ITandDatamanagementhavebeenthemainstaysofDatamanagement.TheyarestilldoingsointheleastData-mature

organizations.Thisposesseveralproblems:

→Businessunitslackautonomyincontrolling

theirData,andbecomerelativelydependentonITorDatadepartmentsforaccesstotheirData,delayingthetime-to-marketofanalysesandprojects;

→Ontheotherhand,ITDepartmentsare

sometimesheldresponsibleforthequalityofDataoverwhichtheyhavenocontrolinordertodeveloptheprocessesusedtogenerate

andprocessit.Attemptstoremedythe

situationofteninvolvereprocessingthestock,withoutaddressingtheflowproblemsatthesourceofdatacreation.

Thechallengeistogivethebusiness

unitsbackthekeystotheirData

assets.

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CHAPTER2

2.Aunifiedrolerepositorytofacilitate

know-howdeduplication,recruitmentandcareermanagement

It'snotuncommontofindorganizationsthatcan'ttellyouhowmany"Dataemployees"theyhave.Data'suniquepositionatthecrossroadsof

businessandtechnologyisamajorcontributingfactor.Theconsequencesaremanifold:

→Difficultyinmanagingtheworkforceandstrategicworkforceplanning;

→Difficultyinmanagingskillsandmaintainingthematthestateoftheartfortheseprofiles,againstabackdropofrapidlyevolving

technologiesandassociatedknow-how;

→Recruitmentandinternalmobilitycanbe

laborious,withcandidatesunableto

understandtheactivitiesbehindtheseprofilesandcareerpathsunclear;

→Data-relatedoperatingmethodsand

responsibilitiesthatneedtobefinelydefinedonaperimeter-by-perimeterbasis,andwhoseunderstandingbytherestoftheorganizationremainsopaque.

So,beyondacoherentorganization,Data/AIroles

aretendingtobestreamlinedtowardsa

common,sharedrepositoryacrossthe

organization.HRteamsarefullyintegratedintothisapproach.

3.Commonstandardsandpracticesthroughouttheorganization

Forthisfederatedorganizationtofunction

effectively,itisessentialtodefineandunifyDatamanagementstandardsandpractices.This

includescommonrulesfordatagovernance,

cataloguingprocedures,documentation

standards,aswellassecurityandcompliancepolicies.Inaddition,anorganization-wideDatacatalogisdefined,whichdescribesbusiness

conceptsviaaDataglossary,documentsDataintheDatadictionary,andrecordsthe

transformationsundergonebykeyData(Datalineage).

UnifiedstandardsensureDataconsistencyandqualitythroughouttheorganization.Theyalso

facilitatesystemintegrationandinteroperability,makingDatamoreaccessibleandusablebyall.Forexample,astandardizedDatamodelenablesteamsfromdifferentareastocollaboratemore

easily,shareinsights,andmultiplythevaluecreated.

What'smore,thesestandardsfacilitateData

sharing,notonlywithintheorganization,butalsoviaothersubsidiaries,andevenwiththirdpartiesandexternalpartners.

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CHAPTER2

FocusonFiDA,thenewEuropeanregulationthatwillmakeamajor

contributiontothegrowingmaturityofDatasharinginfinancialservices:

TheFinancialDataAccess(FiDA)

regulationproposedbytheEuropean

CommissioninJune2023aimstocreatealegalframeworkfortheaccessand

useofconsumerfinancialData.ItispartofthebroaderOpenFinancestrategy,

extendingtherulesalreadyintroducedbythePaymentServicesDirective

(PSD2),whichonlyconcernedpaymentaccounts.

Essentially,FIDAwillmakeitpossibleto:→GreaterDatatransparency,withclear

andtransparentcommunicationonhowcustomerDataisusedand

sharedbetweenfinancialinstitutions;

→Fine-grainedcustomerconsent,

givingcustomerstheabilitytogrant,manageandwithdrawconsentfordatasharing;

→Enhancedsecurity,byimplementing

strictsecuritymeasuresforthe

protectionandprocessingoffinancialData;

→StandardizationofuserDataandtechnicalinterfacestoaccelerateData-sharingcapabilities.

FiDAcompliancewillthereforerequiretheimplementationofrulesfor

accessingandsharingveryspecific

customerData,andwillcontributetoasometimesforcedriseinthematurityoffinancialservicesplayersintheirDatasharingcapabilities.

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CHAPTER2

6.DemocratizetheuseofData

forasmanypeopleaspossible

Theframeworkisthusset,withanorganization,roles/responsibilitiesandoperatingmodes.

AllthatremainsistopreparetheDataandmakeitavailabletothegreatestpossiblenumberofpeople.

a.DataProductstobuildupawealthofDatareadytobeexploited

WiththeexplosionofDataandtheaccelerationinitsuse,anewparadigmhasemergedoverthe

yearstofacilitateitsaccessanduse.TheconceptoftheDataproducthasthusemerged,andisnowbeingadoptedeverywhere.

ADataproductcanbegenericallydefinedas"aproductthatfacilitatesanend-goalthroughtheuseofData".Thetermthusencompassesvariousitems:

→Technicalfoundation"typeproducts,i.e.

technologicalcapabilitiesthatrepresentthefoundationsformanagingandleveragingData,andwhoseconstructionandevolutionarecarriedoutinproductmode(e.g.,aRAGplatform,theDatacatalog...);

→Dataproducts,wheretheDataitselfisofferedasaready-to-consumeproduct(e.g.,

customerrepository,salesperperimeter,etc.);

→Analyticsproducts(e.g.,BIdashboard,

recommendationengine,scoringmodel,etc.).

Inparticular,andbymisuseoflanguage,theDataproductisassimilatedtotheconceptof“Dataasproduct".ItisinthissensethattheDataproductisdefinedthroughtheDataMeshasdefinedby

ZhamakDehghani(seebelow).

ThisDataproducthasseveralbasicfeatures:

→Discoverable:Datais"sorted"bybusinessdomain(thenbysub-domains,families,

businessobjects,etc.)andstoredina

marketplace.PotentialconsumersarethusabletoseewhatDataisavailable,readitsdescription,andrequestaccesstoitif

necessary;

→Self-describing:productsaredocumented

anduserscanindependentlyunderstandwhattheycontain(viaproductdefi