人工智能时代全新的营销技术技术栈

March2026

TheNewMartech“Stack“fortheAIAge

AResearchReportbyScottBrinker

Inpartnershipwith

。chiefmartec

PrefacefortheCMO

1

TheGreatUnstacking

3

TheArcofMartechBendsTowardConvergence

4

AIastheCatalystfora3rdAge

6

AUnifiedDataFoundation:EverythingIsData

9

TheConvergenceofFiveClassesofData

9

TheSemanticLayerastheKeeperofCoherence

11

ContextGraphs:ANewKindofCompanyData

12

ConvergenceofOperationalandAnalyticalWorkloads

13

UniversalDataAccess:DataWithoutthePlumbing

14

MoreSoftware,Everywhere,ByEveryone

16

TheSoftwarePaletteoftheComposableCanvas

16

TheHypertail:CustomSoftwareatScale

18

GoverningtheExplosion:ProliferationWithoutChaos

19

EverythingAssembles:ComposabilityastheDefault

20

TheNewShapeofthe“Stack”

21

FromVerticalLayerstoComposableCapabilities

21

5RingsofCapability,OneCenterofGravity

22

BreakingtheIntegrationBottleneck

24

ComposingDynamicPartnerships

26

GettingThereFromHere

28

StartWiththeDataFoundation

28

ComposabilityasYourNorthStar

29

FromBoxestoBuildingBlocks

30

Conclusion

33

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databricks

。chiefmartec

PrefacefortheCMO

Yourmartechstackhasbecomebothessentialandexasperating.You)veassembleddozensoftoolsovertheyears,eachsolvingarealproblem,butcollectivelycreatingawebofintegrationheadaches.Now,AIisraisingthestakes.Newcapabilitiesemergeweeklyandyourcompetitorsaremovingfast.Willyourstackholdyoubackorhelpyoupullahead?

MarketinghastheopportunitytoleadenterpriseAIadoption,asafullpartnerwith

yourCIOorCTO.Yourcampaignscanbealways-on,data-rich,andpoweredby

continuousfeedbackloops—exactlywhatAIagentsneedtolearn,decide,andactinrealtime.Andbecauseagentsareonlyasgoodasthedatatheyacton,theymakeyourdatafoundationastrategicimperative,notjustahygieneissue.

Thisreportproposesafundamentalshiftinhowmarketingtechnologycanbe

architectedtogiveyoutheagilityandpoweryouneed,transformingarigid“stack”intoafluidcomposablecanvasaroundamoderndataplatformsuchasDatabricks.

Thistransformationwon)thappenovernight.Butwithaclear3-5yearvision)youcanstartmakingdecisionstodaythatturnyourstackfromaconstraintintoacompetitiveadvantage.

Figure1.

Theevolutionofmartechfromarigidstacktoacomposablecanvas

rigidtechstacktransformationcomposablecanvas

>

uncoordinatedsystemsofengagementdynamicandcontextualapps&agents

integrationchallengesbetweeneachboxinthestack

vs.

dataandservicesarefullyconnectedacrosstheorganization

disparatesystemsofrecordsharedsubstrateofunifieddata

Whatreallymatters,ofcourse,istheimpactthisnewarchitecturecanhaveonyourbusinessoutcomes:

Speed:Frommonthstominutes.Today,addinganewmarketingcapabilitymeansintegrationprojects,datapipelines,andITtickets.Inthecomposablecanvas,new

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。chiefmartec

“AllCMOsaretryingto

solvethesamethreethings:

effectiveness,efficiency,andself-service.”

—RickSchultz,CMO,Databricks

toolsandAIagentsplugintoashareddatafoundationinstantly.Spotanopportunityinanewchannel?WanttospinupAIagentsforapromisingusecase?Youcanmovefromideatoexecutionindays,notquarters.

Personalizationatscale:Everycustomer,continuously.Yourdatalivesinsilos:

customerprofileshere)campaignperformancethere)contentengagement

somewhereelse.Connectingwhoyourcustomersarewithwhat’sactuallydriving

resultshasbeendifficulttowrangle.Thecomposablecanvasunifiescustomerand

companydataintoasinglefoundationwhereAIcanorchestratetrulyindividualizedexperiences,informedbyreal-timeperformance,learningandimprovingwitheveryinteraction.

Reducedcostsandcomplexity.Integrationhasbeenmarketing’smostexpensiveinvisibletax.Whensystemsshareacommondatafoundationandspeakcommonprotocols,theintegrationburdendropsbyanorderofmagnitude.Thoseresourcescanshiftfromplumbingtoprogramsthatdrivegrowth.

Adaptabilityascompetitiveadvantage.Marketschange.Customerexpectations

evolve.Newchannelsemerge.Acomposablearchitectureletsyouswapcomponents,addcapabilities)andreshapeworkflowswithoutmassivereplatformingprojects.Yourtechnologybecomesasagileasyourstrategyneedstobe.

Differentiationthroughcustomcapabilities.Wheneverycompetitorcanbuythesamemartechproducts,theadvantagegoestothosewhocantailortheirownAIagents)specializedapps)andproprietaryworkflowsthatencodewhatmakesyourcustomerexperienceunique.Thecomposablecanvasmakesthiskindofcustomdevelopmentdramaticallyfasterandmoreaccessible.

Thisisn’tarip-and-replaceproposition.Thejourneyisincremental,andeverystepdeliversvalue.Butthecompaniesthatorienttowardthisarchitecturenowwillfindthemselveswithmarketingtechnologythatadaptsasfastastheirmarketsdemand.

Thisreportdrawsoninterviewswithleaderswhoarealreadybuildingtowardthis

future:MeagenEisenberg,CMOatSamsara;BrycePeake,formerVPofMarketing

DecisionSciencesatDomino’s;KumarRam,VP/GlobalHeadofMarketingData

SciencesatHP)andChrisWissing)ChiefProductOfficeratEpsilon—aswell

asDatabricks’ownRickSchultz,CMO,andElizabethDobbs,AVPofMarketing

Technology,Data&Growth.Thepioneeringworktheseleadershavealreadyputintopracticepaintsthewaytothecomposablecanvasahead.Ourdeepestgratitudeandthankstothemfortheirparticipation.

Whatfollowsshowshowtogetthere.Yourstackdoesn’thavetobeasourceof

frustration.Itcanbecomeagenuinecompetitiveadvantageandahugeunlockforyourteam’screativityandimpact.

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1IntheAI&DatainMarketingSurveyrunbychiefmartecandMartechTribeinFall2025,integrationremainedaTop3challengeforthemajorityof

respondents.

“We’vebeentryingtomodifythesamemartechstackwe’vehadsincetheinternetstartedinterneting.Folks,we’re

goingtohavetobuildanewone.”

—BrycePeake,formerVPofMarketingDecisionSciences,Domino)s

TheGreatUnstacking

Themartechstack,onceanobscuretechnicalphrase,isfirmlypartoftoday)smarketinglexicon.

Overtheyears,thatstackhasgrown.It)snotunusualforenterprisemarketing

teamstohavedozens(anddozensanddozens)ofproductsintheirstackthatthey)veaccumulated.Itsgrowthhasbeenorganicatmostfirms,inthesamewaythatone

mightrefertoanovergrownyardas“organic.”

Theupsideofthesebest-of-breedstacksisthey)vebeenabletodeliverinnovative,specializedcapabilitiesandaddsupportfornewandemergingchannelsandtactics.Thedownsideisthatintegration—gettingallthesedifferenttoolstoworktogether—hasbeenasourceofgreatfrustration.1

AIhasputthisallintohyperdrive.AwhirlwindofnewAI-nativemartechproducts.

AtorrentofnewAIfeaturesaddedtoincumbentplatforms,whicharefiercely

determinednottoletAI-nativewhippersnapperstaketheirmarketshare.Andnowafloodofcustom-builtAIagents,apps,andautomations—yes,insomecases“vibecoded”—proliferatingacrosstheorg.

Despiteallthesechanges,theoverallarchitectureofmartechstackshaslargely

remainedthesame.Sofar.Butit)screakingunderitsownweight,asnewthings

keepgettingthrownontop,stuckontheside,andwedgedinbetween.Thephrase

“technicaldebt”isnowbecomingpartofmarketing)slexicontoo.Everyhourspentonintegrationworkisanhournotspentoncampaignoptimization,personalization,orgrowthinitiatives.Thearchitectureofyourstackhasbecomeaceilingon

marketingperformance.

ButwhatcouldthemartechstackbecomeinthisnewAIera?

Thatisthequestionthisreportproposestoanswer.Oratleastprovideonepossibleanswer.Becausethearchitectureofthemartechstackoverthenext5-10years

doesn)thavetolooklikeitdoestoday.

It)snotjustAI.Advancesincloudinfrastructurebythehyperscalersandmoderndataplatformsareenablingnewwaystostructureacompany)soveralltechstackand

digitaloperations.Theopportunitytocollaborateacrossorganizationalboundarieswithmorepartnersandprovidersinasafeandseamlesswayisenablingnewservice-as-softwarevendorrelationshipsandnewecosystembusinessstrategies.

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Wehaveagenerationalopportunitytoreimaginemartechwiththisinmind.

We)lllayoutaframeworkofafuturemartechstackarchitecturethatwebelieveis

ambitiousbutachievable.Moststackswillnotlooklikethistoday,orevenwithinthenextyear.Butbygazingfurtherout)onatimehorizonof3-5years)weaimtosetaNorthStarthatcanguidemartechevolutioninthisnewAIera.

Databrickssponsoredthisresearchandprovidedaccesstotheirplatform,customers,andproductteam,givingusalaboratorytoexploretheseconceptsindepth.We

usetheirplatformasanexampletoillustrateourideas.However,theconceptsandprincipleshereareintendedtobeportableandplatform-agnostic.

Let)sstartwiththebigpicturetrajectory.

TheArcofMartechBendsTowardConvergence

We)reatthebeginningofa3rdAgeofMartech.

CommercialSoftware

ProfessionalServices

CustomSoftware

Figure2.

Thethreeagesofmartech,

progressivelyconvergingformerlydisparatechoices

1stAgeofMartech

mid-1990s-mid-2010s

3rdAgeofMartech

mid-2020s-

2ndAgeofMartech

Platform

Ecosystems

Blended

Software&

Services

Customon

Commercial

Platforms

ComposableCanvas

mid-2010s-mid-2020s

Suite

vs.

Best-of-Breed

Software

vs.

Services

Build

vs.

Buy

XorY

XandY

XY

>

increasingconvergenceinthecloudasboundariesblur

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。chiefmartec

2ThemartechindustryhasgeneratedTLAs—three-letteracronyms—

fasterthanit’sgeneratedintegrationsbetweenthem.

The1stAgeofMartech(mid-1990stomid-2010s)wasdefinedbylarge)standaloneproductsthateventuallycoalescedintomulti-productsuites.Itwasanageof

dichotomies:suitevs.best-of-breed,softwarevs.services,buyvs.build.

Buttherigidityofthosedichotomiescouldn’tkeeppacewithmarketing’sacceleratingexpansion:acontinuousfloodofmoredata)morechannels)moreprograms)more

stakeholders)moregovernancerequirements.Thetrade-offsbetweenthoseeither/orchoiceswerepainfullysuboptimal.

The2ndAgeofMartech(mid-2010stomid-2020s)begantobreakdownthoseboundaries:

•Fromsuitevs.best-of-breedtointegratedplatformecosystems.

•Fromsoftwarevs.servicestoblendeddeliverymodelswithboth.

•Frombuildvs.buytocustomizationontopofcommercialplatforms.

It’sbeenanageofincreasinglyopensystems.Productslikecustomerdataplatforms(CDPs)andintegration-platform-as-a-service(iPaaS)toolsgrewinpopularity

becausetheywerebuilttoserveaheterogeneousstack,movingdatabetweensystemsandorchestratingworkflowsacrossindividualproducts.Practitionersandvendors

embracedtheterm“composable”todescribeproductsandarchitecturesdesignedforgreaterinteroperability.

Yetthestructurebetweentheseproductsremainedrigid.Insteadofasuiteasonebigbox,martechstackdiagramsbecameamuralofmultiplesmallerboxes,piecedtogetherinarelativelyfixedconfiguration)oftennotverywellintegrated:

•Multiplesystemsofrecordatthefoundation:separateonesforcustomerdata

(CRM/CDP),content(CMS),brandassets(DAM),productinformation(PIM),etc.

•Systemsofengagementlayeredabovethem:marketingautomationplatforms(MAP),digitalexperienceplatforms(DXP),e-commerceplatforms,ad

managementplatforms(DSP),businessintelligence(BI),etc.2

•Specialistapps(andnowAIagents)orbitingthesesystemsofrecordandsystemsofengagement,forawiderangeofusecases:webinarsandeventmanagement,socialmediamarketing)video/podcastproduction)customerprofileenrichment)loyaltyandreferralprograms,marketingattributiontools,anddozensofother

nicheapplications.

•Dataplatforms,warehouses,lakes,andlakehouses,grewasacommonsubstrateunderneaththeentirestack)poolingdatafromthesedifferentsystemstogether.Initiallythiswasforanalytics,buttheriseof“reverseETL”anddatacleanroomshavemadethesedatacloudplatformsamoreactivepartofmarketingoperations.

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Theword“stack”waspopularizedbecause,well,thearrangementofthese

componentsaretypicallydrawnasboxes,packedtogetherinlayers,stackedontopofeachother.

Figure3.

Theverticallylayeredstructureofmostmartechstackstoday,boxesontopofboxes

AIAgent

SpecialistApp

SpecialistApp

AIAgent

SpecialistApp

SpecialistApp

AIAgent

MAP

DXP

Ecommerce

DSP

BI

(internaluse)

PIM

CDP

CMS

DAM

CRM

DataWarehouses,LakesandLakehouses

Specialist

Apps&Agents

SystemsofEngagement

SystemsofRecord

Data

Repository

Whilemoreflexiblethanthemonolithicsuitesofthe1stAge)thisarchitecturehasstillbeenconstrained.Integrationbetweenboxesisoftenlimitedtoasubsetofdataand

functionality.Andsystemsbeyondmarketing—finance)product)IT)supplychain)HR—arerarelyincluded.

Truecomposabilityacrossthestackhasremainedaspirational.

AIastheCatalystfora3rdAge

AIisprovingtobeacatalystforamoredramaticchange.

ConsideraB2Bcompanywith10)000accounts.Buyingsignalsarefiringconstantly

—websitevisits,contentdownloads,productusagespikes,supporttickets,social

mentions,jobchangesamongcontacts.Eachsignalisambiguousonitsown.Patternsemergeonlywhendatafrommultiplesystemsarecorrelatedinrealtime.Andthe

“right”responsedependsoncontext.Isthisaprospectorcustomer?What)stheir

contractstatus?Whoownstherelationship?Whatactionshavewealreadytakenthisweek?

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Adashboardcansurfacethisinformation.Butorchestratingtheresponse—routingtherightinsighttotherightperson,triggeringtherightoutreach,adjustingtherightads,updatingtherightnurturetrack—acrossthousandsofaccounts,continuously,inrealtime?

Thisiswheredashboardsendandagentsbegin.

“Thetraditionallayered

structurecanbebetter

understoodasagraph.It’sthefoundationthatconsistsofdataandsemantics,

uponwhichamorefluidnetworkofbi-directionalcommunicationbetweenagentsisbuilt.”

Themostvaluableagenticworkflowscrosssystemboundariesasamatterofcourse.Agentsareravenousfor“context”—relevantdata,instructions,andtoolsforthespecificgoalthey)resolvingfor.Theycravedatafromacrosstheorganization)

includingbehavioralstreamsandunstructuredcontentthatexistedbeforebutweretoounwieldytooperationalize.

Simultaneously,aplatformconvergenceisunderway.TraditionalSaaSvendorsare

—TassoArgyros,VPEngineering,Databricks

racingtoembedAIcapabilitiesandagentframeworksintotheirproducts.StandaloneAIagentbuildersareemergingasanewcategory,promisingtoorchestrateacross

anysystem.Anddatacloudplatforms—thehyperscalersandmoderndataplatforms—arepushingupward,arguingthatsincetheyalreadyhousethedata,they)rethe

naturalplatformforsoftwareandAIoperatingwiththatdata.

Everyonewantstobetheplacewhereagentsrun.

rigidtechstacktransformationcomposablecanvas

uncoordinatedsystemsofengagementdynamicandcontextualapps&agents

integrationchallengesbetweeneachboxinthestack

vs.

dataandservicesarefullyconnectedacrosstheorganization

Figure4.

disparatesystemsofrecordsharedsubstrateofunifieddata

Theevolutionofmartechfromarigidstacktoacomposablecanvas

Thispressurefromaboveandbelowiscompressingthestack.Theneatlayersof

the2ndAgearebuckling.Whatemergesisbetterdescribedasaunifiedcomposablecanvas.

Agentsandagenticworkflowsare)bytheirnature)composable.Theyassemblethedata,tools,andcapabilitiestheyneeddynamically,basedonthetaskathand.They

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aren)tanchoredbyhardwired,point-to-pointintegrations.Theycanadapt—ifyourarchitectureletsthem.

Themartech“stack”—thatTetrisarrangementofboxes—startstodissolveinto

aflexibleworkspacewheredata)content)andcapabilitiescanbecomposedand

recomposedonthefly.Rigidlayersgivewaytoafluidarchitectureshapedbytheworkbeingdone,nottheproductspurchased.

Thisisthe3rdAgeofMartechandthebeginningofTheGreatUnstacking.

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AUnifiedDataFoundation:EverythingIsData

Ifthe3rdAgeofMartechisdefinedbyacomposablecanvas)thendataisthecanvasitself.

Inthe2ndAge)datawassomethingthatmovedbetweensystems—extracted)

transformed,loaded,synced,reverse-synced,andoccasionallyprayedover.Each

productinthestackmaintaineditsowndatamodel,itsownversionoftruth,itsowngravitationalpull.Integrationwastheartofbuildingbridgesbetweenthesedata

islands.

Inthe3rdAge)databecomesthesharedsubstrateonwhicheverythingelseruns.Notdataasanassettobemovedaround,butdataasacommonfoundationthatagents,applications,andhumansalloperateupontogether.

Themartechstackdoesn)tsitontopofdata.Itisdata.

WebelievethisistheGrandUnifyingTheoryofMartech:everythingisdata.

Customerprofiles?Data.Marketingcampaigns?Data.Brandguidelines?Data.The

AIagentthatwritesyouremailcopy?Alsodata—amodelwithweights,wrappedinprompts,governedbyrules.Eventhegovernanceframeworksthatkeepeverythingincheckarejustanotherlayerofdata.

Onceyouseemartechthroughthislens)theartificialboundariesbetweensystemsstarttofallaway.Thequestionshiftsfrom“howdoIintegratetheseproducts?”to“whatdoIwantmybusinessoperationsandcustomerexperiencestobe?”

TheConvergenceofFiveClassesofData

Whatdoes“everythingisdata”actuallymeaninpractice?Fiveclassesofdata—previouslysiloedindifferentsystems)managedbydifferentteams)andrarelybroughttogether—nowbecomeinteroperableonaunifiedfoundation.

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Composable

Canvas

CustomerData

FiveClasses

ofData

ContentData

CompanyData

ControlData

CodeData

Figure5.

Everythingisdata—fiveclassesunifiedononesharedsubstrate

“Peoplesometimesforget

abouthowimportant

campaigndataiswhen

talkingaboutCustomer

DataPlatforms(CDPs).I’m

lookingatcampaignsallthetimetofigureoutwhichonesweshouldrun,whichones

areworking,whichonesweshouldkill.Ifyoucanunifyallyourcustomerandcampaigndata—IsometimescallittheC-squaredDataPlatform—youcanbesmarterinyour

decisions.”

—RickSchultz,CMO,Databricks

CustomerDataistheclassmostfamiliartomarketers.Thisencompassesindividualandaccountprofiles)transactionhistories)behavioralsignals(webvisits)product

usage),emails,calls,chattranscripts,supporttickets.Italsoincludessecond-partydatasharedbypartnersandthird-partydataenrichedfromexternalproviders.Inthe2ndAge)thiswasthedomainofCRMsandCDPs.Inthe3rdAge)itremainscentralbutisnolongerlockedinsideanysinglesystem.

CompanyDataisoperationaldatafromacrosstheorganization,notjustmarketing:inventorylevels)financialrecords)campaignspend)salespipeline)logistics)and

supplychainstatus.Thisisthedatathatmarketinghashistoricallyhadtotryto

wrangleoutofpointsolutionsorrequestfromotherdepartmentsthroughformal

processes(orinformalfavors).Whenitlivesonasharedfoundation,marketinggainscontextthatwaspreviouslyinvisible—andotherdepartmentsgainvisibilityinto

marketing)sactivitiesinreturn.

Thisvisibilitychangeshowmarketingprovesitsvalue.Whencampaigndataconnectstopipelineandrevenuedatainthesamefoundation,attributionstopsbeinga

quarterlydebateandbecomesacontinuous,fullyalignedconversationwiththeCFO.

ContentDatareframescreativeassetsasstructureddata.Brandguidelinesbecomequeryable.Productinformation,images,videos,andcopyarestoredwithrich

metadatathatdescribesnotjustwhattheyare,butwheretheycanbeused,who

approvedthem,andhowtheyperform.ThisismorethanatraditionalDAMwith

bettertagging.It)scontenttreatedasafirst-classdatacitizen)readytobeassembledandpersonalizeddynamicallybyagentsthatunderstandtherules.

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3“Incomputerscience,the

expressioncodeasdatareferstotheideathatsourcecodewritteninaprogramminglanguagecanbemanipulatedasdata.”–Wikipedia,

Codeasdata

“Itusedtobethat

contentwouldbethe

lastconsiderationinthe

marketingprocess;however,withagentstakingthelead,andbrandscodifyingtheirbrandguidelinesasagenticcontext,contentbecomes

thetipofthespearfortruepersonalization;itmovesfrombeingthebottleneckattheendoftheprocesstothemainleveragepointthatagentshavetodriveconversion.”

—TassoArgyros,VPEngineering,Databricks

CodeDatamaybethemostunfamiliartomarketing,butitisincreasinglyessentialtotheiroperations:softwareisdata.3AImodelsandtheirweights.Promptsandprompttemplates.Agentskilldefinitions.Sourcecodeforcustomapplications.Configurationsforagentsandautomations.They’reartifactsthatcanbeversioned,governed,and

orchestratedlikeanyotherdataasset.Whenyourmarketingautomationisapromptchainratherthanaflowchart)thepromptisthelogic.

ControlDataistheconnectivetissuethatmakestheotherfourclassescoherentandcompliant:semanticlayerdefinitionsthatcreatesharedmeaningacrosssystems)

governancepoliciesthatdeterminewhocanaccesswhat,businessrulesthatencodeorganizationallogic,andguardrailsthatkeepAIagentsoperatingwithinacceptablebounds.ControlDatatransformsadatalakefromaswampintoanavigablewaterway.

TheSemanticLayerastheKeeperofCoherence

Wheneverythingisdata,theriskisthateverythingbecomesamess.

Differentsystemsusedifferenttermsforthesameconcepts.Whatsalescallsan“opportunity”mightbewhatmarketingcallsa“qualifiedlead”andwhatfinancecalls“projectedrevenue.”Customerlifetimevaluecanhaveadozendefinitionswithinasingleorganization,dependingonwho’scalculatingitandwhatthey’retryingtoprove.

Thisiswhereasemanticlayerbecomesessential.Asemanticlayersitsabovetherawdataandprovidesaconsistent)business-friendlyvocabularyforqueryingit.Itdefineswhat“customer”meansacrosstheorganization.Itstandardizesmetricssothat

everyoneisworkingfromthesamecalculations.Ittranslatesbetweenthetechnicalschemasofamyriadofdatasourcesandtheconceptualmodelsthatbusinessusersactuallythinkin.

Thesemanticlayertakesonheightenedimportanceinthe3rdAgebecauseAIagentsrelyonit.Anagenttryingtoidentifyhigh-valueaccountsatriskneedsashared

definitionof“high-value”and“atrisk.”Withoutasemanticlayer)everyagentbecomesitsownislandofinterpretation—whichishowyougetthreedifferentdashboardsshowingthreedifferentpipelinenumbers)orworse)threedifferentagentstakingthreedifferentactionsbasedoncontradictoryassumptions.

Thesemanticlayeristhekeeperofcoherence.Itdoesn’tjustmakedataaccessible;itmakesdatameaningful.

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4

AI’strillion-dollaropportunity:

Contextgraphs

,JayaGuptaandAshu

Garg)December2025

“Wethinkofmarketingdataintermsofwhatdecision

we’retryingtodrive.Ratherthanmirroringthebusiness’sorgchart,weorganizeour

semanticlayerbydecisionsthatspanmultipleteams

orfunctions.It’sthebest

mentalmodelfororganizingdata,anditmakesdata

easiertofind.Whenyou’remappingacustomer

journey,you’relookingacrossspecificdecisionspaces:consideration,

dealconsideration,valueconsideration.Decisiondatabecomesintuitivetonavigate.”

—BrycePeake,formerVPofMarketingDecisionSciences,Domino)s

ContextGraphs:ANewKindofCompanyData

Traditionalsystemsofrecordcapturewhathappened.Whattheydon)tcaptureiswhyitwasallowedtohappen.

Thereasoningbehinddecisions—theexceptionsgranted,theprecedentsinvoked,thecross-systemcontextsynthesized,theapprovalsobtained—currentlylivesinephemeralplaces:Slackthreads,Zoomcalls,emailchains,andpeople)sheads.

AVPapprovesa25%discountonarenewalcall.TheCRMshowsthenumber;it

doesn)tshowwhoapprovedthedeviation)whatserviceincide