面向未来的AI网络数据管理

Content Introduction3 Industry trends5 Data management evolution6 Target business objectives9 North Star vision11 Dataunificationandfederation12 Dataintegrationanddatapipelineefficiency13 Reference architecture16 Standardizationactivities16 Conclusion17 Authors18 Introduction Over the next decade, the telecommunications landscape will be shaped by the abilitytounlockandutilizethefullpotentialofdataatanunprecedentedscale,speed,andintelligence. As a trusted partner, we have observed how sprawling Hadoop architecturesandon-premisesplatformsinthetelecomsectorcanquicklybecomebrittleasvolumesexpand and new use cases emerge. Inadditiontotheexpansionofdatavolume,datafragmentationacrossdomains,platforms,andschemasisaninevitableconsequence.Autonomousnetworkdomainelements,serviceassurance engines, customer experience agents, and other similar systems produce domain-specificsilos,whichmaydelaythehighlymulti-agenticoperationsthatcommunicationsservice providers (CSPs) seek to achieve. Inthedatamanagementlayer,thechallengeistomanagetheintegrationofhigh-volumedatainflowsfromdisparatesources,governthemovementandtransformationofthatdatasecurelyandwithintegrity,andbringthatdatatoastateofreadinessforavarietyofconsumers,mostnotablythosewhowillapplyvariousartificialintelligence(AI)techniques. Hence,astrategicaugmentationofCSPdatamanagementarchitectureisrequired—onethatnativelyanticipatesdomainfragmentationbydesign,enforcesseamlessandsecureexchange,andguaranteesreadinessforamulti-agentsystemfromdayone. Thisdocumentcapturesthoughtleadershipandareferencearchitectureforatrulyfuture-proofdatamanagementthatscaleselastically,supportsseamlessdataintegration,canbedeployedinahybridmanner,andispurpose-builtforAI-nativeintelligenceandautonomous network and operations. Backgroundcontext This chapter captures the various industry, technological, and business trends thatareinfluencingthenatureofandexpectationsondatamanagementlayerswithinorganizations. Industry trends The growth in data volumes continues to exceed predictions as networks continue to grow.Telecom operators have existing data management architectures, but the costs are high, aslarge datasets usually operate with unnecessary data transit and storage. Thecurrentgrowingdatademandsofthetelecomindustrypresenttwoproblems.Managinglargevolumesofdatawillcontinuetobeimportantandunavoidable,butthatis a well-known engineering issue. However, a more uncommon problem is the ability tocuratedatafromdiversesourcestoensureitsefficientandappropriateuse. Technology trends ThemostobviousdriverintermsoftechnologytrendsistheincreaseduseofAI,whichincludesagenticAI,robotics,andautomationassociatedwithAI.TheuseofAIdemandshigherdataqualityandquantity,whichmeansbetterdatagovernance,integrateddatasets,andthearticulationofrelationships.AIalsofurthermotivatestheneedtomakedatamoreaccessible,whichisasignificantdriverfortheestablishmentofadatafabric,enablingaunifiedapproachtodataaccessthroughintegratedcatalogsandotherfederatedservices. Business trends Asignificantproportionofbusinessesareinvestingheavilyinautomationandautonomousnetworks.Bytheendof2026,approximately80percentofbusinesseswillacceleratetheirautomationeffortstostreamlineoperationsandmaximizerevenues,withmostofthemfocusingonleveragingAItomakemeaningfulgainsintheseareas. Thiswillputextraemphasisondata,itstimeliness,availability,andreadinessforuseinAI processes. As already mentioned, data availability and readiness are critical in the datamanagementdomain,whichdemandshighstandardsinmanyotherrelatedaspectsofdatamanagement. Datamanagementevolution Datamanagementsystemsareresponsibleforhandlingdatafromvarioussources.Onceingested,thesesystemsbecomeresponsibleforcatalogingthedata,applyingde-identificationanddemocratizationprinciples,andrefiningrawdatasetsintoprocessedandreliableones.Thismakesthedatareadyforconsumptionbyvariousconsumersacrossawiderangeofusecases. Modern data management systems provide seamless integration with common data lakeswithon-rampandoff-rampfunctionalitiesthatutilizedatalakesasalong-termstorageandanalyticalplatform.Additionally,thesesystemsaremovingawayfrommonolithicarchitecturetodistributedfederatedsystems,wherecomputeresourcescanbepositionedclosertothedata.Thisapproachleveragesdatafederationtoensuredataisalwaysavailableforconsumers. These systems have to cover several use cases to securely manage data and to properlygovernthecapabilitytoensuredataisprocessedandexposedefficientlyandthroughcommoninterfaces. Someofthekeyfeaturesofmoderndatamanagementsystemsarethefollowing: •data cataloging•dataqualityreporting•data lineage reporting•datamarketplacefordataproducts•data security and audit logging•dataingestionframeworks•data integration with data lakehouses•datafederation•dataclassification,validationandtransformations•knowledgegraphs&contextualizationofdata Datamanagementsystemsfollowafewcoreprinciples: •Data management architecture follows a federated approach:the data ingestionarchitecture can easily