从数据困境到AI就绪型数据网格

Content Introduction Background context Industry trendsTechnology trendsBusiness trends Data management evolution Target business objectives Reference architecture Standardizationactivities Conclusion Authors 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 architectures Inadditiontotheexpansionofdatavolume,datafragmentationacrossdomains,platforms,andschemasisaninevitableconsequence.Autonomousnetworkdomainelements,serviceassurance engines, customer experience agents, and other similar systems produce domain- Inthedatamanagementlayer,thechallengeistomanagetheintegrationofhigh-volumedatainflowsfromdisparatesources,governthemovementandtransformationofthat Hence,astrategicaugmentationofCSPdatamanagementarchitectureisrequired—onethatnativelyanticipatesdomainfragmentationbydesign,enforcesseamlessandsecure Thisdocumentcapturesthoughtleadershipandareferencearchitectureforatrulyfuture-proofdatamanagementthatscaleselastically,supportsseamlessdataintegration, Background This chapter captures the various industry, technological, and business trends thatareinfluencingthenatureofandexpectationsondatamanagementlayerswithin 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, as Thecurrentgrowingdatademandsofthetelecomindustrypresenttwoproblems.Managinglargevolumesofdatawillcontinuetobeimportantandunavoidable,butthatis a well-known engineering issue. However, a more uncommon problem is the ability to Technology trends ThemostobviousdriverintermsoftechnologytrendsistheincreaseduseofAI,whichincludesagenticAI,robotics,andautomationassociatedwithAI.TheuseofAIdemandshigherdataqualityandquantity,whichmeansbetterdatagovernance,integrateddatasets,andthearticulationofrelationships.AIalsofurthermotivatestheneedtomakedatamore Business trends Asignificantproportionofbusinessesareinvestingheavilyinautomationandautonomousnetworks.Bytheendof2026,approximately80percentofbusinesseswillacceleratetheirautomationeffortstostreamlineoperationsandmaximizerevenues,withmostofthem Thiswillputextraemphasisondata,itstimeliness,availability,andreadinessforuseinAI processes. As already mentioned, data availability and readiness are critical in the datamanagementdomain,whichdemandshighstandardsinmanyotherrelatedaspectsofdata Datamanagement Datamanagementsystemsareresponsibleforhandlingdatafromvarioussources.Onceingested,thesesystemsbecomeresponsibleforcatalogingthedata,applyingde-identificationanddemocratizationprinciples,andrefiningrawdatasetsintoprocessedand Modern data management systems provide seamless integration with common data lakeswithon-rampandoff-rampfunctionalitiesthatutilizedatalakesasalong-termstorageandanalyticalplatform.Additionally,thesesystemsaremovingawayfrommonolithic These systems have to cover several use cases to securely manage data and to properlygovernthecapabilitytoensuredataisprocessedandexposedefficientlyandthrough Someofthekeyfeaturesofmoderndatamanagementsystemsarethefollowing: •data cataloging•dataqualityreporting•data lineage reporting Datamanagementsystemsfollowafewcoreprinciples: •Data management architecture follows a federated approach:the data ingestionarchitecture can easily scale up and down, support both batch and streaming data, and be •Data is collected once, and many consumers are allowed:federatedsystemshaveadata ingestion architecture that collects datasets only once, which is then made available •Insightsareshared:applicationsthatproduceinsightscanpublishinsightssootherapplicationscanalsobenefitfromthem. •Data is used in transparent, compliant, and ethical ways, with value for the end-userin mind:data is democratized, meaning it is available to relevant consumers without compromising applicable security policies and regulations, as agreed by and between thedata handler, the customer, employees, and partners. Dataislandscanbeconsideredso-calledlandingzonesfordata,wherethedataarrives,isprocessed,andexposedfornear-real-timeusecases.Dataalsoneedstobestoredforlong-term use cases such as trend analysis and historical reporting. To achieve this, data islands Target business This chapter covers the challenges associated with modernizing data architectures tomeetthedemandsofautonomousnetworkLevel5stateofautonomyinIT,network,and AsnetworksevolvetowardfullLevel5autonomy,whereagenticAIsystemsperceive,reason, and act end-to-end with minimal or probably without human prompts, the datamanagementfuture-statearchitecturemusttransformfrombatch-orientedstoresintoreal-time,context-rich,AI-nativefabrics.Belowisaconcise,structuredviewofthekey Challenges Datafragmentationandfederation: •heterogeneousdomainsilosandmulti-vendorrequirements,suchasRAN,core,edge,in