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爱立信:2026 从数据困境到AI就绪型数据网格白皮书(英文版)(22页).pdf

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1、Ericsson White PaperGFTL-26:000204 UenJanuary 2026From data mess to AI-ready data meshFrom data mess to AI-ready data meshContentJanuary 20262ContentIntroduction 3Background context 4 Industry trends 5 Technology trends 5 Business trends 5Data management evolution 6Target business objectives 9North

2、Star vision 11Dataunificationandfederation12DataenablementandpreparationforAI12Dataintegrationanddatapipelineefficiency 13Datasecurity15Dataproductsandvalue15Reference architecture 16Standardizationactivities16Conclusion 17Authors 18From data mess to AI-ready data meshIntroductionJanuary 20263Introd

3、uctionOver the next decade,the telecommunications landscape will be shaped by the ability tounlockandutilizethefullpotentialofdataatanunprecedentedscale,speed,andintelligence.As a trusted partner,we have observed how sprawling Hadoop architectures andon-premisesplatformsinthetelecomsectorcanquicklyb

4、ecomebrittleasvolumesexpand and new use cases emerge.Inadditiontotheexpansionofdatavolume,datafragmentationacrossdomains,platforms,andschemasisaninevitableconsequence.Autonomousnetworkdomainelements,serviceassurance engines,customer experience agents,and other similar systems produce domain-specific

5、silos,whichmaydelaythehighlymulti-agenticoperationsthatcommunicationsservice providers(CSPs)seek to achieve.Inthedatamanagementlayer,thechallengeistomanagetheintegrationofhigh-volumedatainflowsfromdisparatesources,governthemovementandtransformationofthatdatasecurelyandwithintegrity,andbringthatdatat

6、oastateofreadinessforavarietyofconsumers,mostnotablythosewhowillapplyvariousartificialintelligence(AI)techniques.Hence,astrategicaugmentationofCSPdatamanagementarchitectureisrequiredonethatnativelyanticipatesdomainfragmentationbydesign,enforcesseamlessandsecureexchange,andguaranteesreadinessforamult

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1. **行业趋势**:电信数据量激增,面临数据碎片化、高成本及AI需求(高质量数据、治理、集成)挑战,80%企业将加速AI驱动的自动化(2026年)。 2. **数据管理演进**:从单体架构转向联邦数据网格(Data Mesh),支持弹性扩展、混合部署,核心能力包括数据目录、质量、血缘、安全及AI就绪数据管道。 3. **目标架构**:AI原生数据管理需解决实时处理、上下文管理、治理安全及多智能体协作,通过语义建模、知识图谱实现数据统一与联邦访问。 4. **关键特性**:数据产品化(可追溯、高质量)、安全动态嵌入(隐私保护、访问控制)、自动化管道优化(可观测性、AI驱动自愈)。 5. **标准与愿景**:基于3GPP、O-RAN等标准,构建支持自主网络、AI智能的弹性数据基础,推动行业协同。
**AI数据挑战?** **数据碎片化?** **数据安全新解?**
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