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毕马威(KPMG):2025数据质量“三位一体”:赋能AI成功的数据质量新兴要务研究报告(中译版)(12页).pdf

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1、The DQhome.kpmg/inOctober 2025KPMG.Make the Difference.Data Quality for AI Success:Emerging ImperativesA Perspective Report Trifecta Data Quality for AI Success:Emerging ImperativesTable ofContentsIntroductionEmerging imperatives on DQ for AIa)Business-Led,Process-Fedb)360 Integration within Data Te

2、amsc)Observe,Detect,Act:The Power of Observabilityd)Semantic Intelligence through metadata qualitye)The Full Picture:beyond Mastersf)Agnostic by Principle,Flexible by DesignA Strategic Lens for AI Maturity1232The DQ Trifecta 345AuthorsKey ContactsForeword101112 2025 KPMG Assurance and Consulting Ser

3、vices LLP,an Indian Limited Liability Partnership and a member firm of the KPMG global organization of independent member firms affiliated with KPMG International Limited,a private English company limited by guarantee.All rights reserved.Data Quality for AI Success:Emerging ImperativesData Quality f

4、or AI Success:Emerging Imperatives3The DQ Trifecta We see it in headlines across all industries billions are poured into AI startups,and executives and influencers promote AI as the silver bullet for productivity,creativity and cost efficiency.But the hard reality is your AI programs are only as goo

5、d as the data that flows into them.If data is the critical fuel,then an effective data quality methodology must be the discipline required to ensure trustworthy data.But no surprise,yesterdays data quality methodologies dont quite make the grade in todays overwhelmingly data-driven digital world.If

6、we believe that data is the lifeblood of decision-making,innovation,and trust,what must we do differently in our data quality processes?What new and innovative approaches must we take to pin down the challenges of achieving trusted data for our AI programs?The DQ Trifecta Data Quality for AI Success

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根据《Data Quality for AI Success: Emerging Imperatives》报告,以下是全文关键点: 1. **数据质量的重要性**:数据是AI成功的关键,高质量数据是确保AI模型可靠性和有效性的基础。 2. **数据质量三要素(DQ Trifecta)**: - **业务驱动,流程支持**:将数据质量与业务目标结合,通过流程改进数据质量。 - **360°数据团队整合**:数据质量不再是IT部门的职责,而是整个数据团队的责任。 - **观察、检测、行动**:通过数据可观察性实时监控和解决数据质量问题。 3. **元数据质量**:元数据是数据语义智能的基础,确保数据完整性和一致性。 4. **全面视角**:关注所有数据,而不仅仅是主数据,包括交易数据和分析数据。 5. **原则中立,设计灵活**:采用无工具依赖的方法,使数据质量成为数据生态系统中的核心部分。
数据质量如何影响?" 业务引领还是技术驱动?" AI时代的数据质量全貌"
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