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从愿景到实践:领先的数据治理专注于卓越的工程.pdf

上传人: 芦苇 编号:651620 2025-05-01 20页 1.34MB

1、Apurva WadodkarSenior Manager Enterprise Data,Autodesk IncFrom Vision to Adoption Leading Data Governance focused on Engineering ExcellenceGDPRSOXSecurityArchitecturePlatformsMarket TrendsSOD ConflictsCCPACPRAQualityDomain ConstraintsData LineageData LiteracyLife of an EngineerEmpower Engineering te

2、ams with data practices,frameworks and foundations to curate high-quality,reliable and compliant data that business can depend upon.VisionPosition data products as catalyst for unlocking business valueMissionData GovernanceObjectiveoCreate one-stop shop for all relevant informationoCondense Informat

3、ion in actionable format.oCreate Architecture Frameworks and reusable code templates with Compliance embeddedoGamify the processoBuild a Community of PracticeData Governance ProgramMultiple touchpoints&Audits Daily Tools and TemplatesCondensed InformationEducate Embed EnforceData Governance5671234Da

4、ta LiteracySourcingPlatformsQualitySecurity&ComplianceOperationsCI/CDData Governance PrinciplesData LiteracySourcingPlatformsQualitySecurity&ComplianceOperationsCI/CDData Governance CouncilUtilitiesSelf Assessment Application Publish LeaderboardTeams were given targesLeaderboards were published ever

5、y month in our newsletters.Community of Practice Establish common platform for sharing innovative techniques to solve complex data problems.Operationalize innovative techniques by publishing it for entire data community.GamificationReinforce with constant Narrative Reinforce importance of governance

6、 through continuous discussions.Talk about one idea in every all-hands Captured Maturity Level of each team via self assessment web application.Program Success MetricsLeading IndicatorsAdoption Rate(Enrollment)Maturity ScoresMaturity Model Effectiveness Index(via surveys)Lagging

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Apurva Wadodkar,Autodesk公司的高级经理,在关于数据治理的演讲中强调了从愿景到采纳的重要性。他专注于工程卓越的数据治理,涉及GDPR、SOX、安全、架构、平台、市场趋势、SOD冲突、CCPA、CPRA和质量等领域。Wadodkar提出了一站式信息商店的概念,并提倡将数据产品视为解锁商业价值的催化剂。他提倡建立具有合规性内嵌的架构框架和可重用代码模板,并通过游戏化手段和建立实践社区来提高数据治理的效率。 Wadodkar介绍了数据治理计划的成功指标,包括领先指标如采用率和成熟度得分,以及滞后指标如质量覆盖和合规性得分。他强调了数据治理计划需要根据每个生态系统的特点进行定制。他还提供了数据治理计划的技巧,包括全面沟通、管理变革、逐步增加复杂性、建立社区以及游戏化程序等。 Autodesk通过自我评估应用程序和每月出版物中的排行榜来跟踪团队成熟度,并鼓励分享解决复杂数据问题的创新技术。总之,Wadodkar强调了数据治理在提供高质量、可靠和合规数据方面对业务的重要性,并提出了实施有效数据治理的策略和工具。
"如何通过数据治理提升工程卓越性?" "如何通过数据治理确保GDPR等法规合规?" "如何通过数据治理打造高效工程师团队?"
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