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赞助方:AtlanAI 时代的领域驱动数据治理:与通用汽车和 Atlan 的对话.pdf

上传人: Fl****zo 编号:719014 2025-06-22 24页 21.73MB

1、Forward-looking StatementThis presentation has been prepared for informational purposes only.The information set forth herein does not purport to be complete or contain all relevant information.Statements contained herein are made as of the date of this presentation unless stated otherwise.This pres

2、entation and the accompanying oral commentary may contain forward-looking statements.In some cases,forward-looking statements can be identified by terms such as“may”,“will”,“should”,“expects”,“plans”,“anticipates”,“could”,“intends”,“projects”,“believes”,“estimates”,“predicts”,or“continue”,or the neg

3、ative of these words or other similar terms or expressions that concern Databricks expectations,strategy,plans,or intentions.Forward-looking statements are based on information available at the time those statements are made and are inherently subject to risks and uncertainties that could cause actu

4、al results to differ materially from those expressed in or suggested by the forward-looking statements.Forward-looking statements should not be read as a guarantee of future performance or outcomes.Except as required by law,Databricks does not undertake any obligation to publicly update or revise an

5、y forward-looking statement,whether as a result of new information,future developments or otherwise.2Complete Your SurveysYou will receive a survey for each session attendedOpen the Databricks Events app and select“My Surveys”from the menuSurveys can also be submitted in the Attendee Portal3Your fee

6、dback has a direct impact on Data+AI Summit contentSherri AdameEnterprise Data Governance|GMDomain-driven Governance in the AI EraA Conversation with General Motors and AtlanThe era of AI-native businesses is here.Cloud-native companiesBuilt for the cloud20102019Digital companiesBuilt for the intern

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本文主要内容涉及企业数据治理和AI应用的挑战与策略。关键点如下: 1. 文章提到企业面临AI实验与生产之间的价值差距,指出启动AI项目容易,但实现生产级应用困难。 2. 强调数据意义、治理和上下文的重要性,并提出了跨越这一差距的三个障碍。 3. 介绍了Atlan AI企业级解决方案,通过激活、协作和统一治理来促进数据治理。 4. 举例说明了通用汽车公司的数据哲学和数据成熟度,以及如何通过主动治理和全球流程来提高市场竞争力。 5. 文章提及的数据治理工具和技术解决方案包括:Metaconnect、Anomalo、Immuta和Azure等。 6. 文章最后展望了2024至2025年的目标,包括数据资产增长、用户增加以及全球数据同步等。 核心数据引用: - 企业价值超过10万亿美元的企业正在应对AI应用挑战。 - 通用汽车公司管理的数据资产超过800k,商业智能资产超过40k,用户超过400+。 以上内容概括了文章的核心要点和数据。
"AI时代,企业如何跨越价值鸿沟?" "通用汽车的数据哲学是什么?" "如何实现端到端的数据治理自动化?"
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