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ATscale:数据治理和语义层如何赋能数据网格白皮书(英文版)(27页).pdf

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1、How Data Governance and a Semantic Layer Supports Data MeshBy George FiricanGeorge is a passionate advocate for the importance of data,a frequent conference speaker and a YouTuber,being ranked among Top 5 Global Thought Leaders and Influencers on Big Data,Digital Disruption and Top 15 on Innovation.

2、Wh i t e pap e r2Data are Assets -Valuable and ImpactfulWhen Performance Matters-Insights and Analytics DeliverData is valuable!Data is an asset!We hear this a lot.Clive Humby,a mathematician and the co-creator of the Tesco Clubcard the worlds first supermarket loyalty card,coined the phrase,“data i

3、s the new oil”,in 2006.We also hear the expressions“data is valuable”and“data is an asset”a lot.The message that Mr.Humby and others want to convey is that a companys data is a tangible asset.In fact,data is arguably one of the most important assets that any organization has.Why?We need data for two

4、 key reasons:to help us answer key business questions and to provide feedback about our performance,customers,markets and competitors.We hear so much today about the benefits of using machine learning-but what makes machine learning so valuable is the data that it uses.Enterprises learn from data.Da

5、ta is the catalyst for improving enterprise relevance and distinctiveness.Data helps accelerate productivity,efficiency,and competitive advantage.Companies that learn from their data improve their performance.Research from Tableau confirms that 83%of CEOs want their companies to be data driven,but o

6、nly 46%are achieving that goal.For those that do achieve it,the rewards are plentiful.According to a survey of more than 1,000 senior executives conducted by PwC,highly data-driven organizations are three times more likely to report significant improvements in decision-making compared to those that

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本文主要讨论了数据治理和语义层在数据网格中的支持作用。文章首先强调了数据的重要性,将其视为一种资产,并指出数据对于回答关键业务问题、提供反馈以及提高企业绩效等方面至关重要。接着,文章详细介绍了数据治理的定义和重要性,包括数据治理的核心问题、角色和责任,以及数据政策和标准等。此外,文章还讨论了现代数据治理的五个关键能力,包括基于云的数据平台、数据民主化、将数据视为产品、联邦式和中心辐射式交付模型以及数据可观察性和问责性。最后,文章指出,为了实现数据治理的现代化,需要更快地提供可操作的见解、实现规模化和成本效益,以及减少冗余和优化计算。
数据治理如何支持数据网格? 数据治理在现代数据景观中的作用是什么? 数据治理如何实现速度、规模和成本效益?
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