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ATscale:通过语义层在云优先企业中实现分析民主化白皮书(英文版)(17页).pdf

上传人: Kell****reet 编号:118700 2023-03-16 17页 10.88MB

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语义层分析报告

1、Democratizing Analytics in the Cloud-first Enterprise Using a Semantic LayerWh i t e pap e rBy Kirk BorneKirk has been an influential globally recognized leader in the data science space for 20 years.His areas of passion and focus include Big Data&Data Science,Artificial Intelligence(AI),and Astroph

2、ysics.Kirk is also the co-creator of the field of Astroinformatics.2Data Then,and Data NowData Democratization and its ChallengesIf you are already familiar with databases,BI(business intelligence),and even data mining,you might be thinking:we have had these things for many years,so whats all the fu

3、ss about right now with data analytics,data science,machine learning,and AI?The short answer to that question is this:data science(DS)and machine learning(ML)are hot right now due to the enormous growth in data volumes and data variety in recent years,and due to the significant increase in the capac

4、ity of those techniques to reveal significant new discoveries and insights that deliver new value(and opportunities)to organizations.A longer answer to our question would cover these emergent perspectives:DS/ML now engages three significantly expanded,improved,and mature technologies:?Ubiquitous dat

5、a sources(including sensors,networks,logs,mobile apps,etc.);?Greater access to more powerful computing(including virtual on-demand distributed computing with the Cloud);?Sophisticated and more powerful machine learning algorithms for insights discovery from data.Despite such remarkable advancements,

6、there are a few hurdles that stand in the way of democratizing their benefits across the entire enterprise.These truths might be self-evident,but we will review them here.Several significant truths that have become very clear in the data analytics sphere in recent years include the primary truth(our

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本文主要讨论了在云计算时代,如何通过语义层实现数据分析的民主化。主要观点包括: 1. 数据分析民主化意味着让更多的员工参与到日常的数据密集型工作中,包括数据素养和数据流利度的培训。 2. 语义层可以覆盖每个业务用户访问的数据层,以业务用户熟悉的语言和环境呈现数据洞察,从而使数据科学家能够直接将预测、高级分析结果和最重要的学习模式发布到业务分析师选择的BI工具中。 3. 数据分析民主化还包括采用对业务用户相对容易学习、理解和使用的工具、技术和方法。 4. 数据民主化可以部署并交付显著价值,而不会对现有员工或技术基础设施造成重大干扰。 5. 数据分析的目标是“决策智能”——基于当前数据的最佳行动方案,以实现最佳业务成果。 6. 数据民主化的关键在于提高数据素养和数据流利度,强调数据无处不在,数据分析是自然的人类行为。 7. 数据分析的五个维度包括诊断分析、预测分析、认知分析、规范分析和描述分析。 8. 数据民主化的成功案例包括客户流失预警、国际漫游套餐销售、发动机故障预警和基于购买历史的营销活动。 9. 数据分析的价值不在于更复杂的算法,而在于易用性和可解释性,以及及时提供可信结果。
数据民主化如何帮助企业? 数据科学家如何与业务分析师合作? 数据民主化如何提高业务决策质量?
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