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EPAM:2024生成式人工智能(AI)时代的数据素养白皮书(英文版)(23页).pdf

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1、W H I T E P A P E RData Literacy in the Age of Generative AIDr.Ashwin Mehta Founder&Director,MehtadologyDr.Taryn Hess Director,Talent Enablement&Transformation,EPAMData Literacy in the Age of Generative AI|05/25|2W H I T E P A P E RContentsForeword.3Introduction.4Data Culture.6The Cost of Data Illit

2、eracy.7The Urgency of Data Literacy.8Data,Digital&AI Literacy.9Building a Data Literate Workforce.13Examples of Our Work.14Holistic,Culture-Change Approach to AI&Data Literacy.15Data Literacy.16Success Factors for AI.18Conclusion.21Data Literacy in the Age of Generative AI|05/25|3W H I T E P A P E R

3、ForewordAs generative AI(GenAI)becomes a part of strategy and operations across businesses in all sectors,the dependency on good quality data and data-related skills becomes increasingly important in maximizing the value derived from artificial intelligence(AI).The AI engine runs on the fuel of data

4、,and without the skills in place for data hygiene and management,and a continuously improving data culture,businesses might struggle to reap the rewards promised by the AI revolution.In this article we present best practices for data literacy for any business to establish that will support them on t

5、heir AI journey.This white paper is intended for C-Suite and decision-makers who will lead their businesses across the data frontier towards the utopia of AI-enhancement,as well as the managers and support departments that will ensure that culture and skills related to data become an inherent part o

6、f the value chain.W H I T E P A P E RData Literacy in the Age of Generative AI|05/25|4IntroductionAs AI continues to be both a disruptive force across industries and an opportunity for transforming business,AI literacy is rapidly becoming a fundamental aspect of the skill set of any workforce.AI and

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根据《Data Literacy in the Age of Generative AI》白皮书,以下为全文关键点: 1. **数据素养的重要性**:随着生成式AI的普及,数据素养成为关键技能,对理解AI工作原理、优化决策和提升业务价值至关重要。 2. **数据素养的挑战**:组织面临数据素养技能缺口,缺乏数据素养可能导致决策失误、机会丧失和业务风险。 3. **数据文化**:建立数据文化,包括个人和团队对数据的责任,以及数据收集、管理和分析的技能。 4. **数据素养框架**:包括数据概念、数据收集、数据管理、数据应用和数据评估。 5. **AI成功因素**:战略、运营模式、技术、数据文化、技能和环境。 6. **数据素养对L&D团队的重要性**:L&D团队需利用数据和分析来优化任务和学习,并证明其价值。 7. **案例研究**:EPAM与Regeneron和全球信息服务领导者合作,通过数据素养项目提升员工技能和业务成果。
"数据素养,AI时代的必备技能?" "AI时代,如何打造数据驱动型企业?" "数据素养,助力企业突破增长瓶颈?"
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