Gartner-首席数据和分析官如何领导数据科学和机器学习领域的技能提升计划（英）-15页_314kb

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How CDAOs Can Lead Upskilling Initiatives in Data Science and Machine Learning Summary

Key Findings

• Machine learning literacy remains low in many organizations.
• Upskilling is motivated by educational opportunities and talent acquisition challenges.
• The gap between data scientists' technical expertise and business users' domain knowledge makes culture change difficult.

Recommendations for CDAOs

• Raise overall data science and machine learning awareness through centralized resources and showcasing case studies.
• Identify and develop citizen data scientist (CDS) candidates by creating skills inventories and matching them to appropriate paths.
• Design sustained upskilling programs for three groups: average analytics consumers, CDS candidates, and expert data scientists.
• Encourage learning on the job, foster collaboration, and build communities of practice.
• Targeted training includes formal education, hands-on experimentation, and delivery phases for different user levels.

Evidence and Context

• Based on Gartner research, highlighting talent shortages and the need for strategic leadership to close the data science skills gap.
• Success requires flexible timelines and early, low-risk projects.