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贝恩:高级分析人才市场新趋势(英文版)(7页).pdf

上传人: 科*** 编号:14671 2020-08-01 12页 1.68MB

1、With the supply of talent growing fast, make the most of opportunities inside and outside your company. By Chris Brahm, Arpan Sheth, Velu Sinha and Jessica Dai Solving the New Equation for Advanced Analytics Talent Chris Brahm is a Bain average calculated based on a sample of companies in each indus

2、try Sources: LinkedIn; S company websites; company annual reports Consumer goods 3 Solving the New Equation for Advanced Analytics Talent Building an advanced analytics team Companies should not expect to fill this gap entirely by wooing experienced talent from other em- ployers. The most analytical

3、ly mature sectors plan to expand their teams fastest, and employees are most interested in working for companies with well-established track records in analytics, our recent survey of more than 200 industry participants found. Creative, flexible approaches for expanding the talent base include build

4、ing centers of excellence for pools of hired analytics experts, and also retraining capable existing employees and giving them access to automation tools. Importantly, rather than trying to do everything in-house, a tiered talent strategy should focus a core, in-house analytics team on strategic tas

5、ks while tapping offshore data hubs, third-party service firms and crowdsourcing for other work. Even the most sophisticated companies leverage a combination of internal and external supply chains for analytics capabilities. What is the optimal blend of advanced analytics roles? How are teams best c

6、onfigured? The exact bal- ance varies depending on the sector and maturity of a companys analytics practice, but teams will draw from eight key roles (see Figure 2). With companies hiring to create balanced advanced analytics teams, certain skills are in higher demand. Figure 2:Aneffectiveadvancedan

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本文讨论了全球先进数据分析人才供应的增长以及企业如何应对人才短缺的问题。主要观点包括: 1. 人才供应快速增长:到2020年,先进数据分析人才库预计将从2018年的50万人增长到100万人,其中印度的增长尤其迅速。 2. 美国人才短缺:尽管人才供应增加,但美国在数据工程师和架构师等关键职位上仍面临短缺。 3. 多元化的人才战略:文章建议企业不应期望仅通过吸引经验丰富的外部人才来填补空缺。成功的多层次人才策略应结合新雇员和内部再培训,同时利用数据枢纽、第三方服务公司和众包。 4. 创新与教育:教育领域的变革,特别是印度在培训程序员方面的投资,为数据分析人才的快速增长奠定了基础。 5. 行业分析:不同行业在数据分析人才方面的需求不同,例如,技术公司在数据分析方面的员工比例远高于银行。 6. 自动化和培训:为了克服人才瓶颈,公司可以使用自动化工具培训没有强大编码技能的员工构建模型和工程数据。 总结而言,随着全球数据分析人才库的扩大,企业应采取灵活的人才策略,结合内部培养和外部资源,以应对不断增长的数据分析需求。
"如何应对数据分析人才短缺?" "未来数据分析人才格局如何?" "如何利用外部资源培养数据分析人才?"
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