A Digital Economy Model for Talent Prediction Data Analytic

Haru Purnomo Ipung, Amin Soetomo
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Abstract

This research proposed a model to assist the design of the associated data architecture and data analytic to support talent forecast in the current accelerating changes in economy, industry and business change due to the accelerating pace of technological change. The emerging and re-emerging economy model were available, such as Industrial revolution 4.0, platform economy, sharing economy and token economy. Those were driven by new business model and technology innovation. An increase capability of technology to automate more jobs will cause a shift in talent pool and workforce. New business model emerge as the availabilityand the cost effective emerging technology, and as a result of emerging or re-emerging economic models. Both, new business model and technology innovation, create new jobs and works that have not been existed decades ago. The future workers will be faced by jobs that may not exist today. A dynamics model of inter-correlation of economy, industry, business model and talent forecast were proposed. A collection of literature review were conducted to initially validate the model.
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人才预测数据分析的数字经济模型
本研究提出了一个模型,以协助设计相关的数据架构和数据分析,以支持当前由于技术变革步伐加快而导致的经济、行业和商业变化加速的人才预测。新兴和再新兴的经济模式,如工业革命4.0、平台经济、共享经济、代币经济等。这些都是由新的商业模式和技术创新驱动的。技术能力的提高将使更多的工作自动化,这将导致人才库和劳动力的转变。新的商业模式随着可用性和成本效益的新兴技术,以及新兴或再新兴的经济模式的结果而出现。新的商业模式和技术创新都创造了几十年前不存在的新的就业机会和工作。未来的工人将面临今天可能不存在的工作。提出了经济、产业、商业模式与人才预测相互关联的动态模型。通过文献综述对模型进行初步验证。
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