Advanced predictive analytics for employee turnover

在当前的大数据时代，可以很容易地在各个行业（例如，运输和卡车行业，航空业，某些类型的制造商和服务公司，拥有经过高度培训/认证的员工）中以高速率收集大量各种有价值的员工数据。数据科学解决方案——应用数据挖掘、机器学习（包括深度学习）和统计建模等多学科技术——有助于发现嵌入在大数据中的隐含的、以前未知的、潜在有用的信息和知识。我们的行业合作伙伴DecisionWorks咨询公司报告说，司机更替问题对运输和卡车行业的客户来说非常重要，因为培训一名新司机大约需要5000美元。此外，糟糕的司机（例如，那些装备精良、安全记录不佳或在良好路况下驾驶困难的司机）正在造成诸如延迟或管理不善的交付、高于平均维护成本等问题。类似的问题也存在于其他行业（例如，航空业、拥有训练有素/合格员工的制造商和服务公司），这些行业的每名员工的离职成本很高。因此，利用数据科学解决方案来发现员工的知识和信息，有助于揭示“好”和“坏”员工的特征，从而帮助DecisionWorks增强其业务分析模型，以(a)降低运营成本，(b)为客户改善员工的工作环境。随着Engage项目在运输和卡车行业特定类型司机的营业额预测分析方面的成功，我们计划进一步增强我们的数据科学解决方案，用于(a)其他类型的司机和(b)其他行业的员工的预测分析。此外，我们还计划为我们的数据科学解决方案设计和开发一个可视化分析组件，以便发现的知识可以以易于理解的可视化形式表示。由此产生的解决方案有望执行有效的可视化分析和准确的预测分析，以预测优秀员工的离职风险。

In the current era of big data, high volumes of a wide variety of valuable data about employees can be easilycollected at a high rate in various industries (e.g., transportation & trucking industry, airline industry, certaintypes of manufacturers & service companies with highly trained/accredited employees). Data sciencesolutions--which apply multidisciplinary techniques such as data mining, machine learning (including deeplearning), and statistical modelling--help discover implicit, previously unknown and potentially usefulinformation and knowledge that is embedded in the big data. Our industrial partner, DecisionWorks ConsultingInc, has reported that the issue of driver turnover is significant for its clients in the transportation & truckingindustry because it costs roughly $5K to train a new driver. Furthermore, bad drivers (e.g., those who are hardon equipment, have poor safety records, or who find driving under anything but good road conditions difficult)are causing problems such as late or mismanaged deliveries, higher than average maintenance costs, etc.Similar issues exist in other industries (e.g., airline industry, manufacturers & service companies with highlytrained/accredited employees) where the cost of turnover on a per-employee basis is high. Having data sciencesolutions to discover knowledge and information about employees therefore helps reveal the characteristics of"good" and "bad" employees, which in turn helps DecisionWorks to enhance its business analytic model for (a)reducing operating costs and (b) enhancing the employees' working environment for its clients. With success inour Engage project in predictive analytics of turnover of a specific type of drivers in transportation & truckingindustry, we plan to further enhance our data science solution for predictive analytics of (a) other types ofdrivers and (b) employees in other industries. Moreover, we also plan to design and develop a visual analyticscomponent for our data science solution so that the discovered knowledge can be represented in a visual formthat can be easily comprehend. The resulting solution is expected to perform effective visual analytics andaccurate predictive analytics to forecast good employees at risk of turnover.