Latest Academic Publications on People Analytics (September 2021)

In this series of monthly LinkedIn articles, you'll read about the latest academic publications on People Analytics! Don't miss out on the latest insight, and follow associate professor?Sjoerd van den Heuvel?on LinkedIn! In this edition you find details about the following publications:

• Seeing the Forest for the Trees: Group-Oriented Workforce Analytics
• Harnessing People Analytics for Future-Proofing Human Resource Management
• Workforce Analytics: A Data-Driven Machine Learning Approach to Predict Job Change of Data Scientists
• The framework of talent analytics using big data

Seeing the Forest for the Trees: Group-Oriented Workforce Analytics

Jing Yang, Chun Ouyang, Arthur ter Hofstede, Wil van der Aalst, Michael Leyer

Workforce analytics brings data-driven methods to organizations for deriving insights from employee-related data and supports decision making. However, it faces an open challenge of lacking the capability to analyze the behavior of employee groups in order to understand organizational performance. This paper proposes a novel notion of work profiles of resource groups, informed by the management literature, for characterizing resource group behavior from multiple aspects relevant to workforce performance. This notion is central to the design of a new, systematic approach that supports resource group analysis by exploiting business process execution data. The approach also provides managers and business analysts with an intuitive means of group-oriented resource analysis by applying visual analytics. We demonstrate the applicability of the approach and usefulness of the proposed notion of resource group work profiles using real datasets from five Dutch municipalities.

Harnessing People Analytics for Future-Proofing Human Resource Management

Ramar Veluchamy, Abinaya R A, Aditi Maitra

The prevalence of the HR Analytics concept in the field of HRM is very minimal. To take effective decisions in Employee Retention, HR consultants adopt data-driven approaches. Predicting employee attrition are published incorporating various methods but here the most influential parameter that impacts the employee attrition the most is determined. HR consultants can make effective decisions using the data-driven approach. While numerous HR experts consider analytics as a component of HRM intervention, is study aims to expand the scope of HR analytics in the field of HRM aiming at Employee Attrition. Human resource departments started to concentrate on building a strong analytics capability to best indurate the data-driven world. There are critical monetary and immaterial expenses related to losing steadfast, loyal, and high-performing employees. HR analytics functions serve as a supply for data required by HR management, enabling it to act as a strategic alliance for the welfare of the organization. The research strongly proposes that analytics be integrated with Human Resource Management, which help to formulate strategies to futureproof human resource management.

Workforce Analytics: A Data-Driven Machine Learning Approach to Predict Job Change of Data Scientists

Sohini Sengupta, Sareeta Mahendra Mugde, Renuka Deshpande, Kimaya Potdar

Today the total amount of data created, captured, and consumed in the world is increasing at a rapid rate, as digitally driven organizations continue to contribute to the ever- growing global data sphere. (Holst, Statista Report 2020). This data brings with it a plethora of opportunities for organizations across different sectors. Hence, their hiring outlook is shifting towards candidates who possess the abilities to decode data and generate actionable insights to gain a competitive advantage. A career in data science offers great scope and the demand for such candidates is expected to rise steeply. When companies hire for big data and data science roles, they often provide training. From an HR perspective, it is important to understand how many of them would work for the company in the future or how many look at the training with an upskilling perspective for new jobs. HR has the aim of reducing costs and time required to conduct trainings by designing courses aligning to the candidate’s interest and needs. In this paper, we explored the data based on features including demographics, education and prior experience of the candidates. We made use of machine learning algorithms, viz. Logistic Regression, Naive Bayes, K Nearest-Neighbours Classifier, Decision Trees, Random Forest, Support Vector Machine, Gradient Descent Boosting, and XGBoost to predict whether a candidate will look for a new job or will stay and work for the company.?

The framework of talent analytics using big data

Arnold Saputra , Gunawan Wang , Justin Zuopeng Zhang , Abhishek Behl

The era of work 4.0 demands organizations to expedite their digital transformation to sustain their competitive advantage in the market. This paper aims to help the human resource (HR) department digitize and automate their analytical processes based on a big-data-analytics framework. The methodology applied in this paper is based on a case study and experimental analysis. The research was conducted in a specific industry and focused on solving talent analysis problems. This research conducts digital talent analysis using data mining tools with big data. The talent analysis based on the proposed framework for developing and transforming the HR department is readily implementable. The results obtained from this talent analysis using the big-data-analytics framework offer many opportunities in growing and advancing a company's talents that are not yet realized. Big data allows HR to perform analysis and predictions, making more intelligent and accurate decisions. The application of big data analytics in an HR department has a significant impact on talent management. This research contributes to the literature by proposing a formal big-data-analytics framework for HR and demonstrating its applicability with real-world case analysis. The findings help organizations develop a talent analytics function to solve future leaders' business challenges.

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All articles in the series:

• Latest Academic Publications on People Analytics (September 2021)
• Latest Academic Publications on People Analytics (August 2021)
• Latest Academic Publications on People Analytics (July 2021)