The production of salary profiles of ICT professionals: Moving from structured database to big data analytics

Ramasamy, Ramachandran

doi:10.3233/sji-150891

Cited by 5 publications

(1 citation statement)

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“…Thistechniqueisusedtoanalyzeandextractinformationfromunstructuredaudiodata (Dubeyet al,2015).Itisalsoknownasspeechanalyticswhenappliedtohumanspokenlanguage (Ramasamy, 2015).Healthcareandcustomercallcentersareusingthistechniquetoanalyzethousandsoreven millionsofhoursofrecordedcalls (Habteetal.,2015).Audioanalyticshelpsinimprovingcustomer experiencebygaininginsightintocustomerbehavior,enhancesalesturnoverrates,evaluateagent performance,monitorcompliancewithdifferentpolicies(e.g.,privacyandsecuritypolicies),and identifyproductorserviceissuesandmore. (Hirschbergetal.,2010)(Table6).…”

Section: Audio Analyticsmentioning

confidence: 99%

Big Data and Advance Analytics: Architecture, Techniques, Applications, and Challenges

Verma

2017

International Journal of Business Analytics

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The insights that firms gain from big data analytics (BDA) in real time is used to direct, automate and optimize the decision making to successfully achieve their organizational goals. Data management (DM) and advance analytics (AA) tools and techniques are some of the key contributors to making BDA possible. This paper aims to investigate the characteristics of BD, processes of data management, AA techniques, applications across sectors and issues that are related to their effective implementation and management within broader context of BDA. A range of recently published literature on the characteristics of BD, DM processes, AA techniques are reviewed to explore their current state, applications, issues and challenges learned from their practice. The finding discusses different characteristics of BD, a framework for BDA using data management processes and AA techniques. It also discusses the opportunities/applications and challenges managers dealing with these technologies face for gaining competitive advantages in businesses. The study findings are intended to assist academicians and managers in effectively quantifying the data available in an organization into BD by understanding its properties, understanding the emerging technologies, applications and issues behind BDA implementation.

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Section: Audio Analyticsmentioning

confidence: 99%

Big Data and Advance Analytics: Architecture, Techniques, Applications, and Challenges

Verma

2017

International Journal of Business Analytics

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Computer Science Studies in English from the Perspective of Students and Business

Miłosz

2016

Information Systems: Development, Research, Applications, Education

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Fine-Grained Job Salary Benchmarking with a Nonparametric Dirichlet Process–Based Latent Factor Model

Qing-xin

Xiao

Shen

et al. 2022

INFORMS Journal on Computing

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As a key decision-making process in compensation and benefits (C&B) in human resource management, job salary benchmarking (JSB) plays an indispensable role in attracting, motivating, and retaining talent. Whereas the existing research mainly focuses on revealing the essential impacts of personal and organizational characteristics and economic factors on labor costs (e.g., C&B), few studies target optimizing JSB from a practical, data-driven perspective. Traditional approaches suffer from issues that result from using small and sparse data as well as from the limitations of linear statistical models in practice. Furthermore, there are also important technical issues that need to be addressed in the small number of machine learning–based JSB approaches, such as “cold start” issues when considering a brand-new type of company or job or model interpretability issues. To this end, we propose to address the JSB problem with data-driven techniques from a fine-grained perspective by modeling large-scale, real-world online recruitment data. Specifically, we develop a nonparametric Dirichlet process–based latent factor model (NDP-JSB) to jointly model the latent representations of both company and job position and then apply the model to predict salaries based on company and position information. Our model strengthens the usage of data-driven approaches in JSB optimization by addressing the aforementioned issues in existing models. For evaluation, extensive experiments are conducted on two large-scale, real-world data sets. Our results validate the effectiveness of the NDP-JSB and demonstrate its strength in providing interpretable salary benchmarking to benefit complex decision-making processes in talent management. Summary of Contribution: This paper bridges the cutting-edge machine learning techniques to their implementation in a practical operation research problem in human resources. We focus on optimizing the salary-matching work to help the companies to seek reasonable salaries for their positions by proposing a data-driven approach to capture hidden patterns from user and company profiles. The contributions of this work reside in both operation research and computing. We (1) formulate the JSB optimization problem and (2) solve it by developing a data-driven method along with an effective algorithm optimization. Moreover, the proposed methodology has strengths in addressing the issues of data sparseness and result interpretability.

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The production of salary profiles of ICT professionals: Moving from structured database to big data analytics

Cited by 5 publications

References 10 publications

Big Data and Advance Analytics: Architecture, Techniques, Applications, and Challenges

Big Data and Advance Analytics: Architecture, Techniques, Applications, and Challenges

Computer Science Studies in English from the Perspective of Students and Business

Fine-Grained Job Salary Benchmarking with a Nonparametric Dirichlet Process–Based Latent Factor Model

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