Use of Data Analytics in Supporting the Advising of Undecided Students

2019 ◽  
pp. 152102511988081
Author(s):  
Rong Wang ◽  
James E. Orr
Keyword(s):  
Data Analytics ◽  
Undecided Students ◽  
Use Of Data

scholarly journals Mathematical Issues in Data Science and Applications for Health care

2019 ◽  
Author(s):  
Sitti Zuhaerah Thalhah ◽  
Mohammad Tohir ◽  
Phong Thanh Nguyen ◽  
K. Shankar ◽  
Robbi Rahim
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Use Of Data ◽  
Source Data ◽  
Military Applications

For development in military applications, industrial and government the predictive analytics and decision models have long been cornerstones. In modern healthcare system technologies and big data analytics and modeling of multi-source data system play an increasingly important role. Into mathematical models in these domains various problems arising that can be formulated, by using computational techniques, sophisticated optimization and decision analysis it can be analyzed. This paper studies the use of data science in healthcare applications and the mathematical issues in data science.

Improving the use of analytics and big data by changing the decision-making culture

2017 ◽  
Vol 55 (10) ◽  
pp. 2074-2088 ◽  
Author(s):  
Jane Elisabeth Frisk ◽  
Frank Bannister
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Design Approach ◽  
Action Research Project ◽  
Content Type ◽  
Use Of Data ◽  
Support Organizations ◽  
Novel Approach ◽  
Using Data

Purpose Evolving digital technologies continue to enable new ways to collect and analyze data and this has led some researchers to claim that skillful use of data analytics and big data can radically improve a company’s performance, but that in order to achieve such improvements managers need to change their decision-making culture and to increase the degree of collaboration in the decision-making process. The purpose of this paper is to create an increased understanding of how a decision-making culture can be changed by using a design approach. Design/methodology/approach The paper presents an action research project in which the authors use a design approach. Findings By adopting a design approach organizations can change their decision-making culture, increase the degree of collaboration and also reduce the influence of power and politics on their decision-making. Research limitations/implications This paper proposes a new approach to changing a decision-making culture. Practical implications Using data analytics and big data, a design approach can support organizations change their decision-making culture resulting in better and more effective decisions. Originality/value This paper bridges design and decision-making theory in a novel approach to an old problem.

Data Analytics in Human Resources

2016 ◽  
Vol 15 (4) ◽  
pp. 487-495 ◽  
Author(s):  
Kylie Goodell King
Keyword(s):  
Human Resources ◽  
Human Resource Development ◽  
Human Resource ◽  
Data Analytics ◽  
Review Of Literature ◽  
Human Resource Professionals ◽  
Organizational Benefits ◽  
Use Of Data ◽  
Management And Development

The use of data analytics in the field of human resource development is becoming increasingly common. This rise in popularity is accompanied by skepticism about the ability of human resource professionals to effectively utilize data analytics to reap organizational benefits. This article provides a review of literature both supportive and critical of human resource analytics, argues for the involvement of academia in implementing analytical practices, and uses a case study to illustrate how quantitative tools may positively influence the management and development of human resources.

scholarly journals Data-driven Application of PHM to Asset Strategies

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Sarah Lukens ◽  
Matt Markham
Keyword(s):  
Data Analytics ◽  
Failure Modes ◽  
Health Management ◽  
Data Driven ◽  
Work Processes ◽  
Industrial Companies ◽  
Use Of Data ◽  
Reliability Centered Maintenance ◽  
Daunting Challenge

There are many benefits from implementing a prognostics and health management (PHM) initiative in an industrial facility, such as realizing potentials from reducing unplanned downtime and increased asset efficiency. Many industrial companies would like to take advantage of PHM technologies and algorithms to meet their business objectives, but identifying how to get started can be a daunting challenge. The classical approach is to begin with a Reliability Centered Maintenance (RCM) program supported by failure modes and effects analysis (FMEA) where all possible failure modes, their risks, and mitigating actions are evaluated in the context of asset function. In this framework, application of PHM technologies is viewed as a maintenance strategy effective at mitigating certain failure modes in specific cases that are both feasible and costeffective. However, there are many challenges and limitations to traditional RCM where data-driven analytics embedded in these work processes can help overcome and/or automate. On the other hand, the use of data-driven approaches introduces new challenges surrounding available data, data quality, and identifying numerical methods that are scalable across large datasets. In this paper, we present a case study applied to historical maintenance data for identifying and prioritizing where to start a PHM initiative, and discuss the work processes and various challenges encountered when embedding data analytics in classical reliability approaches.

scholarly journals Data analytics in education: are schools on the long and winding road?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mara Soncin ◽  
Marta Cannistrà
Keyword(s):  
Data Analytics ◽  
School Principals ◽  
Participant Observation ◽  
Central Administration ◽  
Efficient System ◽  
Content Type ◽  
Use Of Data ◽  
Data Scientist ◽  
Concrete Solution ◽  
Effective Use

Purpose This study aims to investigate the organisational structure to exploit data analytics in the educational sector. The paper proposes three different organisational configurations, which describe the connections among educational actors in a national system. The ultimate goal is to provide insights about alternative organisational settings for the adoption of data analytics in education. Design/methodology/approach The paper is based on a participant observation approach applied in the Italian educational system. The study is based on four research projects that involved teachers, school principals and governmental organisations over the period 2017–2020. Findings As a result, the centralised, the decentralised and the network-based configurations are presented and discussed according to three organisational dimensions of analysis (organisational layers, roles and data management). The network-based configuration suggests the presence of a network educational data scientist that may represent a concrete solution to foster more efficient and effective use of educational data analytics. Originality/value The value of this study relies on its systemic approach to educational data analytics from an organisational perspective, which unfolds the roles of schools and central administration. The analysis of the alternative organisational configuration allows moving a step forward towards a structured, effective and efficient system for the use of data in the educational sector.

scholarly journals Insurance pricing discrimination and Aristotelian equality: an application to annuity pricing

2021 ◽  
Author(s):  
David A. Cather
Keyword(s):  
Social Justice ◽  
Mortality Rate ◽  
Medical History ◽  
Data Analytics ◽  
Black Americans ◽  
White Americans ◽  
International Courts ◽  
Insurance Pricing ◽  
Use Of Data ◽  
The Social

International courts often apply the social justice standard of Aristotelian equality—treating like people alike and unlike people differently—to cases involving insurance pricing discrimination. This article examines whether the use of insurance pricing variables like gender and race results in discriminatory pricing categories consisting of heterogeneous policyowners, in violation of Aristotelian equality. This article applies this discrimination standard to the pricing of annuities, drawing from studies investigating the racial mortality crossover, findings that show that the mortality rate of Black Americans falls below the rate of White Americans at advanced ages. Based on the crossover literature, this study demonstrates how race-based annuity pricing would be discriminatory because it results in heterogeneous pricing within racial pricing categories, but that insurers can control for this heterogeneity by using the wider variety of annuity pricing data (e.g., medical history, diseases, and smoking) developed in the enhanced annuity submarket. The article demonstrates how the increased use of data analytics in insurance pricing to control for heterogeneity is consistent with Aristotelian equality.

scholarly journals Balancing Privacy Vs Efficiency in Data Analytics using Nearest Neighbour Randomization

2019 ◽  
Vol 8 (12) ◽  
pp. 2289-2295
Keyword(s):  
Data Analytics ◽  
Large Scale ◽  
Personal Data ◽  
Privacy And Security ◽  
Computing Platform ◽  
Use Of Data ◽  
Digital Era ◽  
Considerable Research ◽  
Group Data ◽  
Security Practices

The Digital era marked by the unrivalled growth of Internet and its services with day-to-day technological advancements has paved way for a data driven society. This digital explosion offers opportunities for extracting valuable information from collected data, which are used by organizations and research establishments for synergistic advantage. However, privacy of online divulged data is an issue that gets overlooked as a consequence of such large-scale analytics. Although, privacy and security practices conjointly determine the ethics of data collection and its use, personal data of individuals is largely at risk of disclosure. Considerable research has gone into privacy preserving analytics, in the light of Big Data and IoT boom, but scalable and efficient techniques, that do not compromise the usefulness of privacy constrained data, continues to be a challenging arena for research. The proposed work makes use of a distance-based perturbation method to group data and further randomizes data. The efficacy of perturbed data is evaluated for classification task that gives results on par with the non-perturbed counterpart. The relative performance of the algorithm is also evaluated on the parallel computing platform Spark. Results show that the technique does not hinder the use of data for holistic analysis while privacy is subjectively maintained.

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