A Framework for Managing Big Data in Enterprise Organizations

Ahmed, Youssef; Medhat, Walaa; Shishtawi, Tarek El

doi:10.4018/ijskd.2020010105

Cited by 11 publications

(2 citation statements)

References 17 publications

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“…The study of ElHamlaoui et al 3 entitled "Big Data Project Management" This study aimed to identify project management approaches and big data projects based on works in the field, with explanations of the use of traditional and rapid project management methods, in particular, big data management projects with citing and identifying various methods of managing big data, and providing some recommendations for the use of agile project management for the successful start of the big data project, with case studies and their results.…”

Section: Previous Studiesmentioning

confidence: 99%

The Reality of the Awareness of the Workers in the National Health Information Center of the Importance of Big Data: A Survey Study

Selmi

Alqarni

2022

Comm. Math. Appl.

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This study aimed at identifying the reality of the awareness of workers in the National Health Information Center of the importance of big data. It used the descriptive approach (survey study) through the personal interview tool with the aim of collecting data to identify the views of the study sample about the main question of the study, which is "What is the reality of workers' perceptions in the National Health Information Center of the importance of big data?"From this main question the following sub-questions derive: First, "What is the awareness of workers at all administrative levels of the concept and importance of managing and analyzing big data?". Second, "What is the importance of managing big data in the National Health Information Center?". Third, "What value does managing big data add to the National Health Information Center?". Out of This third question, emerge the following sub-questions: First, "What is the value of big data in the National Health Information Center follow-up on e-health projects?". Second, "What is the value of big data in decision support for health policy-making?". Third, "What value does big data add to support health innovation?". Fourth, "What value does big data add to enhance clinical trials?". Fifth, "What is the investment value of big data?" The study community consisted of the National Health Information Center, and the study sample was the intentional sample, which consisted of officials, administrative and technical staff directly related to dealing with big data in the center. In its results, the study found a difference in the awareness of the workers in the center of the importance of big data, according to the administrative levels they occupy. The strategic position of the center and its role in making health decisions at the national level and the huge volumes of data it contains and produces illustrate the importance of managing big data in the center. The value added by the management of big data in the center is namely assistance in following up on e-health projects, assisting decision-makers in making health policies. In addition, it is considered one of the most important tributaries to support the process of health innovation and health research, to enhance the aspect of clinical trials, and in investment. In light of these results, the study recommended raising awareness among the center employees through holding seminars and specialized workshops, directing the health sectors and relevant authorities and guiding them with the basic rules for managing and preserving big data, completion of operational plans to actually launch the investment phase of big data.

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Section: Previous Studiesmentioning

confidence: 99%

The Reality of the Awareness of the Workers in the National Health Information Center of the Importance of Big Data: A Survey Study

Selmi

Alqarni

2022

Comm. Math. Appl.

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“…Optimizationreliesonefficiencyinthenecessaryresources,itsperformanceisdirectlysubject tothematchingextentbetweenavailableresourcesandthegivenbusinessprocess.Toguaranteethe matchingqualityandthebusinessprocessfulfilment,itisessentialtoplantheprocessexecutionand resourceallocationatbuildtime.Increasingbusinessefficiencyincludesanincreaseintheutilisation ofcompanyresources,improvementofworkingconditionsandcustomersatisfaction.Information technology (IT) has been used to manage BPs to deal with these challenges (Davenport, 1993) includingusageofbigdata (Ahmed et al, 2020) H4:Firmhedgingoffunctionalriskhasadirect,positive,andsignificantrelationshipwithfirm's abilitytoensurebusinesscontinuity…”

Section: Hypothesis Formulationmentioning

confidence: 99%

Business Continuity During Adversity and Strategies to Revive Certain Sectors

Singh

Jain

2021

International Journal of Sociotechnology and Knowledge Development

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The study investigates the factors influencing business continuity during adversities like COVID-19. It further sheds light on priorities for preparedness measures that need to be taken to ensure continuity during these adversities. Exploratory research was conducted in the form of focussed interviews with 20 senior management industry professionals, and these were analyzed using N-Vivo, and four important determinants of business continuity were identified based on which a research model was conceptualized using business continuity as dependent variable and others as independent variables. The model was further tested using quantitative research. For this purpose, a questionnaire was prepared, and a total of 200 responses were collected representing 26 sectors. These responses were analyzed using factor and variance analysis, and a multiple regression analysis was performed to test the role of these variables on business continuity. It was further concluded that the factors that ensure business continuity differ according to the industry in which the business is operating.

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Research on enterprise economic efficiency assessment and management decision-making based on big data analysis

Du,

Sun

2024

Applied Mathematics and Nonlinear Sciences

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Enterprise economic efficiency is an important indicator for measuring the input and output of enterprises, and it has a certain guiding role in the formulation of enterprise management decisions. This paper constructs an enterprise economic efficiency assessment model using big data analysis and explores the application of big data analysis in enterprise management decision-making. First, we extract the principal component factors for measuring enterprise economic efficiency indexes using principal component analysis, and then construct the comprehensive evaluation function of enterprise economic efficiency based on this. The particle swarm algorithm is used to optimize the BP neural network, and the PSO-BP neural network model is constructed to intelligently evaluate the economic efficiency of the enterprise. This paper extracts three principal components of enterprise economic efficiency, namely “enterprise solvency and profitability,” “enterprise operating ability,” and “net asset growth ability,” and achieves a cumulative variance explanation rate of 84.431%. The PSO-BP neural network model and the standard BP neural network model satisfy the error criterion of MAPE<0.001 when they are trained about 170 times and 820 times, respectively, and the number of operations of the former is only about 20.73% of that of the latter, which greatly shortens the running time of the model and indicates that the PSO-BP-based enterprise economic efficiency assessment model in this paper is practical. Practicality. This paper serves as a reference for the application of big data analysis in enterprise economic benefit assessment and management decision formulation.

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A Framework for Managing Big Data in Enterprise Organizations

Cited by 11 publications

References 17 publications

The Reality of the Awareness of the Workers in the National Health Information Center of the Importance of Big Data: A Survey Study

The Reality of the Awareness of the Workers in the National Health Information Center of the Importance of Big Data: A Survey Study

Business Continuity During Adversity and Strategies to Revive Certain Sectors

Research on enterprise economic efficiency assessment and management decision-making based on big data analysis

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