Benefits Of Big Data Research Articles

Benefits of Big Data in Supporting Better Educational Decision Making

Big data plays an increasingly important role in education, offering great potential to improve educational decision making. Big Data collects, stores, and analyzes large amounts of and diverse data at very high speeds. In the educational context, big data collects data from various sources, including school management systems, student academic records, student satisfaction surveys, and online data. Analysis of this data can provide valuable information to decision makers in the education sector to identify relevant trends, patterns and opportunities. This research discusses the great benefits that can be gained from using Big Data in an educational context to support better decision making. Through research on the educational benefits of big data in supporting better decision making, it is hoped that this will provide an excellent opportunity to improve decision making in education. Through careful data analysis, education can become more effective, efficient and relevant to better meet the needs of students and society. The method used in this research is a quantitative method. Researchers conducted a survey using a Google form consisting of 15 statements related to the title of the research. Researchers found that using big data in education provides great opportunities to improve better educational decision making. And supported by careful selection as material for consideration. The limitation of this research is that the researcher only conducted research in schools and the researcher did not conduct research directly in schools but shared a survey link on a Google form containing a statement about the benefits of big data in supporting better educational decision making.

Big Data in online education

From classifying learners to predicting learner behaviour, the application of Big Data in online education has been vast. Besides the potential benefits of Big Data in education, it is necessary to critically engage with some ethical and social challenges that Big Data presents to the field of online learning. The increasing use of big data by large institutional actors and corporations raises questions not only about data privacy and ownership, but whether this data is used to genuinely improve learner and teacher online learning experiences, or primarily for commercial profits and institutional benefits. When addressing ethical concerns regarding the use of Big Data in education, critiques often follow a reasoning that is in line with corporate interests and neoliberal logic of marketization of education. Given the importance of the pursuit for democratic online education, the need for critical perspectives in the field is ever-more essential. This research tries to critically address the role and impact of Big Data on labour relations and economic fairness in online education by examining both corporate and institutional data practices in online learning. The study puts forward a provisional theory of the use of Big Data in two large online learning platforms (Coursera and Blackboard) using critical grounded theory. The core category of Exploitation of the learning community, the three constituent concepts; the Vendor-Institutional Complex, Use of learner generated value for profit, and the Behavioral monitoring and engineering; and the sustaining category, the Magic Trick, were the foundational blocks for developing an emancipatory theory that addressed ethical issues of economic fairness regarding the use of big data in online education.

BIG DATA-DRIVEN DECISION MAKING IN PROJECT MANAGEMENT: A COMPARATIVE ANALYSIS

This study investigates the impact of big data-driven decision-making in construction project management through a qualitative comparative analysis. By conducting semi-structured interviews with project managers, data analysts, and construction workers across various types of construction projects, the research identifies key themes related to the benefits and challenges of integrating big data analytics. The findings highlight significant advantages such as enhanced operational efficiency, improved decision-making processes, cost reduction, budget management, timely project delivery, and quality control and assurance. However, challenges including data integration complexities, privacy concerns, the need for specialized skills, and organizational resistance to change are also revealed. The study underscores the importance of fostering a data-driven culture and strong leadership support to maximize the benefits of big data in construction project management, while also emphasizing the need for context-specific strategies tailored to different project types.

The Relevance and Challenges of Big Data for Leadership

This research is a study of ‘The Relevance and challenges of Big Data for Leadership’. The purpose of this study is to examine the level of understanding, knowledge, and skill of leadership to drive a data driven organisation and to determine the shortcomings for corrective measures. The research involves an exploratory study of the leadership needs and abilities for effectively leading in the big data environment. Method used include literature review and qualitative analysis of interviews of people at different levels of leadership, as primary research. The analysis is a combination of a deductive and an inductive approach, and the themes help analyse big data benefits and challenges for leadership, and the leadership style adopted by leaders. Findings reveal the underlying issues of lack of skill and knowledge among leaders. The findings enable organisations and leaders understand the gaps in using data efficiently, and recommendations help to take measures to improve effectiveness. Big data and leadership are under explored and there is scope for continuing research in these areas.

Використання великих даних для інформаційного забезпечення ринку праці

The challenges of collecting current labor market data amidst global changes and geopolitical fluctuations increasingly necessitate new approaches and alternative methods of data collection and analysis. This highlights the relevance of research aimed at developing and adapting labor market information provision methods to contemporary challenges. A promising approach in this context is the use of Big Data for labor market assessment, which involves collecting information from sources such as online job search and vacancy portals. This method allows for a deeper analysis of market trends and provides a more accurate and timely assessment of labor market needs and opportunities. The aim of this article is to discuss approaches to developing labor market information systems using Big Data, particularly online data from job vacancy websites. It examines the use of Big Data in labor market analysis based on a database containing over four million job vacancies posted on Ukrainian job search portals over the last five years, provided by the European Training Foundation (ETF). The effectiveness of these approaches in facilitating job search for all interested parties is evaluated, particularly through providing insights into the dynamics of supply and demand in the labor market based on data from these portals. The opportunities and limitations of using Big Data in this context are analyzed, including their impact on employment policy development and labor market planning. The potential benefits of Big Data in providing deeper and more accurate market condition analyses are outlined, along with technical aspects and challenges associated with their processing and interpretation. The article examines methodological approaches to data collection and analytical processing in the context of accelerated transformations, volatility, and limited access to traditional information resources. The scientific novelty of the article lies in the substantiation of the feasibility and appropriateness of using open data from online job portals for labor market information provision under current conditions. In conducting the research, methods of analysis, synthesis, and generalization were applied to identify the main contemporary issues of labor market information provision in Ukraine. The effectiveness of data collection methods based on web scraping and parsing techniques was evaluated, as well as the use of the integrated Snowflake platform to identify key trends and patterns in the labor market. The conclusions summarize the main points and substantiate directions for further research, highlighting the significance of Big Data in developing employment strategies and optimizing the labor market.

Impact of Big Data Analytics on Digital Marketing: Academic Review

Strategic decisions require the use of analytics and big data technologies. Previous study has focused on big data applications, ethics, benefits, drawbacks, and analytical viewpoints, among other things. The goal of this study is to conduct a comprehensive literature assessment in these areas and to fill any research gaps on the impact of big data analytics on digital marketing approaches. We attempted to cover as many as 200 articles, news, publications, and other portals to investigate studies conducted from the past to the present. As a result, this study evaluated the current literature on big data applications and discovered that digital marketing is a vast sector in which big data has a considerable influence on the creation of digital advertising strategies and how advertising is influenced by big data. Using the greatest big data applications, according to past research, can assist selected organizations in overcoming severe limits during one of the world's most catastrophic pandemics. This outcome will benefit academics and industry in two ways: first, the experimental output will navigate the state of mind on the relationship between digital marketing, digital advertising, and big data analytics; second, the data-based result will improve the ability to think more creatively in the future with other industry affiliations.

أخلاقيات الذكاء الاصطناعي: بحث في المعضلات الأخلاقية المرتبطة بالذكاء الإصطناعي

The significance of ethics in artificial intelligence (AI) cannot be overstated, as it encompasses the foundational principles guiding the responsible creation, deployment, and management of AI technologies. As AI systems increasingly permeate every facet of our lives—from healthcare and education to security and entertainment—their decisions and actions have profound implications not only on individual rights and privacy but also on societal norms and values. Ethical considerations in AI are paramount to ensure that these technologies enhance human well-being, uphold fairness, and protect freedoms, rather than perpetuate biases, exacerbate inequalities, or undermine democratic institutions. The importance of AI ethics lies in its ability to provide a framework for navigating the complex moral dilemmas presented by AI, such as the balance between innovation and regulation, the protection of individual privacy versus the benefits of big data, and the prevention of AI misuse. By foregrounding ethical principles, stakeholders—including developers, policymakers, and users—can work towards the development of AI technologies that are not only technologically advanced but also socially responsible and aligned with human values. This emphasis on ethics ensures that as AI systems become more autonomous and integral to our daily lives, they do so in a manner that is transparent, accountable, and inclusive, thereby fostering trust and confidence in their widespread adoption and use.

Caught in a Landslide? Exploring how Far the Increasing Focus on Big Data Benefits or Damages Theoretical Development in Management Studies

AbstractThe author teams in this Point‐Counterpoint (PCP) put forward contrasting views regarding the benefits – or otherwise – of using commercially generated corporate ‘big data’ algorithms to inform scholarly research. In this editorial, I reflect on the lines of reasoning for, and against, whether such data offers a reliable means of building new theory. Are academics who refuse to mine and analyse corporately owned big data taking sensible steps to manage scholarly integrity? Or are they Luddites? I invite readers to consider these timely and provocative PCP articles and to consider the implications, for management studies, of the key arguments presented.

Big data to guide glaucoma treatment.

Ophthalmology has been at the forefront of the medical application of big data. Often harnessed with a machine learning approach, big data has demonstrated potential to transform ophthalmic care, as evidenced by prior success on clinical tasks such as the screening of ophthalmic diseases and lesions via retinal images. With the recent establishment of various large ophthalmic datasets, there has been greater interest in determining whether the benefits of big data may extend to the downstream process of ophthalmic disease management. An area of substantial investigation has been the use of big data to help guide or streamline management of glaucoma, which remains a leading cause of irreversible blindness worldwide. In this review, we summarize relevant studies utilizing big data and discuss the application of the findings in the risk assessment and treatment of glaucoma.

Implementing Electronic Medical Records Through Big Data in Healthcare Facilities

Accessibility of information is one of the main benefits of implementing electronic medical records in the world of health. Through electronic medical records, medical information can be accessed quickly, efficiently, and integrated by authorized parties, bringing positive changes in health data management. This study aims to determine the implementation of electronic medical records through big data in health facilities. The research method used a literature study using data based on EBSCOhost, Science Direct, Proquest, and Google Scholar. Inclusion criteria included: 1) Articles in Indonesian and English; 2) Full-text articles; 3) Articles published in 2017-2023. Keywords have been adjusted to Medical Subject Heading (MeSH), namely: “Big Data AND Electronic Medical Records”. The retrieved articles were manually selected and reviewed from the title, keywords section, abstract, and the main context of the articles. The results of a literature study from the database showed that 5 relevant studies related to the implementation of electronic medical records through big data in health facilities have potential opportunities. Potential opportunities for the benefits of big data in health services are divided into 4 groups, including: 1)The quality of health services increased; 2) Supporting health workers in services; 3) Supporting research and scientific activities; 4) Management and business. The implementation of electronic medical records through big data in health services can provide potential opportunities including increased quality of health services, supporting health workers in services, supporting research and scientific activities, as well as management and business.

DATA MANAGEMENT AND THE CONCEPT OF BIG DATA IN SUSTAINABILITY REPORTING

This article explores the interdependence between management control systems, sustainable development integration, and financial reporting. It highlights the necessity of incorporating Big Data and Big Data Analytics to enhance informed decision-making, especially in the context of sustainability reporting. The study emphasizes the benefits of Big Data, including improved efficiency, risk reduction, and competitive advantages, while also addressing the challenges such as data fragmentation and the difficulty of interpreting unstructured data. Recommendations include investing in education and training to develop necessary skills and fostering data partnerships to achieve sustainability goals. The research provides practical insights and empirical evidence on the successful integration of Big Data into management systems for enhanced organizational performance and sustainable development.

Leveraging Big Data Analytics for Understanding Consumer Behavior in Digital Marketing: A Systematic Review

The integration of big data analytics in digital marketing has fundamentally transformed how organizations understand and influence consumer behavior. This systematic review explores the potential of big data to offer deep insights into consumer preferences and behaviors. The current literature on big data and consumer behavior showcases its potential in enhancing marketing and customer experiences. However, significant gaps exist, such as longitudinal studies on how continuous big data–driven personalization affects consumer trust and loyalty over time. Addressing these gaps will advance academic understanding and offer practical insights for optimizing marketing strategies and improving customer experiences ethically. Focusing on these areas will contribute to a holistic view of big data’s responsible use in digital marketing. By utilizing large datasets, businesses can now tailor their marketing strategies to individual consumers, enhancing customer satisfaction and engagement. Adopting the PRISMA methodology, this review synthesizes recent literature to evaluate the benefits of big data in digital marketing. The research was conducted through a rigorous five‐stage process, encompassing the identification of key research questions, database searches, and the critical analysis of selected articles: (1) defining the initial topic, (2) developing the appropriate research questions, (3) identifying the necessary keywords, (4) identifying and searching databases, and finally (5) accessing and reading the articles. The databases that were searched were as follows: Scopus, Web of Science, Emerald Insight, Springer Link, and ScienceDirect. The articles that were selected were 19, in a total sum of 265 identified articles. The findings consolidate current knowledge on how big data analytics can optimize marketing strategies and consumer experiences. Ultimately, this review underscores the transformative potential of big data in digital marketing, highlighting its role in enhancing customer insights and driving more effective marketing strategies.

Big data and machine learning in cost estimation: An automotive case study

This paper presents a case study on the applicability of machine learning and big data technology for product cost estimation, using data on the material cost of passenger cars. The study provides contributions to six research aspects. First, we show which machine learning algorithms are appropriate when dealing with product cost estimation for highly complex products with more than 2000 parts and hundreds of cost drivers. Second, our case study provides a novel approach to increasing the predictive accuracy of cost estimates for subsequent product generations. Third, we show that the accuracy is up to 3.5 times higher when using big data compared to an intermediate size of data. Fourth, machine learning can outperform cost estimates from cost experts during the early stage of new product development, even when dealing with highly complex products. Then, we evaluate the use cases, issues, and benefits of machine learning and big data from the perspective of cost experts. Specifically, the case study shows that machine learning can reliably select the most important cost drivers (fifth aspect) and calculate the average cost of cost drivers over thousands of product configurations (sixth aspect). However, cost experts must be knowledgeable about the product and remain careful when interpreting machine learning outcomes, as they can yield misleading outcomes in some exceptional cases. In conclusion, machine learning and big data empirically proved to be able to generate additional value in many aspects for managing costs during the early phase of new product development.

Relay learning: a physically secure framework for clinical multi-site deep learning

Big data serves as the cornerstone for constructing real-world deep learning systems across various domains. In medicine and healthcare, a single clinical site lacks sufficient data, thus necessitating the involvement of multiple sites. Unfortunately, concerns regarding data security and privacy hinder the sharing and reuse of data across sites. Existing approaches to multi-site clinical learning heavily depend on the security of the network firewall and system implementation. To address this issue, we propose Relay Learning, a secure deep-learning framework that physically isolates clinical data from external intruders while still leveraging the benefits of multi-site big data. We demonstrate the efficacy of Relay Learning in three medical tasks of different diseases and anatomical structures, including structure segmentation of retina fundus, mediastinum tumors diagnosis, and brain midline localization. We evaluate Relay Learning by comparing its performance to alternative solutions through multi-site validation and external validation. Incorporating a total of 41,038 medical images from 21 medical hosts, including 7 external hosts, with non-uniform distributions, we observe significant performance improvements with Relay Learning across all three tasks. Specifically, it achieves an average performance increase of 44.4%, 24.2%, and 36.7% for retinal fundus segmentation, mediastinum tumor diagnosis, and brain midline localization, respectively. Remarkably, Relay Learning even outperforms central learning on external test sets. In the meanwhile, Relay Learning keeps data sovereignty locally without cross-site network connections. We anticipate that Relay Learning will revolutionize clinical multi-site collaboration and reshape the landscape of healthcare in the future.

App design based on the protection of personal privacy information in China

In today's world, digital technology is rapidly evolving and the way people live their lives is changing dramatically. For example, tools such as computers, the internet and mobile phones are bringing us new experiences like never before, while digital technology is also bringing unprecedented advances and innovations to business and science. New opportunities are growing and traditional hierarchies are beginning to fall apart - and with them comes a breakdown in trust. Google, Instagram, Wechat, Xiaohongshu - these disruptive platforms,networks and technologies have changed the current status of their respective industries, making our lives easier while there are bad actors in the unseen grey areas stealing and exploiting our privacy and data. The emergence of COVID-19 in recent years has forced us to use and rely on digital devices with high frequency, especially in China, which has accelerated our transition to a digital world, with both advantages and disadvantages. This article will explore how to better protect the privacy and data of digital nomads within reasonable and lawful limits, and provide an idea as a solution. The article presents the idea of a data protection app called Data Butler, which is an application dedicated to protecting the privacy of its users. The aim is to enable users to enjoy the benefits of big data while protecting their private data. Data Butler is designed to enable users to enjoy the benefits of big data while their private data is protected.

Big geochemical data through remote sensing for dynamic mineral resource monitoring in tailing storage facilities

Evolution in geoscientific data provides the mineral industry with new opportunities. A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity. This direction is more significant where traditional geochemical data are not ideal, which is the case for evaluating unconventional resources, such as tailing storage facilities (TSFs), because they are not static due to sedimentation, compaction and changes associated with hydrospheric and lithospheric processes (e.g., erosion, saltation and mobility of chemical constituents). In this paper, we generate big secondary geochemical data derived from Sentinel-2 satellite-remote sensing data to showcase the benefits of big geochemical data using TSFs from the Witwatersrand Basin (South Africa). Using spatially fused remote sensing and legacy geochemical data on the Dump 20 TSF, we trained a machine learning model to predict in-situ gold grades. Subsequently, we deployed the model to the Lindum TSF, which is 3 km away, over a period of a few years (2015-2019). We were able to visualize and analyze the temporal variation in the spatial distributions of the gold grade of the Lindum TSF. Additionally, we were able to infer extraction sequencing (to the resolution of the data), acid mine drainage formation and seasonal migration. These findings suggest that dynamic mineral resource models and live geochemical monitoring (e.g., of elemental mobility and structural changes) are possible without additional physical sampling.

A Comprehensive Study of Various Big Data Analytic Techniques and its Benefits

Abstract: Analytics plays significant role in all areas in when making decisions based on particular facts. Big Data analytics has currently arisen as a vital research area because of the admiration of the Internet and the arrival of the different web technologies. Additionally, the widespread use of social media applications has presented both numerous opportunities and difficulties for scholars and practitioners. The combination of users' background information and everyday activity results in the enormous amount of data that is generated by users of social media sites. This huge volume of generated data called Big Data which has been intensively examined presently. A review of the current works is accessible to acquire an extensive perception of the Big Data analytics research area. Furthermore, this study also discusses potential benefits of Big Data analytics techniques as well as their quality features.

The application of big data and the analysis of advantages and disadvantages in its development

Today, all Internet technologies are being constructed on the basis of big data, the so-called Internet of Everything, which is said to be a comprehensive service for individuals, facilitating peoples life, enriching peoples brain, and improving the quality of life and work efficiency. On the other hand, Restrictive laws and regulations have not been developed and improved along with big data, but rather lag behind. This has resulted in loopholes in the big data industry, giving all the bad people the opportunity to take advantage of personal data reselling, privacy disclosure, and even the use of big data to influence personal habits, such as daily work and rest, shopping and consumption, education and cultural beliefs. This paper lists the benefits of big data to various groups, as well as the hidden problems in the development of this technology. Through literature analysis, this paper finds that big data is a great threat to the privacy and security of the masses. At the end of the paper, some constructive suggestions are put forward to protect public privacy.

Pemanfaatan Big Data dalam Pembelajaran Jarak Jauh (PJJ) selama Pandemi pada Daerah 3T (Terdepan, Terpencil dan Tertinggal)

The COVID-19 pandemic has prompted the adoption of distance education worldwide, including in 3T regions facing challenges of limited infrastructure and accessibility. The utilization of Big Data holds great potential in enhancing the quality and accessibility of education in these 3T regions. This study aims to analyze the utilization of Big Data in distance education during the pandemic in 3T regions, identify its benefits in personalized learning, monitoring the learning process, and predicting student needs, and provide recommendations for further research. The research approach employed data analysis from a survey involving 500 schools in 3T regions. Data was collected through questionnaires that encompassed the aspects of Big Data usage in distance education and its impact on educational quality in 3T regions. Data analysis reveals that the utilization of Big Data in distance education in 3T regions has significantly contributed to student data collection and analysis, monitoring the learning process, and predicting student needs. These findings are consistent with previous research that highlights the benefits of Big Data in personalized learning and educational performance management. The utilization of Big Data in distance education in 3T regions during the pandemic holds immense potential in improving the quality and accessibility of education. Adequate training and education should be provided to teachers and school staff regarding the utilization of Big Data.

From Big Data Technologies to Big Data Benefits

In this article, we present an approach used in Vestas Wind Systems A/S for managing the realization of potential benefits from big data technologies. This approach involves dependency networks representing how different domains in a big data analytics project support business benefits.