Potential Of Big Data Analytics Research Articles

The Role of Data Analytics in Digital Transformation: A Study of how Firms Leverage Data for Insights

Big data helps organisations undergo a digital transition. The majority of businesses rely on big data to enhance the dependability, velocity, calibre, and effectiveness of their corporate operations. Large volumes of information are known to be collected, stored, and maintained by big data, which gives businesses a competitive edge. The main research gap addressed by previous writing studies is the lack of a thorough analysis on the use of massive amounts of data for computerised transformation. This is addressed by looking at the key benefits, opportunities, and challenges that big data poses to businesses as they cautiously alter their structure. This research advances knowledge about big data in general by employing a case study technique involving Google and a comprehensive survey of the literature. The study's findings demonstrate how companies work together to leverage the potential of big data analytics, especially for profitability, market expansion, decision-making, and information synthesis. Furthermore, this study recommends the use of business intelligence strategies and long-term planning methodologies to guarantee data protection.

Big Data in Pharmacy: Transforming Patient Care with Analytics

The integration of big data analytics in pharmacy is revolutionizing patient care by providing insights that enhance medication management and therapeutic outcomes. By leveraging vast amounts of health data, pharmacists can deliver more personalized treatment and optimize drug therapy. Despite the potential benefits, existing methods in pharmacy often suffer from data silos, lack of interoperability, and insufficient analytical capabilities, leading to suboptimal patient outcomes and inefficient medication management. These limitations hinder the effective utilization of data for real-time decision-making in patient care. To address these challenges, it propose the Patient Care Transformation using Big Data Analytics (PCT-BDA) framework, which facilitates a holistic approach to patient data integration and analysis within smart grid systems. This framework enables seamless data flow between various healthcare entities, improving collaboration and fostering a comprehensive view of patient health profiles. The proposed method emphasizes real-time analytics, predictive modeling, and machine learning algorithms to enhance decision-making processes in medication management. By utilizing big data analytics, pharmacists can better predict patient responses, identify potential drug interactions, and tailor therapy plans to individual needs. Preliminary findings from the implementation of the PCT-BDA framework indicate significant improvements in patient outcomes, including reduced medication errors, enhanced adherence to treatment protocols, and overall increased satisfaction with pharmacy services. This innovative approach highlights the transformative potential of big data analytics in optimizing patient care in the pharmacy sector.

An Overview of Characteristics and Application of Big Data

Big data is huge volume, heterogeneous, appropriated data. Big data applications where data gathering has developed ceaselessly, it is costly to oversee, catch or concentrate and procedure data utilizing existing programming apparatuses. Clustering Technique for unstructured for big data is a primary errand of exploratory data investigation and data mining applications. This phenomenon, commonly referred to as Big Data, presents both opportunities and challenges across various domains. This paper provides a comprehensive overview of the characteristics and applications of Big Data. it delves into the characteristics of Big Data, including its volume, velocity, variety, veracity, and value. Understanding these characteristics is crucial for harnessing the full potential of Big Data analytics. the paper explores the diverse applications of Big Data across industries such as healthcare, finance, retail, manufacturing, and transportation.

Harnessing big data analytics to revolutionize ESG reporting in clean energy initiatives

The integration of Big Data Analytics into Environmental, Social, and Governance (ESG) reporting holds significant potential to revolutionize clean energy initiatives. This paper examines the current challenges in ESG reporting, such as data collection and integration, data quality, regulatory compliance, transparency, and standardization. Big Data Analytics offers advanced techniques, including machine learning, artificial intelligence, predictive analytics, and real-time data processing, which can address these challenges. By leveraging diverse data sources—both structured and unstructured—clean energy projects can achieve more accurate environmental impact assessments, social impact evaluations, and governance analyses. The applications of Big Data Analytics are exemplified through various case studies, such as renewable energy projects, smart grids, and carbon offset programs. The benefits of these applications are vast, including enhanced data accuracy, improved decision-making, increased transparency, better stakeholder engagement, and cost efficiency. However, implementing Big Data Analytics in ESG reporting also presents challenges like data privacy, technological requirements, skills shortage, ethical considerations, and system integration. The future of Big Data Analytics in ESG reporting looks promising with emerging trends like blockchain for data transparency, IoT and sensor technologies, advanced machine learning models, and collaborative platforms. This paper concludes with a call to action for stakeholders to embrace these technologies, highlighting the transformative potential of Big Data Analytics in creating more effective and accountable clean energy initiatives.

A Review of Leveraging Big Data Analytics for Sustainable Supply Chain Management

As organizations aim to drive sustainability in their supply chain management practices, big data analytics has emerged as one of the effective approaches. However,data security and privacy challenges remain since big data analytics continue evolving in the supply chain. These challenges need to be addressed by organizations to ensure business performance. This paper conducts systematic literature reviews of the potential of big data analytics in driving sustainability in supply chain management. The findings show that big data analytics enables organizations to make data-driven decisions, optimize business processes, and enhance sustainability through techniques like machine learning, predictive, and prescriptive analysis. Future research directions are suggested, focusing on addressing the challenges with appropriate strategies and exploring the impact of other Industry 4.0 technologies on sustainable supply chain management.

The Use Of Big Data Analytics In Healthcare Organization Management

Introduction. The potential of using big data analytics in the management system of healthcare organizations is of interest to healthcare managers, with a notable emphasis from the research community on the correlation between the incorporation of big data analytics and its advantages for healthcare organizations, particularly in terms of resource allocation and operational efficiency. The purpose of the study was to summarize scientific data characterizing the potential of big data analytics on healthcare organizations' management practices. Materials and Methods. A comprehensive search was conducted across PubMed, eLibrary, CyberLeninka and ScienceDirect databases, as well as the World Health Organization datasets, covering the use of big data analytics in healthcare. In this study, keyword and phrase searching on the following requests was performed: ”big data in healthcare organization management”, “big data analytics in healthcare”, “big data”, “analytics”, “management”, “healthcare”. Results. The study involved an in-depth analysis and consolidation of both general and specific aspects of the potential of big data analytics in the management practices of healthcare organizations. The benefits of big data analytics implementation in personnel management, as well as its efficiency in prognosing the number of referrals to emergency departments and tracking the prescription and intake of medications, were identified.

Is it about “them”? leveraging big data research to understand anti-immigrant discourse

The paper explores the potential of big data analytics for researching anti-immigrant discourse. We emphasize contextualization as an essential element of research and follow a hybrid approach inspired by best practices of computational content analysis, combining human hermeneutic expertise with supervised machine learning to classify a corpus of comments in online news communities in Singapore over 6 months ( N = 399,225). The paper highlights how big data analytics can provide a nuanced and critical apprehension of immigrant-related discourse in large social media datasets.

Enhancing sports education through big data analytics: Leveraging models for improved teaching strategies

The incorporation of big data analytics into the realm of sports education marks a pioneering shift towards leveraging advanced technological tools to refine teaching methodologies and elevate student performance to unprecedented levels. This article delves into the application of data-driven models that meticulously analyze a multitude of facets within sports education, ranging from student engagement and skill acquisition to comprehensive performance assessments. Utilizing sophisticated quantitative analysis techniques alongside robust mathematical models, this research meticulously evaluates the impact of embedding big data analytics into the core practices of sports education. The essence of this investigation lies in its rigorous analysis of how data-driven insights can be harnessed to inform and enhance coaching strategies, tailor training programs to individual athlete needs, and objectively evaluate performance metrics. By systematically analyzing large datasets that encompass performance statistics, physiological data, and even psychological assessments, educators and coaches can gain a deeper understanding of each student-athlete's unique profile, thereby facilitating more personalized and effective training interventions. The findings from this study highlight the transformative potential of big data analytics in sports education, illustrating how such technological integration can lead to more scientifically informed teaching strategies, optimized training regimens, and significantly improved outcomes for student-athletes. This research not only underscores the benefits of adopting a data-centric approach in sports education but also sets the stage for future innovations that could further revolutionize the field, making it more data-informed, precise, and tailored to the needs and potential of each individual athlete. Through this exploration, it becomes evident that the future of sports education and training lies in the effective integration of big data analytics, promising a new era of enhanced performance and educational excellence.

REVIEWING THE ROLE OF BIG DATA ANALYTICS IN FINANCIAL FRAUD DETECTION

Financial institutions grapple with the escalating nature of fraudulent activities, necessitating innovative and timely detection methods. The review underscores the transformative potential of Big Data Analytics, emphasizing its pivotal role in the ongoing fight against fraud. Delving into the specifics, the paper explores diverse data sources, such as transaction and user behavior data, alongside external data from sources like social media, employing machine learning algorithms and predictive modeling for anomaly detection and risk assessment. Real-time processing emerges as a critical component for swift and effective fraud identification. Critically addressing implementation challenges, including data quality assurance and privacy concerns, the paper showcases case studies of successful Big Data Analytics implementations, highlighting their positive impacts on fraud prevention and financial security. Looking ahead, the review anticipates the role of emerging technologies like blockchain and artificial intelligence in enhancing fraud prevention strategies, emphasizing integration with cybersecurity for robust defense against sophisticated attacks. The paper concludes with recommendations for financial institutions, advocating collaborative efforts and information sharing within the industry. In summary, the review underscores the transformative contributions of Big Data Analytics to financial fraud detection, shaping the future of fraud prevention strategies and fortifying the resilience of the global financial ecosystem..
 Keywords: Big, Data, Analytics, Finance, Fraud, Detection.

Leveraging big data and analytics for enhanced public health decision-making: A global review

In recent years, the proliferation of big data and analytics technologies has revolutionized various sectors, including public health. This review presents a comprehensive review of how leveraging big data and analytics has enhanced public health decision-making on a global scale. The review encompasses diverse applications, methodologies, challenges, and opportunities within this burgeoning field. Big data analytics in public health encompasses the collection, processing, and analysis of vast datasets from heterogeneous sources, including electronic health records, social media, wearable devices, and environmental sensors. These data sources offer valuable insights into disease patterns, risk factors, healthcare utilization, and population health trends. By applying advanced analytical techniques such as machine learning, predictive modeling, and data visualization, public health officials can extract actionable intelligence to inform decision-making processes. Several case studies highlight the efficacy of big data analytics in various public health domains. For instance, predictive modeling techniques have been utilized to forecast disease outbreaks, enabling timely resource allocation and intervention planning. Social media mining has facilitated real-time surveillance of public sentiment and health-related behaviors, aiding in targeted health promotion campaigns. Additionally, electronic health record analysis has facilitated personalized medicine initiatives and improved patient outcomes. Despite the significant potential of big data analytics in public health, several challenges exist. These include data privacy concerns, data quality issues, interoperability barriers, and the digital divide. Furthermore, ethical considerations regarding consent, transparency, and equity must be carefully addressed to mitigate potential biases and ensure responsible data usage. Looking ahead, the future of leveraging big data and analytics for public health decision-making appears promising. Advancements in data integration, interoperability standards, and artificial intelligence hold immense potential for improving the accuracy, timeliness, and relevance of public health interventions. Collaborative efforts between governments, academia, industry, and civil society are essential to harness the full potential of big data analytics in safeguarding population health and promoting well-being on a global scale.

THE NEXUS OF BIG DATA ANALYTICS, KNOWLEDGE SHARING, AND PRODUCT INNOVATION IN MANUFACTURING

In today‘s highly competitive business environments, manufacturers face stiff competition. As digital technologies have become more pervasive, many businesses in the manufacturing sector have begun to tap into the potential of big data analytics to gain an edge in their markets. Companies in the manufacturing sector can gain a significant competitive advantage by strategically utilizing big data analytics to uncover profound insights that have the potential to significantly enhance their capabilities in product innovation. This research delves into communication’s role as a go-between for big data analytics and product innovations’ success at manufacturing firms. The validity and reliability of the measurement scales were first thoroughly examined in this study. The research model was then tested using structural equation modeling and process macro analysis. The analytical findings unveil those big data analytics exert a pronounced, positive, and statistically significant impact on product innovation performance and information-sharing dynamics. Furthermore, it is discerned that information-sharing exerts a substantial and affirmative influence on the capacity for product innovation. Additionally, it is established that the impact of big data analytics on product innovation performance undergoes moderation by the information-sharing mechanism.

BIG DATA ANALYTICS: A REVIEW OF ITS TRANSFORMATIVE ROLE IN MODERN BUSINESS INTELLIGENCE

In the dynamic landscape of modern business intelligence, Big Data Analytics has emerged as a transformative force, reshaping the way organizations derive insights from vast and diverse datasets. This paper provides a concise overview of the key themes explored in the comprehensive review of Big Data Analytics and its impact on modern business intelligence. Big Data Analytics represents a paradigm shift in decision-making processes, offering organizations the capability to harness the full potential of their data assets. The review delves into the multifaceted role of Big Data Analytics, emphasizing its significance in strategic planning, risk management, operational optimization, and customer-centric initiatives. Strategic planning takes a quantum leap forward as organizations leverage predictive analytics to anticipate market trends. The integration of analytics-derived insights aligns decision-making with overarching organizational objectives, driving a more informed and forward-thinking approach to strategic initiatives. Risk management becomes more proactive with the integration of Big Data Analytics, particularly in fraud detection and prevention. The ability to process large volumes of data in real-time enhances vigilance, mitigating financial risks associated with fraudulent activities cenario modeling further empowers organizations to assess and address potential risks before they materialize. Operational optimization becomes a focal point as analytics uncovers inefficiencies in manufacturing processes, supply chains, and retail operations. Real-time decision-making in retail, enabled by data analytics, ensures agility and responsiveness to changing market dynamics and customer preferences. Customer-centric initiatives are elevated through personalized marketing campaigns and predictive analytics in customer support. The review explores how Big Data Analytics enables organizations to craft personalized experiences, enhancing customer satisfaction and loyalty. The study encapsulates the transformative journey of Big Data Analytics in modern business intelligence, emphasizing its role in navigating strategic complexities, mitigating risks, optimizing operations, and placing the customer at the center of decision-making processes. The comprehensive review provides insights for organizations seeking to harness the transformative potential of Big Data Analytics in the data-driven era.
 Keywords: Big Data, Business Intelligence, Data Analytics, Modern Business, Review.

Meningkatkan Kualitas Official Statistics Melalui Integrasi Big Data

This paper delves into the transformative impact of incorporating big data into official statistical practices. The objective is to elevate the overall quality of data by leveraging the potential of big data analytics. The study employs a comprehensive methodology that encompasses data collection, processing, and analysis. The primary aim is to explore how the integration of big data can enhance the accuracy, timeliness, and relevance of official statistics. The research methodology involves the systematic collection of large-scale datasets, followed by advanced analytics techniques to extract meaningful insights. The findings underscore the substantial improvements achieved in the precision and reliability of official statistics through the infusion of big data methodologies. The transformative potential of integrating big data into official statistics is explored, offering valuable insights for policymakers, researchers, and practitioners in the field.

Digital social innovation based on Big Data Analytics for health and well-being of society

BackgroundAny nation's health policy aims to properly care for its citizens and the society's quality of life. Since in the healthcare system, the population's health is the essential component of national wealth, health is treated as a public good and a social value. Therefore, in recent times, in addition to traditional instruments for promoting healthy lifestyles, health policy has increasingly turned to information instruments and digital technologies.AimsThe paper aims to recognize the role of Big Data Analytics (BDA) in developing Digital Social Innovation (DSI) in the healthcare field. The proposal of the author's Individual Health Plan Platform (IHPP) solution is presented as an example of Digital Social Innovation.Material and methodsThe research is based on a critical analysis of the literature, followed by a Focus Group study aimed at determining the potential of Big Data Analytics, including especially analysis based on data from wearables, to help manage one's health and improve the society's well-being. The focus research also included verification of the research model presented in the article and evaluation of the proposed solution, the Individual Health Plan Platform, including the possibilities of its implementation as a solution to complement healthcare provided by medical facilities in Poland.ResultsThe results of the focus group interviews show that analysing data from wearables and the proposed IHPP could be helpful in managing one's own health and helping to level important problems in healthcare.ConclusionOne of the goals of this paper was to determine the role of BDAs in the development of social innovations in healthcare. From the analysis of the literature, results of the focus group study and discussion, it was concluded that Big Data Analytics has the potential to develop social innovation in healthcare.

Navigating ePolicy Cycle: Big Data, Citizen, and Government

This study explores the ePolicy cycle, with a specific focus on the intricate relationship between big data, citizen engagement, and government decision-making. The primary objective is to shed light on the complexities, opportunities, and challenges inherent in this dynamic interplay. The study aims to equip policymakers, government officials, data analysts, and engaged citizens with the knowledge and insights needed to effectively navigate this evolving landscape. This research examines the key components of ePolicy within the context of data-driven governance, emphasizing the principles of data privacy, security, and transparency. Furthermore, it explores the innovative ways in employed by governments to leverage the potential of big data analytics for making more informed and responsive policy decisions, as well as the growing role of citizens in co-creating public policies through digital channels. As it navigates this multifaceted terrain, the aim is to foster a deeper understanding of the ePolicy cycle and its implications for modern governance. This study seeks to empower stakeholders at all levels to responsibly leverage big data, engage citizens effectively, and ultimately forge a more collaborative, data-informed, and participatory future for government policy-making.

A Review of the Role of Big Data Analytics in Healthcare

The healthcare industry generates massive amounts of data, from patient records to clinical trial results, which can be utilized to improve patient outcomes, reduce costs, and enhance the efficiency of healthcare delivery. Big data analytics is a technology that can help healthcare organizations make sense of these vast amounts of data. The potential benefits of big data analytics in healthcare include improved patient outcomes, reduced costs, enhanced efficiency, and more accurate diagnoses. However, big data analytics also presents significant challenges, including data privacy and security, and the complexity of healthcare data. This research paper aims to explore the role of big data analytics in healthcare, focusing on its potential benefits, challenges, and applications. By understanding the potential of big data analytics in healthcare and the challenges it presents, healthcare organizations can leverage this technology to improve healthcare delivery and patient outcomes.

Exploring the Potential of Big Data Analytics in Improving Library Management in Indonesia: Challenges, Opportunities, and Best Practice

This column aims to explore the potential of big data analytics in improving library management in Indonesia and identify the challenges, opportunities, and best practices for libraries when applying big data analytics. Using of literature review of published studies, this column examines the current state of big data analytics in library management in Indonesia and provides recommendations, including a SWOT analysis, for implementing big data analytics in operations. It also highlighted several challenges such as limited resources, limited access to technology and digital literacy among librarians, and limited data security measures which could put sensitive patron data at risk.

BIG DATA IN THE OIL SECTOR: A REVIEW OF HOW ANALYTICS IS REVOLUTIONIZING SUPPLY CHAIN OPERATIONS

In recent years, the oil sector has been at the epicenter of a transformative wave, primarily driven by the integration of big data and analytics. This study embarked on a comprehensive exploration of the evolving dynamics in the oil sector, focusing on the pivotal role that big data analytics plays in reshaping the industry’s operational and strategic frameworks. The research delineated the historical context of supply chain operations, the advent of digital transformation, and the current challenges faced by the oil sector, providing a robust foundation for the analysis of the transformative potential of big data analytics in the industry. The methodology adopted in this study was both robust and comprehensive, encompassing a detailed literature search strategy, well-defined inclusion and exclusion criteria, and a methodical approach to data extraction and synthesis. This facilitated a deep analysis of the available literature, enabling the drawing of informed conclusions based on empirical evidence. The study identified significant gaps in existing literature, pointing towards areas ripe for further research and exploration. Findings from the study underscored the transformative potential of big data analytics in the oil sector, offering solutions that can revolutionize the industry by fostering efficiency, innovation, and growth. The integration of advanced data interpretations and predictive analytics emerged as a central theme, promising to reshape the industry by offering solutions to complex problems and fostering innovation. Furthermore, the study delineated strategic recommendations for the oil sector, guiding the industry towards a future marked by innovation, sustainability, and prosperity. These recommendations emphasized embracing digital transformation, fostering sustainability, enhancing safety protocols, and developing a skilled workforce, among others. In conclusion, the study offers a promising outlook for the oil sector, highlighting the opportunities and challenges that lie ahead. As the industry stands at the cusp of a new era, it is incumbent upon stakeholders to leverage the opportunities presented by big data analytics, fostering a culture of innovation and growth that will propel the sector into a prosperous future.

Unlocking the potential of artificial intelligence for big data analytics

This article comprehensively examines the use of artificial intelligence (AI) in big data analytics. It focuses on machine learning and deep learning methods that are leveraged to develop innovative algorithms and solutions across domains like finance, healthcare, environment, and education. The article discusses the benefits of applying AI to big data analysis such as improved efficiency and accuracy of predictions, as well as optimization of decisions. However, it also highlights downsides and challenges such as information processing and security, privacy concerns, and ethical considerations. The opportunities and technological challenges associated with processing huge volumes of data are elaborated. The need for an interdisciplinary approach and importance of proper implementation of AI across various spheres of activity is emphasized to maximize impact on societal and economic advancement. Specifically, the article delves into cutting-edge AI and machine learning techniques that enable identifying complex patterns and extracting meaningful insights from massive, heterogeneous data sources. Real-world case studies demonstrate applied AI transforming decision-making in areas like personalized medicine, predictive maintenance, demand forecasting, and more. The piece highlights best practices and cautions around data quality, algorithmic transparency, model interpretability, and ethical AI to tap the potential of big data analytics while mitigating risks like biases and breaches. It underscores the need for holistic solutions blending AI, domain expertise, and purposeful data science. Overall, the article provides a balanced perspective on modern AI amid the big data revolution. It informs technical and non-technical readers about prospering at the intersection of big data and AI – by being realistic about the challenges, following principles for responsible AI, and focusing on human-centered design.

Data analytics in quality 4.0: literature review and future research directions

ABSTRACT The quality level in manufacturing processes increasingly concerns manufacturing firms, as they respond to pressures such as increasing complexity and variety of products, more complex value chains and shortened time-to-market. Quality management is becoming increasingly challenging as model variety, and highly complex products harbour the danger of distributing defective products in the market. Data analytics has started gathering the interest of quality researchers and practitioners, who investigate approaches, algorithms, and methods for supporting the manufacturing quality procedures in the context of Industry 4.0. This trend is facilitated by the wide expansion of sensory technology and the accelerated adoption of information systems by the manufacturing firms. Since quality and process control has been identified as one of the major challenges with a high potential of big data analytics, in this paper we investigated the manufacturing quality research field from a data analytics perspective. Specifically, we examined the existing literature, we provided clarity to the Quality 4.0 research field, we synthesized the literature review outcomes, and we identified the research gaps and challenges. On top of them, we proposed directions for future research.