Implementation Of Big Data Analytics Research Articles

Implementation of Big Data Analytics and its Challenges in Digital Transformation Era: A Literature Review

Implementation of Big Data Analytics and its Challenges in Digital Transformation Era: A Literature Review

Big Data Analytic Tools Usage among Academic Libraries in Tanzania

This study examines the implementation of Big Data analytics in Tanzanian University libraries. Employing a qualitative approach within the interpretive paradigm, the study conducted interviews to collect data from 12 library staff across various University libraries in Tanzania. The qualitative data collected was analyzed using Atlasi.ti 7, focusing on emerging themes. The findings established that most University libraries in Tanzania utilize social networking sites for marketing their products and services. However, only a few academic libraries employ analytics tools such as Apache Hadoop, MongoDB, Apache Spark, Tableau, Zoho Analytics, Apache Storm, and others due to insufficient skills. The study concludes that library administrators could greatly benefit from integrating Big Data analytics into their daily operations. Recommendations include developing Big Data analytics policies to ensure proper handling of user data and offering training on the use of such analytic tools to librarians to equip them with necessary skills and techniques.

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.

Big Data Analytics Implementation and Practices in Medical Institute Libraries of Pakistan

Abstract The aim of this research was to analyze the contemporary practices of Big Data Analytics (BDA) in medical libraries of Pakistan and to explore ways for its implementation in these libraries. A cross-sectional study was carried out among medical librarians in Pakistan by using a Modified Technology Acceptance Model (MTAM) model, with collected data analyzed using Smart-PLS. The results revealed that lack of resources, technical expertise, and some other factors were significant hurdles in smooth implementation of BDA. It is concluded that the perception of medical librarians about adoption of BDA is very encouraging, which resultantly will improve the quality of healthcare services in the country.

Exploring the role of big data analytics and dynamic capabilities in ESG programs within pharmaceuticals

AbstractAchieving the United Nations’ Sustainable Development Goals (SDGs) requires environmental, social, and governance (ESG) programs in the pharmaceutical industry. Using the Millennium Development Goals, the 2030 agenda aims to transform European Union companies toward sustainability. In pharmaceuticals, in particular, ESG programs come with complexities such as employee skills, corporate goals, and management expectations. Managing these programs effectively requires advanced technologies such as big data analytics (BDA) and dynamic capabilities (DC). In this study, DC theory is used to develop an architecture for managing ESG criteria, focusing on provenance, traceability, and availability. BDA’s role in ESG programs is explored, along with its use cases and benefits, and how DC drives success in ESG implementation. The study examined five pharmaceutical companies in Germany, Portugal, and Switzerland, all consulting the same firm for BDA systems, to identify the characteristics of effective BDA implementation. The research explores how BDA and DC jointly enhance ESG efforts, the essential skills needed, and how DC aids in real-time decision-making in BDA projects aligned with ESG standards. It highlights the BDA system’s accuracy and effectiveness in managing ESG programs, with DC as a pivotal facilitator. Findings reveal BDA’s value in operational efficiency and aligning business models with ESG goals, underscoring the need for diverse skills in BDA implementation and DC’s importance in integrating various managerial capacities into effective strategies. The study promotes a dynamic, data-driven approach in the pharmaceutical industry for managing complex ESG initiatives. It stresses continuous learning, adaptation, and integrating technological advances with ethical business practices. The research concludes by emphasizing BDA and DC’s vital roles in advocating ethical, socially responsible, and environmentally sustainable practices in the pharmaceutical sector, marrying technology with ethical business strategies.

Leveraging social media, big data, and smart technologies for intercultural communication and effective leadership: Empirical study at the Ministry of Digital Economy and Entrepreneurship

The objective of this study was to evaluate the impact of social media, big data, and smart technology on intercultural communication and effective leadership inside the Ministry of digital & entrepreneurship. The main objective was to investigate the influence of these technical elements on organizational behavior and the efficacy of leadership within the particular setting of a government ministry dedicated to digital economy and entrepreneurship. In order to accomplish this goal, a thorough empirical inquiry was done, which included gathering data from important individuals involved in the Ministry. The study intentionally selected a sample size of 379 individuals, who represented various responsibilities within the Ministry. The process of data gathering entailed the distribution of surveys and the conduction of interviews to acquire valuable insights and viewpoints from the participants. The utilization of this approach yielded a resilient dataset that is well-suited for thorough investigation. The study explored the complex connection between the use of social media platforms, the implementation of big data analytics, and the incorporation of smart technologies in influencing the dynamics of intercultural communication and leadership inside the Ministry. The results emphasized the substantial influence of social media in promoting intercultural communication and cooperation among personnel within the Ministry. Moreover, the implementation of big data analytics has become a crucial element in improving decision-making processes, impacting several facets of leadership efficacy, strategic planning, and employee involvement. Smart technologies were recognized as crucial elements in establishing efficient communication channels and facilitating effective leadership practices. The study's findings emphasized the beneficial impacts of utilizing social media, big data, and smart technology in the Ministry of digital & entrepreneurship. The research highlighted the significance of government organizations incorporating these technologies in a proactive manner to foster a work environment characterized by improved multicultural communication, well-informed decision-making and efficient leadership. This study makes a substantial contribution to the comprehension of how technological improvements might influence organizational behavior and leadership practices in a government setting. It provides essential insights for policymakers, leaders, and researchers. The findings have broader significance beyond the Ministry, serving as a basis for additional investigation into the use of technology in intercultural communication and leadership effectiveness inside government institutions.

Big Data Analytics for Crisis Management From an Information Processing Theory Perspective: A Multimethodological Study

COVID-19 pandemic has created disruptions and risks in global supply chains. Big data analytics (BDA) has emerged in recent years as a potential solution for provisioning predictive and pre-emptive information to companies in order to preplan and mitigate the impacts of such risks. The focus of this article is to gain insights into how BDA can help companies combat a crisis like COVID-19 via a multimethodological scientific study. The advent of a crisis like COVID-19 brings with it uncertainties, and information processing theory (IPT) provides a perspective on the ways to deal with such uncertainties. We use IPT, in conjunction with the Crisis Management Theory, to lay the foundation of the article. After establishing the theoretical basis, we conduct two surveys towards supply chain managers, one before and one after the onset of the COVID-19 pandemic in India. We follow it up with qualitative interviews to gain further insights. The application of multiple methods helps ensure the triangulation of results and, hence, enhances the research rigor. Our research finds that although the current adoption of BDA in the Indian industry has not grown to a statistically significant level, there are serious future plans for the industry to adopt BDA for crisis management. The interviews also highlight the current status of adoption and the growth of BDA in the Indian industry. The article interestingly identifies that the traditional barriers to implementing new technologies (like BDA for crisis management) are no longer present in the current times. The COVID-19 pandemic has hence accelerated technology adoption and at the same time uncovered some BDA implementation challenges in practice (e.g., a lack of data scientists).

Ethical considerations in the use of big data for business analytics: а cross-industry perspective

Purpose. The primary objective of the article is to explore the ethical challenges and limitations associated with the use of big data in business analytics. It aims to identify and analyze the key ethical issues, providing insights into the implications of using large datasets for business analysis. The article considers the multifaceted aspects of ethics in big data applications and emphasizes the need for comprehensive solutions. Results. The article presents a comprehensive analysis of the ethical problems related to big data implementation in business analytics. It discusses the existing challenges, categorizes them into distinct types, and offers a subtle understanding of each. The results highlight the importance of addressing ethical concerns to ensure the responsible use of big data in the business sphere. Scientific novelty. The scientific novelty of the article lies in its in-depth examination of the ethical issues surrounding big data in the context of business analytics. It contributes to the existing body of knowledge by categorizing ethical challenges and proposing a set of potential solutions. The article integrates perspectives from diverse fields, offering a holistic view of the ethical implications, thereby advancing the scientific understanding of this critical subject. Practical value. The article provides practical insights for businesses, regulatory bodies, and researchers involved in the implementation of big data analytics. By identifying ethical issues and proposing solutions, it offers actionable recommendations for developing ethical standards and norms. The practical value lies in guiding stakeholders to navigate the ethical landscape of big data, promoting responsible practices, and ensuring the protection of privacy and integrity in business analytics.

A multiple-case study on the adoption of customer relationship management and big data analytics in the automotive industry

PurposeThis paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19).Design/methodology/approachA qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities.FindingsResults highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed.Research limitations/implicationsThe study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample.Practical implicationsFindings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach.Originality/valueThis research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation.

Forest Fire Incident Forecasting System In Permanent Reserved Forest In Peninsular Malaysia Using Big Data Analytics

Purpose – This paper summarized the research project on the implementation of big data analytics to forecast the forest fire incident in Peninsular Malaysia. Design/methodology/approach – The research project has developed a Forest Fire Incident Forecasting System in Permanent Reserves Forest (PRF) to forecast forest fire incidence in Peninsular Malaysia. This project was conducted for the Forestry Department Peninsular Malaysia (JPSM) in collaboration with the Ministry of Natural Resources, Environment and Climate Change (NRECC) using Big Data Analytics (BDA). Findings – The results from the system have been summarized into four conclusions. Firstly, the forecast of areas with fire potential can be identified as early as 7 days; secondly, the location of relevant agencies to deal with forest fires close to the site of the fire incident can be identified. Third, the water source close to the fire scene can be located; and finally, the estimated cost of the extinguishing operation can be determined in advance. Practical Implications - To overcome these obstacles and accomplish wise forest management, modern science, and technology must be improved. This research project has successfully implemented BDA via the Forest Fire Incident Forecasting System in PRF. It has improved the performance of the JPSM for its forest management system. Originality – The project is originally conducted with collaborations between the JPSM and NRECC in the Malaysian federal government and has been successfully implemented by the forestry department.

A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era

The significance of big data analytics (BDA) has benefited the health sector by leveraging the potential insights and capabilities of big data in decision making. However, every implementation of BDA within the healthcare field faces difficulties due to incomplete or flawed information that necessitates attention and resolution. The purpose of this systematic literature review is to accomplish two main objectives. Firstly, it aims to synthesize the various elements that contribute to imperfect information in BDA and their impact on decision-making processes within the healthcare sector. This involves identifying and analyzing the factors that can result in imperfect information in BDA applications. Secondly, the review intends to create a taxonomy specifically focused on imperfect information within the context of BDA in the health sector. The study conducted a systematic review of the literature, specifically focusing on studies written in English and published up until February 2023. We also screened and retrieved the titles, abstracts, and potentially relevant studies to determine if they met the criteria for inclusion. As a result, they obtained a total of 58 primary studies. The findings displayed that the presence of uncertainty, imprecision, vagueness, incompleteness, and complexity factors in BDA significantly impacts the ability to sustain effective decision-making in the healthcare sector. Additionally, the study highlighted that the taxonomy for imperfect information in BDA provides healthcare managers with the means to utilize suitable strategies essential for successful implementation when dealing with incomplete information in big data. These findings have practical implications for BDA service providers, as they can leverage the findings to attract and promote the adoption of BDA within the healthcare sector.

The barriers and prospects related to big data analytics implementation in public institutions: a systematic review analysis

The barriers and prospects related to big data analytics implementation in public institutions: a systematic review analysis

Implementasi Big Data Analytics dan Opini Audit terhadap Kualitas Audit dengan Audit Delay Sebagai Variabel Mediasi

This research is based on the relationship between Big Data Analytics and its impact on the quality of audit evidence and the level of assurance felt by end users of financial statements. Big Data Analytics potentially play an important role in innovative auditing practices. Big Data Analytics can increase the efficiency and effectiveness of financial statement audits, but additional competencies and technological capabilities are required to implement Big Data Analytics. With Big Data, auditors can minimize the risks posed by sampling, and therefore, provide a reasonable level of assurance. This study aims to examine Big Data Analytics variables and audit opinion whether these two variables are antecedent variables of audit delay and affect audit quality. This study uses secondary data in the form of the company's annual financial report. The population in this study are all companies listed on the Indonesia Stock Exchange (IDX). The population in this study were 100 companies in the telecommunications, transportation and banking sub-sectors that met the criteria and had been determined to be related to problems during the 2016 to 2020. The data analysis technique in this study was to use the Structural Equation Model to examine the mediating effect. The implementation of big data analytics has no effect on the audit delay variable. Based on the research results, there is sufficient evidence that audit opinion has a significant positive effect on audit delay. However, there is not enough evidence that BDA has a significant positive effect on audit delay

A resource orchestration perspective of organizational big data analytics adoption: evidence from supply chain planning

PurposeThis paper investigated the organizational adoption of big data analytics (BDA) in the context of supply chain planning (SCP) to conceptualize how resources are orchestrated for organizational BDA adoption and to elucidate how resources and capabilities intervene with the resource management process during BDA adoption.Design/methodology/approachThis research elaborated on the resource orchestration theory and technology innovation adoption literature to shed light on BDA adoption with multiple case studies.FindingsA framework for the resource orchestration process in BDA adoption is presented. The authors associated the development and deployment of relevant individual, technological and organizational resources and capabilities with the phases of organizational BDA adoption and implementation. The authors highlighted that organizational BDA adoption can be initiated before consolidating the full resource portfolio. Resource acquisition, capability development and internalization of competences can take place alongside BDA adoption through structured processes and governance mechanisms.Practical implicationsA relevant discussion identifying the capability gap and provides insight into potential paths of organizational BDA adoption is presented.Social implicationsThe authors call for attention from policymakers and academics to reflect on the changes in the expected capabilities of supply chain planners to facilitate industry-wide BDA transition.Originality/valueThis study opens the black box of organizational BDA adoption by emphasizing and scrutinizing the role of resource management actions.

Big data analytics implementation issues: A case study of a large bank in Malaysia

This case study discusses the concept of big data analytics, which involves managing and analyzing large, complex data to discover patterns and insights. Implementing big data analytics has many benefits, such as improving decision-making processes, enhancing accounting processes and compliance, boosting marketing campaigns, and providing better credit risk assessment procedures. This case study aims for the student to understand the challenges of big data analytics implementation in a Malaysian commercial bank. Students take on the role of a Senior IT Manager who needs to consider solutions to the implementation problems. Although the team proposed using cloud-based services as a solution to these issues, the CEO expressed concerns about security risks and requested other alternative solutions. The team has also been requested to come up with solutions to the data quality issues experienced by the case study bank.

Big data analytics and key success factor in achieving competitive advantage and performance of small medium enterprises: literature review

Along with the rapid changes in technology, many small and medium-sized enterprises (SMEs) have leveraged Big Data Analytics and other factors to drive digital transformation to gain a competitive advantage and improve performance. Making smart decisions based on data will help small and medium-sized companies get through the wave toward larger investment targets. The successful implementation of Big Data Analytics and other factors depends on alignment with aspects of an organization, technology, and human resources. Identifying critical success factors (CSFs) in driving Big Data innovation is critical to addressing the challenges surrounding Big Data Analytics and its application. This study aims to examine a systematic literature review that explains the concept of Big Data Analytics and the key success factors in achieving competitive advantage and performance of small and medium-sized companies. By conducting a systematic literature review analysis for studies related to key success factors and Big Data Analytics during the research period on data needs, there were 50 sources of articles analyzed to identify the concept based on its classification. Referring to the descriptive qualitative analysis method for the selected literature section, the findings of this study succeeded in identifying 48 critical success factors from the implementation section of Big Data Analytics by proposing schemes and frameworks in 4 categories, namely: Organization, Technology, Human Resources (People) and Management Data Governance. These findings can be used as a reference for successful strategies and implementation of Big Data Analytics by formulating them into a more effective database form to improve the performance of small and medium-sized companies in achieving competitive advantage.

Is the Implementation of Big Data Analytics in Sustainable Supply Chain Really a Challenge? The Context of the Indian Manufacturing Sector

Purpose : In this age, characterized by the incessant generation of a huge amount of data in social and economic life due to the widespread use of digital devices, it has been well established that big data (BD) technologies can bring about a dramatic change in managerial decision-making. This work addresses the challenges of implementation of big data analytics (BDA) in sustainable supply chain management (SSCM). Design/methodology : The barriers to the implementation of BDA in SSCM are identified through an extensive literature survey as per PESTEL framework which covers political, economic, social, technological, environmental and legal barriers. These barriers are then finalized through experts’ opinion and analyzed using DEMATEL and AHP methods for their relative importance and cause-and-effect relationships. Findings : A total of 13 barriers are identified out of which the lack of policy support regarding IT, lack of data-driven decision-making culture, compliance with laws related to data security and privacy, inappropriate selection and adoption of BDA technologies, and cost of implementation of BDA are found to be the key barriers that have a causative effect on most of the other barriers. Research limitations : This work is focused on the Indian manufacturing supply chain (MSC). It may be diversified to other sectors and geographical areas. The addition of missed-out barriers, if any, might enrich the findings. Also, the fuzzy or grey versions of MCDM methods may be used for further fine-tuning of the results. Practical implications : The analysis presented in this work gives hierarchy of the barriers as per their strength and their cause-and-effect relationships. This information may be useful for decision makers to assess their organizational strengths and weaknesses in the context of the barriers and fix their priorities regarding investment in the BDA project. Social implications : The research establishes that the successful implementation of BDA through minimizing the effect of critical causative barriers would enhance the environmental performance of the supply chain (SC) which in turn would benefit society. Originality/value : This is one of the first studies of BDA in SSCM in the Indian manufacturing sector using PESTEL framework.

Performance measurement to evaluate the implementation of big data analytics to SMEs using benchmarking and the balanced scorecard approach

Performance measurement to evaluate the implementation of big data analytics to SMEs using benchmarking and the balanced scorecard approach

An exploration into the factors influencing the implementation of big data analytics in sustainable supply chain management

PurposeWith big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).Design/methodology/approachThe factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM).FindingsA total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM.Research limitations/implicationsThis research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model.Practical implicationsThis research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project.Social implicationsThe result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society.Originality/valueThis research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions.

Identifying barriers to big data analytics adoption in circular agri-food supply chains: a case study in Turkey.

Big data analytics (BDA), along with the resource efficiency and sustainability perspectives of a circular economy, supports the transition to circular agri-food supply chains (AFSCs), contributing to a country's achievement of the United Nations' Sustainable Development Goals. However, there is still limited research demonstrating the importance and awareness of BDA implementation in circular AFSCs in developing countries. As a result of the barriers to BDA adoption in these regions, circular AFSCs in developing countries are still in their infancies. This study sought to identify the barriers to BDA adoption in circular AFSCs in Turkey using a Delphi-based Pythagorean fuzzy analytic hierarchy process. The proposed method removes the potential for bias and produces consensus among managers of companies in various AFSCs in Turkey. The findings of this study show that the most impactful barriers to BDA are technical, economic and social, followed by environmental and organisational. The most crucial sub-barriers to BDA adoption are "lack of trust, privacy and security", "lack of financial resources" and "lack of skilled human resources". This research can guide industry managers and policymakers in the development of strategies for overcoming barriers to BDA adoption in circular AFSCs in developing nations.