Big data analytics and application for logistics and supply chain management

This special issue explores big data analytics and applications for logistics and supply chain management by examining novel methods, practices, and opportunities. The articles present and analyse a variety of opportunities to improve big data analytics and applications for logistics and supply chain management, such as those through exploring technology-driven tracking strategies, financial performance relations with data driven supply chains, and implementation issues and supply chain capability maturity with big data. This editorial note summarizes the discussions on the big data attributes, on effective practices for implementation, and on evaluation and implementation methods

THE IMPACT OF SUSTAINABLE BRANDING USING BIG DATA AND BUSINESS ANALYTICS IN THE MARKET RESEARCH INDUSTRY

Abstract Aim: The research aimed to explore how sustainable branding and big data analytics could enhance brand equity and sustainability in the market research industry. It reviewed existing literature, analysed branding strategies of data-driven companies, identified key attributes for sustainable positioning, used qualitative research methods to investigate competitive advantage, and created a theoretical framework to demonstrate how sustainable branding could improve performance in data-driven companies using big data and analytics. Methodology: This research used qualitative methods for a systematic review of sustainability, branding, and business analytics in the market research industry. It involved semi-structured interviews with 38 senior managers and directors from 24 companies across 8 countries. Despite the impact of COVID-19 on data collection due to changes in working patterns, this study showcased the potential of modern qualitative methods such as the 'inductive a priori' model. It utilized advanced technologies and multi-disciplinary research to tackle complex industry concepts. The research sought to bring about sustainable change in the market research industry. Results: The results of the study indicated that sustainable branding was positively related to consumer behaviour, corporate reputation, and financial performance. Big data and business analytics offered valuable insights into consumer preferences, attitudes, and behaviour which helped companies to develop and manage successful sustainable branding strategies. The study provided a comprehensive framework for understanding the role of sustainable branding, big data, and business analytics in the market research industry. Contribution to knowledge: The contribution of the study lies in identifying the importance of sustainable branding and its relationship with big data and business analytics. The study highlighted the potential benefits of integrating sustainability practices into branding strategies and suggested practical implications for companies to adopt sustainable branding approaches. The findings of the study offered insights into the value of big data and business analytics in the market research industry and provided a basis for future research in this field

Knowledge Management and Data Analysis Techniques for Data-Driven Financial Companies

In today’s fast-paced financial industry, knowledge management and data-driven decision making have become essential for the success of financial technology (FinTech) companies. Big data (BD) is a prevalent phenomenon that can be found across many industries, including finance. Despite its complexity and difficulty to comprehend, big data is a critical component of financial services enterprises and technology architectures. We examine BD from various aspects, considering data science (DS) techniques and methodologies that can be applied during the operation of an enterprise. Our aim is to provide an overview of knowledge management (KM) practices and data analysis (DA) strategies and techniques in the daily operations of financial companies. We address the role of knowledge management, data analytics in a financial institution. The paper demonstrates financial institutions’ enablement for new services resulting from technological advancements

Global Fintech

How the global financial services sector has been transformed by artificial intelligence, data science, and blockchain. Artificial intelligence, big data, blockchain, and other new technologies have upended the global financial services sector, creating opportunities for entrepreneurs and corporate innovators. Venture capitalists have helped to fund this disruption, pouring nearly $500 billion into fintech over the last five years. This book offers global perspectives on technology-fueled transformations in financial services, with contributions from a wide-ranging group of academics, industry professionals, former government officials, and current government advisors. They examine not only the struggles of rich countries to bring the old analog world into the new digital one but also the opportunities for developing countries to “leapfrog” directly into digital. The book offers accessible explanations of blockchain and distributed ledger technology and explores big data analytics. It considers, among other things, open banking, platform-based strategies for banks, and digital financial services. Case studies imagine possible future fintech-government interaction, emphasizing that legal and regulatory frameworks can help to create trust in financial processes. The contributors offer novel takes and unexpected insights that will be of interest to fintech experts and nonexperts alike. Contributors Ajay Bhalla, Michelle Chivunga, John D'Agostino, Mark Flood, Amias Moore Gerety, Oliver R. Goodenough, Thomas Hardjono, Sharmila Kassam, Boris Khentov, Alexander Lipton, Lev Menand, Pinar Ozcan, Alex Pentland, Matthew Reed, David L. Shrier, Markos Zachariadi

Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis

Financialization has contributed to economic growth but has caused scandals, misselling, rogue trading, tax evasion, and market speculation. To a certain extent, it has also created problems in social and economic instability. It is an important aspect of Enterprise Security, Privacy, and Risk (ESPR), particularly in risk research and analysis. In order to minimize the damaging impacts caused by the lack of regulatory compliance, governance, ethical responsibilities, and trust, we propose a Business Integrity Modeling and Analysis (BIMA) framework to unify business integrity with performance using big data predictive analytics and business intelligence. Comprehensive services include modeling risk and asset prices, and consequently, aligning them with business strategies, making our services, according to market trend analysis, both transparent and fair. The BIMA framework uses Monte Carlo simulation, the Black–Scholes–Merton model, and the Heston model for performing financial, operational, and liquidity risk analysis and present outputs in the form of analytics and visualization. Our results and analysis demonstrate supplier bankruptcy modeling, risk pricing, high-frequency pricing simulations, London Interbank Offered Rate (LIBOR) rate simulation, and speculation detection results to provide a variety of critical risk analysis. Our approaches to tackle problems caused by financial services and the operational risk clearly demonstrate that the BIMA framework, as the outputs of our data analytics research, can effectively combine integrity and risk analysis together with overall business performance and can contribute to operational risk research

Marketing relations and communication infrastructure development in the banking sector based on big data mining

Purpose: The article aims to study the methodological tools for applying the technologies of intellectual analysis of big data in the modern digital space, the further implementation of which can become the basis for the marketing relations concept implementation in the banking sector of the Russian Federation‘ economy. Structure/Methodology/Approach: For the marketing relations development in the banking sector in the digital economy, it seems necessary: firstly, to identify the opportunities and advantages of the big data mining in banking marketing; secondly, to identify the sources and methods of processing big data; thirdly, to study the examples of the big data mining successful use by Russian banks and to formulate the recommendations on the big data technologies implementation in the digital marketing banking strategy. Findings: The authors‘ analysis showed that big data technologies processing of open online and offline sources of information significantly increases the data amount available for intelligent analysis, as a result of which the interaction between the bank and the target client reaches a new level of partnership. Practical Implications: Conclusions and generalizations of the study can be applied in the practice of managing financial institutions. The results of the study can be used by bank management to form a digital marketing strategy for long-term communication. Originality/Value: The main contribution of this study is that the authors have identified the main directions of using big data in relationship marketing to generate additional profit, as well as the possibility of intellectual analysis of the client base, aimed at expanding the market share and retaining customers in the banking sector of the economy.peer-reviewe

IPO Ready? Illuminating the Dark Box of Private Equity

The use of public equity data can help combat the challenges private equity funds currently face regarding data availability. The goal is to create a model to provide guidance to both investors and entrepreneurs in the decision-making process. The data gathered would provide insight on how close a private company is to a successful Initial Public Offering (IPO). The idea is that a model, showing the average financial metrics of companies within certain industries during an IPO, can provide new perceptiveness as to how the private company is performing

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions