Big Data is Watching You: Power, Complexity and Algorithms in the Digital Era - Petter Törnberg and Justus Uitermark, Seeing Like a Platform: An Inquiry into the Condition of Digital Modernity (London, Routledge, 2025, 164 pages.)

Today's supply chains are becoming more competitive, complex, costly, risky and unpredictable. Usage of Information and Communications Technology (ICT), digital technology, and big data analysis develops traditional supply chains into smarter supply chains and enable effective management of future challenges in digital era. These technologies of digital era are found to achieve the basic objectives of supply chain such as, faster, cheaper and better product availability and customer reach. The digital era consists of Internet of Things (IoT), Big Data and Analytics, Interconnectivity of digital devises and it requires advanced technologies to capture, collect, store, analyze and interpret huge information. The IoT is a network of hardware, software, devices, databases, objects, sensors, and systems, all working at the service of humanity. With IoT smart devices, supply chain firms would be able to reduce the information acquisition costs. The current evolution of big data in the digital era and an appropriate analysis of this data would provide a huge benefit in terms of positive outcomes in supply chain for companies which utilize and implement them during the next decade. The data driven revolution is going to sweep the way the future business is done and the way supply chain is managed. Big data analytics (BDA), known as `supply chain analytics' in supply chain management can help firms gain competitive advantage and achieve the status of `Smart Supply Chain'. Supply chain management in Digital Era is influenced through the analysis of wide range of data available in supply chain through various sources such as i) Product Design, ii) Supply Chain Design, iii) Sourcing, iv) Production, v) Warehousing, vi) Transportation, vii) Point-of-Sale to viii) Consumer. Supply chains will be operated in a more networked manner than they are today in the digital era. Organizations need to develop new collaborative models and scalable approaches for managing their supply chains more effectively in the digital era. Digital marketplaces and data cleanroom will enable the data transparency and smooth coordination with strategic supply chain partners. Organizations need to achieve new competitive capabilities like visibility, real-time response and digitization in the digital era in supply chain management. Though some leading companies have already commenced to modify their customer value propositions, and attempting to transform their supply chain operations using digital technologies, consensus has not yet evolved among researchers on what strategies to be adopted by the organizations to achieve supply chain related competitive advantage in the digital era. Therefore, there is a need to study the future strategies of supply chain to be adopted by business organizations to achieve positive outcomes in the digital era. This paper attempts to fill this gap in the body of knowledge by developing a framework of supply chain strategies and their enablers in digital era. It would aid organizations in redesigning their supply chain strategies to achieve supply chain related competitive advantage.

The growing role of integrated and insightful big and real-time data analytics platforms

The ‘digital era’ of informatics and knowledge integration has changed the roles and experiences of patients, research participants and health consumers. No longer figured (merely) as passive recipients of healthcare services or as beneficiaries of top-down biomedical information, individuals are increasingly seen as active contributors in healthcare and research. They are positioned into multiple roles that are experienced simultaneously by those who access and co-produce digital content that can easily be transformed into data. This is contextualised by ‘big data’ technologies that have altered biomedicine, enabling collation and analysis of myriad data from digitised records to personal mobile data. Social media facilitate new formations of communities and knowledge enacted online, while novel kinds of commercial value emerge from digital networks that enable health data commodification. In this paper, we draw from exemplary digital era shifts towards participatory medicine to cast light on the rapprochements between patienthood, participation and consumption, and we explore how these rapprochements are mediated by, and materialise through, the use of participatory digital technologies and big data. We argue that there is a need to use new conceptual tools that account for the multiple roles and experiences of patient–participant–consumers that co-emerge through digital technologies. We must also ethically re-assess the rights and responsibilities of individuals in the digital era, and the implications of digital era changes for the future of biomedicine and healthcare.

This study focuses on the role of the Arctic Strategy of Russia in the Great Eurasian Partnership.Aim. The study aims to identify the features of the Arctic Strategy of Russia in the Great Eurasian Partnership.Tasks. The development of principles of geopolitical forecasting and work with big data, with the aim of synchronizing the integration processes contradictory in nature and interests of the actors in Greater Eurasia, identifying the features of the geopolitical regionalization of Eurasia, and analyzing the global security analytics of the Great Eurasian Partnership in the coming “digital era”.Methods. The transition from the “analog” to the “digital era” at the beginning of the 21st century meant an essentially revolution in the data itself, which was associated with the emergence of their new quality — predictive. The methodology for research and forecasting the prospects of Eurasian integration is modeling the multi-agent interaction of geopolitical factors of coalitions based on mathematical game theory. The strategy of transition to a polycentric world order implies the need to move from “geopolitical fiction” to geopolitics, based on mathematical modeling of global geopolitics processes using game theory methods.Results. The geopolitical regionalization of Eurasia is a challenge to time. On the expanses of the Eurasian continent, several multi-format long-term “Eurasian projects” are increasingly clearly emerging. This is, firstly, the Eurasian Economic Union, secondly, the Chinese One Belt-One Way and, thirdly, Indo-Pacifica. The Russian Arctic is a region of the Great Eurasian Partnership. The tasks of the modern Arctic policy of Russia are focused on ensuring the priority growth of raw materials exports and industrial development of the Arctic territories, rather than on solving the problems of improving the quality of life of the local population, although in the Russian Arctic strategy until 2035 these tasks are formulated quite correctly.Conclusion. The analytics of the Eurasian partnership and the wording of expert assessments should be formed on the basis not only of political discourse, but also of exact sciences. In modern conditions, the development of scenario projects for the construction of the Great Eurasian Partnership, which includes the Greater Arctic (which, in turn, require an independent theoretical and methodological study), involves the full use of the apparatus of mathematical geopolitics (game theory), scenario modeling and big data technologies.

We read a recent editorial that discusses the importance of big data in conjunction with analytical methods in food science (Foegeding, 2018). The editorial prompted us to ponder the relationship between big data and food science, and we found that it would be worth examining the relationship in the context of the history of food science. In this letter, we report our findings and share our perspective on the result with the readers of this journal. In today's digital era, the amount of information grows exponentially, generating a massive volume of data every day that humanity has never seen before. The term "big data" has been used to describe those massive data sets (McAfee & Brynjolfsson, 2012). Even though the term "big data" has been widely used, there is no formal consensus on the definition of the term. A group recently proposed a definition for the term based on a literature search: "Big data represents the information assets characterized by such a high volume, velocity, and variety to require specific technology and analytical methods for its transformation into value" (De Mauro, Greco, & Grimaldi, 2015). While big data usage has occurred in numerous fields including business, science, and media industries (Mayer-Schönberger & Cukier, 2014), it was introduced to biological science about ten years ago (Howe et al., 2008). How can big data make sense to the users? Holmes succinctly states in her recent book (Holmes, 2018) that "Big data is only useful if we can extract useful information from it." One of the essential tools in extracting information from big data is quantitative skills, which include mathematical, statistical, and computational techniques (Kepner & Jananthan, 2018; EMC Education Services, 2015; Pierson, 2015). Therefore, we thought that it may be worth evaluating food science with respect to its readiness for the big data era as discussed in the editorial (Foegeding, 2018). How can a certain academic discipline be evaluated, preferentially, quantitatively? In our previous publications (Kang & Purnell, 2011; Kang, Park, Venkat, & Gopinath, 2015), we showed that the number of articles in a certain academic field archived in an indexation system could represent the activity of the academic field. There are many indexation services developed, such as PubMed, EMBASE, and SCOPUS (Balhara, 2012). While it is not an easy task to select the best one among those indexation systems, PubMed, a search engine for biomedical papers archived in MEDLINE (https://www.ncbi.nlm.nih.gov/pubmed), is considered one of the best databases (Farassati, 2017). In our previous research (Kang & Purnell, 2011; Kang et al., 2015), we used PubMed to assess several biomedical sciences including biochemistry, biophysics, bioinformatics, and systems biology. Employing the strategy, we searched the PubMed database to examine how much quantitative skills have been utilized in food science. According to [Eq. 3], the asymptotic maximum value of the proportion of quantitative food science papers is 3.7%. The value of h(2018) is 3.1%, which is 83% of the maximum value. Both Figure 1B and 1C clearly show that the usage of quantitative skills in food science has exponentially grown for the last 30 years. Nonetheless, the magnitude of their contribution is not significant as its projection is just 4% at most (Figure 1C), which means that only 4 out of 100 papers in food science use quantitative skills substantially. As we indicated above, big data analysis requires a great usage of quantitative skills. The current status of big data usage in food science is primitive according to our search of PubMed with a search phrase "food" AND "big data": so far, 117 papers have been published in food science research using big data since the first food science publication with big data analysis in 2013 (the search was done in August 2018 when we revised this letter). The degree of the usage of big data in food science can be compared with that in other fields, for example, bioinformatics or immunology. The number of publications in bioinformatics and immunology using big data is 500 and 156, respectively, as of August 2018. Finally, we would like to comment on the education of food science based on our findings shown in this paper. It is highly desirable to adjust the academic curriculum of any academic disciplines according to their development, especially if the field is growing rapidly (Kang & Purnell, 2011; Kang et al., 2015). Considering the wide and rapid application of big data to other areas (Mayer-Schönberger & Cukier, 2014), we expect that big data will play an important role in food science in the near future as suggested in the editorial (Foegeding, 2018). Therefore, we suggest that the food science community should consider developing courses that introduce quantitative skills to students in food science, who will be the future users and/or producers of "big data" in the 21st century. Sincerely,

Purpose - The digital era has fundamentally transformed the business landscape, presenting new challenges and opportunities for Human Resources (HR) management. This article investigates the effects of the utilization of Artificial Intelligence (AI), Big Data, and Automation on HR performance in an increasingly digital business environment. Findings - This research aims to identify how these technologies influence HR, both positively and negatively. Design/methodology/approach - Research methods include surveys, data analysis, and case studies across various industries. The results indicate that the presence of AI has altered how HR handles routine tasks. AI enables HR to optimize recruitment processes, deliver tailored training, and forecast future workforce needs. Big Data has become a valuable resource in HR decision-making. Data collected from various sources allows for in-depth analysis of employee performance, workplace happiness, and productivity trends. Automation has also reshaped the job landscape by taking over repetitive tasks, freeing up HR to focus on more strategic work. Practical implications - However, this research also underscores several challenges. Organizational culture changes are required to effectively integrate these technologies, and concerns about data privacy need to be addressed with stringent policies. Originality/value - In conclusion, the use of Artificial Intelligence, Big Data, and Automation has a significant impact on HR performance in the digital era. Organizations capable of integrating these technologies with an innovative work culture will emerge as leaders in optimizing their human resources and gaining a competitive edge in the ever-evolving digital age.

The COVID-19 pandemic has given rise to the use of several technologies to control disease transmission. Whilst these applications are intended to empower people to be informed, alerted or to be able to pursue a so-called return to normality their use has also raised several questions around the way in which data is being gathered and used across countries. However, the potential of big data and real time data in better managing health and disease as well as in leaving nobody behind may have also been underestimated to date. This roundtable will permit a deep dive and follow up discussion to some of the points raised in the plenary debate on Capturing the breadth and depth of the digital health era - beyond the COVID-19 pandemic.The workshop will kick off with three lightning presentations:COVID-19 contact tracing apps: Have they really been useful in the European Region?Clayton HamiltonThe EU Digital Vaccination Certificate - Where do freedom of movement, equity, and public health coincide? - EU representative tbcThe use of Big Data to improve people's health: Big Brother watching or genuine potential to improve health and well-being on a massive scale - David NovilloThe COVID-19 pandemic has accelerated the use of new and existing technologies to help support the national public health response and mitigate the impacts on the continuity of essential health services. While many digital applications employed in the context of COVID-19 have been designed to inform and empower individuals, or to enable a gradual relaxation of public health and social measures, their continued use beyond the scope of the pandemic has raised questions as to the gathering and use of data by governments and its potential to be repurposed. At the same time, the potential of big data and real-time data to improve the quality of care and disease management and in working towards universal health coverage have also been underestimated to date. Addressing the underlying issues of public trust for digital technologies and data sharing, appropriate regulation and accountability mechanisms to protect the privacy of individuals, and the development of health workforce competencies in using digital tools are also part of the complex picture of barriers and enablers to putting people at the centre of their own health and well-being in the digital era.This roundtable will involve a “technical deep dive” and follow up of the discussions and points raised in the plenary debate “Capturing the breadth and depth of the digital health era — beyond the COVID-19 pandemic”. The session will start with three lightning presentations to contextualize the application of digital technologies during the pandemic and will be followed by reflections and questions posed to a panel of civil society representatives in a discussion facilitated by the European Observatory on Health Systems and Policies.Speakers/Panelists Clayton Hamilton Digital Health Flagship, WHO/Europe Ioana-Maria Gligor European Reference Networks and Digital Health DG SANTE, EC David Novillo Data, Metrics and Analytics, WHO/Europe Dineke Zeegers Paget EUPHA Caroline Costongs EuroHealthNet Paolo Benanti Moral Theology, Bioethics, and Neuroethics, Gregorian Pontifical University, Rome, Italy Carlos Martins Faculty of Medicine of Porto, PortugalKey messages As we have learned from COVID-19, the use of several technologies to help overcome the pandemic also raises questions around the way data is being gathered and used.

The 2024 National Education Work Conference pointed out that we should adhere to the hard truth that education serves high-quality development, and digital intelligence brings excellent opportunities and challenges for the high-quality development of education. The digital intelligence era refers to the new economic era characterized by big data and intelligence, and the development of new technologies such as the Internet and artificial intelligence is constantly reshaping the shape of education. The digital era has also brought new development opportunities and challenges to construction enterprises. Traditional construction enterprise accounting teaching materials can no longer meet the needs of construction enterprise accounting personnel training under digital intelligence, and it is necessary to vigorously carry out the construction of construction enterprise accounting teaching materials incorporating the trend of digital intelligence, and to construct teaching materials construction evaluation indexes from the dimensions of the degree of meeting the needs of enterprise development, the degree of recognition of teachers and students, and the degree of enterprise development of digital intelligence, which can help to test the applicability of teaching materials for teaching accounting in construction enterprises, and help to improve the teaching of teaching materials. The evaluation indexes of teaching material construction are constructed from the dimensions of the conformity of enterprise development demand, the recognition of teachers and students, and the degree of digital intelligence of enterprise development, which can help to test the applicability of teaching materials for construction enterprise accounting, analyse the path of teaching material construction integrating with the trend of digital intelligence in depth, and introduce the teaching materials by following the dynamics of the industry and emphasizing the cultivation of talents in the aspect of application orientation. So we can effectively promote the construction of teaching materials of construction enterprise accounting in depth, and enhance the high quality of teaching materials, and provide useful references and guidance for the practice of teaching.

This study aims to analyze the effect of a holistic strategy on a company's success in maintaining competitive advantage in the digital transformation era. The holistic strategy includes technology adoption, human resource development, business model innovation, customer orientation, and risk management. This study uses a quantitative approach with a survey method through a closed questionnaire distributed to managers and executives in 100 companies spread across various industrial sectors, including technology, manufacturing, services, and retail. Respondents were selected based on their company's involvement in digital transformation initiatives for at least two years. The collected data were analyzed using descriptive statistics and linear regression to evaluate the relationship between the independent (holistic strategy) and the dependent (competitive advantage) variables. The study results show that technology adoption, especially AI, big data, and IoT, significantly affects company performance and strengthens its competitive advantage. In addition, employee digital skills development, innovation in business models, and a focus on improving customer satisfaction have also been shown to play an important role in driving business growth. Effective risk management helps companies deal with market disruption and economic uncertainty to maintain their operations' sustainability. These findings emphasize the importance of integrating various elements in a holistic strategy to ensure a company's success in the era of dynamic digital competition. This research provides theoretical and practical contributions for business managers in formulating adaptive and sustainable strategies amidst technological disruption. The implications of these findings suggest that companies need to accelerate their digital transformation by strengthening the synergy between technology, human resources, innovation, and risk management to maintain their competitiveness in the global market.

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in national economic growth through job creation and GDP contribution. However, in today’s digital disruption era, they face challenges in operational efficiency, limited financing, and weak human resource management. This study explores the strategic role of management accounting in supporting MSME sustainability, focusing on cost control, capital structure, and HR development. Using a systematic literature review (SLR) of international journals and academic sources from the past decade, the study finds that digital-based management accounting such as big data, digital accounting systems, and activity-based costingimproves cost efficiency, decision-making quality, and HR capabilities. The research concludes that digital transformation in management accounting is a strategic necessity for MSME resilience and long-term sustainability.

This paper aims to explore the impact of the digital era on economic management, encompassing technological-driven economic transformation, data-driven decision-making and management, challenges faced by economic management, digital innovation, opportunities, and prospects. By analyzing the applications of cutting-edge technologies such as artificial intelligence, big data, cloud computing, and the Internet of Things, along with practical experiences of businesses in traditional model adjustments, data privacy and security, and digital innovation, it reveals the opportunities and challenges that enterprises face in the digital era. Based on research findings, recommendations are proposed, including strengthening the introduction of digital technologies, focusing on organizational culture and structural adjustments, establishing innovative management models, and enhancing social responsibility practices to guide businesses in better adapting to the digital era. Future research directions involve in-depth studies on emerging technologies, global trends in the digital era, exploration of social impacts and ethical issues, and deepening research on the sustainable development path of enterprises. This comprehensive study provides theoretical guidance and practical suggestions for businesses to achieve sustainable development in the digital era.

The purpose of this study is to analyze the challenges of the accounting profession in the digital era. The study focuses on how the challenges of the accounting profession in the digital era and how accountants can respond to these challenges and adapt to an increasingly complex economy. Digital transformation requires accountants to adapt to technology-based systems such as automation, artificial intelligence, big data, and blockchain. The research methodology includes a comprehensive literature review on topics relevant to the study. We quantitatively analyze the data used to identify the main challenges faced by accountants in the digital era. The study shows that accountants face a variety of challenges, including the complexity of data security, complex system integration, and the need for advanced technology skills. The impact of the digital era has changed the paradigm of financial information management, Enabling more detailed data analysis and opening up new opportunities in consulting services. This study provides valuable insights for accounting practitioners, academics, and other stakeholders to understand the role of accountants in the rapidly evolving digital era.

Geographic information science in the era of geospatial big data: A cyberspace perspective

In today's digital era, organizations are inundated with vast amounts of data, presenting both challenges and opportunities for businesses. Big data analytics has emerged as a powerful tool to extract valuable insights from this abundance of data. However, for organizations to derive maximum benefit from big data analytics, alignment with business objectives is crucial. This paper explores the relationship between big data analytics and business alignment, aiming to elucidate the ways in which organizations can effectively integrate analytics into their strategic decision-making processes. The research begins by providing an overview of big data analytics and its significance in the contemporary business landscape. It then delves into the concept of business alignment, elucidating its importance for organizational success. Drawing on existing literature and empirical studies, the paper examines the various dimensions of alignment between big data analytics initiatives and business goals, including organizational culture, leadership support, technological infrastructure, and human resources capabilities. Furthermore, the paper investigates the challenges and barriers that organizations may encounter in aligning big data analytics with business objectives, such as data quality issues, skill shortages, and cultural resistance to change. Strategies and best practices for overcoming these challenges are also discussed, drawing on real-world examples and case studies. Ultimately, this research contributes to the growing body of knowledge on big data analytics by highlighting the critical role of business alignment in maximizing the value of analytics investments. By fostering alignment between analytics initiatives and strategic business objectives, organizations can enhance their competitiveness, drive innovation, and achieve sustainable growth in an increasingly data-driven world.

This study aims to examine the impact of Big Data Analytics on Audit Quality using the Structural Equation Modeling-Partial Least Squares (SEM-PLS) approach. The research involved 120 respondents consisting of auditors from Big Ten public accounting firms in Indonesia. The Big Data Analytics variable was measured based on five main dimensions: volume, velocity, variety, veracity, and value. The results indicate that Big Data Analytics has a positive and significant effect on Audit Quality. This relationship is demonstrated by a path coefficient in the moderate category, with a significance level below the five percent threshold. The coefficient of determination shows that nearly half of the variation in Audit Quality can be explained by Big Data Analytics. These findings confirm that effective implementation of Big Data Analytics can enhance the effectiveness, efficiency, and reliability of the audit process. The study also supports the application of the Technology Acceptance Model framework, where perceived usefulness and ease of use of technology contribute to improved audit quality. The practical implications of this research highlight the importance of data-driven strategies in enhancing audit quality in today's digital era.