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The rise of big data and analytics companies has significantly changed the business playground. Big data and the use of data analytics are being adopted more frequently, especially in companies that are looking for new methods to develop smarter capabilities and tackle challenges in the dynamic processes. Working with big data and applying a series of data analysis techniques require strong multidisciplinary skills and knowledge of statistics, econometrics, computer science, data mining, law and business ethics, etc. Higher education institutions (HEIs) are concerned by this phenomenon which is also changing learning needs and require a reorientation toward the development of novel approaches and advancements in their programs. The purpose of this paper is to introduce and define big data analytics as having an immense potential for generating value for businesses. In this context, one prominent strategy is to integrate big data analytics in educational programs to enrich student’ understanding of the role of big data, especially those who want to explore their entrepreneurial ways and improve their effectiveness. So, the main purpose of this article consists, on the one hand, in why HEIs must carefully think about how to provide powerful learning tools and open a new entrepreneurship area in this field, and, why, on the other hand, future entrepreneurs (students) have to use data analytics and how they can integrate, operationally, analytics methods to extract value and enhance their professional capabilities.

The author has established an expert viewpoint to discuss the notion of data analytics, identify new ways and re-think what really is new, for both entrepreneurs and HEIs, in the area of big data. This study provides insights into how students can improve their skills and develop new business models through the use of IT tools and by providing the ability to analyze data. This can be possible by bringing the tool of analytics into the higher educational learning system. New analytics methods have to help find new ways to process data and make more intelligent decisions. A brief overview of data analytics and its roles in the context of entrepreneurship and the rise of data entrepreneur is then presented. The paper also outlines the role of educational programs in helping address big data challenges and transform possibilities into opportunities. The key factors of implementing an efficient big data analytics in learning programs, to better orientate and guide students’ project idea, are also explored. The paper concludes with suggestions for further research and limitations of the study.

The findings in this paper suggest that analytics can be of crucial importance for student entrepreneurial practice if correctly aligned with their business processes and learning needs and can also lead to significant improvement in their performance and quality of the decisions they make. The added value of big data is the ability to identify useful data and turn it into usable information by identifying patterns and exploiting new algorithms, tools and new project solutions. So, the move toward the introduction of big data and analytics tools in higher education addresses how this new opportunity can be operationalized.

There are some limitations to this research paper. The research findings have significant implications for HEIs in the field of analytics (mathematics and computer science), and thus, it is not generalizable with any further context. Also, the viewpoint is centered on the data analytics process as a value generator for entrepreneurial opportunities.

This research can be considered as a contribution with literature about educational quality, entrepreneurship and big data analytics. This study describes that new analytics thinking and computational skills are needed for the newer generation of entrepreneurs to handle the challenges of big data. But, preparing them to capture, analyze, store and manage the large amounts of data available today – so they can see value in data – is not just about implementing and using new technologies. This is also, about, a dynamic, operational and modern educational learning process from which a student can extract the maximum benefit. In another words: How to make new opportunities from these data? Which data to select for the analysis? and How to efficiently apply analytical techniques to generate value?

The purpose of this paper is to analyze the inadequacies of current business education in the tackling of the educational challenges inherent to the advent of a data-driven business world. It presents an analysis of the implications of digitization and more specifically big data analytics (BDA) and data science (DS) on organizations with a special emphasis on decision-making processes and the function of managers. It argues that business schools and other educational institutions have well responded to the need to train future data scientists but have rather disregarded the question of effectively preparing future managers for the new data-driven business era.

The approach involves analysis and review of the literature.

The development of analytics skills shall not pertain to data scientists only, it must rather become an organizational cultural component shared among all employees and more specifically among decision makers: managers. In the data-driven business era, managers turn into manager-scientists who shall possess skills at the crossroad of data management, analytical/modeling techniques and tools, and business. However, the multidisciplinary nature of big data analytics and data science (BDADS) seems to collide with the dominant “functional silo design” that characterizes business schools. The scope and breadth of the radical digitally enabled change, the author are facing, may necessitate a global questioning about the nature and structure of business education.

For the sake of transparency and clarity, academia and the industry must join forces to standardize the meaning of the terms surrounding big data. BDA/DS training programs, courses, and curricula shall be organized in such a way that students shall interact with an array of specialists providing them a broad enough picture of the big data landscape. The multidisciplinary nature of analytics and DS necessitates to revisit pedagogical models by developing experiential learning and implementing a spiral-shaped pedagogical approach. The attention of scholars is needed as there exists an array of unexplored research territories. This investigation will help bridge the gap between education and the industry.

The findings will help practitioners understand the educational challenges triggered by the advent of the data-driven business era. The implications will also help develop effective trainings and pedagogical strategies that are better suited to prepare future professionals for the new data-driven business world.

By demonstrating how the advent of a data-driven business era is impacting the function and role of managers, the paper initiates a debate revolving around the question about how business schools and higher education shall evolve to better tackle the educational challenges associated with BDADS training. Elements of response and recommendations are then provided.

The emerging attention in big data has led businesses to improve big data analytics talent capability to enrich firm performance. The big data capability pays off for some companies but not for all, and it appears that very few have achieved a big impact through big data. Rooted in the latest literature on the knowledge-based view, IT capability, big data talent capability and business intelligence, this study aims to examine how big data talent capability impact on business intelligence infrastructure to achieve firm performance.

The primary survey data of 272 IT managers and big data analysts from Chinese firms was analyzed by using the structural equation modeling and partial least squares (Smart PLS 3.0). The analysis uncovers a positive and significant relationship in the proposed model.

The finding shows that the big data analytics talent capability positively impacts on business intelligence infrastructure that in turn directs to achieve firm financial and marketing performance.

This study theorized on the multitheoretic lenses, and findings suggest the managers and industry practitioners to develop business intelligence infrastructure capabilities from big data analytics talent capability.

The article's objective is to analyze the consequent factors of Big Data Analytics Capability for firms in the competitive scenario, using different analytical models.

The research had a quantitative approach, using a survey of data from firms located in the state of São Paulo – Brazil. Structural Equation Modeling (SEM) was used to validate the model.

The results reveal that all hypotheses were accepted. Business value was the construct that had the most explanatory power in the model. It is necessary to invest more in analytical tools, as well as people trained in the analysis of these models, in addition to a change of mindset, which will dictate the bias of the firm's strategic decision-making. The Big Data analysis is evident from firms' growing investments, particularly those that operate in complex and fast-paced environments.

The proposed theoretical model makes it possible to verify firms' analytical structure and whether they are better positioned to analyze customer data and information in real-time, generate insights and implement solutions to maintain and improve their market position.

The contribution of this article is to present a proposal to expand the research models in the literature that analyzed the direct and indirect relationship between “Big Data Analytics Capability” and “Product Innovation Performance”.

The purpose of this paper is to explore the demand for big data and analytics curriculum, provide an overview of the curriculum available from the SAP University Alliances program, examine the evolving usage of such curriculum, and suggest an academic research agenda for this topic.

In this work, the authors reviewed recent academic utilization of big data and analytics curriculum in a large faculty-driven university program by examining school hosting request logs over a four-year period. The authors analyze curricula usage to determine how changes in big data and analytics are being introduced to academia.

Results indicate that there is a substantial shift toward curriculum focusing on big data and analytics.

Because this research only considered data from one proprietary software vendor, the scope of this project is limited and may not generalize to other university software support programs.

Faculty interested in creating or furthering their business process programs to include big data and analytics will find practical information, materials, suggestions, as well as a research and curriculum development agenda.

Faculty interested in creating or furthering their programs to include big data and analytics will find practical information, materials, suggestions, and a research and curricula agenda.

Introduction: In recent years, fresh big data ideas and concepts have emerged to address the massive increase in data volumes in several commercial areas. Meanwhile, the phenomenal development of internet use and social media has not only added to the enormous volumes of data available but has also posed new hurdles to traditional data processing methods. For example, the insurance industry is known for being data-driven, as it generates massive volumes of accumulated material, both structured and unstructured, that typical data processing techniques can’t handle.

Purpose: In this study, the authors compare the benefits of big data technologies to the needs for insurance data processing and decision-making. There is also a case study evaluation concentrating on the primary use cases of big data in the insurance business.

Methodology: This chapter examines the essential big data technologies and tools from the insurance industry’s perspective. The study also included an analytical analysis that supported several gains made by insurance companies, such as more efficient processing of large, heterogeneous data sets or better decision-making support. In addition, the study examines in depth the top seven use cases of big data in insurance and justifying their use and adding value. Finally, it also reviewed contemporary big data technologies and tools, concentrating on their key concepts and recommended applications in the insurance business through examples.

Findings: The study has demonstrated the value of implementing big data technologies and tools, which enable the development of powerful new business models, allowing insurance to advance from ‘understand and protect’ to ‘predict and prevent’.

In recent years, the rise of big data has led to an obvious shift in the competence profile expected from the controller and management accountant (MA). Among others, business analytics competences and information technology skills are considered a “must have” capability for the controlling and MA profession. As it still remains unclear if these requirements can be fulfilled by today’s employees, the purpose of this study is to examine the supply of business analytics competences in the current competence profiles of controlling professionals in an attempt to answer the question whether or not a skills gap exists.

Based on a set of 2,331 member profiles of German controlling professionals extracted from the business social network XING, a text analytics approach is conducted to discover patterns out of the semi-structured data. In doing so, the second purpose of this study is to encourage researchers and practitioners to integrate and advance big data analytics as a method of inquiry into their research process.

Apart from the mediating role of gender, company size and other variables, the results indicate that the current competence profiles of the controller do not comply with the recent requirements towards business analytics competences. However, the answer to the question whether a skills gap exist must be made cautiously by taking into account the specific organizational context such as level of IT adoption or the degree of job specialization.

Guided by the resource-based view of the firm, organizational theory and social cognitive theory, an explanatory model is developed that helps to explain the apparent skills gap, and thus, to enhance the understanding towards the rationales behind the observed findings. One major limitation to be mentioned is that the data sample integrated into this study is restricted to member profiles of German controlling professionals from foremost large companies.

The insights provided in this study extend the ongoing debate in accounting literature and business media on the skills changes of the controlling and MA profession in the big data era. The originality of this study lies in its explicit attempt to integrate recent advances in data analytics to explore the self-reported competence supplies of controlling professionals based on a comprehensive set of semi-structured data. A theoretically founded explanatory model is proposed that integrates empirically validated findings from extant research across various disciplines.

Although big data analytics (BDA) have great benefits for higher education institutions (HEIs), due to lack of sufficient evidence on how BDA investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The purpose of this paper is to propose a big data academic and learning analytics enabled business value model to explain BDA potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs.

The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the BDA current and potential benefits and business value creation path for big data academic and learning analytics success in HEIs.

The pressure of compliance with all legal and regulatory requirements and competition had pushed HEIs hard to adopt BDA tools. However, the study found out that application of risk and security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, the results provide new insights to higher education administrators on ways to create BDA capabilities for HEIs transformation and suggest an empirical foundation that can lead to more thorough analysis of BDA implementation.

A distinctive theoretical contribution of this study is its conceptualization of understanding business value from BDA in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of BDA and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area.

Despite the variety of supply chain management (SCM) research, little attention has been given to the use of Big Data Analytics for increased information exploitation in a supply chain. The purpose of this paper is to contribute to theory development in SCM by investigating the potential impacts of Big Data Analytics on information usage in a corporate and supply chain context. As it is imperative for companies in the supply chain to have access to up-to-date, accurate, and meaningful information, the exploratory research will provide insights into the opportunities and challenges emerging from the adoption of Big Data Analytics in SCM.

Although Big Data Analytics is gaining increasing attention in management, empirical research on the topic is still scarce. Due to the limited availability of comparable material at the intersection of Big Data Analytics and SCM, the authors apply the Delphi research technique.

Portraying the emerging transition trend from a digital business environment, the presented Delphi study findings contribute to extant knowledge by identifying 43 opportunities and challenges linked to the emergence of Big Data Analytics from a corporate and supply chain perspective.

These constructs equip the research community with a first collection of aspects, which could provide the basis to tailor further research at the nexus of Big Data Analytics and SCM.

The research adds to the existing knowledge base as no empirical research has been presented so far specifically assessing opportunities and challenges on corporate and supply chain level with a special focus on the implications imposed through Big Data Analytics.

The purpose of this paper is to scrutinize and classify the literature linking Big Data analytics and management phenomena.

An objective bibliometric analysis is conducted, supported by subjective assessments based on the studies focused on the intertwining of Big Data analytics and management fields. Specifically, deeper descriptive statistics and document co-citation analysis are provided.

From the document co-citation analysis and its evaluation, four clusters depicting literature linking Big Data analytics and management phenomena are revealed: theoretical development of Big Data analytics; management transition to Big Data analytics; Big Data analytics and firm resources, capabilities and performance; and Big Data analytics for supply chain management.

To the best of the authors’ knowledge, this is one of the first attempts to comprehend the research streams which, over time, have paved the way to the intersection between Big Data analytics and management fields.