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This study focuses on leadership in organizations where big data analytics (BDA) is an essential component of corporate strategy. While leadership researchers have conducted promising studies in the field of digital transformation, the impact of BDA on leadership is still unexplored.

This study is based on semi-structured interviews with 33 organizational leaders and subject-matter experts from various industries. Using a grounded theory approach, a framework is provided for the emergent field of BDA in leadership research.

The authors present a conceptual model comprising foundational competencies and higher order roles that are data analytical skills, data self-efficacy, problem spotter, influencer, knowledge facilitator, visionary and team leader.

This study focuses on BDA competency research emerging as an intersection between leadership research and information systems research. The authors encourage a longitudinal study to validate the findings.

The authors provide a competency framework for organizational leaders. It serves as a guideline for leaders to best support the BDA initiatives of the organization. The competency framework can support recruiting, selection and leader promotion.

This study provides a novel BDA leadership competency framework with a unique combination of competencies and higher order roles.

Public-funded research in digital humanities (DH) enhances institutional and individual research missions and contributes open data to a growing base of globally networked knowledge. The Digging into Data 3 challenge (DID3) (2014–2016) is an international, interdisciplinary and collaborative grant initiative, and the purpose of this paper is to explore skills that faculty and students brought to projects and others they acquired and shared on collaborative teams.

Rooted in the naturalistic paradigm, this qualitative case study centers on semi-structured interviews with 53 participants on 11 of the 14 DID3 projects. Documentary evidence complements empirical evidence; analysis is constructivist and grounded.

Hailing from diverse academic research institutions, centers and repositories, participants brought 20 types of discipline-based or interdisciplinary expertise to DID3 projects. But they reported acquiring or refining 27 other skills during their project work. While most are data-related, complementary programming, management and analytical skills push disciplinary expertise toward new frontiers. Project-based learning and pedagogy function symbiotically; participants therefore advocate for aligning problem-solving skills with pedagogical objectives at home institutions to prepare for public-funded DH projects. A modified content analysis juxtaposes DID3 skills with those advanced in 23 recent DH syllabi to identify commonalities and gaps.

Pedagogy has an important yet under-researched and underdeveloped role in public-funded DH research.

The lack of clarity in defining data science is problematic in both academia and industry because the former has a need for clarity to establish curriculum guidelines in their work to prepare future professionals, and the latter has a need for information to establish clear job description guidelines to recruit professionals. This lack of clarity has resulted in job descriptions with significant overlap among different related professional groups. This study examines the industry view of five professions: statistical analysts (SAs), big data analytics professionals (BDAs), data scientists (DSs), data analysts (DAs) and business analytics professionals (BAs). The study compares the five fields with the unified backdrop of their common semantic dimensions and examines their recent dynamics.

1,200 job descriptions for the five Big Data professions (SA, DS, BDA, DA and BA) were pulled from the Monster website at four points in time, and a document library was created. The collected job qualification records were analyzed using the text analytic method of Latent Semantic Analysis (LSAs), which extract topics based on observed text usage patterns.

The findings indicated a good alignment between the industry view and the academic view of data science as a blend of statistical and programming skills. This industry view remained relatively stable during the 4 years of our study period.

This research paper builds upon a long tradition of related studies and commentaries. Rather than relying on subjective expertise, this study examined the job market and used text analytics to discern a space of skill and qualification dimensions from job announcements related to five big data professions.

The purpose of this paper is to understand genomics scientists’ perceptions in data quality assurances based on their domain knowledge.

The study used a survey method to collect responses from 149 genomics scientists grouped by domain knowledge. They ranked the top-five quality criteria based on hypothetical curation scenarios. The results were compared using χ2 test.

Scientists with domain knowledge of biology, bioinformatics, and computational science did not reach a consensus in ranking data quality criteria. Findings showed that biologists cared more about curated data that can be concise and traceable. They were also concerned about skills dealing with information overloading. Computational scientists on the other hand value making curation understandable. They paid more attention to the specific skills for data wrangling.

This study takes a new approach in comparing the data quality perceptions for scientists across different domains of knowledge. Few studies have been able to synthesize models to interpret data quality perception across domains. The findings may help develop data quality assurance policies, training seminars, and maximize the efficiency of genome data management.

This paper aims to investigate emerging trends in data analytics and knowledge management (KM) job market by using the knowledge, skills and abilities (KSA) framework. The findings from the study provide insights into curriculum development and academic program design.

This study traced and retrieved job ads on LinkedIn to understand how data analytics and KM interplay in terms of job functions, knowledge, skills and abilities required for jobs, as well as career progression. Conducting content analysis using text analytics and multiple correspondence analysis, this paper extends the framework of KSA proposed by Cegielski and Jones‐Farmer to the field of data analytics and KM.

Using content analysis, the study analyzes the requisite KSA that connect analytics to KM from the job demand perspective. While Kruskal–Wallis tests assist in examining the relationships between different types of KSA and company’s characteristics, multiple correspondence analysis (MCA) aids in reducing dimensions and representing the KSA data points in two-dimensional space to identify potential associations between levels of categorical variables. The results from the Kruskal–Wallis tests indicate a significant relationship between job experience levels and KSA. The MCA diagrams illustrate key distinctions between hard and soft skills in data across different experience levels.

The practical implications of the study are two-fold. First, the extended KSA framework can guide KM professionals with their career planning toward data analytics. Second, the findings can inform academic institutions with regard to broadening and refining their data analytics or KM curricula.

This paper is one of the first studies to investigate the connection between data analytics and KM from the job demand perspective. It contributes to the ongoing discussion and provides insights into curriculum development and academic program design.

A key finding in the extant literature on adopting information systems has been the importance of management support and a champion. Further research has indicated that business managers need to have appropriate IT knowledge and skills to lead adoption adequately. In the context of small and medium enterprises (SMEs), this role is usually assumed by the owner/manager. This research aims to synthesise these two tenets by identifying and understanding the type of business intelligence and analytics (BI&A) leadership skills that owners/managers need to develop during the adoption of BI&A.

Five BI&A knowledge areas are identified and connected to different types of BI&A leadership skills through qualitative in-depth case studies of fourteen Australian SMEs.

The case studies reveal that several BI&A leadership skills need to be developed to bring SMEs to higher stages of BI&A adoption.

This study proposes a BI&A leadership skills development framework that allows practitioners to develop progressive BI&A skills concerning managing data, analytical skills, business processes, social and cultural change, and investment decisions to achieve sustainable operational, management and strategic goals.

The paper takes a unique approach that links five knowledge areas to BI&A leadership skills that owners/managers need to ensure for effective adoption and orchestration of BI&A in their organisations. The BI&A leadership framework includes a developmental approach that relates to the iterative and complex nature of BI&A adoption.

Accounting education is re-inventing itself as technology impacts the practical aspects of accounting in the real world and education tries to keep up. Big Data and data analytics have begun to influence elements of accounting including audit, accounting preparation, forensic accounting and general accountancy consulting. The purpose of this paper is to qualitatively analyse the current skills provision in accounting Masters courses linked to data analytics compared to academic and professional expectations of the same.

The academic expectations and requirements of the profession, related to the impact of Big Data and data analytics on accounting education were reviewed and compared to the current provisions of this accounting education in the form of Masters programmes. The research uses an exploratory, qualitative approach with thematic analysis.

Four themes were identified of the skills required for the effective use of Big Data and data analytics. These were: questioning and scepticism; critical thinking skills; understanding and ability to analyse and communicating results. Questioning and scepticism, as well as understanding and ability to analyse, were frequently cited explicitly as elements for assessment in various forms of accounting education in the Masters courses. However, critical thinking and communication skills were less explicitly cited in these accounting education programmes.

The research reviewed and compared current academic literature and the requirements of the professional accounting bodies with Masters programmes in accounting and data analytics. The research identified key themes relevant to the accounting profession that should be explicitly developed and assessed within accounting education for Big Data and data analytics at both university and professional levels. Further analysis of the in-depth curricula, as opposed to the explicitly stated topic coverage, could add to this body of research.

This paper considers the potential combined role of professional qualification examinations and master’s degrees in skills provision for future practitioners in accounting and data analysis. This can be used to identify the areas in which accounting education can be further enhanced by focus or explicit mention of skills that are both developed and assessed within these programmes.

The paper considers the interaction between academic and professional practice in the areas of accounting education, highlighting skills and areas for development for students currently considering accounting education and data analytics.

While current literature focusses on integrating data analysis into existing accounting and finance curricula, this paper considers the role of professional qualification examinations with Masters degrees as skills provision for future practitioners in accounting and data analysis.

The purpose of this study was to analyse the skills required by lecturers to be able to support research data management effectively; assess the research data management literacy levels amongst lecturers at Strathmore University; and suggest how research data management capacity can be strengthened to mitigate the knowledge gaps identified.

This study was conducted as a mixed methods research. Explanatory sequential mixed methods approach was used to collect, analyse and interpret quantitative and qualitative data from lecturers at Strathmore University in Nairobi, Kenya. Quantitative data was collected using questionnaires while qualitative data was collected through focus group discussions. Quantitative data was analysed using SPSS while qualitative data was analysed thematically.

The findings of this study indicate varied levels of research data management literacy amongst lecturers at Strathmore University. Lecturers understand the need of having literacy skills in managing research data. They also participate in data creation, collection, processing, validation, dissemination, sharing and archiving. This is a clear indication of good research data management. However, the study also revealed gaps in research data management skills amongst the lecturers in areas such as sharing of research data on open access journals, data legislation and securing research data.

The study has been conducted in one university in Kenya. However, the findings have been contextualised in the global landscape through suitable references.

The findings of this study may be used to attract the attention of lecturers and librarians to research data management. The findings may also be used to develop institutional policies on research data management at Strathmore University and beyond. The suggested ways of research data capacity strengthening can be adopted or adapted by other universities to enhance research data management.

This is an original study.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

In today's global and competitive corporate environment characterised by rapidly changing information, knowledge and technology (IKT), researchers must be upskilled in all aspects of research data management (RDM). This study investigates a set of capabilities and competencies required by researchers at selected South African public universities, using the community capability model framework (CCMF) in conjunction with the digital curation centre (DCC) lifecycle model.

The post-positivist paradigm was used in the study, which used both qualitative and quantitative methodologies. Case studies, both qualitative and quantitative, were used as research methods. Because of the COVID-19 pandemic rules and regulations, semi-structured interviews with 23 study participants were conducted online via Microsoft Teams to collect qualitative data, and questionnaires were converted into Google Forms and emailed to 30 National Research Foundation (NRF)-rated researchers to collect quantitative data.

Participating institutions are still in the initial stages of providing RDM services. Most researchers are unaware of how long their institutions retain research data, and they store and backup their research data on personal computers, emails and external storage devices. Data management, research methodology, data curation, metadata skills and technical skills are critically important RDM competency requirements for both staff and researchers. Adequate infrastructure, as well as human resources and capital, are in short supply. There are no specific capacity-building programmes or strategies for developing RDM skills at the moment, and a lack of data curation skills is a major challenge in providing RDM.

The findings of the study can be applied widely in research, teaching and learning. Furthermore, the research could help shape RDM strategy and policy in South Africa and elsewhere.

The scope, subject matter and application of this study contribute to its originality and novelty.