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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 the article is to gain more insight into factors that can explain students' success in business subjects. The focus is on the connection between performance in introductory courses in business mathematics (BM) and business statistics (BS) and success in various business subjects.

Use of a regression model with administrative data from a business school in Norway over a period of 10 years.

The findings show a strong correlation, especially in quantitative subjects. The results suggest that statistical skills are more strongly related to academic success than mathematical skills.

The data are collected from only one school. No information on undergraduates' personalities and behaviours is available.

There are limited published studies that have explored the relationship between success in statistics and later achievements in business courses. This is useful knowledge for planning the content of the bachelor's programme.

The purpose of this study is to identify the specific skills and abilities within the broad category of data analytics that current business professionals believe are most important for accounting graduates. Data analytics knowledge is clearly important, but this category is broad. Therefore, this study identifies the specific skills and abilities that are most important for accounting graduates so that faculty can create classroom materials most beneficial for the future accounting graduates. In 2013, the Association to Advance Collegiate Schools of Business developed new standards for accounting programs, including standard A7, related to information technology and analytics. The intent of the standard clearly focuses on increasing the level of technology and analytics studied within the accounting curriculum. However, the specific details and methods for achieving the intent of A7 remain an open question. This chapter uses prior research focused on business analytics education to identify potential analytic skills, tools, techniques, and management issues of concern within the accounting profession. A survey of 342 accounting professionals identifies suggested areas of analytic competencies for accounting graduates. Specifically, the authors find preferences for skills related to data interpretation and communication over any individual technical skills or statistical knowledge. These skills suggest a role for accountants as intermediaries who may need to translate analytic activities into business language. Post hoc, the authors examine the survey results for differences based on respondent characteristics. Interestingly, female respondents report lower beliefs about the importance of analytic skills. The authors also find some differences when examining different demographics within the respondents.

The purpose of this study is to investigate the state of undergraduate business statistics education in the Middle East and North Africa (MENA) and assess its alignment with the best practices in equipping business graduates with the knowledge and skills demanded by the labor market.

A survey of 108 instructors from 80 business schools in 17 MENA countries was conducted to gauge information on the delivery of business statistics courses. The survey results were benchmarked to a proposed framework for best practices in business statistics education.

The gap analysis identified deficiencies in the delivery of business statistics education in the region as compared to international best practices. This study revealed a need to revise statistics education as part of a comprehensive reform of business education with the aim to meet international quality standards in business education.

The study relied on the self-reported responses of business statistics instructors in MENA. One hundred eight questionnaires were completed, corresponding to a response rate of 40 per cent. Moreover, the study did not measure the effectiveness of teaching and learning in business statistics courses.

Recommendations from the study are intended to guide business statistics instructors in improving the quality of business statistics education through adopting more effective ways to enhance student learning experience and graduate employability.

This study is the first of its kind to investigate and assess the business statistics education in the MENA region.

Little attention is paid to understanding generic skills in business. Even less attention is paid to collecting evidence of students' development of these skills. This paper aims to fill this gap.

Four generic skills in business undergraduate and graduate programs are examined – written communication; critical thinking; use of mathematical and statistical tools; and information literacy. A total of 341 individual student assessments were reviewed.

Results suggest that there are skills deficits in effectively using language and coherence in writing; taking different perspectives and integrating ideas; understanding, presenting and solving a problem; and evaluating information to produce new and original thought.

This paper presents some important findings from the evaluation of student development of four different generic skills promoted in business disciplines.

Existing research on the relationship between big data and organizational decision quality is still few and far between, and what does exist often assumes direct effects of big data on decision quality. More recent research indicates that such direct effects may be too simplistic, and in particular, an organization’s overall human skills are often not considered sufficiently. Inspired by the knowledge-based view, we therefore propose that interactions between three aspects of big data usage and management accountants’ data analytics skills may be key to reaching high-quality decisions. The purpose of this study is to test these predictions based on a survey of US firms.

The authors draw on survey data from 140 US firms. This survey has been conducted via MTurk in 2020.

The results of the study show that the quality of big data sources is associated with higher perceived levels of decision quality. However, according to the results, the breadth of big data sources and a data-driven culture only improve decision quality if management accountants’ data analytics skills are highly developed. These results point to the important, but so far unexamined role of an organization’s management accountants and their skills for translating big data into high-quality decisions.

The present study highlights the importance of an organization’s human skills in creating value out of big data. In particular, the findings imply that management accountants may need to increasingly draw on data analytics skills to make the most out of big data for their employers.

This study is among the first, to the best of the authors’ knowledge, to provide empirical proof of the relevance of an organization’s management accountants and their data analytics skills for reaching desirable firm-level outcomes. In addition, this study thus adds to the further advancement of the knowledge-based view by providing evidence that in contemporary big-data environments, interactions between tacit and explicit knowledge seem crucial for driving desirable firm-level outcomes.

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.

Statistical process control (SPC) is a powerful technique which organisations can use in their pursuit of continuous improvement of both product and service quality. Many organisations in the UK are still learning about the successful introduction, development and implementation of SPC, even though it has been widely and commonly used in many Japanese organisations with great success. Research in the UK academic institutions has clearly indicated that the only thing taught to engineers in relation to SPC is control charting and the mathematical aspects of the subject rather than the implementation aspects of the technique. It can be argued that it is not just control charts which makes SPC initiative successful in organisations, rather the emphasis should be on the critical factors which are essential for the success of SPC program and also issues such as “how to get started” and “where to get started”. This paper compares the existing frameworks for SPC implementation in terms of their strengths and weaknesses and then illustrates a conceptual framework for the successful introduction and application of SPC program in any organisation. The framework also shows a systematic approach to apply the SPC technique in an industrial setting.

The purpose of this study is to critically evaluate the current status of Design of experiments (DoE) in the service industry or non-manufacturing processes in the manufacturing sector. This involves the identification of critical success factors and challenges in the use of DoE in the context of the service industry as well as the key skills needed for its successful applications.

In order to identify the critical success factors and challenges, an online survey instrument was developed and distributed across Six Sigma Black Belts (SSBB), Six Sigma Master Black Belts (SSMBB), process improvement champions, continuous improvement professionals, DoE experts and specialists as well as academics who are involved in DoE research and applications.

A total of 109 participants from over 20 countries responded to the study. One of the first findings of the study is that over 40% of the process improvement professionals in the service sector do not have any hands-on experience with the application of DoE. Further, this study highlights the following critical factors for successful DoE applications: dedicating adequate resources in terms of time, budget and people for the experiment; getting a clear understanding of the problem at hand; securing a reliable and validated measurement system for measuring the response or quality characteristics of interest; and identifying input independent variables, which influence the quality characteristics during the brainstorming stage. Some respondents also mention the importance of having training or education in DoE as a critical success factor. At the same time, the lack of adequate planning to experimentation, mathematical knowledge and statistical reasoning remains as challenge to be solved.

As data were collected at an individual level, inter-reliability, which measures the consistency of judgements on a particular survey item, could not be gauged. Moreover, due to low sample size across some continents, an intercontinental comparative study on the extent of the application of DoE across various continents could not be performed.

To the best of the authors’ knowledge, this is the first global survey carried out to understand the status of DoE in the service industry. The results of the global study can be used to encourage quality, continuous improvement and process improvement professionals in the service sector to drive wider acceptance of one of the most powerful tools in the Six Sigma toolkit for problem-solving scenarios. Moreover, senior managers in the service industry can take away some of the key benefits, challenges and success factors in the implementation of DoE.