Comparing Business Intelligence and Big Data Skills

While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.     

Teaching information technology alongside business analytics: Case study

Business analytics can be described as the statistical analysis of data to make decisions and meaningful conclusions. As the demand to advance the curriculum of undergraduate business education increases, courses in business analytics aim to provide students with fundamental skills in critical thinking. Educators have found that spreadsheet applications that include statistical features are easy to use and facilitate student learning. The authors analyzed student performance in an introductory business analytics course that used Microsoft Excel as a statistical tool by comparing scores from this introductory course with those from an information technology course in which only Excel skills were learned.     

Business curriculum redesign: Integrating data analytics

Data analytics is an integral part of planning and decision making in business. Priorities have shifted to hiring skilled employees to support a company’s analytics requirements. The authors discuss the background of big data and data analytics, demand for trained professionals, and information on the development of a data analytics curriculum. Curriculum design models are explored and emphasis placed on university curriculum redesign. This is a perspective piece that also addresses interdisciplinary collaboration, accreditation, and related challenges.     

Business (un)usual: Critical skills for the next normal

The impact of COVID-19 on global human resource (HR) management has been swift, dramatic and has fundamentally changed HR processes. The prompt online migration of business has altered the skills required by employees to succeed in the workplace of the future. This research examines the hard and soft skill gaps that exist in the digital marketing and advertising industry. Through the use of two data collection points in 2019 and 2020, the research identified a renewed importance being placed on soft skills in the wake of the COVID-19 pandemic. Soft skills and the development thereof have become a key focal area of training for new employees as a result of remote working. The identified hard skill gaps are indicative of the future growth areas of the industry, focusing on data analytics, marketing automation and user experience. Future research should consider an expansion to other industry-specific skills and contrast country-level skill gaps.     

Combining sell-out data with shopper behaviour data for category performance measurement: The role of category conversion power

Retailers need to manage a series of complex decisions relating to numerous products. To reduce this complexity, they have introduced category management practices, which consider groups of similar products (categories) that can be managed separately as single business units (SBUs). Although the concept that the store offer should be organised as a category mix and that this strategy allows for better overall store management is already consolidated, retailers still struggle to adopt an approach to the store performance measurement starting from a category level perspective. Nowadays, the available methods for measuring categories’ performance are quite limited. The current trend sees the measurement of category performance mainly based on sell-out data that are ill-equipped to fully address category management issues. Retailers should broaden their field of analysis not only by focusing on the product/sales perspective but also by including other methodologies such as shopper behaviour analysis. In this regard, the use of technology offers the retail sector new perspectives for those analysis. Therefore, we intend to contribute to the ongoing debate on the retail analytics topic by presenting a shopper behaviour analytics system for category management performance monitoring. More in detail, we could derive a new key performance indicator, category conversion power (CCP), aimed at analysing and comparing the single categories organised within the store. The research is based on a unique dataset obtained from a real-time locating system (RTLS), which allowed us to collect behavioural data togheter with sell-out data (from POS scanner). We argue that retailers could exploit this new analytical method to gain more understanding at the category level and therefore make data-driven decisions aimed at improving performance at the store level.     

The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources

Big data analytics have transformed research in many fields, including the business areas of marketing, accounting and finance, and supply chain management. Yet, the discussion surrounding big data analytics in human resource management has primarily focused on job candidate screenings. In this article, we consider how significant strategic human capital questions can be addressed with big data analytics, enabling HR to enhance overall firm performance. We also examine how new data sources that help assess workforce performance in real time can assist in the identification and development of the knowledge stars that contribute to firm performance disproportionately as well as help reinforce firm capabilities. But in order for big data analytics to be successful in the HR field, regulatory and ethical challenges must also be addressed; these include privacy concerns and, in Europe, the General Data Protection Regulation (GDPR). We conclude by discussing how big data analytics can facilitate strategic change within HR and the organization as a whole.     

Data Science,Predictive Analytics,and Big Data: A Revolution That Will Transform Supply Chain Design and Management

We illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.     

The Business Economist’s Toolkit

Business economists are not members of the high church of academic orthodoxy. Our tool kit includes research functions, such as business cycle analysis, demographics research, data analytics, primary research, diversity of thought, and client research. It also includes personal skills that should be cultivated and practiced, such as curiosity, maintaining a nonpartisan view, humility, courage, and effective communication.     

Big data analytics and firm performance: Effects of dynamic capabilities

Drawing on the resource-based view and the literature on big data analytics (BDA), information system (IS) success and the business value of information technology (IT), this study proposes a big data analytics capability (BDAC) model. The study extends the above research streams by examining the direct effects of BDAC on firm performance (FPER), as well as the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between BDAC and FPER. To test our proposed research model, we used an online survey to collect data from 297 Chinese IT managers and business analysts with big data and business analytic experience. The findings confirm the value of the entanglement conceptualization of the hierarchical BDAC model, which has both direct and indirect impacts on FPER. The results also confirm the strong mediating role of PODC in improving insights and enhancing FPER. Finally, implications for practice and research are discussed.     

Data Science,Predictive Analytics,and Big Data in Supply Chain Management: Current State and Future Potential

While data science, predictive analytics, and big data have been frequently used buzzwords, rigorous academic investigations into these areas are just emerging. In this forward thinking article, we discuss the results of a recent large‐scale survey on these topics among supply chain management (SCM) professionals, complemented with our experiences in developing, implementing, and administering one of the first master's degree programs in predictive analytics. As such, we effectively provide an assessment of the current state of the field via a large‐scale survey, and offer insight into its future potential via the discussion of how a research university is training next‐generation data scientists. Specifically, we report on the current use of predictive analytics in SCM and the underlying motivations, as well as perceived benefits and barriers. In addition, we highlight skills desired for successful data scientists, and provide illustrations of how predictive analytics can be implemented in the curriculum. Relying on one of the largest data sets of predictive analytics users in SCM collected to date and our experiences with one of the first master's degree programs in predictive analytics, it is our intent to provide a timely assessment of the field, illustrate its future potential, and motivate additional research and pedagogical advancements in this domain.     

Business analytics: Why now and what next?

Business analytics is a revolution that is impossible to miss. At its core, business analytics is about leveraging value from data. Instead of being referred to as the ‘sludge of the information age,’ data has recently been deemed ‘the new oil.’ While data can be employed for purposes such as detecting new opportunities, identifying market niches, and developing new products and services, it is also notoriously amorphous and hard to extract value from. In this Guest Editors’ Perspective, we first present a structural framework for deriving value from business analytics. Extracting value from data requires aligning strategy and desirable behaviors to business performance management in conjunction with analytic tasks and capabilities. We then introduce three special articles that provide in-depth insights regarding how business analytics is being employed in the management of healthcare, accounting, and supply chains.     

Start-ups by the unemployed: characteristics,survival and direct employment effects

Fostering and supporting start-up businesses by unemployed persons has become an increasingly important issue in many European countries. These new ventures are being subsidized by various governmental programs. Empirical evidence on skill-composition, direct job creation and other key variables is rather scarce, largely because of inadequate data availability. We base our analysis on unique survey data containing a representative sample of over 3,100 start-ups founded by unemployed persons in Germany and subsidized under two different schemes: the bridging allowance (BA) and the start-up-subsidy (SUS). We are able to draw on extensive pre- and post-founding information concerning the characteristics of the business (start-up capital, industry, etc.) and of the business founders (education, motivation, preparation, etc.). Our main results are: (1) The two programs attracted very different business founders (higher skilled for the BA, more female persons for the SUS), and different businesses were created (less capital intensive for the SUS). (2) We find that formerly unemployed founders are motivated by push and pull factors. (3) Survival rates 2.5 years after business founding are quite high (around 70%) and similar for both programs and across gender. (4) However, the newly developed businesses differ significantly in terms of direct employment effects. While around 30% of the founders with the BA already have at least one employee, this is true for roughly 12% of the founders with the SUS.     

Identification of Marketing Needs and Means of Communication of Romanian Marketing Companies in the Context of the Knowledge Society

A few decades ago, academics and economic pundits used to say that information is the main source of power. However, in the Knowledge Society, as we experience it today, information is readily available for everyone, and the real challenge is to master modern and complex information analysis tools, which can make sense of the information overflow of modern society, thus constituting the true competitive advantages of major economic players. For a competent analyst, data generated by a survey, for instance, can reveal paramount information about consumer behavior, competitive strategies, or any other economic and social environment-related aspect deemed important. The marketing needs of Romanian marketing companies are very diversified, commencing with the research of the needs and demand on the target market, going down to the 4Ps (Product, Price, Promotion, and Placement) and their components. The present marketing research was performed using seven categories of information sources: specialized publications in marketing; specialized publications in economics; information provided by specialized institutions in consultancy and marketing research; information provided by individual marketing specialists; information provided by advertising agencies; information provided by the economic sections of the daily newspapers; and job Websites.     

The Role of Big Data and Predictive Analytics in Retailing

The paper examines the opportunities in and possibilities arising from big data in retailing, particularly along five major data dimensions—data pertaining to customers, products, time, (geo-spatial) location and channel. Much of the increase in data quality and application possibilities comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. The importance of theory in guiding any systematic search for answers to retailing questions, as well as for streamlining analysis remains undiminished, even as the role of big data and predictive analytics in retailing is set to rise in importance, aided by newer sources of data and large-scale correlational techniques. The Statistical issues discussed include a particular focus on the relevance and uses of Bayesian analysis techniques (data borrowing, updating, augmentation and hierarchical modeling), predictive analytics using big data and a field experiment, all in a retailing context. Finally, the ethical and privacy issues that may arise from the use of big data in retailing are also highlighted.     

“I can get no e-satisfaction”. What analytics say? Evidence using satisfaction data from e-commerce

This study mines customer satisfaction (CS) segments using almost 270 thousand responses from a CS survey which ran in 140 e-commerce stores of a European country. To achieve this, it develops and applies a business analytics (BA)-informed framework. Then, it presents examples of how one e-commerce store exploited the extracted CS segments to build automated marketing actions for its customers, ranging from social media sharing strategies for the satisfied segments, to discounts for the less satisfied. This study contributes to customer satisfaction and segmentation literature. The extracted insights can be utilized to support decision making, ranging from targeted advertising for specific customer segments, to benchmarking for companies in similar industries.     

Advanced technologies and international business: A multidisciplinary analysis of the literature

Advanced digital technologies, such as the Internet of Things, blockchain, big data analytics and augmented reality, are gradually transforming the way multinational firms do business. Due to the extent of this transformation many scholars argue that the integration of these technologies marks the commencement of the fourth industrial revolution (Industry 4.0). However, the question how these advanced technologies impact international business activities needs further attention. To this end, we adopt a multidisciplinary approach to review the related literature in international business (IB), general management, information systems, and operations research. We include the two latter fields, because advanced technologies have received more attention in these bodies of literature. Based on our analysis, we discuss the implications of these technologies for international business. Further, we highlight the drivers of technology utilisation by multinational firms and likely outcomes. We also provide future research avenues.     

Developing International Business Skills Through the Study of Eastern European Transitional Economies—An Inter-Cultural Case Method Approach

Abstract

This paper describes the educational experience of students enrolled on the BA (Hons) International Accounting and Finance degree at the University of Brighton, who undertake a case study visit to Kraków, Poland to study the transition of an economy and an enterprise and at the same time acquire skills relevant for international business. The paper is supported by a questionnaire survey of all five cohorts of students who followed this course. Three of these cohorts have now left the University and are in a position to reflect on their educational experience in the light of the skills required in their current employment. The course is also proposed as a forerunner of an MBA to be followed by the student later in their development.