Data analytics skills and employability among accounting graduates: perceptions of accounting professionals in the UAE

Purpose –The study aims to reveal the current status of the skills of those working in audit firms in today's changing business world. In addition, the aim of the study is to reveal the differences between Global audit firms and local audit firms in Türkiye.Design/methodology/approach –In this study, descriptive analysis method were used to reveal the existing situation. It is aimed to reach the top 20 audit firms with the highest turnover in the world and 364 audit firms registered with the Public Oversight Agency in Türkiye as of January 2022. LinkedIn corporate pages of 19 of the top 20 audit firms with the highest turnover in the world and 58 of 364 audit firms in Turkey have been reached.Findings –It has been revealed that the soft skills of employees in Big4 firms are an important complement to their technical skills. This situation is more limited in local audit firms. While Data Analytics (DA) skills are also observed in employees of Big4 firms,no one stated skills with the word “data analytics” in local audit firms. It has been determined that Python, R, SQL, Power BI and Tableau tools are used as DA skills. Engineering education stands out for those who indicate Python, R, and SQLskills.Discussion –The study's empirical evidence shows that Big4 firms need DA skills more and more as they move from the accounting and auditing fields to consulting, technology, and digital services. It shows the lack of both soft and DA skills in local audit firms. This study develops empirical knowledge of what employee skills are and should be for global and local audit firms

Oil and gas companies are increasingly using data analytics to improve drilling performance. This paper provides an example of using a business intelligence (BI) tool to analyze drilling data in the Permian Basin. The BI tool helped to improve operation decisions through the use of a visual report. A database, consisting of massive amounts of historical drilling data, is analyzed using the BI tool to better understand drilling performance and predict average operations performance in the area. A historical drilling database is created based on the bottomhole assembly (BHA) run data and analyzed by the BI tool to review the well performance, in addition to identifying any hazards and summarizing the optimum drilling system during the planning phase. With the help of the BI tool, the drilling database can be displayed in an interactive way to further understand the drilling performance in the area; e.g., the top performing drill bit, drilling system, downhole mud motor configuration, and estimated drilling time for the section of interest. As a result, engineers will find it easier to identify the potentially top performing wells along with drilling hazards in offset wells. The engineers can evaluate the well details and identify the best drilling practices to optimize drilling performance and eliminate downhole incidents. Using the BI tool helps reduce the data mining time and offers a fast, improved method for gaining technical insights into the drilling operation. These descriptive analytics help to simplify the complex data sets, which are valuable for uncovering patterns that offer data set understanding. With the visualization results, experts can focus on data diagnostics analytics to make suggestions for drilling operation improvements and corrections. Furthermore, these analytical data can be used as inputs for more advanced predictive (forecast drilling performance) or prescriptive analytics (drilling optimization) that deliver real-time insights for making improved business decisions.

How do organizations develop and manage employees’ data analytics skills to create business value and enhance organizational competitive advantage? In order to address this prominent and critical research question for IS research, we conceptualize and operationalize data analytics skills at the individual level and develop a nomological network model to examine its critical antecedents and outcomes from the lens of adaptation structuration theory. We test our core proposition and research model using survey data collected from 258 frontline employees of three data-intensive research institutes in China. We discover that data-driven culture, data analytics affordance, and individual absorptive capacity are positively associated with employees’ data analytics skills, which in turn, have positive influences on their task and innovative performance. We classify the employees into digital immigrants and digital natives based on age and examine the different influences of three salient antecedents on data analytics skills between the two groups. The research findings suggest that data-driven culture plays a more significant role in driving data analytics skills for digital immigrants, while data analytics affordance exhibits a stronger influence on data analytics skills for digital natives.

The accountancy profession is now challenged by the pace of technological advancement and the ubiquitous digitalization leading to data explosion and advanced analytics. Digital technology is also replacing mundane tasks and manual work which accountants undertook in the past. Besides data analytics skills, accountants now need to possess critical thinking skills, knowledge of data science tools and communication skills. Consequently, equipping accounting professionals with data analytics skills is critical. Professional accounting bodies address this need by emphasizing continuing professional education and developing guidelines for data analytics. At the same time, higher education institutions are taking the initiative to integrate data analytics into their accounting curricula. However, given the numerous professional accreditation requirements that higher education institutions must fulfill, a big challenge remains for any institution to insert rigorous data analytics training into their existing curriculum. This chapter describes the development of a data analytics roadmap for undergraduate accountancy education—from reviewing our academic and industry data analytics curricula and evaluating existing modules that could be integrated with relevant data analytics topics, to seeking feedback from industry partners regarding the curriculum model we had developed. In delivering our curricula across the levels of study, a spaced retrieval teaching technique was opted to ensure that students could progressively develop data analytics competencies.KeywordsAccounting EducationData Analytics CompetenciesUndergraduate CurriculumSpaced Retrieval

Data assets that become very vast, unstructured, and move in a fast pace as a result of the digitalization of organizational business processes have a substantial impact and may have implications for greater advantages from data analytics. This creates a need for modifications to accounting education. This study examines how data analytics is integrated into the accounting curriculum. Through an online survey of 238 accounting educators. The findings indicate a gap between the need and actual conditions incorporating data analytics capabilities into the curriculum, with a scarcity of resources with data analytic skills serving as the main cause. The findings have implications for all stakeholders involved in accounting education in Indonesia, encouraging them to collaborate to anticipate the potential effects of the Industry 4.0 revolution, particularly about the graduates’ data analytics skills and ability to make the best business decisions.

This paper reports on Data Analytics Research (DAR), a course-based undergraduate research experience (CURE) in which undergraduate students conduct data analysis research on open real- world problems for industry, university, and community clients. We describe how DAR, offered by the Mathematical Sciences Department at Rensselaer Polytechnic Institute (RPI), is an essential part of an early low-barrier pipeline into data analytics studies and careers for diverse students. Students first take a foundational course, typically Introduction to Data Mathematics, that teaches linear algebra, data analytics, and R programming simultaneously using a project-based learning (PBL) approach. Then in DAR, students work in teams on open applied data analytics research problems provided by the clients. We describe the DAR organization which is inspired in part by agile software development practices. Students meet for coaching sessions with instructors multiple times a week and present to clients frequently. In a fully remote format during the pandemic, the students continued to be highly successful and engaged in COVID-19 research producing significant results as indicated by deployed online applications, refereed papers, and conference presentations. Formal evaluation shows that the pipeline of the single on-ramp course followed by DAR addressing real-world problems with societal benefits is highly effective at developing students' data analytics skills, advancing creative problem solvers who can work both independently and in teams, and attracting students to further studies and careers in data science.

Undertaking a multistage qualitative approach, this study explores the accounting profession's demands for information communication technology (ICT) skills as well as the opportunities, challenges and influential factors that accounting academics encounter in embedding ICT and data analytics skills in the accounting curriculum. We employ content analysis of the course syllabi of all major Australian and New Zealand universities using term frequency‐inverse document frequency (TF‐IDF), and survey accounting heads of departments (or equivalents) and interview members of the industry. Our findings reveal diverse pedagogical approaches to embedding ICT and data analytics in the accounting curriculum. The findings also portray the gap between university accounting curricula and the professional bodies' ICT competencies requirements at the point of data collection. As such, challenges and future opportunities for the integration of topics related to ICT and data analytics are identified, which should be beneficial to academics and practitioners interested in future‐oriented curriculum designs that are fundamental to accounting graduates and the future of the profession.

This paper discusses business intelligence algorithms and data analytics capabilities of an integrated digital production platform implemented in a giant gas condensate field. The advanced workflow focuses on helping the user navigate through the bulk of data to identify patterns and make predictions utilizing exception-based intelligence alarming. This helps derive insightful findings and provides recommendations for users to make efficient business decisions for achieving field potential optimization objectives. An Integrated digital production platform within a giant gas condensate field is implemented with numerous production optimization workflows encompassing daily well and facility performance monitoring and surveillance. The data integration within the systems is enhanced by integration with powerful Business Intelligence (BI) tools, enabling users to create customized dashboards, KPI screens, and exception-based alarm screens. An additional integration to the production platform is carried out with data from real-time sources like PI Asset Framework and corporate databases, improving the integrated production system's daily well and facility surveillance capabilities. The advanced integration of BI tools provided users with various opportunities to identify bottlenecks, production improvement chances, and troubleshooting areas by capitalizing insights from various dashboards and business KPI screens. Further, integrating these dashboards with several corporate data sources and a real-time asset data framework enabled users to harness maximized information embedded in the bulk of data. This also enabled end-users to harness maximized system potential, with all information available under a single collaborative platform. The integration powered by various inbuilt complex algorithms extended scripting capabilities, and enhanced visualization assisted the asset in realizing business KPIs requirements. Business intelligence algorithms in user interface established a drill-down approach to utilize information associated with multiple variables on top of one another. This allowed for the quick identification of trends and patterns in data. The customization approach helped the user to draw maximum information out of data as per their engineering requirements and current practices. This advanced integration facilitated users to minimize their efforts in traditional data analysis such as gathering, mapping, filtering, and plotting. With the help of these powerful features embedded in an integrated platform, the user was able to drive more focus on optimization and minimize time and effort on system configuration. This unique integration was one of its kind. An online integrated digital production platform comprising of wells, networks, and various workflows was integrated with business intelligence tools, thereby providing end-users tremendous opportunities related to system optimization.

This edited volume examines the rapidly evolving intersection of Artificial Intelligence (AI), Machine Learning (ML), and business analytics, offering a comprehensive analysis of how these technologies redefine organizational decision-making and competitive strategy. The book explores a broad spectrum of themes, including enterprise-scale AI implementation, predictive and prescriptive analytics integration, data governance, organizational readiness, ethical frameworks, and future-focused analytical capabilities. Contributions span multiple domains such as e-commerce sentiment analysis, digital transformation, AI-enabled strategic decision-making, IoT-driven quality monitoring, workforce AI literacy, and chatbot-driven retail innovation. Drawing from empirical research, case studies, theoretical frameworks, and emerging technological trends, the volume highlights both the opportunities and challenges associated with AI adoption—including algorithmic performance, real-time adaptability, data quality, privacy, and sociotechnical tensions. The collective insights emphasize the need for scalable, interpretable, and ethically governed AI systems that align with organizational culture and strategic goals. This work serves as a critical resource for researchers, practitioners, executives, and policymakers seeking to advance organizational excellence through AI-driven analytics and future-ready decision-making. Keywords Artificial Intelligence, Machine Learning, Business Analytics, Decision-Making, Predictive Analytics, Prescriptive Analytics, Data Governance, Digital Transformation, Organizational Readiness, AI Ethics, Sentiment Analysis, E-Commerce Marketing, Big Data Analytics, Chatbots, Generative AI, IoT, Edge AI, Workforce Transformation, AI Literacy, Strategic Management, Data-Driven Decision-Making, Reinforcement Learning, Multimodal Analytics.

This study explores the implications of market digital transformation in the United Arab Emirates (UAE) for the undergraduate accounting curriculum. Responding to a number of government initiatives toward artificial intelligence (AI) transformation, corporations and government agencies in the UAE have recently started to test and adopt AI, Blockchain Technology (BT), and Data Analytics (DA) in their operations. This digital transformation in the business environment raises concerns as to whether existing accounting curricula are preparing accounting graduates for the emerging IT needs relevant to the existing accounting job market. To this end, this study explores the extent to which the current accounting curriculum in the UAE reflects the current digital transformation in the country.

In an aging field, where the intensive activity of new wells and interventions are taking place, commingled production is established and water flooding project is reaching maturity, it is of paramount importance to have high frequency, high accuracy production data to fine-tune models, to evaluate, and to estimate optimization impacts and to reduce potential deferred losses. A digital solution was implemented to allow early identification of events, estimate production losses, and thus classify and prioritize events for optimizations using data analytics. Production monitoring and optimization have been difficult to accomplish because of problems at surfacefacilities such as cross flow at manifold valves, shared production lines, unstable production from commingledwells and low well test repeatability. Low well test repeatability was caused by a reduced number of operative equipment such as separators and multiphase flowmeters. With the application of big data and data analytics, a virtual flowmeter (VFM) was created, calibrated and run-in real time for several pilot wells. The virtual flowmeter (VFM) uses the electric submersible pump (ESP) data and the fluid characteristics, integrated with a dashboard from a business intelligence tool that was developed to rank critical wells to intervene for optimizations. The pilot application enables us to quantify the volume lost during operative events in the wells, as well as the gained production from optimization activities, integrating a powerful production monitoring and electrical submersible pump (ESP) parameters surveillance tool to guarantee the continuity of operations, maximize well optimizations, and increase operational efficiency and productivity on real time. An average of 475 BOPD are associated with deferred production. Using a data-base platform and the connectivity provided by a supervisory control and data acquisition (SCADA) fiber-optics, all the variables can be monitored in real time to develop data analytics and make an early identification of events, give a rapid response and to reduce the production losses. This digital implementation has shown remarkable results, enabling us to reduce 80% of our manual processing time, optimizing our field operator's mobilizations, reduced transport time, carbon dioxide (CO2) emissions/year, and mobilizations risk, reducing the response time from days to minutes, and ensuring the operative continuity of the production, optimizing costs, maximizing people's efficiency, and evolving the monitoring process. This paper shows the pilot wells selection, the VFM creation using data analytics, calibration, and connections to run the digital application in real time with a dashboard from a business intelligence tool. This solution is a clear example of what the digital transformation capability brings to any oilfield, showing the industry that is not only an example of optimizing production, but also to demonstrate that edge computing, data analytics, and data science can be applied at all maturity levels in oil and gas fields becoming a game changer for the next generations.

In the existing environment characterized by information abundance, decision-making skills determine the organization’s competitive position preservation. BI encompasses several technologies that have evolved in the recent couple of decades, as follows: It t allows enterprises effectively translate large volumes of raw data into productive insights hence enhancing decision making. This article evaluates progress on the state of art on modern BI technological solutions that have transformed the way that businesses manage information for value creation. This focus is on AI, ML, predictive analytics and cloud computing that have revolutionized business intelligence. In this research, the outcomes obtained from using modern BI tools are discussed depending on the type of industry. In eligible health care, AI practicing forecasting models estimate the number of patients and resources; thus enhances the health care working system. This is whereby financial institutions find it useful to use real-time data analysis in order to detect such activities and prevent risks. The case examples given are emblematic for the fact that reliance on knowledge is growing and underline how BI technologies can enhance efficiency, reduce expenditure and thus improve general organizational performance. However, the above developments have been noted, the use of current BI tools has the following challenges. The issues regarding privacy and security of the data, the problems associated with the roll out of large technological infrastructures coupled with the scarcity of trained professional labor to gather, analyze and understand the data remain difficulties. Furthermore, it could also be stated that data quality and governance are the major drivers for BI system impact. This research continues to indicate areas of future research as far as BI tool compatibility with large data sets, improving data governance, and more within moral theory as it relates to AI-based decision making. That said let me make it clear that as all these innovations are gradually embraced by enterprises BI tools remain pivotal in charting the future direction of analytical decision making.

In the age of digital transformation and data abundance, data analytics has become a fundamental driver of strategic decision-making in modern enterprises. This systematic review critically examines the role of data analytics in shaping business strategy, with a focus on analytical typologies, technological tools, and the mechanisms through which analytics contributes to competitive advantage. Guided by the PRISMA methodology, a total of 162 peer-reviewed journal articles published between 2015 and 2025 were systematically identified, screened, and analyzed from major academic databases including Scopus, Web of Science, and IEEE Xplore. The review synthesizes findings across multiple domains, revealing that descriptive, diagnostic, predictive, and prescriptive analytics each contribute uniquely to strategic planning and performance optimization. Business Intelligence tools such as Tableau and Power BI, along with AI-driven forecasting models and cloud-based analytics infrastructure, emerged as critical enablers of data-informed strategic decisions. Moreover, the study highlights the organizational conditions—such as leadership support, data governance, and analytics maturity—that determine the effectiveness of analytics implementation. Sectoral and geographic analyses further reveal disparities in adoption and outcomes, emphasizing the contextual nature of analytics strategy. Theoretical foundations including the Resource-Based View (RBV), Dynamic Capabilities View (DCV), and Technology-Organization-Environment (TOE) framework provide a conceptual lens for interpreting the findings. By consolidating evidence from 162 scholarly sources with over 40,000 combined citations, this review offers a comprehensive perspective on how analytics capabilities are developed, deployed, and leveraged to create strategic value. The study also identifies persistent gaps in longitudinal ROI assessments and calls for more diverse empirical research across industries and global contexts. This review provides actionable insights for researchers, practitioners, and policymakers aiming to harness analytics for sustained organizational advantage.

This study explores the evolution and current state of business intelligence (BI) tools and their strategic role in driving business growth. The research utilizes a combination of market analysis, industry case studies, and theoretical frameworks, including the DIKW hierarchy and Resource-Based View, to examine BI adoption trends. The results highlight the importance of data quality, cross-functional collaboration, and user adoption in maximizing BI effectiveness. Key findings indicate that cloud-based and self-service BI tools significantly improve data-driven decision-making, while challenges remain in data governance and integration. To address these challenges, organizations must implement robust data policies and empower users through training and self-service capabilities. The study concludes that integrating BI tools into digital transformation initiatives provides a competitive edge, enabling strategic planning, operational efficiency, and innovation. This research offers new insights into how organizations can leverage BI tools for sustained growth and enhanced decision-makingl.

This study explores the evolution and current state of business intelligence (BI) tools and their strategic role in driving business growth. The research utilizes a combination of market analysis, industry case studies, and theoretical frameworks, including the DIKW hierarchy and Resource-Based View, to examine BI adoption trends. The results highlight the importance of data quality, cross-functional collaboration, and user adoption in maximizing BI effectiveness. Key findings indicate that cloud-based and self-service BI tools significantly improve data-driven decision-making, while challenges remain in data governance and integration. To address these challenges, organizations must implement robust data policies and empower users through training and self-service capabilities. The study concludes that integrating BI tools into digital transformation initiatives provides a competitive edge, enabling strategic planning, operational efficiency, and innovation. This research offers new insights into how organizations can leverage BI tools for sustained growth and enhanced decision-making.