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There are plenty of definitions proposed for business analytics – some of them focus on the scope/coverage/problem, some on the nature of the data, and some concentrate on the enabling methods and methodologies. The common denominator of all of these definitions is that business analytics is the encapsulation of all mechanisms that help convert dat...
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... moving from one level to the next essentially means that the maturity at one level is completed and the next level is being widely exploited. Figure 3 shows a graphical depiction of business analytics taxonomy developed by INFORMS' initiative and Figure 4 shows how this taxonomy can be leveraged along two axis -value proposition and computational sophistication -for a deeper understanding. ...
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... moving from one level to the next essentially means that the maturity at one level is completed and the next level is being widely exploited. Figure 3 shows a graphical depiction of business analytics taxonomy developed by INFORMS' initiative and Figure 4 shows how this taxonomy can be leveraged along two axis -value proposition and computational sophistication -for a deeper understanding. ...
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... Many studies are descriptive (Ghahfarokhi, Soroush, & Hasanbeigi, 2022;Matos et al., 2021) and predictive (Lee, Kim, Kim, & Lee, 2022;Leitner & Richlan, 2021a) with limited theoretical work (Webb, 2021) and no diagnostic or prescriptive analyses (Delen & Ram, 2018). In research, theoretical reviews occur when a topic has sufficient investigations. ...
The COVID-19 pandemic enabled a natural experiment examining how spectator absence impacted home advantage in football. This systematic review analysed research characterizing home advantage dynamics with no fans present across diverse leagues worldwide. The aim was assessing the profile, trends, methodologies, procedures, and developments in this emerging field. Searches in specialized databases identified 50 relevant studies following PRISMA guidelines. These works were systematically assessed to extract key details related to design, country, competition level, analytic approach, performance variables, and home advantage findings. Overall methodological quality was categorized as excellent. Most research occurred in top European men's leagues, with limited attention to other levels. Predictive (58%) and descriptive (36%) investigations predominated, chiefly utilizing regression and group comparisons. Points, goals, cards, and result were the primary metrics. Results demonstrated home advantage decreases without spectators in most leagues, conforming to social facilitation theories stipulating performance declines minus audience encouragement. Additional research is warranted across female competitions, youth categories, amateur settings, and knockout tournament stages. Maintaining methodological rigor while expanding domains will solidify understanding of this intricate phenomenon to guide teams performing both home and away.
... With these new data fields, data will serve as an intuition enhancing tool. To do so, it is crucial to think beyond just applying traditional statistical or machine learning methods to ordinary problems in conventional ways (Delen and Ram 2018). ...
Building a successful robotics team is a long journey with much effort before the first surgery. It starts with sound business development and, in case of acquiring a system, an implementation plan, to make it a success. The pathway to a successful Robotics program is much more than choosing the desired robotic arm-assisted surgery (RAS) brand. Success also depends on the specific pathway optimization aspects of RAS. High-over, all systems introduce the computer and robotic-arm into the operating room. But all systems have subtle, but significant, differences. An essential aspect of a successful RAS project is the implementation phase. After deciding to purchase a Robotic system, the following training and OR setup phase should be prepared and executed. When the robotic system enters the operating room, aspects like arm position using the arm board should be evaluated critically since the robot needs sufficient working space. A suboptimal positioning will disrupt the team dynamics and lead to preventable delay. RAS requires new or adjusted skills. Two crucial aspects are eye-hand-feet coordination in combination with a different focus of the surgeon (also screen instead of 100% surgery field) and new cognitive decision making features. Robotic surgery is a perfect example of how technology can change a surgical field. This data acquisition is probably the most fundamental, powerful aspect of adding the computer into the surgical process. The most used robotic-arm systems in hip and knee arthroplasty are semi-automatic systems, and practically all major orthopedic manufacturers offer a device. ORs are a highly capitalized section of hospitals, generating high costs and critical revenues. Therefore, taking a closer look at workflows, inventory management, and team efficiencies is crucial. Improving these aspects in the OR has a high return on investment. RAS helps accurately observe everything occurring within and around the surgical process. These new data opportunities open the opportunity to work with surgical data science (SDS).
... Tabular data analysis plays a crucial role in various fields, and automated data analysis has the potential to enhance people's work efficiency significantly (Delen and Ram 2018a). The emergence of large language models has shown promising capabilities to accelerate tabular data analysis (Chen 2023;Ye et al. 2023;Ma et al. 2023;Jiang et al. 2023). ...
... We introduce the involved tasks one by one as follows: 1. Rudimentary Operations: These operations encompass a set of functions and procedures that can be executed using the Structured Query Language (SQL) (Date 1989). Their primary purpose is to enable users to perform data management, manipulation, and transformation on multidimensional structured data. ...
Tabular data analysis is crucial in various fields, and large language models show promise in this area. However, current research mostly focuses on rudimentary tasks like Text2SQL and TableQA, neglecting advanced analysis like forecasting and chart generation. To address this gap, we developed the Text2Analysis benchmark, incorporating advanced analysis tasks that go beyond the SQL-compatible operations and require more in-depth analysis. We also develop five innovative and effective annotation methods, harnessing the capabilities of large language models to enhance data quality and quantity. Additionally, we include unclear queries that resemble real-world user questions to test how well models can understand and tackle such challenges. Finally, we collect 2249 query-result pairs with 347 tables. We evaluate five state-of-the-art models using three different metrics and the results show that our benchmark presents introduces considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.
... descriptive, predictive, and prescriptive analytics (Delen & Ram, 2018). Other category of business analytics is diagnostic analytics. ...
In today's data-driven world, the utilization of business intelligence and machine learning has become imperative for companies seeking a competitive edge and informed decision-making. This article examines the employment of machine learning algorithms towards improving business actionable insights. An apple dataset and three classifiers namely, Logistic Regression, K-Nearest Neighbor, and Naïve Bayes were employed. The performance of developed models is evaluated using precision, recall, f1-score, and accuracy metrics. Results indicate that all three classifiers offer better performances, although K-Nearest Neighbor outperforms the others with a 90% accuracy, precision, recall and f1-score rates. This suggests that K-Nearest Neighbor is the best classifier among the three, with the highest fitness for the dataset used in this study. This study also highlights the significance of integrating business analytics and machine learning in driving business success and providing better business insights in business organizations.
... The surge in the availability of enormous textual data, coupled with the increasing complexity of financial markets, has necessitated the application of advanced NLP models to extract meaningful insights. Many finance-related areas benefit from these technologies, including financial research [1][2][3], business analytics [4][5][6][7], risk assessment [8][9][10], stock market prediction [11][12][13], and financial sentiment analysis [14][15][16][17]. ...
The task of company classification is traditionally performed using established standards, such as the Global Industry Classification Standard (GICS). However, these approaches heavily rely on laborious manual efforts by domain experts, resulting in slow, costly, and vendor-specific assignments. Therefore, we investigate recent natural language processing (NLP) advancements to automate the company classification process. In particular, we employ and evaluate various NLP-based models, including zero-shot learning, One-vs-Rest classification, multi-class classifiers, and ChatGPT-aided classification. We conduct a comprehensive comparison among these models to assess their effectiveness in the company classification task. The evaluation uses the Wharton Research Data Services (WRDS) dataset, consisting of textual descriptions of publicly traded companies. Our findings reveal that the RoBERTa and One-vs-Rest classifiers surpass the other methods, achieving F1 scores of 0.81 and 0.80 on the WRDS dataset, respectively. These results demonstrate that deep learning algorithms offer the potential to automate, standardize, and continuously update classification systems in an efficient and cost-effective way. In addition, we introduce several improvements to the multi-class classification techniques: (1) in the zero-shot methodology, we TF-IDF to enhance sector representation, yielding improved accuracy in comparison to standard zero-shot classifiers; (2) next, we use ChatGPT for dataset generation, revealing potential in scenarios where datasets of company descriptions are lacking; and (3) we also employ K-Fold to reduce noise in the WRDS dataset, followed by conducting experiments to assess the impact of noise reduction on the company classification results.
... BAC is still at its incipient stage in the business world. It simply portrays the firm's capacity in "the discovery of meaningful patterns, new and novel information and knowledge in a data" (Delen & Ram, 2018). BAC herald a state-of-the-art opportunity for firms on digital technologies and data usage in their business processes (Chen et al., 2012;Müller et al., 2016). ...
... The terms business intelligence (BI) and business analytics (BA) are commonly used interchangeably, although they vary in some ways. Business intelligence is often used to describe business data collecting, online analytical processing, and business reporting [66,76,77]. Business analytics, on the other hand, correspond to mathematical and statistical methods for explanatory and predictive modelling [33,78]. ...
In the market-shaping literature, markets are viewed as the results of intentional and planned acts. Market shapers do not often create technology themselves despite the fact that technical advancements can hasten changes in smart sustainable marketing. This study assessed the development of emerging technologies in business informatics with a focus on smart sustainable marketing and its relevant emerging technologies. Data were collected using an online questionnaire from 320 marketing professionals in Greece. The study revealed that emerging technologies have a significant relationship with smart sustainable marketing. Drivers of smart sustainable marketing positively enhance the effectiveness of smart sustainable marketing, and innovations in business informatics have a positive effect on smart sustainable marketing. It is clear from our results that the area of emerging technologies and smart sustainable marketing has not received much attention since such developments depend on complementary technologies that make it possible for emerging methods or technologies to integrate resources and co-create value. This study conceptually distinguishes between technology as a market offering and technology as a business platform for market shaping and innovation. Managers in the business industry and elsewhere are urged by this research to more closely examine the development of ancillary technologies, which could serve as platforms for market innovation and market shaping. Discovering and taking advantage of these opportunities is crucial for success in smart sustainable marketing. This study also presents a significant addition to the academic community because of the empirical evidence on the relationships between business informatics, smart sustainable marketing, and new technology. In terms of technology, it expands the theoretical framework of market dynamics and offers a novel perspective on the ways in which technology influences the structure and sustainability of markets.
... Resp 13 stated that corporate leadership was instrumental in driving BA maturity. Further, examination of specific factors that drove BA maturity revealed that few of the respondents believed that deep customization could be a specific factor while there were others ( Resp 4,11,12 & 15) and leadership needs to own the agenda and other factors will follow through towards maturity ( Resp 8,9,10). Whereas, there were other respondents who felt that culture played a vital role along with leadership in driving BA maturity. ...
In the present day, business analytics (BA) is known to play a vital role within all processes through which organizations are known to make vital business decisions. The power of BA is such that it presents organizations with data that enable them to reach strategic, operational and tactical solutions. BA offers tremendous scope to business organizations in terms of cost-reductions, reduction in time required to execute and complete analytical tasks. However, it is imperative for organizations that are keen to invest in adopting analytical technologies to overcome certain challenges, enabling them to comprehend the benefits from adopting BA. While studies that are conducted on BA in the past have taken developed nations into account, there is a paucity of studies that investigate BA in Indian organizations. This research adopted a qualitative method where data collection was facilitated with semi-structured interviews from Indian analytics decision makers & subject matter experts and secondary sources. Semi-structured interviews were carried out with 15 respondents, while secondary data was collected from reputed sources to add a theoretical perspective to the research.The data collected was segregated into themes to facilitate analysis. The findings from the semi-structured interviews revealed diverse perspectives of BA in the Indian context where several respondents believed that BA was in a nascent stage while others believed that certain aspects of BA were well-developed in India. Secondary analysis revealed that BA maturity success required a BA maturity model.
... Frequently, the term data analytics is used to describe an interdisciplinary approach and its implementation for retrieving knowledge to solve complex challenges [9]. Generally, data analytics can be categorized into business intelligence and advanced analytics [9]. ...
... Frequently, the term data analytics is used to describe an interdisciplinary approach and its implementation for retrieving knowledge to solve complex challenges [9]. Generally, data analytics can be categorized into business intelligence and advanced analytics [9]. The former category includes descriptive and diagnostic analytics, while the latter category comprises predictive and prescriptive analytics [9]. ...
... Generally, data analytics can be categorized into business intelligence and advanced analytics [9]. The former category includes descriptive and diagnostic analytics, while the latter category comprises predictive and prescriptive analytics [9]. In this context, various techniques are described, such as data mining [10], process mining [11], visual analytics [12] and knowledge extraction [13]. ...
Implementing processes for traceability is required in various industries to assure product quality during manufacturing, provide evidence on required processing conditions or facilitate product recalls. Commonly, radio-frequency identification (RFID) or code recognition techniques (e.g. Data Matrix) are applied to track the flow of workpieces through a manufacturing system and link processing data accordingly.
Although the analysis of tracking data is well-examined, we still see a gap in the research on the trade-off between data acquisition, data analytics and data quality. Here, we present a framework to increase the value of existing data by enabling data analytics while addressing common pitfalls and reducing the costs of data management.
... With this emerging trend for business analytics, many organizations are looking for employees who are capable of analyzing the data to support decision making (Power, Heavin, McDermott, & Daly, 2018). In most simple terms, business analytics refers to generating insights (actionable information) from existing data using sophisticated mathematical, statistical, machine learning and network science methods along with expert knowledge to support faster, better, and timely decision making (Delen & Ram, 2018). Therefore, business analytics function as a major enabler for effective decision making. ...
... Therefore, business analytics function as a major enabler for effective decision making. The use of business analytics for decision making is not a recent phenomenon, but business analytics tools and technologies are continuously evolving (Delen & Ram, 2018). Globally, many industries have now started using business analytics. ...
... Business Analytics is a combination of all mechanisms that are used to transform data into insightful information for better and faster decision making (Delen & Ram, 2018). Although business analytics as a name is new, the use of business analytics has been around for several decades. ...
In this era of data evangelism, organizations are evolving with a paradigm shift from intuition-driven decision making to evidence-based decision making. Thus, business analytics has become a popular buzzword in modern business. Banking is a lavish industry that generates massive amounts of data and has copious opportunities for the utilization of business analytics to reap its maximum return. Therefore, the significance of business analytics in banking cannot be ignored. Though some
international studies have indicated the utilization of business analytics in banks, studies on Sri Lankan banks are quite scarce. To fill this gap, this paper investigates the current utilization of business analytics in the Sri Lankan banking industry. The paper adopted a qualitative approach with multiple case studies, interviewing five professionals from different private commercial banks who are well-versed in business analytics. The paper covers eight aspects of business analytics utilization: duration of use, delegation, beneficiaries, technological aid, essentiality, value realization, perceived usage, and future direction. It was found that most banks have started using business analytics during the last three to four years, mainly for digital banking and card operations with basic technologies such as Power BI and Tableau. Further, it was also found that most banks have realized the importance and potentiality of business analytics, but neither integrated it fully with their core business nor achieved the expected value creation. The paper concludes that Sri Lankan banks are still in the early stages of business analytics maturity. Thus, there is a need to overcome the challenges and implement strategies to leverage business analytics for their benefit. Importantly, this study will contribute to the body of knowledge in the field of business analytics and the Sri Lankan banking industry where related studies are scarce.
Keywords: Banking Industry, Business Analytics, Sri Lanka, Utilization.
