Showing papers on "Business analytics published in 2023"

Deep learning in business analytics: A clash of expectations and reality

Abstract: Our fast-paced digital economy shaped by global competition requires increased data-driven decision-making based on artificial intelligence (AI) and machine learning (ML). The benefits of deep learning (DL) are manifold, but it comes with limitations that have – so far – interfered with widespread industry adoption. This paper explains why DL – despite its popularity – has difficulties speeding up its adoption within business analytics. It is shown that the adoption of deep learning is not only affected by computational complexity, lacking big data architecture, lack of transparency (black-box), skill shortage, and leadership commitment, but also by the fact that DL does not outperform traditional ML models in the case of structured datasets with fixed-length feature vectors. Deep learning should be regarded as a powerful addition to the existing body of ML models instead of a “one size fits all” solution. The results strongly suggest that gradient boosting can be seen as the go-to model for predictions on structured datasets within business analytics. In addition to the empirical study based on three industry use cases, the paper offers a comprehensive discussion of those results, practical implications, and a roadmap for future research.

A theory of predictive sales analytics adoption

Abstract: Abstract Given the pervasive ubiquity of data, sales practice is moving rapidly into an era of predictive analytics, using quantitative methods, including machine learning algorithms, to reveal unknown information, such as customers’ personality, value, or churn probabilities. However, many sales organizations face difficulties when implementing predictive analytics applications. This article elucidates these difficulties by developing the PSAA model—a conceptual framework that explains how predictive sales analytics (PSA) applications support sales employees’ job performance. In particular, the PSAA model conceptualizes the key contingencies governing how the availability of PSA applications translates into adoption of these applications and, ultimately, job performance. These contingencies determine the extent to which sales employees adopt these applications to revise their decision-making and the extent to which these updates improve the decision outcome. To build the PSAA model, we integrate literature on predictive analytics and machine learning, technology adoption, and marketing capabilities. In doing so, this research provides a theoretical frame for future studies on salesperson adoption and effective utilization of PSA.

Analytics Applications in Fashion Supply Chain Management—A Review of Literature and Practice

Abstract: Fashion companies’ chance to survive the current pandemic is much dependent on their analytics skills. Despite this urge and the arising possibilities in the “data era,” analytics activities are still underestimated and scattered across different fashion supply chain functions. Therefore, this article positions itself at the important intersection of analytics and fashion supply chain management. This article analyzed analytics applications across all relevant supply chain functions within the fashion industry. We conducted our literature review with a focus on different forms of data-driven decision making applied within fashion supply chain functions. We systematically compared the findings from a structured literature review and a content analysis of corporate annual reports and detailed state-of-the-art analytics examples. We highlight deviations in the analytics level: Research papers have a strong focus on advanced analytics methods while most companies are struggling to establish descriptive analytics capabilities. Based on this, we derive and detail managerial and research implications. Having created a holistic overview, this article presents itself as a cornerstone for further analytics-focused research within the fashion industry. Also, it provides managers with insights into the current landscape of analytics applications and develops the vision of a future analytics-driven fashion supply chain.

A critical reflection on analytics and artificial intelligence based analytics in hospitality and tourism management research

Abstract: Purpose This work consists of a critical reflection on the extent to which hospitality and tourism management scholars have accurately used the term “analytics” and its five types (i.e. descriptive, exploratory, predictive, prescriptive and cognitive analytics) in their research. Only cognitive analytics, the latest and most advanced type, is based on artificial intelligence (AI) and requires machine learning (ML). As cognitive analytics constitutes the cutting edge in industry application, this study aims to examine in depth the extent cognitive analytics has been covered in the literature. Design/methodology/approach This study is based on a systematic literature review (SLR) of the hospitality and tourism literature on the topic of “analytics”. The SLR findings were complemented by the results of an additional search query based on “machine learning” and “deep learning” that was used as a robustness check. Moreover, the SLR findings were triangulated with recent literature reviews on related topics (e.g. big data and AI) to generate additional insights. Findings The findings of this study show that: there is a growing and accelerating body of research on analytics; the literature lacks a consistent use of terminology and definitions related to analytics. Specifically, publications rarely use scientific definitions of analytics and their different types; although AI and ML are key enabling technologies for cognitive analytics, hospitality and tourism management research did not explicitly link these terms to analytics and did not distinguish cognitive analytics from other forms of analytics that do not rely on ML. In fact, the term “cognitive analytics” is apparently missing in the hospitality and tourism management literature. Research limitations/implications This study generates a set of eight theoretical and three practical implications and advance theoretical and methodological recommendations for further research. Originality/value To the best of the authors’ knowledge, this is the first study that explicitly and critically examines the use of analytics in general, and cognitive analytics in particular, in the hospitality and tourism management literature.

Tech-Business Analytics – a Review-based New Model to Improve the Performances of Various Industry Sectors

Abstract: Purpose: Integration of ICCT underlying technologies and big data technology to develop a new kind of Business analytics that can be used to solve semi-structured and unstructured problems of various industry sectors i.e., primary, secondary, tertiary, and quaternary industry sectors. The new study is known as Tech-business analytics (TBA). The goal of this study is to better understand the idea of TBA and how it influences a company's innovation outcomes. Design/Methodology/Approach: The originality of business analytics products/services in many industries is based on how business analytics is directly influenced by data-driven culture, but product/service importance is influenced indirectly by environmental scanning, which is further influenced by the usage of ICCT underlying technologies. Through a comprehensive review, analysis of the existing state, anticipating ideal/desired status, identifying research gaps, and analysing of research objectives in business analytics, this have developed a new concept of Tech-Business Analytics in this work. The paper also examines the importance of Business Analytics (BA) and how to predict the importance and applications of projected business analytics in future business sectors using the ABCD analytical framework. Findings/Result: This review based a new concept of integrated technology that combines ICCT underlying technologies with big data leads to a new model of tech-business analytics for improving the performance of different industry sectors. With all the resources, templates, technologies, opportunities, and capabilities of integrating Data science with other ICCT underlying technologies, Tech-business analytics will a paradigm shifter with a lot of potentials in solving industrial problems. Originality/Value: The new model of tech-business analytics is developed which is a review-based new concept opportunity for improving Industry Performance in Various Industries. A generic architecture is also developed, which looks at Tech Business Analytics in Primary, Secondary, Tertiary, and Quaternary industry sectors and is useful for research for technical efficiency improvement purposes. Paper Type: Exploratory research.

A Review on Business Analytics: Definitions, Techniques, Applications and Challenges

Abstract: Over the past few decades, business analytics has been widely used in various business sectors and has been effective in increasing enterprise value. With the advancement of science and technology in the Big Data era, business analytics techniques have been changing and evolving rapidly. Therefore, this paper reviews the latest techniques and applications of business analytics based on the existing literature. Meanwhile, many problems and challenges are inevitable in the progress of business analytics. Therefore, this review also presents the current challenges faced by business analytics and open research directions that need further consideration. All the research papers were obtained from the Web of Science and Google Scholar databases and were filtered with several selection rules. This paper will help to provide important insights for researchers in the field of business analytics, as it presents the latest techniques, various applications and several directions for future research.

Big Data Applications and Analytics

Abstract: The usage of smart-device applications among users and the business world is rapidly growing in India and globally. Establishments provide unsurpassed digital application services to their customers through interconnected devices to solve customer-centric problems and provide the best solutions. New trends in digital marketing influence the number of users to adopt social media and customized applications by organizations. Big data (BD) is an opportunity for service providers to get the behaviour of the users through BD analytics (BDA). This chapter aims to outline BD applications and analytics methods. Organizations are taking advantage of BD by leveraging the data collected through interconnected devices and providing the best services to their customers. The usage of the internet and its applications are growing quickly. Organizations are focusing on BD and use artificial intelligence for big data analytics to get the trends of customer requirements and provide valuable solutions. Big data and analytics empower the business world and transform Digital India to the next level.

Augmenting Decision Intelligence Results into the Business Workflow

Abstract: In today’s rapidly changing business environment, organizations are under increasing pressure to make informed decisions that drive growth and competitiveness. Decision intelligence (DI) is a powerful tool that can help organizations make better decisions by leveraging the power of artificial intelligence (AI) and data analytics. However, to fully realize the benefits of DI, it is essential to integrate the outputs of DI models into existing business workflows.

Using Part of Business Analytics for Learning and Working

Abstract: The purpose of this study was to demonstrate some parts of business analysis, more specifically, to show the application of knowledge in business management and real-life situation. The research explores some aspects of business analysis, including enterprise management, foundations of business analysts, planning and monitoring, and data science statistics. Business analysis is a crucial discipline in the growth and development of business through implementing change. This paper covers the overview of business analysis and the application of knowledge in real life and business management. This brief demonstrates a summary of the business analysis programme, including the business analysis foundations and statistics for data science. The primary concepts outlined in business analysis foundations include business competencies, enterprise analysis, requirements, and solutions. Similarly, the paper covers statistics for data science, where vital concepts such as regression analysis, numerical and categorical variables, fundamentals, distribution, and hypothesis testing are presented. In addition, the analysis presents the most exciting discovery during the course elaborating on the birth and development of business analysis from the 1940s until today. Additionally, the paper covers the most crucial information presented in the course: business analysis application and its benefits in organizations. Also, it presents the application of business knowledge in daily life to define needs and solve problems. Furthermore, business analytics knowledge is applied during doctoral research and personal healthcare management. The brief covers the practical application of business analysis skills in large corporations such as Apple Inc., including big data analysis, HR management, communication, and manufacturing. Besides large corporations, business analytics skills apply in small companies in mitigating risks, operation analysis, and market analysis. Lastly, the paper demonstrates the practical application of knowledge in individual entrepreneurship, such as innovation analysis, revenue generation, system analysis, and mind mapping.

Decision-Making Capabilities in Management

Abstract: Data is more valuable than ever as the digital age continues to progress rapidly. Despite its potential value to a company, raw data is often presented in a format that makes it difficult to visualise and comprehend without devoting substantial resources. As a result, many companies now use business intelligence and analytics solutions to help them transform raw data into meaningful insights and practical strategies. To be effective, business intelligence software must take into account the many factors that affect the market and provide a means for businesses to put that information to use. The primary focus is on satisfying clients while also outperforming the competition.

Using Analytics to Manage and Predict Employee Performance

Abstract: This chapter provides an overview of several human behavior/psychological analytics that can be used to help assess current statuses and performances and predict future performance. Moreover, the chapter presents case illustrations for the use of analytics in attaining meaningful data that improve corporate performance. The objective is to help understand ways of reducing uncertainty through various analytics and to enhance data-driven, performance-managed organizations. The overview of multiple analytics with corresponding case illustrations attempts to fill a gap in the literature and help readers understand that using analytics in the business environment can engender productive analysis and change. This is important because organizations are being judged on metrics that are based on their internal and external impacts.

Big Data Analytics as a Game Changer for Business Model Innovation in Small and Medium-Sized Enterprises in South Africa

Abstract: Digital transformation has caused an abrupt change to business processes and practices whereby small and big businesses are developing business model innovation (BMI). Big data analytics is seen as one of the suitable technologies to offer business model innovation solutions and also as a strategy to improve business operations and performance in Small and Medium-sized Enterprises (SMEs). This study thus reviewed literature on the adoption of big data analytics, with a view to highlight how this technology can support the development of big data business model innovation applicable to SMEs. The study revealed that big data analytics has the potential to enhance SMEs' business processes, practices, performance, and to respond to their increasingly competitive environment. It is therefore recommended that SME owners and managers should adopt and implement technologies such as big data analytics to support business innovation processes as that could sustain organizational performance and improve their innovative and dynamic capabilities.

Dangerous data: Analytics and information behaviour in the commercial world

Abstract: Data has become an increasingly important component in contemporary business operations, epitomised by the rise of the Business Intelligence system, data analytics, and data visualisations. It has been associated with increased productivity and the development of new business opportunities. But the use of data is sometimes also associated with poor decision-making, either because of the quality of the data on which decisions are made, or because of the ways in which that data is used. This paper explores the problem of dangerous data in commercial contexts: those situations where the use of data contributes to worse outcomes.t

HR Analytics: History, Present & The Future

Abstract: -HR analytics is an information driven way to deal with further develop HR-related choices. Human resource analytics, otherwise called individuals analytics, labor force analytics, or ability analytics, involves gathering, breaking down, and revealing HR information. It permits your organization to survey the impact of different HR KPIs on by and large business execution and go with information driven choices. HR analytics, in different terms, is an information driven way to deal with Human Resources The board. Human Resource domain has advanced impressively during the last 100 years. It has developed from a functional to a more essential discipline. This is exemplified by the prominence of the phrase Vital Human Resource The board (SHRM). HR analytics, which is described by an information driven approach, is in accordance with this pattern. The goals of this examination are to figure out what HR Analytics is? And furthermore the importance, development and pattern in the field of HR analytics. Most famous HR analytics apparatuses utilized as of now are examined exhaustively. The most recent practices in the field of HR Analytics (In light of a conspicuous association) is likewise exhibited in this exploration study. The gap in human asset capital administration and system acknowledgment has been made sense of. How business intelligence can actually support and improve the fate of HR Analytics is examined exhaustively. One more justification behind this study is to look at the checks and entryways that business firms have while planning HR analytics as a gadget in their associations. This examination likewise gives a hypothetical idea of HR analytics in light of optional information gathered from earlier exploration papers, journals distributed somewhere in the range of 2006 and 2022, web journals, and sites that give HR analytics ongoing information. The advancement of research in HR analytics is examined utilizing bibliometrics. Key Words: HR analytics, evolution, history, bibliometrics, human resource, HR management, business intelligence, Human resource capital management, strategy realization, Human capital analytics, people analytics, workforce,human resource analytics.

Enabling inter-organizational analytics in business networks through meta machine learning

Abstract: Abstract Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within (inter-organizational) business networks is still severely constrained. As data is distributed across several legal units, potentially even across countries, the fear of disclosing sensitive information as well as the sheer volume of the data that would need to be exchanged are key inhibitors for the creation of effective system-wide solutions—all while still reaching superior prediction performance. In this work, we propose a meta machine learning method that deals with these obstacles to enable comprehensive analyses within a business network. We follow a design science research approach and evaluate our method with respect to feasibility and performance in an industrial use case. First, we show that it is feasible to perform network-wide analyses that preserve data confidentiality as well as limit data transfer volume. Second, we demonstrate that our method outperforms a conventional isolated analysis and even gets close to a (hypothetical) scenario where all data could be shared within the network. Thus, we provide a fundamental contribution for making business networks more effective, as we remove a key obstacle to tap the huge potential of learning from data that is scattered throughout the network.

From Meaningful Data Science to Impactful Decisions: The Importance of Being Causally Prescriptive

Abstract: This article proposes a framework for transition from traditional data science where the focus is on extracting value from available data to goal-driven analytical decision making where the business objective is defined first. We discuss the link between predictive analytics and prescriptive analytics in the context of formulating the problem, and assert that all prescriptive analytics problem formulations assume a causal link between decisions and outcomes. We emphasize the role of predictive analytics and causal inference in specifying the causal link between decisions and outcomes accurately, and ultimately in aligning the analysis with the business objectives. We offer practical examples that integrate various required analytics tasks and describe scenarios where causal inference is required versus not required.

Business Analytics Capabilities and Decision Quality: The Mediating Roles of Decision Speed and Comprehensiveness

Abstract: This paper examines the nexus between business analytics and strategic decision facets using the resource-based theory. Analysis with partial least squares demonstrate that decision comprehensiveness and speed mediate the relationship between business analytics and decision quality. While fuzzy sets analysis demonstrates that business analytics, decision comprehensiveness, and speed are sufficient (but not necessary) conditions for decision quality. When firms utilize business analytics, decision speed and comprehensiveness are mutually inclusive. Theoretical and practical implications are offered.

Interactive Business Intelligence System Using Data Analytics and Data Reporting

Abstract: Nowadays, data has grown tremendously. Business intelligence (BI) is emerging technology to identify the progress of the industry. To be successful in organization, this strategy is mandatory with analytics. Many organizations have challenges to analyze user demands. In order to resolve this, data analytics process is used in business intelligence. This system helps to organize and share data among organization which provides potential to determine and provide strategic opportunities to make decision. It leads to accessing data and enabling analytics for service to enrich the all categories of users. Connectors combine real-time data from anywhere and automate reporting within minutes then those simple and beautiful user interactions help new technologies and promising initiatives gain high user adoption.

Data Analytics Advisory: Maximizing Business Performance through Data Analysis

Abstract: Data Analytics Advisory: Maximizing Business Performancethrough Data Analysis

A Study of Business Insight Tool using Big Data Analytics

Abstract: In the generation of modern and advanced technologies, an immense proportion of information is accessible. Big Data is a huddle of huge quantities of data which keeps on growing exponentially with time. Because of the expeditious widening of day to day information and data, resolutions have to be investigated and given with the aim of managing and bringing out important valuable insights from the information databases. Moreover, it is mandatory for business strategists to obtain knowledge from huge swiftly changing data. Big data analytics are acquiring huge dominance in all the domains of business development and management. Further research illustrates that logistic networks and business activities are one of the most gigantic sources of information in the company. Hence, their business strategy building methodology would be beneficial from accumulated utilization of business data analytics technologies. However, there is still a deficiency of recognizing what influences the capability of a company to build business data analytics technologies to gain competitive insights from it. In this study, we focus on drawing helpful insights from the data stored in the company's database. In today’s scenario where we have a large database with n number of tables having millions of rows, it becomes impossible for a human to explore the information and bring out perceptions from it. Tools like MS Excel fail in analyzing such a large amount of data. As a solution to it, we as a BI Developer made a simplified tool in Power BI which acts as a coupling between the data warehouse and employees. Using that tool, the employees can pull out inner sights from the large database within seconds.

Leveraging HR Analytics using Business Intelligence and Analytics

Abstract: Organizations are becoming more and more interested in human resources (HR) analytics. Businesses now have new perspectives on their employees thanks to the application of business intelligence (BI) and analytics in HR, which helps them make wiser decisions. The use of BI and analytics tools to leverage HR analytics is covered in this article. We look at how HR analytics may help in identifying talent, lowering employee turnover, and boosting engagement. We also go over the difficulties with HR analytics, including issues with data quality and privacy, and how to overcome them. Lastly, we give examples of companies who successfully applied HR analytics through utilizing BI and analytics tools, along with the effects it had on their business operations. Overall, this paper offers insights into the potential value of HR analytics as a tool for businesses aiming to enhance both their HR operations and overall business performance