Use Of Business Analytics Research Articles

Business analytics in managerial decision-making: top management perceptions

Purpose Firms seek to improve their decision-making and enable more “fact-based” decisions by using business analytics. While the benefits of using business analytics to monitor, develop and improve daily operations have been reported by many scholars, using it in more complex top management decisions has received less attention. Building on the resource-based view of the firm, this study aims to investigate top management perceptions of using business analytics for making decisions on firm resources. Design/methodology/approach This study uses semi-structured interviews to collect perceptions of 12 top managers in large firms on when and why they use business analytics in their decision-making. Findings Top managers use business analytics output as their main source of information for monitoring ongoing business performance against set targets and taking corrective actions. Concerning future-oriented planning and strategic decision-making involving more complex changes on the firms’ resource base, top managers proactively complement knowledge derived via business analytics with other sources of knowledge, such as stakeholder and expert opinions. Moreover, top managers use of business analytics depends on their own expectations of its value potential and on the expectations of their organization. Originality/value This study adds to the extant literature on the business value of business analytics by outlining the purposes and reasons for top management business analytics use. By demonstrating when and why top managers apply business analytics when making decisions on the firm’s current and future resource base, this study contributes to the discussion on the resource-based view and decision-making practices of the firm.

The impact of organizational capabilities on business analytics use: the moderating role of environmental dynamism

The impact of organizational capabilities on business analytics use: the moderating role of environmental dynamism

DATA MINING AND BUSINESS INTELLIGENCE IN MANAGING THE BUSINESS PROCESSES OF THE PROJECT OFFICE OF AN OUTSOURCING COMPANY

The article carries out a comprehensive study of the possibilities of applying modern business analytics and intelligent data analysis methods to optimize project management processes in the conditions of an outsourcing IT company. The work justifies the relevance of the problem associated with the avalanche-like growth of data volumes during the implementation of IT projects and the possibilities of innovative technologies for their intellectual analysis to identify hidden patterns. Scientific works of leading experts in project management and informatization have been critically analyzed, but a lack of comprehensive studies regarding data analysis specifically for project offices was noted. To achieve the research goal, a thorough analysis of the essence and possibilities of business analytics and intelligent data analysis was carried out, their role in managing modern organizations was examined in detail, and the scientific advantages of implementation in project offices of outsourcing companies were justified. The study of the practical use of business analytics and intelligent data analysis tools to solve the specific task of searching for new projects in the marketing activities of the IT outsourcing company was conducted. Effective methods of data collection, processing, and analysis concerning clients, market, competitors, communication channels, etc., were thoroughly analyzed. Thus, the article carries out a comprehensive study of the theoretical foundations and applied aspects of using advanced business analytics and intelligent data analysis tools with the aim of improving and optimizing the marketing activities of the modern project office of an outsourcing IT company by making timely well-founded managerial decisions based on deep analysis of available data sets.

Research on Barriers to Implementing Business Analytics in Organizations

Business Analytics has been widely applied to multiple aspects with a data-explosive background. People utilize data mining tools and models to guide marketing strategy, organization management, evaluate financial budgets, etc. However, what comes after its popularity is various implemental obstacles, this is why only a few companies have the capacity to run their own business analytics teams and adopt conclusions stemming from data and models into their organizations operation and decision-making. After reading and organizing previous related papers, and distilling and comparing the findings of different researchers this paper divides the barriers to the use of Business Analytics into six parts and summarizes the causes and possible effects of these barriers. In this context, barriers to implementing business analytics in organizations are introduced in dimensions of data quality, security and accessibility, analytical tools and knowledge, financial investment, human resources, analytics procedure, and organizational barriers. This article provides a basic overview of the barriers and may be useful to guide and improve the practical application of Business Analytics.

The Impact of Organizational Culture on the Use of Business Analytics in Sri Lankan Companies

The Sri Lanka Journal of Management (SLJMS) is an international, peer-reviewed journal which is published by the Faculty of Management Studies, The Open University of Sri Lanka with high quality, and original research contributions to management knowledge.

The Influence of a Data-Driven Culture on Product Development and Organizational Success through the Use of Business Analytics

A Data-Driven Culture (DDC) is believed to facilitate a business-focused cultural shift inside a company. Providing considerable returns to the businesses' goods and procedures breakthroughs is evaluated. Several companies have started using various sophisticated technology-integrated Business Analytics (BA) technologies to enhance their company performance. The progress in information and communication technologies has enabled companies to consider using BA solutions, including Artificial Intelligence (AI). This has significantly transformed companies' business-focused cultural environments, allowing them to make precise decisions to enhance their creativity and performance. This research examines the influence of a company’s DDC on procedure efficiency and product development, which leads to improved overall company outcomes and increased business value. Using a systematic approach, developing a theoretical framework and a collection of hypotheses is first supported by existing research and insights from the resource-based perspective and adaptive capacity concept, specifically in the company's environment. These findings are then statistically verified using a survey that includes 513 genuine replies from workers of various firms utilizing business analytics tools equipped with AI capabilities. The results indicate that fostering a DDC inside an organization has a significant impact on goods development as well as procedure improvement. It improves business worth by enhancing overall efficiency.

Business analytics: an example of integration of TD-ABC and the balanced scorecard

PurposeThe purpose of this paper is twofold. First, to combine a holistic model – in our case the balanced scorecard – with the time-driven activity-based costing model. The inspiration for this stems both from Kaplan and Norton and from the intense discussions and use of business analytics (BA) and performance management (PM). Second, to use numerical experiments – more specifically Monte Carlo simulation – to design and explore four hypothetical scenarios within such a holistic model. The paper also aims to emphasise the role played by statistics in increasing the confidence in using such a framework.Design/methodology/approachThe author runs four numerical experiments using different assumptions to show how a decision-maker can improve the outcome by making small changes in the key performance indicator (KPI) input variables.FindingsThe paper gives recommendations for the assumptions that each decision-maker has to consider when setting out to conduct this kind of analysis. Small changes in some input variables may completely change the output and hence the decision result.Practical implicationsThe paper shows why practitioners and researchers need to better understand the limitations of deterministic analysis to make realistic models when combining more accounting models. To choose the relevant probability distributions for the input resources is an important issue for the decision-maker as they have a very large impact on the result.Originality/valueThe real value of the paper lies in making students and practitioners as well as researchers aware of the opportunities for stochastic modelling and also to point at the problems and limitations of combining elements from BA with performance measurement and management.

A modest IoT based smart parking system with business analytics integration

Traditional parking systems are challenging and tedious as it is occupied at irregular intervals for various reasons. Without proper design, this could lead to loss of business revenue in commercial establishments. The objective of this study is to implement an IoT-based system to address issues faced by visitors at parking lots. The proposed system would include capabilities that can display the status of availability of the lot it is placed in and will report the status through a web service. This availability status is transmitted through WiFi, which can provide insight into the parking activities of the establishment. This provides valuable data for the use of business analytics. This study highlights the advantages a basic and low-cost IoT-based Smart Parking Lot system could present. This is visualized using custom and open-source parking datasets. These features provide the basis for discussion on future improvements and scalable parking solutions.

Knowledge identity (KI): a determining factor in the effective use of analytics

ABSTRACT Enterprise systems can play a fundamental role in the management of business analytics by facilitating knowledge management. Nonetheless, and despite extensive studies about enterprise systems and knowledge management over the past four decades, the information systems discipline still lacks a clear and practical understanding of what types of knowledge should be managed by enterprise systems, and how to facilitate the adoption and effective use of business analytics through knowledge management. To a high degree, the issue is rooted in the ambiguity about the nature and sources of knowledge. Motivated by this need, we conducted an exploratory study to address the research question, what knowledge is critical for data analysts to be able to use business analytics effectively? We interviewed 41 data analysts. Using thematic analysis, we propose the theory of knowledge identity, which explains the role of data analysts’ knowledge in the adoption and effective use of analytics in organisations. This study offers significant theoretical and practical implications.

Business analytics in service operations—Lessons from healthcare operations

AbstractWe present an expanded framework for the use of business analytics in projects. To the commonly used descriptive, predictive, and prescriptive analytics, we add comparative analytics, wherein we compare the performance of systems under different interventions. This framework provides a conceptual roadmap for the implementation of business analytics projects. We then demonstrate this framework using recent operations research literature on analytics in healthcare, summarizing papers focusing on one of these aspects. Next, we discuss queue mining as an example of theory and practice illustrative of these aspects. We conclude there is room for further work by operations researchers and management scientists within business analytics projects generally and the healthcare industry more specifically. We argue future work should consider both theory and practice, especially within prescriptive analytics projects, where analysis through the lens of operations research and management science is imperative. We provide some thoughts on the current and future state of operations research and management science in business analytics.

Making sense of business analytics in project selection and prioritisation: insights from the start-up trenches

PurposeThe study aims to provide insights in the sensemaking process and the use of business analytics (BA) for project selection and prioritisation in start-up settings. A major focus is on the various ways start-ups can understand their data through the analytical process of sensemaking.Design/methodology/approachThis is a comparative case study of two start-ups that use BA in their projects. The authors follow an interpretive approach and draw from the constructivist grounded theory method (GTM) for the purpose of data analysis, whereby the theory of sensemaking functions as the sensitising device that supports the interpretation of the data.FindingsThe key findings lie within the scope of project selection and prioritisation, where the sensemaking process is implicitly influenced by each start-up's strategy and business model. BA helps start-ups notice changes within their internal and external environment and focus their attention on the more critical questions along the lines of their processes, operations and business model. However, BA alone cannot support decision-making around less structured problems such as project selection and prioritisation, where intuitive judgement and personal opinion are still heavily used.Originality/valueThis study extends the research on BA applied in organisations as tools for business development. Specifically, the authors draw on the literature of BA tools in support of project management from multiple perspectives. The perspectives include but are not limited to project assessment and prioritisation. The authors view the decision-making process and the path from insight to value, as a sensemaking process, where data become part of the sensemaking roadmap and BA helps start-ups navigate the decision-making process.

Sensegiving in organizations via the use of business analytics

PurposeBusiness analytics (BA) has been a breakthrough technological development in recent years. Although scholars have suggested several solutions in using these technologies to facilitate decision-making, there are as of yet limited studies on how analysts, in practice, improve decision makers' understanding of business environments. This study uses sensemaking theory and proposes a model of how data analysts generate analytical outcomes to improve decision makers' understanding of the business environment.Design/methodology/approachThis study employs an interpretive field study with thematic analysis. The authors conducted 32 interviews with data analysts and consultants in Australia and New Zealand. The authors then applied thematic analysis to the collected data.FindingsThe thematic analysis discovered four main sensegiving activities, including data integration, trustworthiness analysis, appropriateness analysis and alternative selection. The proposed model demonstrates how these activities support the properties of sensemaking and result in improved decision-making.Research limitations/implicationsThis study provides strong empirical evidence for the theory development and practice of sensemaking. It brings together two distinct fields – sensemaking and business analytics – and demonstrates how the approaches advocated by these two fields could improve analytics applications. The findings also propose theoretical implications for information system development (ISD).Practical implicationsThis study demonstrates how data analysts could use analytical tools and social mechanisms to improve decision makers' understanding of the business environment.Originality/valueThis study is the first known empirical study to conceptualize the theory of sensemaking in the context of BA and propose a model for analytical sensegiving in organizations.

Usage of Business Analytics and Supply Chain Performance – An Empirical Study of Sri Lankan Aapparel Sector

Advances in technology and innovation require companies to embrace these new trends to compete and stay ahead in the business world. In particular, there is a need for companies to incorporate Business Analytics practices within their organizations. Business Analytics consists of two components: Information Systems and Business Process Orientation. This study aims to investigate the impact of the use of Business Analytics on the Supply Chain Performance in apparel companies in Sri Lanka. This research focuses on discussing the objectives developed to achieve the purpose of the study. To achieve this objective, this current study investigates the relationship between the Information System, Supply Chain Performance and the effect of the use of the Information System in the supply chains of Sri Lankan apparel companies. The study uses a quantitative approach. In this study, for quantitative analysis study performs regression analysis and decision tree analysis. This study identifies a positive relationship between the Information System and the Supply Chain Performance. For further future studies, it is advisable to extend this study by examining the performance of medium-and large-scale companies in the country.

Business Analytics in E-Commerce: A Literature Review

This paper attempts to identify the value and the use of business analytics (BA) in E-commerce, and challenges and trends associated with BA in E-commerce. It systematically examines the literature about BA, with a particular focus on E-commerce. BA has critical value in E-commerce. BA has been used in customer analysis and website usage analysis. The most important factor for the value of E-commerce BA is customer. This paper also suggests an E-commerce BA research model which describes the BA iterative process from data to analysis, to decision, to estimation, and separates BA analysis results as functional-level and competitive-level results. Challenges identified in this paper hold theoretical and practical implications. Future studies could seek an enhanced understanding of BA through quantitative analysis.

Business Analytics in Service Operations

We present an expanded framework for the use of business analytics in projects. To the commonly used descriptive, predictive, and prescriptive analytics, we add comparative analytics, wherein we compare the performance of systems under different interventions. This framework provides a conceptual roadmap for the implementation of business analytics projects. We then demonstrate this framework using recent operations research literature on analytics in healthcare, summarizing papers focusing on one of these aspects. Next, we discuss queue mining as an example of theory and practice illustrative of these aspects. We conclude there is room for further work by operations researchers and management scientists within business analytics projects generally and the healthcare industry more specifically. We argue future work should consider both theory and practice, especially within prescriptive analytics projects, where analysis through the lens of operations research and management science is imperative. We provide some thoughts on the current and future state of operations research and management science in business analytics.

Digitization of the budgeting process: determinants of the use of business analytics and its effect on satisfaction with the budgeting process

Since the use of business analytics promises automation of business processes and time savings, the budgeting process seems predestined for the integration of analytical methods. Therefore, this study examines the determinants of the use of business analytics in the budgeting process and its effect on satisfaction with the budgeting process. Specifically, we focus on one technical determinant (data infrastructure sophistication) and the importance of the two major budgeting functions (the planning and the evaluation function), which could affect the degree of dissemination of using analytical methods. Based on a survey among German companies, we find, as predicted, that the sophistication of the data infrastructure is positively associated with the use of business analytics in the budgeting process. Further, the more a company emphasizes the planning function, the greater the extent to which business analytics is used in the budgeting process. In contrast, we find no association between the evaluation function and the use of business analytics in the budgeting process. Finally, we find that the use of business analytics is positively associated with satisfaction with the budgeting process. Thus, the use of business analytics can help to overcome dissatisfaction with traditional budgeting systems. Overall, our findings provide practitioners with valuable indications under which circumstances the use of analytical methods appears reasonable.

Business analytics, revenue management and sport: evidence from the field

We surveyed 72 managers and executives from Major League Baseball (MLB), the National Football League (NFL), the National Basketball Association (NBA), the National Hockey League (NHL), and Major League Soccer (MLS), as well as other North American professional sport organisations including auto racing, golf, and tennis regarding the use of business analytics in sport marketing and revenue generation. We explore the use of business analytics to drive improvement in ticket pricing, customer retention, lead scoring, sponsorship, premium sales, digital marketing, food and beverage, merchandising, and fan/game experience. The main contribution of this paper is to show how managers of professional sport franchises in North America utilise business analytics to drive revenue generation and to provide possible theoretical avenues of future research. Managerial implications are that customer relationship management (CRM) and food, beverage, and merchandising are increasing areas of interest. Areas of prevalence are highlighted as well as areas of potential improvement for the use of analytics in professional sports organisations.

Business analytics, revenue management, and sport: evidence from the field

We surveyed 72 managers and executives from Major League Baseball (MLB), the National Football League (NFL), the National Basketball Association (NBA), the National Hockey League (NHL), and Major League Soccer (MLS), as well as other North American professional sport organisations including auto racing, golf, and tennis regarding the use of business analytics in sport marketing and revenue generation. We explore the use of business analytics to drive improvement in ticket pricing, customer retention, lead scoring, sponsorship, premium sales, digital marketing, food and beverage, merchandising, and fan/game experience. The main contribution of this paper is to show how managers of professional sport franchises in North America utilise business analytics to drive revenue generation and to provide possible theoretical avenues of future research. Managerial implications are that customer relationship management (CRM) and food, beverage, and merchandising are increasing areas of interest. Areas of prevalence are highlighted as well as areas of potential improvement for the use of analytics in professional sports organisations.

Use of Business Analytics in Accounting Firms—Taking Deloitte as an Example

This paper aims to introduce business analytics strategies that one of the most prominent public accounting firms, Deloitte, has applied to manage hundreds of thousands of clients and discover valuable insights within the organization to increase efficiency and improve risk management. According to Deloitte official website, 76% of audit committee members within the organization believe that advanced technology should be used more extensively [1]. The insights gained from the data analytics are approved by the most occupied position in the accounting firm, which is the auditors. This paper introduces insight-driven methods used in the company, including Profit model, Network, Structure, Process, Product performance, Product system, Service, Channel, Brand, and Customer engagement. All of these elements can each be integrated into a business analytic approach. Lastly, this paper will provide some limitations associated with the data analysis in the firm.

A case study on accounting faculty's perceptions of technology in accounting classes

The accounting profession is undergoing a transformation because of the explosion of the amount of financial data and information available to organisations, the demand for real-time access to information and the use of business analytics. The accountants of the future need to be qualified to address these challenges. This means that higher education accounting degree programs must adapt their curriculums and courses to leverage technology tools and practices to ensure that their accounting faculties are prepared to teach these courses. The purpose of this exploratory descriptive case study was to evaluate accounting instructors' perceptions and experiences with technology to identify best practices to develop their technological intelligence to enable them to implement technology into accounting programs and courses to meet the demands that technology is placing on the accounting profession.