Business Intelligence Applications Research Articles

The Role of Business Intelligence in Developing Tax Performance: An Applied Research in The General Taxing Authority

Objective: The present research aims at examining the role of business intelligence in developing tax performance in the General Tax Authority of a developing county. Theoretical Framework: To enlighten the main concepts of this study and underpin their theoretical foundations, the business intelligence tools and applications were presented. Their characteristics, dimensions, virtues, as well as the reasons behind their implementation were also detailed. Therefore, the role of business intelligence in improving tax performance was justified, providing a solid basis for understanding the context of our investigation carried out in the taxing authority of the Iraqi capital. Method: To conduct our investigation, a questionnaire was administrated to the target respondents, i.e. 97 employees of the General Tax Authority, located in Baghdad. Following the data collection process, descriptive, then regression analyses were undertaken. Results and Discussion: The obtained findings support that business intelligence exerts a positive influence on tax performance of the general tax authority. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into the awareness of the importance of business intelligence and software applications at the workplace. Departments’ directors of the general taxing authority are invited to cooperate with the corresponding authority whose work could be better performed electronically. Decisions regarding citizens could also be taken more wisely. Originality/Value: The relevance of the present research is essentially evidenced by its main practical implications regarding the improvement of tax performance of taxing authorities of developing countries, as well as the betterment of tax services dedicated to citizens.

Implementasi Penggunaan Aplikasi Microsoft Power BI dalam Melihat Statistik Hutang Negara di Dunia

In the era of globalization, an in-depth understanding of sovereign debt statistics is crucial to ensure economic stability. One solution that can significantly contribute to the analysis of sovereign debt data is the application of Business Intelligence (BI) technology, such as Microsoft Power BI. The importance of in-depth understanding of sovereign debt statistics is crucial, considering that sovereign debt can affect economic policy, currency stability, and various other aspects. Therefore, an efficient approach is needed to manage and analyze data related to sovereign debt in a holistic manner. One of the solutions that can make a significant contribution to the analysis of data on data analysis is the application of Business Intelligence (BI) technology, such as Microsoft Power BI. Power BI enables effective data visualization that can be accessed by various stakeholders, making it easier to understand trends, patterns, and the impact of sovereign debt. This research will focus on the application of Power BI to analyze sovereign debt statistics in various parts of the world, with the aim of contributing positively to the understanding and management of sovereign debt globally through the perspective of advanced information technology

Application of business intelligence under deep neural network in credit scoring of bank users

This paper aims to improve the level of social credit system and the accuracy and efficiency of bank users’ credit scoring by using business intelligence technology based on deep neural network (DNN). Firstly, based on the theory of personal credit evaluation factors, a comprehensive credit evaluation factor system is constructed, taking into account social and economic background, consumption habits, behavior patterns and other factors. Meanwhile, back propagation neural network (BPNN) theory is introduced as the core method of modeling to cope with the nonlinear relationship in the credit scoring task and the demand of large-scale data processing. Secondly, by analyzing the operation process of BPNN in detail, the specific application in credit scoring model is emphasized. Finally, on the basis of theory and operation, this paper implements a credit scoring model for bank users based on BPNN theory. The experimental results show that the model realized in this paper can automatically discover the key attributes and internal rules in the sampled data, and adjust the weight and threshold of the network by modifying the parameters and network structure to meet the expected requirements. The accuracy of the credit score of the predicted sample data reaches 99.5%, and the prediction error is very small, which has a good prediction effect. This paper provides a feasible solution for business intelligence and DNN in the field of credit scoring, and also provides strong empirical support for improving the level of social credit system.

UTILIZATION OF BUSINESS INTELLIGENCE IN BUSINESS DECISION MAKING

Making wise and well-informed company decisions at a time where data is the most valuable resource requires the application of business intelligence (BI). Big Data is handled by modern BI, which also benefits from more processing power, fast data access, opportunity detection, and risk mitigation. In order to correctly represent the facts, this research employs a qualitative technique and gathers data from several sources. The study's findings demonstrate the importance of business intelligence in corporate decision-making. In order to assist educated decision-making, plan development plans, and offer insight into internal and external performance, business intelligence (BI) gathers data from a variety of sources. Implementing BI necessitates a business and technological expertise, but it also makes difficult data analysis easier, boosts productivity, and helps identify possibilities and resolve issues. Business Intelligence (BI) is critical to success in the current day because it gives reliable information, increases operational efficiency, and assists management in making decisions that are responsive to changes in the market. The study's findings also demonstrate how BI gathers information from a variety of sources to offer perception into the market, consumer behavior, and internal business performance.

Application of Business Intelligence for the automation in the Epidemiological Surveillance: XBI a successful case study from Municipal Health Department of Ribeirão Preto, Brazil (2020-2024)

Application of Business Intelligence for the automation in the Epidemiological Surveillance: XBI a successful case study from Municipal Health Department of Ribeirão Preto, Brazil (2020-2024)

Using Power BI to Apply Business Intelligence to Product Sales

Using Power BI to Apply Business Intelligence to Product Sales

Business Intelligence: A Quantitative Approach

Business Intelligence has become in the front of demanding technologies. Many companies need technology to adapt to changing requirements, to deal with rich amounts of data; to get correct information, analysis data, and finally to quickly support decision-makers. Developing an agile BI application can be defined as a short-delivering software application. This process is based on frequently adapting to changing requirements, involving customer participation, and delivering high-quality applications. Agile teams need a set of reliable metrics to measure their performance at different three levels: functionality, content, and scalability. This paper proposes Goal Question Agility Metrics that can be used in measuring the performance of agile teams working in developing business intelligence applications. GQAM is based on the Goal Question Metrics method that was developed by Victor Basili in 1994 [19]. In addition, GQAM depends on agile and BI concepts. GQAM proposes a set of metrics that are used to measure performance. The GQAM will be subjected to experiments using a real data set in the future.

Aplikasi Business Intelligence Pada Sistem Penjualan CV. Muda Jeans

This research explains the application of business intelligence in Point of Sales sales applications in CV. Young Jeans. Muda Jeans is a private company that buys, sells, and manufactures women's jeans. The authors used a descriptive method in this study and conducted observations and interviews with store owners and employees. The sales system currently used by CV. Muda Jeans uses a traditional sales system where the entire sales transaction process is carried out directly through store visits and every transaction is recorded in the general ledger, this results in data recording errors, data duplication and data presentation which can cause problems in data utilization. Data utilization is used in decision making in creating and developing new products. This problem can be overcome with Point of Sales applications that apply business intelligence, allowing applications to analyze current and historical data displayed through data visualization. Data visualization in the form of sales graphs per product to make it easier for owners to see, understand and analyze product movements through sales transactions that occur, which products will later be increased production, which products are decided to be reduced or not produced at all.

Evaluation of a software positioning tool to support SMEs in adoption of big data analytics

Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability, customer demand forecasting, cheaper development of products, and improved stock control. Small and medium sized enterprises (SMEs) are the backbone of the global economy, comprising of 90 % of businesses worldwide. However, only 10 % SMEs have adopted big data analytics despite the competitive advantage they could achieve. Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics. The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK. This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners. The results of the evaluation are presented with a discussion on the results, and the paper concludes with recommendations to improve the scoring tool based on the proposed framework. The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.

Analysis of Hospital Length of Stay in Each Diagnostic -Related Groups (DRGs) Carried Out Using the Smart Hospital Research Application

Abstract Background The application of business intelligence (BI) tools in hospitals can enhance the quality and efficiency of care by providing insights into diagnostic, therapeutic, and business processes. BI tools aid in infection monitoring, clinical decision -making, and analysis of hospitalisation durations within Diagnostic-Related Groups (DRGs), identifying inefficiencies and optimizing resource use. Objectives This study aims to analyse hospital length of stay and identify the DRGs with the most inefficient hospitalization times using the BI -driven Smart Hospital application. Materials and methods The Smart Hospital application, developed on the Qlik Sense BI platform, analysed data from the National Health Fund (NFZ), Statistics Poland, e -health Centre (CEZ), and hospitalisations billed by DRG sections. The dataset included 20,376,405 hospitalisations from 2017–2019. Results The average length of stay (ALOS) was 6.2 days, with an effective length of stay (ELOS) of 4.33 days. Ineffective hospitalisation days totalled 30,307,086, accounting for 28.99% of all hospitalizations. The most inefficient DRGs were E53G (Cardiovascular failure), A48 (Complex stroke treatment), N01 (Childbirth), T07 (Trauma conservative treatment), and D28 (Respiratory and thoracic malignancies), contributing to about 14% of all ineffective hospital days. Conclusions Understanding the factors influencing hospitalisation durations in DRGs can improve patient flow management. Future research should compare treatment effectiveness concerning hospitalisation duration to develop optimal strategies for specific patient groups.

Driving Business Success: Exploring the Implementation and Improvement of Big Data Analysis and Business Intelligence in Selected Companies in Zheijiang, China

The study investigates the application and enhancement of big data analysis and business intelligence in selected companies in Zheijiang province, focusing on aspects such as business profile, adoption levels, benefits, implications, and challenges. Limited to selected companies in Zheijiang, the research aims to propose a tailored enhancement plan based on survey data. While it doesn't compare with other organizations and relies on self-reported data, the study delves into specific aspects of the selected companies, potentially requiring customization for wider applicability. Conducted from June to October 2023, the research utilizes a descriptive-quantitative design, emphasizing systematic data collection. With Zhejiang, China as the locale, 100 respondents from five companies are chosen through purposive sampling. A comprehensive survey questionnaire covers demographics, adoption levels, benefits, and challenges.
 Results highlight the selected companies in Zheijiang's adaptability in Healthcare, Finance, and Marketing sectors, emphasizing strong data analytics for real-time insights and strategic decisions. The system excels in data accuracy, integration, governance, security, compliance, scalability, and skills development. Recommendations include cross-sector collaborations, continuous training, customer-centric strategies, addressing skills gaps, and fostering interdepartmental collaboration, ensuring the selected companies optimize strengths and foster innovation and adaptability.

Proposed Metrics for Agile Business Intelligence

Business Intelligence has become the front of demanding technologies. The large or midsize companies need technology to adapt to changing requirements, to deal with a rich amount of data; to get correct information, to analyze data, and finally to quickly support decision makers. Developing an agile BI application can be defined as a short-delivering software application. This process is based on frequently adapting to changing requirements, involving customer participation, and delivering high-quality applications. Agile teams need a set of reliable metrics to measure their performance at different three levels: functionality, content, and scalability. This paper proposes Goal Question Agility Metrics that can be used in measuring the performance of agile teams working in developing business intelligence applications. Goal Question Agility Metrics is based on the Goal Question Metrics method that was developed by Victor Basili in 1994 [19]. In addition, Goal Question Agility Metrics depend on agile and BI concepts. GQAM proposes a set of metrics that are used to measure performance. The GQAM will be subjected to experiments using a real data set in the future.

Optimizing Industrial Data Analysis: The Convergence of Business Intelligence and Dynamic Simulations in Chemical Process Management

Purpose: The project aims to leverage Business Intelligence integrated with dynamic simulations in the context of Industry 4.0, focusing on the implementation of a system for the dynamic analysis of a chemical reaction process. The system is designed to optimize data analysis and decision-making processes in large-scale industrial settings. Theoretical Framework: In the evolving landscape of Industry 4.0, the effective management of industrial data and processes is paramount. Traditional methodologies often overlook the potential of integrating Business Intelligence with dynamic simulations. This project proposes a novel approach, combining these elements to enhance process oversight and decision-making efficacy. Method: Utilizing PI System AVEVA/OSIsoft for structured data organization, the project implements a Business Intelligence framework applied to the dynamic simulation of a chemical reaction process. This method focuses on creating a harmonious integration of data analysis tools with real-time process simulations, aiming to improve operational efficiency and adaptability. Results and Conclusion: The implementation of this integrated system in a simulated industrial environment demonstrated a notable improvement in process analysis and decision-making efficiency. This indicates a significant advancement in the application of Business Intelligence in industrial operations, particularly in dynamic process simulations. Research Implications: This study underscores the importance of advanced data analysis techniques in modern industrial operations. The results suggest a substantial shift towards more data-driven, efficient, and adaptable process management strategies in Industry 4.0. Originality/Value: This research highlights the innovative application of Business Intelligence in conjunction with dynamic simulations for industrial processes. It showcases a pioneering approach in enhancing data analysis and decision-making capabilities in the context of Industry 4.0.

The Implementation of Business Intelligence to Analyze Sales Trends in the Indofishing Online Store Using Power BI

The growth of online sales businesses by utilizing E-Commerce is increasing rapidly. One way that can be used as a tool to face competition is to apply Business Intelligence as a basis for making the right business strategy decisions. Indofishing Shop is an online shop selling various fishing gear and equipment. So far, online sales transaction data processing uses Microsoft Excel, which has created problems with the need for more information to analyze product trends. This research applies Business Intelligence to detect data extracted and collected in a data warehouse. The data will be analyzed statistically and visualized using Microsoft Power BI. Data warehouse design uses Kimball's Nine-Step method. The results of this research are in the form of a visualization dashboard that is used to analyze sales trends for the most sold fishing equipment products from the Indofishing Shop. Diagram visualization helps Indofishing understand the trend conditions of the most in-demand products and the cities of origin of the buyers who buy the most products.

Revolutionizing Energy Harvesting: Solar Cell Integration with Radio Waves for AI, IT, and Business Applications

The dynamic convergence of solar cell technologies, radio waves, information technology (IT), artificial intelligence (AI), and business applications is examined in this study. The combination of these technological components has created a diverse array of innovative opportunities that provide cost-effective, environmentally friendly, and game-changing solutions for a range of businesses. We start by exploring the basic ideas behind solar cells, demonstrating their ability to transform light from the sun into electrical energy, and looking at the various kinds of solar cells and how they work together to gather energy. With uses ranging from off-grid, rural locations to space exploration and residential and commercial installations, solar cells have emerged as key players in the shift to clean, renewable energy sources. Next, we delve into the world of radio waves, which are essential to radar systems, wireless communication, remote sensing, and other fields. We clarify the various uses of radio waves and the difficulties they pose, paving the way for their cooperation with solar cell technology. The combination of radio waves with solar cells opens up new opportunities for business, IT, and artificial intelligence applications. With this connection, off-grid communication networks may be made more sustainable and wireless devices and sensors can be installed in remote or energy-constrained areas. There could be a huge impact on AI-driven applications such as Internet of Things (IoT), smart cities, precision agriculture, and remote data collection. Integration of solar-powered radio wave technology improves sustainability, resilience, and connectedness in the field of information technology. It enables companies to improve supply chain operations, lower energy costs, operate profitably in isolated or disaster-prone places, and support sustainable practices. Among the noteworthy trends influencing the IT sector are edge computing and digital inclusion. We also examine the significant effects of solar-powered radio wave technologies on the business sector. It increases supply chain optimization, energy efficiency, environmental monitoring, and remote monitoring and control. Key themes in this environment include resilience, scalability, and sustainability. Businesses are looking for creative ways to adapt to changing customer demands and legal obligations.

Selection of the best Big Data platform using COBRAC-ARTASI methodology with adaptive standardized intervals

The advanced technologies emerging in Industry 4.0 are forcing companies in different industries to review their business models and become more compatible with advanced technological practices. While traditional business models are increasingly inadequate in the face of increasing competition, business models developed thanks to advanced technologies such as deep learning and machine learning have begun to replace them. However, developing business intelligence and intelligent applications using these technologies requires more data processing. In this context, Big Data technology is a unique instrument in providing the data businesses need to design more intelligent systems. In conclusion, the Big Data platform can significantly speed up the processes of structuring and processing the data and information generated and increase businesses' efficiency, performance, and agility. However, being a relatively new concept, the knowledge about the Big Data concept is limited, leading to several challenges for decision-makers concerning choosing the appropriate platform. Also, the number of studies on this subject is highly scarce. Hence, practitioners in various industries lack sufficient support from the research society on this issue. We could not find crisp and definite values to evaluate the BD alternatives despite comprehensive investigation. In that regard, as data, we addressed appraisals and opinions of IT professionals with vast knowledge and experience in assessment, selection, installation, and operation. We developed a novel decision-making model to evaluate and select the most proper BD platforms by processing these data. In this connection, the current investigation suggests a novel, robust, practical decision-making model for defining the combination of the weight of criteria based on pairwise comparisons of adjacently ranked criteria (COmparisons Between RAnked Criteria- COBRAC) and the ARTASI (Alternative ranking technique based on adaptive standardized intervals -ARTASI). It can handle complex ambiguities encountered in appraisal processes to address the Big Data platform selection problem. In addition, the current work developed a negotiation process quantitificated to determine the influential criteria affecting the selection of the Big Data platform. When we evaluate the outcomes of the suggested model, the most influential criterion affecting the selection of the Big Data platform is C12 “Ease of Use.” in addition, the most suitable Big Data platform for large-scale enterprises has been identified as A3 Microsoft SQL Server. The proposed model and its results have been validated based on extensive sensitivity and comparison analysis. These results also offer practical and managerial implications for the industry. Although many studies indicate that installation cost and speed are the most critical factors, this research found that, unlike these studies, ease of use is the most critical factor in choosing a BD platform. In this context, the BD alternative that provides the highest ease of use can produce more efficient results and reduce complexities in collecting and processing high volumes of structured and unstructured data.

Analyzing the Impact of the Threshold on Machine Learning Models for Credit Risk Prediction Using Business Intelligence

Making decisions based on predictions generated from machine learning models requires users to have a clear understanding of the mechanisms and logic behind every prediction. From one side, business users must be convinced in the ability of the models to generate correct predictions. Predictive power, expressed by the different performance measures, is not sufficient for building trust and acceptance of machine learning models. Business users need additional techniques and tools for model interpretation and evaluation of the effects from decisions based on machine learning predictions. In this research paper we propose an approach for analyzing the impact of different thresholds for converting probabilities into predictions for binomial classification machine learning models applicable for credit risk prediction in Peer-to-Peer Lending platforms. We define a set of measures to explore global and local impact on decision-making process and present different scenarios in a Business Intelligence application built in an analytical and business intelligent platform. Based on the presented results we can draw conclusions that when choosing the best model and threshold users should consider a broad set of measures not only for model accuracy, but also should consider misclassification costs, financial results, asset portfolio structure, etc.

Framework for Project Sustainability for Power Installations Using Business Intelligence Approach: A Systematic Literature Review

The growing concerns about environmental sustainability and energy conservation have led to increased interest in optimizing electricity consumption and billing processes in various projects. This research article presents a comprehensive study on the development and application of Business Intelligence (BI) frameworks for enhancing project sustainability through data-driven energy management. Through the integration of BI tools and techniques, this research investigates the analysis of electricity consumption patterns, billing accuracy, and cost-effectiveness in diverse project contexts. The article emphasizes the significance of data preprocessing, statistical analysis, and predictive modelling in uncovering valuable insights to support informed decision-making. Additionally, the review examines the concept of project sustaibility, emphasizing its significance in achieving desired outcomes, meeting stakeholder expectations, and ensuring the project’s viability in an ever-changing environment. Traditional project management approaches often fail to adequately address sustainability concerns, leading to project failures or limited long-term impact. Hence, the review highlights the growing importance of leveraging BI-driven frameworks to enhance project sustainability in various sectors, including in Information and Communication Technology (ICT) domain. The Systematic literature review (SLR) method was used involving the scooping of 230 articles from over 8 global academic databases. With the use of exclusion criteria, only 61 articles were used in the study. The analysis of the articles shows that 57% were journal articles, 39% were conference proceedings, 2% were thesis/dissertations and 2% were generic. Within the scope of this literature review, key terms and keywords were identified to provide insights into the development of a novel BI-driven framework for project sustainability. Consequently, future research directions are identified to further explore the integration of renewable energy sources, AI and machine learning applications, and behaviour-based energy management strategies within BI frameworks for sustainable project outcomes. This review lays the foundation for future research endeavours in developing innovative BI-driven frameworks that foster sustainable practices and contribute to a greener and more resilient future across diverse industries and projects.

A Comparative Study of Parallel and Distributed Big Data programming models: Methodologies, Challenges and Future Directions

According to a survey conducted in 2021, users share about 4 petabytes of data on Facebook daily. The exponential increase in data (called big data) plays a vital role in machine learning, internet of things (IoT), and business intelligence applications. Due to the rapid increase in big data, research in big data programming models gained much interest in the past decade. Today, many programming paradigms exist to handle big data, and selecting an appropriate model for a project is critical for its success. This study provides an in-depth analysis of big data programming models such as MapReduce, Directed Acyclic Graph (DAG), Bulk Synchronous Parallel (BSP), and SQL. We conduct a comparative study of distributed and parallel big data programming models and categorize these models into three classes: traditional data processing, graph-based processing, and query-based processing models. Furthermore, we evaluate these programming models based on different parameters like performance, data processing, storage, fault-tolerant, suitable language, and machine learning support. Finally, we highlight the benchmark datasets used for big data programming models and discuss the challenges of models along with future directions for the research community.

Towards a Conceptual Framework for Data Management in Business Intelligence

Business intelligence (BI) refers to technologies, tools, and practices for collecting, integrating, analyzing, and presenting large volumes of information to enable improved decision-making. A modern BI architecture typically consists of a data warehouse made up of one or more data marts that consolidate data from several operational databases. BI further incorporates a combination of analytics, data management, and reporting tools, together with associated methodologies for managing and analyzing data. An important goal of BI initiatives is to improve business decision-making for organizations to increase revenue, improve operational efficiency, and gain a competitive advantage. In this article, we analyze qualitatively various prominent business intelligence (BI) frameworks in the literature and develop a comprehensive BI framework from these. Through the technique of qualitative propositions, we identify the properties, respective advantages, and possible disadvantages of the said BI frameworks to develop a comprehensive framework aimed mainly at data management, incorporating the advantages and eliminating the disadvantages of the individual frameworks. The BI landscape is vast, so as a limitation, we note that the new framework is conceptual; hence, no implementation or any quantitative measurement is performed at this stage. That said, our work exhibits originality since it combines numerous BI frameworks into a comprehensive framework, thereby contributing to conceptual BI framework development. As part of future work, the new framework will be formally specified, followed by a practical phase, namely, conducting case studies in the industry to assist companies in their BI applications.