Business Intelligence Applications Research Articles

The Application of Business Intelligence in Human Resources in the 21st Century

The management of human resources is one of the most critical aspects of any company's strategy, as it significantly influences the achievement of organizational goals. Modern human resource managers are increasingly utilizing advanced information technology tools and data analysis techniques to better understand the relationships between employees and organizational outcomes. This article explores the integration of Business Intelligence (BI) into the implementation of HR strategies, highlighting its benefits and the key performance indicators (KPIs) that enable the measurement and evaluation of these strategies. A systematic literature review methodology was employed, focusing on high-quality studies published in reputable journals and conferences indexed in databases such as ResearchGate and ScienceDirect. This rigorous approach ensured the inclusion of relevant and reliable research, providing a comprehensive synthesis of current knowledge and emerging themes in the application of BI in HR management. The findings aim to advance the theoretical and practical understanding of leveraging BI for strategic HR initiatives in the 21st century.

Development of a Decision Support System for University Administration: Integrating Business Process Management and Multidimensional Data Analysis

The paper provides an overview of decision support systems to demonstrate their role in supporting decision-making in university administration. Enhancing university performance and modernizing university administration must be top priorities for university leaders and decision-makers since quality is the foundation that supports innovation. Good decision-making is the first step towards efficiency and quality in the academic setting. The article’s main topic is developing IT solutions to support decision-making business processes of student admission. By utilizing models of university business processes and applying business intelligence principles, with the support of multidimensional data analysis, it can be effectively introduced and integrated. The study attempts to provide selection criteria for selecting a development environment for creating a support system focused on university management based on data from the student information system database and the author’s personal experience. The contributions include creating OLAP analytical models and a data warehouse model to support university managerial decisions.

QueryAI: A Conversational Interface for SQL Database Querying Using Natural Language Processing

QueryAI is an interface leveraging natural language for SQL database querying. By incorporating large language models (LLMs) and natural language processing (NLP) techniques, QueryAI enables users to query a MySQL database using English-based commands. This interface translates user inputs into SQL statements and presents the outcomes, offering access to those without SQL proficiency. This paper explores QueryAI's design, implementation, and potential applications in data analysis and business intelligence.

Enhancing Predictive Analytics in Business Intelligence through Explainable AI: A Case Study in Financial Products

Today, when the importance of data-based decision-making is impossible to question, the use of Explainable Artificial Intelligence (XAI) in business intelligence (BI) has inestimable benefits for the financial industry. This paper discusses how XAI influences predictive analytics in BI systems and how it may improve interpretability, and useful suggestions for financial product companies. Thus, within the context of this study, an XAI framework helps the financial institutions to employ higher-performing and more accurate models, like gradient boosting and neural networks, while sustaining interpretability required in tendentious planning and satisfying governance and supervision necessities. These studies reveal that, as applied to the credit scoring dilemma, XAI techniques such as SHAP and LIME do not only enhance prediction consistency and performance, but also offer a detailed understanding of customer behaviours, risk profiles and product performance. They help in interacting and acting within fields that involve decision making on aspects like customer loyalty, probable risks and audit. Furthermore, the study establishes that by incorporating XAI into BI improves model interpretability, which helps financial experts provide tangible rationale for analytical results and conform to regulatory directives. This framework and findings also support the importance of introducing XAI for financial BI applications to improve analytics practice within the sector. , enabling the generation of higher confidence, reliable decisions, which place the subject of XAI as a profound evolution of business intelligence in finance.

Supplier relationship management for sustainability: A comprehensive review of business intelligence applications

Supplier relationship management for sustainability: A comprehensive review of business intelligence applications

Application of Business Intelligence in the Brazilian Educational System

In this article, we will discuss an application of Business Intelligence (BI) in analyzing data from the Brazilian educational system, specifically working with INEP (The Instituto Brasileiro de Geografia e Estatística — IBGE). This paper then discusses BI implementation's positive contributions and critical challenges in this domain. From the construction of an integrated BI system, our study aimed to offer a helpful tool that was easy to handle and touched on visuals such as dashboards, tables, and graphs to help us understand and manage the educational scenery throughout Brazil. The methodology used has been applied, with case studies and bibliographical research, based on data analysis techniques such as ETL (Extract, Transform, Load) and Data Warehousing. This is all leveraged to interactive dashboards, which should enable data-driven decisions and forge the way for advanced analytics like data mining.

Application of business intelligence based on big data in E-commerce data evaluation

With the vigorous development of e-commerce, more and more goods are sold online. The electronic platform not only brings convenience to people's lives but also gives more people the opportunity of employment and entrepreneurship, which contributes to the promotion of economic value and the creation of wealth. With the gradual maturity of network technology represented by Big Data, it has also led to the further development of e-commerce. In the past, e-commerce mostly used business intelligence systems for data analysis. However, as the data becomes more and more complex, the innovation ability and data analysis ability of traditional business intelligence systems are relatively conservative, so it is of great significance to update and strengthen business intelligence to analyze its role in e-commerce data. Therefore, this paper proposed the application of business intelligence based on Big Data in e-commerce data analysis. By combining Big Data and a business intelligence system and taking the e-commerce data of a certain brand of beverage as the research object, the Days-Times-Money (DTM) model was established, and the data mining technology was used to classify the brand consumers. The data results showed that among the three consumption attributes, the highest consumption density of consumption days, consumption times, and consumption amount was 68.63 %, 67.99 %, and 69.72 % respectively. These were not more than 70 %, which indicated that the consumption of this brand of beverages had a high space for growth. According to the consumption density, the users of this brand of beverage were divided into four consumer groups. According to the classification results, it provided feasible marketing reference opinions for the brand beverage and could provide direction for brand value-added by mining the hidden data of the brand beverage. This paper hoped to use the brand beverage as a case to provide reference and reality for the development of e-commerce enterprises and provide a reference for the application and promotion of business intelligence based on Big Data in e-commerce data analysis.

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.

Towards a Business Intelligence Application for Evidence-based Maintenance

Maintenance plans are crucial to ensure the ongoing operation of the assets, extending their lifespan, and minimizing costs as much as possible. Various methods, including Reliability-Centered Maintenance and Risk-Based Maintenance, are available in the literature for formulating such plans. However, these approaches may pose significant challenges, particularly for Small and Medium Enterprises (SMEs). In this context, Evidence-based Maintenance (EbM) has emerged as a promising method, aiming to achieve comparable operational outcomes while minimizing initial overhead. Leveraging the tools provided by digital transformation, this work proposes the utilization of Business Intelligence (BI) to support the development of EbM. A methodology is introduced for this purpose and validated through a case study. Nevertheless, its effective implementation requires leadership and commitment to grant consistency through the standardization of vocabulary, formats, and processes within the organization.

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.