Business Intelligence Techniques Research Articles

Optimizing Retail Business Strategies with Advanced Analytics and Improved Business Intelligence Techniques

The retail landscape thrives on the synthesis of advanced analytics and business intelligence techniques, pivotal in navigating the complexities of consumer behavior and market dynamics. This study addresses the imperative to optimize retail strategies by leveraging historical sales data from 45 diverse stores with multifaceted departments. The challenge of predicting retail sales prices guided our methodology, employing convolutional neural network architectures and Root Mean Square Error (RMSE) as the principal error metric. Through iterative computations and feature extractions, our model aimed to discern intricate patterns and correlations within the retail domain, underpinning strategic decision-making processes. Analysis of the integrated methodologies illuminated critical insights into the intricate interplay of factors impacting retail operations. The findings underscored the significance of these techniques in informing strategic decisions, highlighting their potential to elevate sales performance and operational efficiencies. Our study culminates in advocating for the application and refinement of predictive models across diverse retail contexts, proposing further research into real-time application and interpretability methods.

A Business Intelligence System for the Aeronautical Sector

This article describes a company’s business intelligence system in the aeronautical sector that is specialized in the production of aerostructures and the supply of aircraft maintenance services. The goal of this system is to help the company’s managers make better decisions and improve all the business areas of the company, which operates in “Maintenance”, “Aircraft Painting”, “Manufacturing” and “Engineering” services. “Maintenance” services consist of “Engines”, “Components”, “Defense aviation” and “Civil aviation” maintenance. Here the indicator “I1-General Productivity”, for the “Maintenance” services is detailed. The aeronautical company felt the need to begin the process of digital transformation across all its areas and the business intelligence system that is being developed is a component of the company digital transformation. This digital transformation process is in line with the use of business intelligence techniques. The market has become more and more competitive in the aeronautical sector and this company wanted to stay at the forefront of technology and implement a business intelligence system to become increasingly a reference in its business branch. For this digital transformation, several systems were used, the Oracle database management system to manage company internal databases, Oracle’s SQL Developer tool and Microsoft’s Power BI tool.

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.

An Optimized Approach for Maximizing Business Intelligence using Machine Learning

The subject of study known as business intelligence is responsible for the development of techniques and tools for the analysis of business information with the goal of assisting in the management and decision-making processes of corporations. In the current climate, business intelligence is essential to the process of formulating a strategy and carrying out operations that are data-driven. Throughout the many stages of the company operation, an organization will need assistance evaluating data and making decisions; a decision support system may provide this assistance by including business intelligence as an essential component. The fact that this enormous quantity of data is distributed over a number of different types of platforms, however, makes it a difficult challenge, in particular to understand the information that is actually relevant and to make efficient use of it for business intelligence. One of the most important challenges facing modern society is maximizing business intelligence through the application of machine learning. It offers a full analysis that is based on predictions and is extracted for Business Intelligence techniques along with current application fields. This anomalous gap has been pointed up, and solutions and future research areas have been offered to overcome it in order to create effective business strategies.

Utilizing Big Data Analytics and Business Intelligence for Improved Decision-Making at Leading Fortune Company

The present study evaluates Walmart’s existing big data analytics with business intelligence techniques, accentuating their strengths and weaknesses, and suggests improvements for implementation and maintenance through the literature review of the scholarly journals addressing similar topics. Big data analytics is receiving loads of attention globally in the business environment within every sector of the economy. Incorporating the job plan as an additional input component in their models would be beneficial for Walmart to improve the precision and appropriateness of their data analysis and decision-making procedures. Walmart is a company that heavily invests in utilizing big data to improve its operations; this includes optimizing in-store experiences and predicting product trends. Scholarly articles emphasize the importance of advanced data analytics tools like MapReduce and Apache Spark for effective big data strategies. Social media content influences engagement and sentiment. Social network data aids sales forecasting but presents challenges. Big data analytics with business intelligence enhances performance and decision-making. Walmart's success in big data analytics relies on a data-driven culture but faces security challenges. Many Fortune 1000 companies adopt innovative solutions to improve performance and customer experiences but require significant resources. Embracing big data analytics with business intelligence remains a compelling investment for sustaining a competitive edge. Walmart's success in big data analytics is due to a data-driven culture and advanced infrastructure, including the Data Café.

A Decision Support System for Credit Risk Assessment using Business Intelligence and Machine Learning Techniques

Credit risk assessment is a critical task for financial institutions to determine the creditworthiness of their potential customers. Business intelligence (BI) and machine learning (ML) techniques have gained popularity in recent years as effective tools for credit risk assessment. In this paper, we propose a decision support system (DSS) for credit risk assessment that integrates BI and ML techniques. The proposed DSS employs BI tools to extract and transform data from various sources, and ML techniques to analyze the data and generate predictive models for credit risk assessment. We evaluate the proposed DSS using a real-world dataset of a financial institution. The results show that the proposed DSS achieves a high level of accuracy in credit risk assessment. The results showed that the system was able to accurately predict credit risk, with an accuracy of 88%. The system also outperformed traditional credit scoring models, which highlights the potential of our system for credit risk assessment. The system provides decision-makers with actionable insights to make informed decisions, thereby reducing the risk of default and increasing the profitability of the financial institution.

Towards Financial Sustainability: Integrating Business Intelligence in Fintech Operations

This study explores the integration of Business Intelligence (BI) techniques to foster sustainability within the Financial Technology (Fintech) sector. The background elucidates the transformative evolution of the Fintech industry and the increasing imperative to align its practices with sustainable principles. The problem statement addresses the gap in understanding the intricate relationship between BI strategies and sustainability within Fintech enterprises. Employing a mixed-methods approach, including literature review synthesis and the application of the Autoregressive Integrated Moving Average (ARIMA) model, our methodology seeks to provide a systematic and replicable framework for empirical investigation. The results encompass additive and multiplicative summaries, detrended and deseasonalized analyses, and partial and autocorrelation plots, shedding light on critical temporal dynamics and patterns within the Fintech domain.

Big data analytics to depict patient journey: demographics, calculation and the relative role of the CHA2DS2Vasc score components in patients diagnosed with atrial fibrillation in a tertiary hospital

Abstract Introduction and purpose Atrial fibrillation (AF) is the most common arrhythmia worldwide, with a considerable prevalence, high morbidity, mortality, and finantial cost in Europe. To optimise the quality of medical care received in patients with AF, we need to know and investigate their accurate demographic and clinical typology and the actual patient journey, which involves many data to review and a high number of patients included. The CHA2DS2Vasc score classifies the risk of stroke in patients with atrial fibrillation, one of the most critical complications of this arrhythmia, and assists decision-making. A part of Big Data, Data mining, process mining, and business intelligence techniques can analyse a high volume of clinical and non-clinical data. Methods Big Data pre-processing, data mining, process mining and business intelligence techniques were applied. Databases storing clinical and administrative information related to hospital discharges, highly complex procedures, emergency care and specialised practices from 2016 to 2020 were used. Patients with a principal or secondary diagnosis of AF were selected. Data sources included the Basic Minimum Set of Data (BMSD), containing administrative and clinical information at hospital discharge and resources at the Cardiology outpatient clinic. Once the databases were free of noise, inconsistencies, anomalies and duplicates, they were simultaneously reduced into smaller datasets. CHA2DS2Vasc score at the time of the patient's first contact with the system was calculated using BMSD information. Data integration techniques were applied to combine the extracted datasets into a single data source. Data transformation and reduction techniques were used, and a global dataset was generated for exploitation with process mining and business intelligence tools. Results After analysing 10942 individual BMSD, the CHA2DS2Vasc score was calculated for 6870 unique patients from 2016 to 2020. The most prevalent score was 5 (36.07%) (Figure 1), 4.19% of patients had a score of 1, and the first quartile was score 4. Nearly half of the patients (49.6%) were women. A large portion of patients (69.46%) were aged ≥75 (Figure 2). Diabetes mellitus was present in 25.21% of patients, high blood pressure in 64.76%, previous stroke in 8.59%, and none had a history of arterial disease. Conclusions Patients with atrial fibrillation treated in a tertiary university institution show a very high risk of stroke, according to the CHAD2DS2Vasc score. Almost 70% of them are aged 75 years and over. A high number of data and a high volume of patients can be analysed through data mining and business intelligence. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): BMS-PfizerBoehringer

Business Intelligence for the Evaluation of Customer Satisfaction in E-Commerce Websites- A Case Study

Background/Purpose: Advancement of technology has been proven in every field. To establish a good business or any organization is very crucial nowadays. Businesses are moving tremendously and competitors also high in number. It is very important to take a good decision based on reviews and feedback of customer, etc. By combining the strength of technology and business knowledge, Business Intelligence allows you to make good decisions that are fully informed and stay one step ahead of the competition. With the help of business intelligence tools businesses can use their data in a better way. Business intelligence incorporates Data mining, Data Analytic, Data Visualization and Machine learning to help organization for the analysis of data. This article provides the analysis of Business intelligence and machine learning techniques used in e-commerce website and ABCD framework to inspect the key factors. Objective: Analyses the business intelligence technology and sentiment analysis on E-commerce website. Design/Methodology/Approach: The information and details for this case study is obtained from different scholarly articles published in various journals and company websites. Findings/Result: The study of this paper delivers the importance of customer behavior and how it helps in growth of the industry using machine learning and business intelligence. Originality/Value/Novelty: The result of this paper gives an explanation of business intelligence and machine learning approach for customer feedback in e-commerce companies and advantages of e-commerce websites. Paper Type: Case study paper to study the advantages of business intelligence and machine learning for customer satisfaction.

Big data analytics to depict patient journey: demographics, calculation and the relative role of the CHA2DS2Vasc score components in patients diagnosed with atrial fibrillation in a tertiary hospital

Abstract Introduction and purpose Atrial fibrillation (AF) is the most common arrhythmia worldwide, with a considerable prevalence, high morbidity, mortality, and finantial cost in Europe. To optimise the quality of medical care received in patients with AF, we need to know and investigate their accurate demographic and clinical typology and the actual patient journey, which involves many data to review and a high number of patients included. The CHA2DS2Vasc score classifies the risk of stroke in patients with atrial fibrillation, one of the most critical complications of this arrhythmia, and assists decision-making. A part of Big Data, Data mining, process mining, and business intelligence techniques can analyse a high volume of clinical and non-clinical data. Methods Big Data pre-processing, data mining, process mining and business intelligence techniques were applied. Databases storing clinical and administrative information related to hospital discharges, highly complex procedures, emergency care and specialised practices from 2016 to 2020 were used. Patients with a principal or secondary diagnosis of AF were selected. Data sources included the Basic Minimum Set of Data (BMSD), containing administrative and clinical information at hospital discharge and resources at the Cardiology outpatient clinic. Once the databases were free of noise, inconsistencies, anomalies and duplicates, they were simultaneously reduced into smaller datasets. CHA2DS2Vasc score at the time of the patient's first contact with the system was calculated using BMSD information. Data integration techniques were applied to combine the extracted datasets into a single data source. Data transformation and reduction techniques were used, and a global dataset was generated for exploitation with process mining and business intelligence tools. Results After analysing 10942 individual BMSD, the CHA2DS2Vasc score was calculated for 6870 unique patients from 2016 to 2020. The most prevalent score was 5 (36.07%) (Figure 1), 4.19% of patients had a score of 1, and the first quartile was score 4. Nearly half of the patients (49.6%) were women. A large portion of patients (69.46%) were aged ≥75 (Figure 2). Diabetes mellitus was present in 25.21% of patients, high blood pressure in 64.76%, previous stroke in 8.59%, and none had a history of arterial disease. Conclusions Patients with atrial fibrillation treated in a tertiary university institution show a very high risk of stroke, according to the CHAD2DS2Vasc score. Almost 70% of them are aged 75 years and over. A high number of data and a high volume of patients can be analysed through data mining and business intelligence. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): BMS-PfizerBoehringer

Determination of Business Intelligence and Analytics-Based Healthcare Facility Management Key Performance Indicators

The use of digital technologies such as Internet of Things (IoT) and smart meters induces a huge data stack in facility management (FM). However, the use of data analysis techniques has remained limited to converting available data into information within activities performed in FM. In this context, business intelligence and analytics (BI&A) techniques can provide a promising opportunity to elaborate facility performance and discover measurable new FM key performance indicators (KPIs) since existing KPIs are too crude to discover actual performance of facilities. Beside this, there is no comprehensive study that covers BI&A activities and their importance level for healthcare FM. Therefore, this study aims to identify healthcare FM KPIs and their importance levels for the Turkish healthcare FM industry with the use of the AHP integrated PROMETHEE method. As a result of the study, ninety-eight healthcare FM KPIs, which are categorized under six categories, were found. The comparison of the findings with the literature review showed that there are some similarities and differences between countries’ FM healthcare ranks. Within this context, differences between countries can be related to the consideration of limited FM KPIs in the existing studies. Therefore, the proposed FM KPIs under this study are very comprehensive and detailed to measure and discover healthcare FM performance. This study can help professionals perform more detailed building performance analyses in FM. Additionally, findings from this study will pave the way for new developments in FM software and effective use of available data to enable lean FM processes in healthcare facilities.

Business Intelligence for Risk Management: A Review

This paper provides a quick review about business intelligence approaches and techniques in risk management. The important research articles from 2007 to 2021 are involved in this review. We found a little contribution from researchers in this research direction, however the vital role of business intelligence in risk management. Moreover, we provide a novel business approach for risk management. This approach includes deploying the trendiest techniques in this era which are social media and big data analysis. Social media represents the source of identifying risks through the discussions of social media users as well as big data analysis techniques which represent the control tool for potential risks. The new approach will help firms and organizations in many sectors to manage risks efficiently and make the best decisions. Further, we provide the challenges of the new framework and the further research points.

CHARACTERIZATION OF ENTERPRISES IN THE DOMINICAN REPUBLIC FROM BUSINESS INTELLIGENCE TECHNIQUES

The Dominican Republic has more than 1.200.000 Micro, Small and Medium Enterprises (MSMEs) in different Economic Sectors. Although the State recognizes the value that these have for the economic development of the country, there is no systematized and automated characterization that allows for an analysis that helps decisionmaking. This article shows the results when applying Business Intelligence Techniques for the characterization of Enterprises, taking into account the Systematization of Information through a web application. In this sense, 179 Micro, Small and Mediumsize d Enterprises were analyzed, using an instrument that allowed knowing different variables associated with the economic development of each of the Enterprises. Results show the incidence of different factors in the generation of income for the Enterprise.

An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentation

Nowadays, banks use data mining and business intelligence tools and techniques to analyze their customers’ behavior. Customer segmentation is a widely adopted analytical tool to identify distinct groups of customers. Most studies have adopted a static segmentation approach, leading to missing important trends and patterns. In this study, we propose a framework that represents each customer behavior as a time-series sequence of the Recency, Frequency, and Monetary variables, and then exploits time-series clustering algorithms to carry out customer segmentation. The framework consists of state-of-the-art clustering algorithms, including hierarchical, spectral, and k-shape algorithms to divide customers into homogeneous groups and implement point of sale devices transaction data of grocery and appliance retailers. We divide customers into four segments, analyze the behavioral trends, and provide marketing suggestions for each segment. Our results show that, in terms of the computed validity indices, the best clustering model for grocery retailers is reached using hierarchical clustering with the Complexity-Invariant Distance measure, and for appliance retailers, the best segmentation is achieved by applying the spectral clustering with the Complexity-Invariant Distance measure.

Critical Examination Using Business Intelligence on the Gender Gap in Information Technology in Brazil

In the early 1990s, cyberfeminism emerged as an area of knowledge to discuss the connection between gender and technology. According to UNESCO, women are underrepresented in the field of science, technology, engineering, and mathematics and less than a third of women worldwide work in scientific research and development. However, this number has grown and this reality is constantly changing. In this context, using business intelligence techniques, this study analyzes data from the computer and information and communication technology market to characterize the impact of the performance of women in these areas. It is expected to show that this performance in the highlighted fields is still a challenge in Brazil. Based on this hypothesis, results indicates that public policies must be focused on the base, i.e., to encourage young women to work in STEM areas. This study could encourage policymakers to find solutions to the challenges presented in this research.

Software Tools for Conducting Real-Time Information Processing and Visualization in Industry: An Up-to-Date Review

The processing of information in real-time (through the processing of complex events) has become an essential task for the optimal functioning of manufacturing plants. Only in this way can artificial intelligence, data extraction, and even business intelligence techniques be applied, and the data produced daily be used in a beneficent way, enhancing automation processes and improving service delivery. Therefore, professionals and researchers need a wide range of tools to extract, transform, and load data in real-time efficiently. Additionally, the same tool supports or at least facilitates the visualization of this data intuitively and interactively. The review presented in this document aims to provide an up-to-date review of the various tools available to perform these tasks. Of the selected tools, a brief description of how they work, as well as the advantages and disadvantages of their use, will be presented. Furthermore, a critical analysis of overall operation and performance will be presented. Finally, a hybrid architecture that aims to synergize all tools and technologies is presented and discussed.

Designing a Clinical Education Tracking System: An Innovative Approach.

In clinical education, there is a need for constant evaluation, assessment, and at times immediate feedback for students to recognize areas of success, learning, and areas for improvement. The clinical educator is expected to manage the number of student evaluations and assess their performance, regularly, as needed, and over time. This allows appropriate and timely formative, as well as summative feedback to students and the program. Clinical programs face challenges in finding innovative and efficient methods to track and manage this large volume of seemingly disparate needed and required data. Traditional solutions entail paper-based systems or a paid proprietary clinical tracking system. However, both of these options have their own unique challenges. This perspective article proposes an innovative approach of collecting data electronically via online forms, processing and storing data in cloud-based databases and providing appropriate visualizations for the end users (i.e., students and educators) to analyze and assess the information. Migrating data to be collected in this manner allows for the integration of business intelligence (BI) techniques that provide data mining and machine learning in various user needed methods. This article presents a perspective and an innovative guide for clinical educators on how to develop and design a system that leads to positive sentiments and immediate student feedback regarding their progress to improve student outcomes.

Leveraging Business Intelligence and Operations Research for Enhanced Decision-Making in Healthcare

This paper explores the potential of leveraging business intelligence (BI) and operations research (OR) techniques to enhance decision-making in healthcare organizations. We propose a novel BI framework that includes three main components: data collection and management, data analysis and reporting, and decision-making support. Our framework leverages existing BI tools and techniques, such as data mining and visualization, to provide healthcare organizations with a comprehensive and integrated view of their operations. The framework also integrates clinical data with financial and operational data to provide a more holistic view of the organization. Healthcare organizations face numerous challenges, including rising costs, changing regulations, and the need to improve patient outcomes. By leveraging the proposed framework, healthcare organizations can make data-driven decisions that optimize resource allocation, streamline processes, and improve patient care. The paper provides use cases of how BI and OR have been successfully applied in healthcare organizations and discusses the potential for future research and applications in this field. Ultimately, our framework highlights the importance of using data-driven approaches to improve decision-making in healthcare organizations and suggests that the integration of BI and OR techniques has significant potential to achieve this goal.

Review of Public Procurement Fraud Detection Techniques Powered by Emerging Technologies

Numerous studies and various methods have been used to detect and prevent corruption in public procurement. With the development of IT technology and thus the digitization of the Public Procurement Process (PPP), the amount of available data is increasing. Studies have shown progress in this area and have revealed many challenges and open issues geared to the various goals outlined in this paper. Different data mining and business intelligence techniques and methods are being used to develop models that will find any suspicious public procurement process, contracts, economic operators, or to classify observations as corrupt. In addition to using classification models, methods such as association rules and graph databases are used to find relationships between economic operators and contracting authorities, as well as to find daughter companies that participate in PPP collusion. Therefore, this paper addresses a comprehensive review of the emerging techniques and models used for the detection of suspicious or corrupted observations, their goals, open issues, challenges, methods and metrics used, tools, and relevant data sources. The findings show that models are mostly fitted on historical data and move in the direction of an early warning system. Moreover, the efficiency of fraud or anomaly detection depends on data set quality and detection of the most important red flags. The study is presenting a summary of identified fraud detection model objectives such as predicting fraud risk in contracts and contractors or finding split purchases, and detection of used data sources such as public procurement process or economic operator data.

Transforming E-commerce Operations: An Intelligent Business Intelligence Approach for Improving Customer Transaction Management

In the fast-paced world of e-commerce, understanding customer behavior is essential for success. Business intelligence (BI) tools provide valuable insights into customer transactions and can be used to model and predict customer behavior. This paper explores the use of BI techniques for modeling customer transaction behavior in e-commerce. We discuss the various types of BI tools available and their use in analyzing customer data. We then outline a framework for using BI to develop a customer transaction behavior model, including data collection, preprocessing, feature selection, and model selection. Finally, we present a case study in which we apply this framework to a real-world e-commerce dataset and demonstrate the effectiveness of our approach in predicting customer behavior. Our results show that BI techniques can be an effective tool for modeling customer behavior in e-commerce, providing valuable insights for businesses looking to optimize their operations and increase customer satisfaction.