Business Intelligence Tools Research Articles

Tourism Reputation Index for Assessing Perceptions on Destinations Using Collaborative Text Data

The tourism sector is experiencing a change in trend due to the widespread use of Web 2.0 by tourists. This phenomenon has prompted tourism agents to apply Big Data techniques and Business Intelligence tools to find out what tourists think. In this sense, the analysis of the Electronic Word Of Mouth in Social Networks is particularly relevant, and the academic literature has faced some difficulties in extracting data, determining its meaning and linking it to the destination. The aim of this work is to is to present the results that can be generated by the design of a tourism reputation index, through the analysis of data created collaboratively on the Twitter Social Network. For this purpose, a BI tool has been created to extract data from short text messages posted by users (tweets) and process them by implementing an Extract, Transform and Load process. In this process, Natural Language Processing is carried out, focusing on Sentiment Analysis tasks, applying an automatic classification model (Multinomial Naive Bayes) and then an automatic thematic categorisation of these texts depending on the words they contain. Subsequently, the data is analysed, calculating a global online reputation index based on sub-indexes of perception on different tourism aspects. These ratings are shown to users through an interactive dashboard, which allows comparison between different queries associated with a tourist location and a temporal period. This research helps tourism agents to better understand the public perception about destinations and make appropriate and well-informed decisions in real time, facilitating the intelligent management of data that enables the creation of smart tourism destinations with competitive, unique and sustainable advantages.

Industry 4.0 and Chemical Industry: The Technology Enablers behind Strategic Growth of Chemical Companies

Abstract: Industry 4.0, also known as the fourth industrial revolution, focuses on various digital transformations within manufacturing units that help them improve their connections and networks, leading to better product evaluation according to the diversified and changing market. Connections are at the heart of the golden age: the link between a company and its related departments, people and products, and global relationships between consumers, suppliers, and producers. All stakeholders must communicate effectively and agree on best practices to ensure smooth daily operations and prompt problem resolutions to maximise the power and potential of such connections. Inculcating and reviewing the applications of Industry 4.0 aspects in a Chemical Industry is the subject of this paper. The paper would assist enterprises in understanding how digitalisation will help them increase production efficiency and integrate their business. It also explains the various enabling technologies under Industry 4.0 that help capture process data from industrial devices, connectivity for data captured, and business intelligence tools to convert that data into impact. This paper also emphasises the challenges associated with promoting Industry 4.0. Methodologies for putting the fourth revolution strategy into action in the chemical industry would be efficiently carried out as a result of this.

Effect of Applying Business Intelligence on Export Development and Brand Internationalization in Large Industrial Firms

Possessing an international brand in an exclusive field can play a critical role in developing exports. On the other hand, monitoring market conditions and predicting the changes caused by the physical separation and distance between the upstream and downstream markets and asymmetric information is challenging in export markets. Accordingly, it is necessary to manage this issue by adopting business intelligence tools. To this end, using a descriptive–correlation method, the present study investigated the effect of applying business intelligence on export development and brand internationalization in large industrial firms. To collect the data, three questionnaires were distributed among 161 employees at the headquarters of Isfahan’s Mobarakeh Steel Company. The validity and reliability of the research questionnaires were confirmed. Data analysis was performed with SmartPLS and SPSS software. The findings indicated the effect of applying business intelligence on export development and brand internationalization in large industrial firms. We also performed additional analyses to deepen the results of the verified hypotheses to identify the best business intelligence dimensions for the prediction of export development and brand internationalization. The conclusion that can be drawn from our findings is that business intelligence and its tools can provide companies with an optimal understanding of organizational processes, appropriate responses to the behavior of competitors, and the ability to identify the needs of global customers by developing the best value chain.

Technology Platforms and Approaches for Building and Evaluating Machine Learning Methods in Healthcare.

Artificial intelligence (AI) methods are becoming increasingly commonly implemented in healthcare as decision support, business intelligence tools, or, in some cases, Food and Drug Administration-approved clinical decision-makers. Advanced lab-based diagnostic tools are increasingly becoming AI driven. The path from data to machine learning methods is an active area for research and quality improvement, and there are few established best practices. With data being generated at an unprecedented rate, there is a need for processes that enable data science investigation that protect patient privacy and minimize other business risks. New approaches for data sharing are being utilized that lower these risks. In this short review, clinical and translational AI governance is introduced along with approaches for securely building, sharing, and validating accurate and fair models. This is a constantly evolving field, and there is much interest in collecting data using standards, sharing data, building new models, evaluating models, sharing models, and, of course, implementing models into practice. AI is an active area of research and development broadly for healthcare and laboratory testing. Robust data governance and machine learning methodological governance are required. New approaches for data sharing are enabling the development of models and their evaluation. Evaluation of methods is difficult, particularly when the evaluation is performed by the team developing the method, and should ideally be prospective. New technologies have enabled standardization of platforms for moving analytics and data science methods.

The impact of business intelligence tools on sustaining financial report quality in Jordanian commercial banks

The objective of this study was to showcase the influence of Business Intelligence (BI) tools, such as Online Analytical Processing (OLAP), Extract, Transform, Load (ETL) processes, Data Mining (DM), Relational Database Management Systems (RDBMS), and Document Management Systems (DMS), on maintaining the quality of financial reports in Jordanian commercial banks listed on the Amman Stock Exchange. Two approaches were employed to achieve the research objectives: a descriptive-analytical approach involving the development of a questionnaire to gather primary data on the independent variables associated with BI tools (OLAP, ETL, DM, RDBMS, DMS), and an applied approach to evaluate the dependent variable represented by the sustainability of financial report quality, utilizing the financial statements of commercial banks listed on the Amman Stock Exchange from (2016 to 2021). Data analysis and hypothesis testing were conducted using statistical software (SPSS) through multiple regression analysis. The results of the statistical data analysis and input from the research community indicated that the sustainability of financial report quality, as a valuable asset for banks, relies on the utilization of Business Intelligence tools. IT professionals in commercial banks perceive a statistically significant impact of BI tools on maintaining the quality of financial reports. Consequently, the management of commercial banks listed on the Amman Stock Exchange should prioritize the effective utilization of Business Intelligence tools, as their potential lies in aiding the accounting process to achieve its objectives, which ultimately contribute to the sustainability of financial reports. By employing these tools accurately and efficiently in accounting practices, all stages of the accounting process can be influenced, enabling the transformation of available data into information that benefits decision-makers both internally and externally within the banking environment.

Shipbuilding—An Analysis from the Perspective of Enterprise Resources Planning System

Designed as a tool for business intelligence, resource planning is used to manage the essentials of a business by integrating processes into a single system. Enterprise Resource Planning (ERP) solutions ensure all the organizational, operational, and procedural structures interconnection, which can be both an advantage and a disadvantage, knowing that inefficiency within one structure will lead to inefficiency in others. The originality of the approach consists in the unitary analysis of all processes which shipbuilding involves (design; evaluation of ship construction possibilities; contracting of works; contracting of supply of materials and equipment; presentation of inspections for the completed works reception; delivery of the ship into operation). The systemic approach is a notable contribution by highlighting the input and output quantities for the analyzed processes. Also, where it was found relevant, the diagrams highlight the owners of the process and show the interconnectivity of the functional departments within a shipyard. Although it is obviously the expression of in-depth knowledge of the peculiarities of Romanian shipyards, the authors hope that the work can support any shipyard that may be interested in knowing these structures (as a logical scheme mandatory to design a custom ERP software system).

A Novel Comparison of Data Analytics and Business Intelligence Tools: A Information Preservation and Ledger Management Solution

A Novel Comparison of Data Analytics and Business Intelligence Tools: A Information Preservation and Ledger Management Solution

A novel comparison of data analytics and business intelligence tools: an information preservation and ledger management solution

A novel comparison of data analytics and business intelligence tools: an information preservation and ledger management solution

A New Data Fusion Framework of Business Intelligence for Mining Educational Data

Student academic performance can be affected by social, economic, and educational factors. Many research works studied these factors applying to different levels in the educational organizations’ models. The importance spans giving professional educational advice to vulnerable students, supporting the student’s development of special education-related skills, and encouraging students to handle their education challenges. For educational organizations, dealing with pandemics and other obstacles has proven to be essential for education sustainability. One way is to be proactive and use the power of exploring and discovering educational data to predict students’ performance and attitude. Mining educational data can benefit from Business Intelligence (BI) in visualizing, organizing, and extracting insights for student’s performance. Educational Data Mining (EDM) is used in this research to predict students' performance. A novel data fusion framework is introduced for Business Intelligence using educational data mining. This study aims to show the techniques that predict students' performance and the most effective methods for each of them. The proposed framework used the advantage of business intelligence concepts and tools to highlight the metrics providing better statistical and analytical understanding.

Clinical Decision Support systems: A step forward in establishing the clinical laboratory as a decision maker hub - A CDS system protocol implementation in the clinical laboratory

BackgroundNew tools for health information technology have been developed in recent times, such as Clinical Decision Support (CDS) systems, which are any digital solutions designed to help healthcare professionals when making clinical decisions. The study aimed to show how we have adopted a CDS system in the San Juan de Alicante Clinical Laboratory and facilitate the implementation of our protocol in other clinical laboratories. We have user experience and the motivation to improve healthcare tools. The improvement, measurement, and monitoring of interventions and laboratory tests has been our motto for years. Materials and methodsA descriptive research was conducted. All stages in the design of the project are as follows: 1. Set up a multidisciplinary workgroup. 2. Review patients’ data. 3. Identify relevant data from main sources. 4. Design the likely outcomes. 5. Define a complete integration scenario. 6. Monitor and track the impact. To set up this protocol, two new software systems were implemented in our laboratory: AlinIQ CDS v8.2 as Rule Engine, and AlinIQ AIP Integrated Platform v1.6 as Business Intelligence (BI) tool. ResultsOur protocol shows the workflow and actions that can be done with a CDS system and also how it could be integrated with other monitoring systems, as well as some examples of KPIs and their outcomes. ConclusionsCDS could be a great strategic asset for clinical laboratories to improve the integration of care, optimize the use of laboratory tests, and add more clinical value to physicians in the interpretation of results.

An exploratory cybercrime analysis and its impact on india

In the modern era, the rapid use of the internet has led to reports of cybercrimes. The number of cybercrimes is constantly increasing. The purpose of this paper is to identify the root causes of cybercrime and find insights that can be used by the administration to take fruitful decisions to overcome cybercrime offenses. It has collected data related to the registered cybercrime cases in various Indian states on different parameters for the years 2016 to 2019. The preprocessing was applied to individual datasets, and a common data set was prepared. Data analysis using Power BI (business intelligence) and Microsoft (MS) Excel tools was performed for the Indian states, and its impact was analyzed through year-wise analysis. The outcome of this paper can be utilized by the Indian administration, cyber experts, working professionals, and users to take care of the affecting factors and limit cybercrimes.

Factors that drive the selection of business intelligence tools in South African financial services providers

Factors that drive the selection of business intelligence tools in South African financial services providers

Utilizing business intelligence and digital transformation and leadership to enhance employee job satisfaction and business added value in greater Amman municipality

The goal of this study was to find out how business intelligence systems, AI, and digital leadership affect how satisfied employees are with their jobs and how much value they add to companies in the Greater Amman Municipality. After the study samples were taken and looked at, a total of 246 samples were approved to be used in the PLS software-based analysis. The results of this study showed that putting in place business intelligence tools, artificial intelligence, and digital leadership all made employees happier with their jobs and gave businesses more value. The research showed that there are four key parts to digital leadership: commander, communicator, collaborator, and co-creator. The main parts of business intelligence are Data Warehouse, Data Mining, Business Process Management, and Competitive Intelligence. Findings show that digital transformation is made up of three key parts: changing processes, developing business models, and changing domains. The results also show that an employee's level of job satisfaction, which includes things like business success, work commitment, and job thinking, is linked to how much value they add to the company. Intriguingly, the current results go against those of earlier studies, which said that the variables of interest have no effect on how happy employees are with their jobs or how much value companies add for their customers. When the results of this study are looked at as a whole, they say that businesses should start doing things that make employees happier at work and increase the value of the business. The current study is innovative because it focuses on the most important parts of business intelligence, artificial intelligence, and digital leadership in order to improve employee satisfaction at work and the quality of business learning with added value in Greater Amman Municipality.

Role of Data Mining in Business Intelligence and Quality Decision Making: A Quantitative Investigation

Data mining is a critical business intelligence tool that enables organizations to discover and extract valuable insights from large datasets. Data mining, which employs advanced algorithms and statistical techniques, aids businesses in identifying patterns and relationships that would not be obvious through manual analysis. These learnings can be applied to improve decision-making, business processes, and overall profitability. One of the most significant benefits of data mining is its ability to improve the quality of decision-making. By providing a better understanding of their data, data mining enables decision-makers to make more informed and strategic decisions. This can lead to a variety of benefits, including increased efficiency, increased customer satisfaction, and improved financial performance. Data mining can also be used to spot trends and predict future outcomes. Historical data can be used by businesses to identify patterns and trends that can be used to predict future behaviour. This data can be used to create targeted marketing campaigns, improve pricing strategies, and boost customer retention. Overall, data mining is a valuable tool for businesses seeking a competitive advantage in today's fast-paced market. Businesses can make better decisions, improve processes, and increase their bottom line by providing valuable insights into their data.

Managerial self-actualisation in the era of business intelligence

The new technological evolution has influenced the global business with the use and application of business intelligence tools that provide information accuracy and conformity to managerial decisions, leading to organisational efficiency and corporate performance. In the global business environment, there is a need of social awareness for the improvement of the human well-being. Managers need to create a knowledge base with the purpose to understand the needs of the global society. This paper aims to present literature insights, from both philosophical and IT dimension, proposing initial research undertakings that should enhance our understanding of the new managerial self-actualisation era. Thus, it is argued that corporate performance is a well-balanced combination between technological trends and human values based on the self-actualisation continuous managerial decision process.

Conceptual Design of Project Portfolio Management Using Business Intelligence Tools for Small and Medium Enterprises

Conceptual Design of Project Portfolio Management Using Business Intelligence Tools for Small and Medium Enterprises

Factors That Drive the Selection of Business Intelligence Tools in South African Financial Services Providers

Factors That Drive the Selection of Business Intelligence Tools in South African Financial Services Providers

Visualization of Business Intelligence Insights into Aviation Accidents

Despite the recent tragic loss activity, flying is often said to be the safest form of transport, and this is at least true in terms of fatalities per distance travelled. The Civil Aviation Authority reports that the death rate per billion kilometers travelled by aircraft is 0.003, which is much lower than the rates of 0.27 for train travel and 2.57 for vehicle travel. Despite the fact that safety has been the aviation industry's top focus for the last century and a half, accidents involving aircraft continue to be a source of horror even in the present day. Hence, the aim of this project is to identify the major causes and reasons that led to accidents in the aviation industry and to carry out research, finding, design, build and suggest a Business Intelligence (BI) solution to the problem. Throughout the project, it will discover problems, both elementary and critical which needs to be corrected or changed in order to prevent major negative happenings and improve the current situation in a positive way. Tableau will be the primary BI tool used in this process. Data visualization is the graphic depiction of information and data. Data visualization tools offer an easy approach to data analysis that observe and find patterns, outliers, and patterns in data by employing visual elements like charts, graphs, and maps. The project will also cover the initial to building and deployment stage of the BI solution to improve and prevent further accidents.

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.

THE ROLE OF BUSINESS INTELLIGENCE IN SOCIAL MEDIA MARKETING AND ITS IMPACT ON FIRM PERFORMANCE

Social media business intelligence assists business owners achieve more accurate data and assessments of existing and potential customers. Today, the COVID-19 pandemic has disrupted business operations globally, and various governments have implemented policies and grants to facilitate business recovery; however, the initiatives have brought limited success. Therefore, it is essential to adopt social media business intelligence tools that help predict potential sales and analyze key performance indicators through effective virtual data collection. By generating such data, a business will be better equipped to plan on past, present, and future marketing and consumer trends. In addition, business owners can design a personalized dashboard that integrates consumer data from diverse sources to counter competition and understand overall performance. Subsequently, business intelligence tools can facilitate organizing the data into clear visualizations that are easy to understand and interpret. Therefore, a business can respond swiftly to changes, understand the cause of the changes, and mitigate adverse impacts in time.