Business Intelligence Tools Research Articles

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.

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.

AUTOMATION IN DECISION MAKING AND IT AS A BUSINESS AGENT

Making decisions such as checking numbers, analyzing movements, data, values is a very important factor for a company. These are activities that make it a daily challenge for a manager, making it even more difficult if performed manually. If the calculation of the data occurs erroneously, the leader can make the wrong decision and the time lost to get to the result ends up occupying his working schedule completely. The work presents the problems that lead to the stock of a glass factory in Manaus-AM, since the main system of the company in question, does not have the ability to provide the necessary data to do studies of materials, and will be implemented a Business Intelligence tool (BI - Qlik Sense) for the construction of dashboards, able to integrate together with the companys system, automating the updating of data and facilitating the manager to read the data clearly and objectively, facilitating the study of inventory.

Business Intelligence for Reducing NPA in Indian Banking Sector

Indian Banking sector is a blend of Private as well as public sector banks. In the Economy of a Nation, banks keep a deposit with themselves and interest the depositors. The banks are responsible for the majority of the lending operations that took place in the economy. However, since 2008, the returns on Indian banks’ assets are continuously falling. A healthy banking system is the backbone of a healthy economy. Hence, to help banks make wiser decisions about creating fruitful assets in the future, the paper advises Business Intelligence to help decision-makers make sound decisions and ultimately prevent productive assets in the future.This paper is an attempt to use the Business Intelligence tools to predict if the loan application will turn stressed or not. The outcome of this paper will be on suggesting parameters for the Indian Banks to judge an assetto-be about its fruitfulness so that the NPAs of these banks will be reduced in the future.

Intention to use business intelligence tools in decision making processes: applying a UTAUT 2 model.

The pressure on the speed of information processing ranks business intelligence technologies among the fastest growing decision support tools. The main goal of this article is, applying the UTAUT 2 (the unified theory of acceptance and use of technology), to verify the factors determining the implementation of business intelligence tools in business processes, especially decision-making, and their subsequent optimal use in business practice. The researched scheme was modified according to the specifics of business intelligence tools and was supplemented by user behaviour in decision-making. The verification was performed using a questionnaire survey based on UTAUT 2 theory and 152 respondents were included in the analysis. According to the results, the most important variable of influence on both the behavioural intention and the users' behaviour itself in decision-making was the factor of habit. And surprisingly, some previously recognised links were not confirmed, especially the factors influencing the intention of behaviour (effort expectancy, social influence, facilitating conditions). So, there is room after almost 10years and experience gained during the Covid-19 pandemic to modify the latest version of a model.