Business Intelligence Research Articles

Implementation of Universal Hepatitis C Virus Screening in a Tertiary Cancer Center.

The prevalence of chronic hepatitis C virus (HCV) infection in the United States is ≤1%. Universal HCV screening is recommended nationwide. Here we describe our experience implementing universal HCV screening at a cancer center. In October 2016, universal HCV screening with HCV antibody (anti-HCV) was initiated for all new outpatients. Universal screening was promoted through widespread provider education, orders in the Epic electronic health records (EHRs), SmartSets, and automated EHR reminders. The effort focused on patients with solid tumors, because universal screening in patients with hematologic malignancies was already standard practice. Primary outcomes were the proportion of patients screened and the proportion of patients with reactive anti-HCV test results linked to HCV care. The secondary outcome was the incidence of HCV-associated hepatocellular carcinoma as a second primary malignancy (HCC-SPM) in patients with a history of other cancers before HCC diagnosis. Epic's Reporting Workbench Business Intelligence tools were used. Statistical significance was defined as P<.05 on chi-square analysis. From April 2016 through April 2023, 56,075 patients with solid tumors were screened for HCV, of whom 1,300 (2.3%) had reactive anti-HCV test results. The proportion of patients screened was 10.1% in the 6 months before study implementation and 34.4% in the last 6 months of the study (P<.001). HCV screening was ordered using SmartSets in 39,332 (45.8%) patients and in response to automated EHR reminders in 10,972 (12.8%) patients. Most patients with reactive anti-HCV test results were linked to care (765/1,300; 59%), most with proven HCV infection were treated (425/562; 76%), and most treated patients achieved sustained virologic response (414/425; 97%). The incidence of HCC-SPMs was 15% in historical controls treated from 2011 to 2017 and 5.7% following implementation of universal screening (P=.0002). Universal HCV screening can be successfully implemented in cancer hospitals using an EHR-based multipronged approach to eliminate HCV and prevent HCV-associated HCC-SPMs.

Customer Churn Prevention For E-commerce Platforms using Machine Learning-based Business Intelligence

Aims &amp; Background: Businesses in the E-commerce sector, especially those in the business- to-consumer segment, are engaged in fierce competition for survival, trying to gain access to their rivals' client bases while keeping current customers from defecting. The cost of acquiring new customers is rising as more competitors join the market with significant upfront expenditures and cutting-edge penetration strategies, making client retention essential for these organizations. Objective: The main objective of this research is to detect probable churning customers and prevent churn with temporary retention measures. It's also essential to understand why the customer decided to go away to apply customized win-back strategies. Methodology: Predictive analysis uses the hybrid classification approach to address the regression and classification issues. The process for forecasting E-commerce customer attrition based on support vector machines is presented in this paper, along with a hybrid recommendation strategy for targeted retention initiatives. You may prevent future customer churn by suggesting reasonable offers or services. Results: The empirical findings demonstrate a considerable increase in the coverage ratio, hit ratio, lift degree, precision rate, and other metrics using the integrated forecasting model. Conclusion: To effectively identify separate groups of lost customers and create a customer churn retention strategy, categorize the various lost customer types using the RFM principle.

Analysis of the “Global Niche Top” Strategy of Regional Companies in Japan – RHEON Automatic Machinery Co., Ltd.

Abstract This paper illustrates how Rheon Automatic Machinery Co., Ltd., the largest manufacturer of food molds in the world, uses operational and manufacturing complexity to create sustainable competitive advantage in a highly volatile business environment. In these days of increased digitalization, high-tech and intelligent business solutions, it is still of interest to review some rather analogue, but highly successful manufacturing companies which do not rely on high-tech to charm their customers, create employee engagement, global advantage and social impact, while nourishing sustainably business opportunities. This research further explores core elements of talent attraction, retention, workforce planning and development, as well as organizational development techniques unique to Rheon Automatic Machinery Co., Ltd.

Data Science, Business Intelligence, and the Internet of Everything: A Scientometric Education Perspective with a Mountain Focus

Data Science, Business Intelligence, and the Internet of Everything: A Scientometric Education Perspective with a Mountain Focus

Transformative potential of Generative Artificial Intelligence (GenAI) in business

Objective:This study investigates the transformative potential of Generative Artificial Intelligence(GenAI) within the business domain and the entrepreneurial activity.Methodology:A comprehensive research design is adopted, integrating text-mining techniques to analysedata obtained from publicly available innovation repositories. A systematic literaturereview (SLR) is developed based on the literature obtained from all databases indexedin Web of Science (WoS), incorporating preprints from arXiv, alongside industry-relatedinnovation data in the form of patents from Google Patents. This method enables the derivationof valuable insights regarding the impact and prospective developments of GenAIacross diverse business sectors and industries by leveraging Natural Language Processing(NLP) and network analysis.Results:The research outcomes highlight the significant potential of GenAI in enabling informeddecision-making, enhancing productivity, and revealing new growth opportunities inthe business landscape. The continuously evolving business environment is examined,emphasising GenAI's role as a catalyst for data-driven innovation. However, there are stillrelevant limitations to overcome.Limitations:The selection of data sources and the study period may have excluded relevant or recentlypublished articles and patents within the scope of the present research. The language ofthe databases analysed is only English.Practical Implications:The practical implications of this study carry significant weight, serving as a valuableresource for decision-makers, researchers, and practitioners navigating the constantlyshifting terrain of business innovation through the lens of GenAI. Understanding thepotential advantages and challenges associated with GenAI adoption equips stakeholdersto make informed decisions and develop future business strategies.

Enhancing Business Intelligence with Real-Time Sales Dashboards: Integrating SQL Databases and Power BI

Abstract: In today's fast-paced business environment, the ability to make informed decisions swiftly is crucial for maintaining a competitive edge. This project proposes the integration of SQL databases and Power BI to develop a real-time sales dashboard, aimed at transforming how sales data is visualized and analysed within the organization. By harnessing the robust capabilities of SQL databases for structured data management and Power BI for data visualization and analytics, the project seeks to provide comprehensive insights into sales trends, performance metrics, and customer behaviours. The integration will enable the automatic synchronization of sales data from SQL databases to Power BI, facilitating the creation of interactive and dynamic dashboards. These dashboards will not only display key sales indicators and metrics but also allow users to drill down into specific data points for deeper analysis. The real-time nature of the data presented will empower sales managers and executives to make data-driven decisions rapidly, adjust strategies on the fly, and identify opportunities for growth and improvement with unprecedented speed and accuracy.

ARTIFICIAL INTELLIGENCE WITH MICROSTRATEGY: ENHANCING DATA INGESTION AND CUSTOMER BENEFITS WITH AI INTEGRATION

The integration of Artificial Intelligence (AI) with MicroStrategy is revolutionizing data management and analytics, significantly enhancing organizational productivity and efficiency. By incorporating advanced technologies such as large language models (LLMs) and generative AI, MicroStrategy offers features like Auto Answers and Auto Dashboard, which streamline data analysis and provide rapid, reliable insights. This integration is particularly impactful in fields such as healthcare, where AI-driven solutions like CerviCARE AI improve diagnostic accuracy in cervical cancer screening. The implementation of AI in MicroStrategy optimizes data ingestion, processing, and analysis, boosting data processing speed and analytical precision by up to 40%. This comprehensive integration distinguishes MicroStrategy in the business intelligence landscape, offering superior capabilities compared to traditional BI tools.

From Data to Decisions: Optimizing Supply Chain Management with Machine Learning-Infused Dashboards

This paper examines how business users can leverage machine learning and data analytics through dashboards to optimize their decision making in demand-side supply chain management. We present a case study of an Austrian B2B hygiene product retailer that needed to provide its top management, sales representatives, and marketing managers with more relevant information to improve business intelligence and to enhance customer acquisition and retention. To generate this information, we utilized various data analysis and machine learning methods, including RFM analysis, market basket analysis, TURF analysis, and demand forecasting, using real-life transaction data. To provide business users with easy access to this information, we developed dashboards that integrate these methods providing an interactive and visual tool for data exploration and understanding. We conclude that dashboards can enable, business users to make better informed and effective decisions on the demand side of supply chains leading to improved sales performance and increased customer satisfaction.

Business Intelligence HRIS Monitoring Dashboard to Increase User Interaction Using Power BI

Business Intelligence HRIS Monitoring Dashboard to Increase User Interaction Using Power BI

Improving value chain data lifecycle management utilising design for Lean Six Sigma methods

PurposeThis project aims to optimise a secondary agricultural company’s reporting and data lifecycle by providing self-help business intelligence at an optimal price point for all business users.Design/methodology/approachA design for Lean Six Sigma approach utilising the define, measure analyse, design and verify methodology was utilised to design a new reporting and data product lifecycle.FindingsThe study found that this approach allowed a very structured delivery of a complex program. The various tools used assisted greatly in delivering results while balancing the needs of the team.Practical implicationsThis study demonstrates how improving data analysis and enhanced intelligence reporting in agribusinesses enable better decision making and thus improves efficiencies so that the agribusiness can leverage the learnings.Social implicationsImproving data analysis increases efficiency and reduces agrifood food wastage thus improving sustainability and environmental impacts.Originality/valueThis paper proposes creating a standardised approach to deploying Six Sigma methodology to correct both the data provisioning lifecycle and the subsequent business intelligence reporting lifecycle. It is the first study to look at process optimisation across the agricultural industry’s entire data and business intelligence lifecycle.

Maximizing Organizational Performance: The Synergy of AI and BI

Objective: This paper focuses on ways artificial intelligence and business intelligence technologies can be utilized to improve the efficiency of an organization, this paper aims to propose a framework for organizational use to leverage AI and its performance in organizations. Theoretical Framework: In this topic, the author proposes a framework for integrating AI and BI, which includes the following components: data collection and statistical analysis, machine learning algorithms, visualization, and integration with BI systems. Method: This study will employ a statistical methodology called multiple regression, the analysis helps to examine the relationship between a dependent variable (organizational performance) and multiple independent variables (extent of BI integration, extent of AI integration, and AI and BI integration). Results and Discussion: Combining AI and BI in business processes improves decision-making, increases efficiency, and enhances customer experiences. AI automates complex processes, detects patterns, and predicts based on vast data, enhancing BI systems. AI-powered BI systems give businesses a competitive edge by improving decision-making, performance, and customer engagement. Integrating AI and BI leads to improved organizational performance. Research Implications: This paper stresses the significance of evaluating organizational structure before implementing AI-based systems. It guides infrastructure requirements for successful AI implementation in business, emphasizing security implications when making decisions based on AI-generated data. Originality/Value: Integrating AI and BI can lead to faster and more efficient business processes, resulting in higher performance. AI technology can revolutionize decision-making across industries, including business. Practical recommendations are available on how to use AI to improve decision-making in organizations.

Critical Appraisal of Definitions on Intelligence within the Organizational Context

This article explores the different forms of intelligence in relation to expanding organizational design and enhancing development, and how it entertains the notions of growth and success. Furthermore, it embraces how the amalgamation of the different forms of intelligence contributes to the relevancy and longevity of the organization. Thereafter, leadership is discussed as crucial for organizational success that reflects engagement of decision intelligence, business intelligence, and competitive intelligence as key pillars of strategic decision-making. These concepts are taken to enable leaders in understanding complex problems that lead to developing innovative and creative solutions. The proposition here is transformative intelligence that is taken to combine these three concepts of intelligence that potentially would revolutionize organizational leadership and consequent decision-making. Simultaneously, the five pillars of leadership, or the 5Qs (Cognitive Intelligence, Digital Emotional Intelligence, Moral Intelligence, Innovation Intelligence, and Spiritual Intelligence), also contribute to strategic decision-making, enabling organizations to achieve desired transformation. This spectrum of reviewing intelligence as a combination concept benefits organization through streamlined decision-making, customer-centricity, anticipating disruptive forces, improved innovation, and better organizational culture. This article examines the scopes and potential drawbacks of each type of intelligence and showcases how adopting a business model which utilizes and combines the aforementioned types of intelligence ultimately creates a stronger and more resilient organization.

HUMAN JUDGMENT IN ARTIFICIAL INTELLIGENCE FOR BUSINESS DECISION-MAKING: AN EMPIRICAL STUDY

The deployment of AI systems has increased across several industries as they exhibit progressively stronger predictive performance. Due to safety, moral, and legal considerations, full automation is frequently undesirable. However, fully manual methods might be erroneous and time-consuming. The idea of using AI to support human decision-making is therefore gaining popularity in the scientific community. The flourishing subject of AI decision-making needs to embrace empirical methodologies in addition to building AI technologies for that purpose to establish a solid understanding of how people interact and collaborate with AI to make decisions. This research intends to analyse how artificial intelligence uses human judgment for decision-making in business. Researchers gathered survey results from high-tech employees in India via email, media, and other means. The sample size was 196, and the sampling strategy most likely employed was convenience sampling. With the data collected measurement and structural model are performed and found that artificial intelligence-based decision-making impacts the organizations’ business value and artificial intelligence capability impacts the organization created with the moderating effect of business intelligence. Also, it is concluded that AI-based decision-making impacts knowledge management.

How a System Is Closing the Evidence-Based Data Management Gap.

To translate raw data into information that is understandable and actionable, healthcare leaders must leverage decision-making tools that can drive strategic innovation, improve processes, and shape the future of healthcare. Continuous changes in healthcare delivery require constant monitoring of an expanding range of data. Population demographics, psychographics, and availability of care all must be considered, as well as provider practice patterns, patient utilization, clinical and service quality, costs, and many other key variables over time. RWJBarnabas Health is navigating significant changes in its approach to managing data. A unified operating model is driving standardization, continuous quality improvement, and cost reductions across the system. The solution is based on an electronic health record system designed to meet the needs of the entire system, an array of carefully selected external data sources, and a business intelligence tool to enable leaders to quickly draw insights from all the available data.

Delivering data analytics: A step‐by‐step guide to driving adoption of business intelligence from planning to launchLondon, UK: Kogan Page2022

Delivering data analytics: A step‐by‐step guide to driving adoption of business intelligence from planning to launchLondon, UK: Kogan Page2022

Revolutionizing Organizational Decision-Making for Banking Sector: A Machine Learning Approach with CNNs in Business Intelligence and Management

This research investigates the transformative impact of deep learning, particularly Convolutional Neural Networks (CNNs) such as VGG16, ResNet50, and InceptionV3, on organizational management and business intelligence within the banking sector. Employing a comprehensive methodology, the study emphasizes the crucial role of high-quality datasets in harnessing deep learning for improved decision-making. Results reveal the superior performance of CNN models over traditional algorithms, with CNN (VGG16) achieving an impressive accuracy rate of 90%. These findings underscore the potential of deep learning in extracting valuable insights from complex data, presenting a paradigm shift in optimizing various banking processes. The article concludes by highlighting the importance of investing in infrastructure and expertise for successful CNN integration, while also addressing ethical and privacy considerations. This research contributes to the evolving discourse on deep learning applications in organizational management, offering valuable insights for banks navigating the challenges of the global market.

Smart resilience through IoT‐enabled natural disaster management: A COVID‐19 response in São Paulo state

AbstractNatural disaster management approach establishes stages of prevention, preparation, response, and recovery. With the Internet of Things (IoT), Bigdata, Business Intelligence, and other Information Communication Technologies, data can be gathered to support decisions in stages of the response to natural disaster events. In biological natural disasters, the ICTs can also support efforts to promote social distancing, public health, and economic monitoring to face the threads. São Paulo state used IoT in scenarios to face COVID‐19, such as monitoring vehicular interurban mobility, social distancing, and economic activity. Frameworks, strategies, data views, and use cases are presented to support the decision‐making process to face this biological natural disaster. The data‐driven approach supports several purposes, including the communication of social distancing indices, economic recovery, the progression of contagion, and deaths. It also played a pivotal role in fostering transparency initiatives for society and supporting the crisis committee by facilitating situational analyses, and this approach became standard practice for pandemic response. Studies and innovative visualisation perspectives have produced positive outcomes, guiding the decision‐making process through data analysis. Noteworthy use cases were interurban traffic fence monitoring; mapping of virus spreading; tracking the economic impact concerning recovery plans; and, evaluating the effectiveness of public policies.

Virtual Digital Metaverse Chartered Accountant

A globally renowned financial expert that oversees customers' budgets, audits, taxes, and company strategy is a chartered accountant. You can work for companies, the government, or private citizens as a CA Assisting clients with money management and offering professional financial advice are your duties. For the accounting sector, the emergence of virtual reality (VR) and the metaverse offers an exciting opportunity. Modern technologies like blockchain, artificial intelligence (AI), and data analytics, which come together to form a virtual environment that reimagines the function of the accountant, are the foundation of this paradigm shift. Virtual Digital Metaverse Chartered Accountant is what we're working on. By using artificial intelligence (AI), one may automate repetitive processes related to financial reporting, taxation, predictive analytics, business intelligence, and automation of regular tasks. Chartered accountants are becoming increasingly important to the growth and recovery of the Indian economy. They guarantee honest, ethical trade and commercial practices, support the smooth operation of businesses and organizations, and apply their financial know-how to different economic and financial initiatives aimed at improving the country. They are therefore crucial to boosting the economy. Forecasting the budget, tax audits, creating monthly financial reports, capital planning, tax planning, and other duties are all part of the CA's job description. Additionally, since the GST was implemented, their function has grown stronger and more significant

Utilizing machine learning to predict employee turnover in high-stress sectors

This study investigates the application of machine learning techniques to predict employee turnover in high-stress sectors. The primary objective is to enhance retention strategies by accurately identifying potential turnover risks. The research utilizes a comprehensive dataset comprising various factors, including employee demographics, job satisfaction, performance metrics, and stress levels. Multiple machine learning algorithms, such as logistic regression, decision trees, random forests, and neural networks, are employed to build predictive models. The methodology involves data preprocessing, feature selection, model training, and evaluation. Cross-validation and hyper parameter tuning are performed to ensure the robustness and accuracy of the models. The performance of each algorithm is assessed using metrics such as accuracy, precision, recall, and the area under the receiver operating characteristic curve (AUC-ROC). Key findings reveal that machine learning models can effectively predict employee turnover, with random forests and neural networks demonstrating superior performance. Significant predictors of turnover include job satisfaction, stress levels, and performance ratings. The study concludes that integrating machine learning models into human resource practices can provide valuable insights for preemptive interventions, ultimately reducing turnover rates in high-stress environments. Future research should explore the integration of real-time data and the potential of deep learning techniques to further enhance predictive accuracy. Additionally, the ethical implications of using predictive models in HR decisions warrant careful consideration to ensure fairness and transparency. Keywords: Machine Learning (ML), Employee Turnover, Predictive Analytics, Human Resources (HR), High-Stress Sectors, Decision Trees, Random Forests, Extreme Gradient Boosting (XGBoost), Personalized Retention Strategies, Business Intelligence (BI) Tools, Data Quality, Ethical Considerations, Data Privacy, Natural Language Processing (NLP), Deep Learning, Real-time Data Analysis, Employee Engagement, Work-Life Balance, Organizational Performance, Data-Driven Insights.

A novel Bayesian optimizable ensemble bagged trees model for cryptocurrency fraud prediction approach

AbstractNowadays, the prediction of cryptocurrency side effects on the critical aspects of the exchange rates in intelligent business is one of the main challenges in the financial market. Cryptocurrency is defined as a set of digital information concerning internal financial protocols of digital marketing, such as blockchain, which operates according to a decentralized architecture. On the other hand, fraud activities in Ethereum transfer and management of cryptocurrency now increase and affect safe transactional processes. This article presents a new machine‐learning approach to Ethereum fraud Detection based on Bayesian Optimizable Ensemble Bagged Trees (BOEBT) algorithm. Moreover, the main goal of this study is to derive the accuracy of the cryptocurrency prediction model using different machine‐learning algorithms and compare their evaluation parameters together. The performance of the proposed prediction model using the machine learning algorithms was evaluated by the MATLAB tool. The experimental results show that the proposed BOEBT algorithm merits achieving 99.21% accuracy and 99.14% F1‐Score to other machine learning algorithms for cryptocurrency fraud prediction.