Business Intelligence Research Articles

Implementasi Intelejen Bisnis dengan Visualisasi Data Gaji dan Algoritma Linear Regresion

To understand these employment dynamics, it is important to explore how long Data Scientists typically work in this field and what sectors they are more likely to be involved in. This information is very important for companies looking to recruit Data Scientists or determine the average salary of their employees. With this activity, managers can more clearly know the employee's salary range based on their work experience and area of specialization. This gives managers a more precise tool for assessing compensation appropriate to the level of experience and expertise possessed by employees. Nowadays, the concept of Business Intelligence can be applied by all industrial sectors, as long as the industry has a database system to develop its business. Business Intelligence processes and analyzes large amounts of raw data and then displays it in a business report with interactive visuals, namely a dashboard. This research aims to contribute to the understanding of average salary predictions and provide valuable information for business owners to make informed decisions. The results show that linear regression is accurate and forms a linear regression model with an R-square result of 0.263429. The salary prediction calculation process is carried out by considering influencing factors, and the results are then presented visually using Power BI to provide business owners with more interactive information.

Assessing the Moderating Role of Customer Orientation on the Impact of Business Intelligence Tools on Digital Marketing Strategy Optimization

This research examines the interplay between Business Intelligence tools and digital marketing strategy optimization, with a focus on the moderating role of customer orientation. Utilizing a quantitative research design, the study explores how BI tools enhance digital marketing strategies and how customer orientation amplifies this effect among 207 respondents from the Jordanian telecommunications sector, reflecting a diverse range of experiences and perceptions within the industry. Through Structural Equation Modeling - Partial Least Squares (SEM-PLS), based on a stratified sample of these employees, the results confirmed that BI tools positively influence digital marketing strategy optimization. Additionally, customer orientation was found to significantly moderate this relationship, highlighting the importance of aligning technological capabilities with a customer-centric approach. The findings contribute valuable insights to marketing and business intelligence fields, suggesting strategies for organizations to enhance their market competitiveness and customer satisfaction, underpinned by a substantial engagement rate from the targeted demographic.

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.

Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review.

In order to comprehensively understand the characteristics of Adaptive Business Intelligence (ABI) in Healthcare, this study is structured to provide insights into the common features and evolving patterns within this domain. Applying the Sheridan's Classification as a framework, we aim to assess the degree of autonomy exhibited by various ABI components. Together, these objectives will contribute to a deeper understanding of ABI implementation and its implications within the Healthcare context. A comprehensive search of academic databases was conducted to identify relevant studies, selecting AIS e-library (AISel), Decision Support Systems Journal (DSSJ), Nature, The Lancet Digital Health (TLDH), PubMed, Expert Systems with Application (ESWA) and npj Digital Medicine as information sources. Studies from 2006 to 2022 were included based on predefined eligibility criteria. PRISMA statements were used to report this study. The outcomes showed that ABI systems present distinct levels of development, autonomy and practical deployment. The high levels of autonomy were essentially associated with predictive components. However, the possibility of completely autonomous decisions by these systems is totally excluded. Lower levels of autonomy are also observed, particularly in connection with prescriptive components, granting users responsibility in the generation of decisions. The study presented emphasizes the vital connection between desired outcomes and the inherent autonomy of these solutions, highlighting the critical need for additional research on the consequences of ABI systems and their constituent elements. Organizations should deploy these systems in a way consistent with their objectives and values, while also being mindful of potential adverse effects. Providing valuable insights for researchers, practitioners, and policymakers aiming to comprehend the diverse levels of ABI systems implementation, it contributes to well-informed decision-making in this dynamic field.

Managing the E-commerce Data Deluge through Text Analytics and Web Management (Overview of Amazon.com)

Today, more than 80% of the big data handled in the e-commerce industry is text and unstructured data. Text analytics is an automated process for analyzing text and extracting useful information from it. It can discover trends and relationships in data. Web analytics is the collection, processing, and analysis of data in order to draw conclusions to optimize usability on a website. Web analytics can be used to improve the usability of a site by analyzing user behavior patterns such as time spent on the site, abandonment rates, most frequently accessed products, click-through rates, etc. It can also help analyze the interests of different user demographics, as it tracks granular details such as user demographics, age and gender, geography, and devices used as data. In order to obtain UpToDate information, the business can utilize business intelligence for real-time data processing, then they can practice stream analysis to analyse continuous flow of data. For instance, the business can collect instant information in Twitter or other social media and analyse it by using social media analysis. For website management, business can practice web analysis to analyse the customer’s behaviours. Tracking the customer’s activity, page view and conversion rate is important for business to analyse how to improve the website performance. Text analytics of comments received on Amazon can be used to group text data and produce results in terms of word frequency distribution and sentiment analysis. Text analytics could be used for decision making, improving service quality, and developing new business models.

Supply Chain Optimization through Real-Time Data Analysis and Visualization with Power BI

Supply chain optimization is a critical aspect of modern business operations, aiming to enhance efficiency, reduce costs, and improve customer satisfaction. In today's dynamic business environment, supply chains face increasing complexity and uncertainty. The ability to access and analyse data in real-time is paramount for organizations to stay competitive and responsive to market dynamics. Effective management requires timely access to accurate data and actionable insights. By harnessing the power of real-time data and integration of real-time data analysis techniques, coupled with visualization and Business Intelligence tools like Power BI, organizations can make informed decisions promptly, leading to agile and responsive supply chain management. Raw data has been transformed into meaningful insights through interactive dashboards and reports by using Power BI.

Transformative Impact of Deep Learning in Stock Market Decision-Making: A Comparative Study of Convolutional Neural Networks

This research delves into the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs) such as VGG16, ResNet50, and InceptionV3, on organizational management and business intelligence. The study follows a comprehensive methodology, emphasizing the importance of high-quality datasets in leveraging deep learning for enhanced decision-making. Results demonstrate the superior performance of CNN models over traditional algorithms, with CNN (VGG19) achieving an accuracy rate of 89.45%. The findings underscore the potential of deep learning in extracting meaningful insights from complex data, offering a paradigm shift in optimizing various organizational processes. The article concludes by emphasizing the significance of investing in infrastructure and expertise for successful CNN integration, ensuring ethical considerations, and addressing data privacy concerns. This research contributes to the growing discourse on the application of deep learning in organizational management, providing a valuable resource for businesses navigating the dynamic landscape of the global market.

Croatian consultancy practices: a nurse’s experience report

INTRODUCTION: The article analyses the professional path of a nurse consultant. Nursing consulting is an expanding and stimulating profession with advanced practice roles and responsibilities. It emphasizes the nurse consultancy practice created through persistent effort, challenges, successes, and personal growth. Engaging in partnerships with healthcare organizations to enhance patient care, optimize workflows, and address challenges within the healthcare system. OBJECTIVE: To report on a nurse’s work experience as a nursing consultancy practice in Croatia's healthcare system. METHODOLOGY: The methodology utilizes descriptive, qualitative, and narrative approaches, with a specific focus on personal experiences and reflections. This methodology aims to share the nurse's journey and the transformative impact of their work in the nursing field. The study emphasizes the significance of authenticity, tenacity, and ongoing learning in nursing management and the execution of nurse consultancy practice in Croatia. RESULTS: The author underscores the significance of embracing a fresh outlook on Croatia's nursing and healthcare systems, particularly workflow integration and a comprehensive perspective of organizational and management business concepts. It provides insights into the process of establishing new hospitals, encompassing expertise in general management as well as specialized abilities in leadership and healthcare management. It also explores the evolution of patient rights and advocates for increased empowerment of nurses in influencing healthcare policies and decision-making, utilizing business Intelligence data to guide the advancement of the field. CONCLUSION: In summary, the nurse is dedicated to transforming the nursing profession by implementing innovative ideas and procedures, focusing on changing patient care attitudes and practices. The narrative journey embarked upon in this has provided a profound understanding of the nurse's career, offering insights into their thoughts, struggles, attitudes, and triumphs, the process of growth, adaptation, and unwavering commitment to bringing about a positive impact in the field of the nursing profession.

IMPACT OF ARTIFICIAL INTELLIGENCE ON BUSINESS ANALYTICS

IMPACT OF ARTIFICIAL INTELLIGENCE ON BUSINESS ANALYTICS

A STUDY ON IMPROVING SUPPLY CHAIN USING BUSINESS INTELLIGENCE CONCERNING DHARA LOGISTICS

Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs. BI tools enable business users to access different types of data — historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Users can analyze this information to gain insights into how the business is performing. According to CIO magazine: “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.” Organizations can use the insights gained from business intelligence and data analysis to improve business decisions, identify problems or issues, spot market trends, and find new revenue or business opportunities. BI platforms traditionally rely on data warehouses for their baseline information. A data warehouse aggregates data from multiple data sources into one central system to support business analytics and reporting. Business intelligence software queries the warehouse and presents the results to the user in the form of reports, charts and maps. Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. “OLAP provides powerful technology for data discovery, facilitating business intelligence, complex analytic calculations and predictive analytics,” says IBM offering manager Doug Dailey in his data warehousing blog. “One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data to improve product quality, customer interactions and process improvements.” Some newer business intelligence solutions can extract and ingest raw data directly using technology such as Hadoop, but data warehouses are still the data source of choice in many cases.

Comparison between Tableau and Power bi: A Case Study of COVID19

Things have come a long way since then, with ever-increasing amounts of data being accessible for modern businesses. As a result of such an influx of data, various organizations have produced software to help businesses make sense of the vast swathes of information. There are now many excellent BI tools on the market today. Tableau first launched in 2004, providing a drag-and-drop way for users to create interactive visuals and dashboards. In reality, Tableau has a variety of products that help businesses store, analyze, and visualize data. Microsoft Power BI arrived on the scene several years later, first launching to the public in 2011. Again, the focus was to create an easy-touse interface to enable non-programmers to organize and visualize data.. Business intelligence is a practice that pre-dates the digital age. The first usage of the term dates back to the 1860s, referring to an American. banker who gathered and analyzed data to gain actionable insights and get ahead of the competition. Things have come a long way since then, with ever-increasing amounts of data being accessible for modern businesses. As a result of such an influx of data, various organizations have produced software to help businesses make sense of the vast swathes of information. There are now many excellent power BI tools on the market today. Tableau first launched in 2004, providing a drag-and-drop way for users to create interactive visuals and dashboards. In reality, Tableau has a variety of products that help businesses store, analyze, and visualize data. Microsoft Power BI arrived on the scene several years later, first launching to the public in 2011. Again, the focus was to create an easy-touse interface to enable non-programmers to organize and visualize data. Like Tableau, Power BI .

Business intelligence and cognitive loads: Proposition of a dashboard adoption model

Decision makers in organizations strive to improve the quality of their decisions. One way to improve that process is to objectify the decisions with facts. Data-driven Decision Support Systems (data-driven DSS), and more specifically business intelligence (BI) intend to achieve this. Organizations invest massively in the development of BI data-driven DSS and expect them to be adopted and to effectively support decision makers. This raises many technical and methodological challenges, especially regarding the design of BI dashboards, which can be seen as the visible tip of the BI data-driven DSS iceberg and which play a major role in the adoption of the entire system. In this paper, the dashboard content is investigated as one possible root cause for BI data-driven DSS dashboard adoption or rejection through an early empirical research. More precisely, this work is composed of three parts. In the first part, the concept of cognitive loads is studied in the context of BI dashboards and the informational, the representational and the non-informational loads are introduced. In the second part, the effects of these loads on the adoption of BI dashboards are then studied through an experiment with 167 respondents and a Structural Equation Modeling (SEM) analysis. The result is a Dashboard Adoption Model, enriching the seminal Technology Acceptance Model with new content-oriented variables to support the design of more supportive BI data-driven DSS dashboards. Finally, in the third part, a set of indicators is proposed to help dashboards designers in the monitoring of the loads of their dashboards practically.

Driving Business Growth from Research to Innovation in The Deployment of Business Intelligence

This research evaluates a company's development and performance based on its intelligence and BI utilization. A study using quantitative poll data from numerous firms examines the relationship between leveraging business knowledge, promoting innovation, and important performance metrics. The findings demonstrate that BI adoption and receptivity to new ideas improve corporate development. Companies that invest heavily in business intelligence (BI) programmes and foster innovation generally outperform their rivals in sales, earnings, and market share. Creative thinking and business knowledge are crucial to corporate success, according to the findings. These gives workers’ suggestions on how to work faster and more creatively. More research, sector-specific analysis, and continuing studies are needed to determine how innovation culture and BI adoption effect firm performance. Finally, this research adds to what is already known about how BI adoption and innovation culture effect firm performance in today's competitive business environment.

Rise of Artificial Intelligence in Business and Industry

The integration of artificial intelligence (AI) into business and industry is catalyzing a paradigm shift in how organizations operate, innovate, and interact with stakeholders. This abstract explores the multifaceted implications of AI across various domains, highlighting its role in automation, predictive analytics, personalized customer experiences, supply chain optimization, enhanced decision-making, natural language processing, product innovation, risk management, fraud detection, healthcare advancements, and workforce augmentation. By leveraging AI technologies, businesses can automate repetitive tasks, anticipate trends, tailor experiences, optimize operations, mitigate risks, and foster innovation. However, the widespread adoption of AI also poses ethical and societal challenges, including concerns about job displacement, data privacy, and algorithmic bias. Therefore, a holistic approach that balances technological advancement with ethical considerations is essential to harness the full potential of AI while ensuring its responsible and equitable deployment in business and industry.

The Mediating Role of Organization Agility between Business Intelligence &amp; Innovative Performance

The Mediating Role of Organization Agility between Business Intelligence &amp; Innovative Performance

Importance of the use of business intelligence for the control of diabetic patients

Importance of the use of business intelligence for the control of diabetic patients

AI-driven adaptive learning for enhancing business intelligence simulation games

This study investigates how automated adaptable learning (BSG) may improve business simulating games (BSG) and its transformation possibilities. The rapid increase of information, combined with the intricate nature of today's company contexts, means that classic BSG education techniques frequently fail to adequately prepare students for the difficulties they will face in everyday life. This study provides a revolutionary architecture that continually evolves learning paths, responses, and testing situations based on specific student progress and desires. It does this by utilizing modern artificial intelligence methods, including machine learning. This technique seeks to maximize users' preservation of expertise, ability to make decisions, and capacity for creative thinking through tailoring their education. In summary, our study raises possibilities for better and more interesting educational opportunities throughout the modern age by adding to the continuing conversation about the relationship between AI, training, and corporate strategies.

IMPACT OF ARTIFICIAL INTELLIGENCE ON BUSINESS ANALYTICS

Artificial Intelligence (AI), a subset of Advanced Analytics (AA), involves the automation of steps that would typically require human intervention to complete a comprehensive analysis. AI is a multidisciplinary field with the objective of automating tasks that currently necessitate human intelligence. The purpose of this research paper is to explore the impact of Business Analytics (BA) and Business Intelligence (BI) on business activities, and to analyze the scientific advancements in BA and BI to identify new research directions in this area. An analysis is needed to highlight findings that offer recognition and comparison of results, enabling an understanding of the current dynamics, its significance for organizations, and its effectiveness in addressing the new challenges posed by global trade requirements. This paper examines the broad implications of AI in BA and BI, investigating the overall influence of AI from research and innovation to implementation in BA and BI. The hypotheses derived from the research will provide a better understanding of the innovations and effectiveness of AI on businesses and society at large. It will also offer insights into how AI can transform business operations. Keywords: Artificial Intelligence, Advanced Analytics, Business Analytics, Business Intelligence.

Big Data Analytics: Analyzing the Significance of it in Light to Indian Banking Sector

Abstract: Banks are the engines that drive the financial sector, monetary markets, and economic expansion. Since the introduction of information technology (IT) in the 1980s, 1990s, and 2000s, the Indian banking sector has experienced a number of changes in terms of how IT is applied to various banking operations from different perspectives, such as cutting expenses, earning revenue, detection of fraud, security concerns, etc. These changes are still ongoing with the emergence of new trends like business intelligence and big data analytics. Banks are utilizing big data to their advantage. To obtain a general competitive edge, they push for innovations in data and analytics through the use of big data and data science. Due to the vast amount of data about transactions that banks collect, big data has the ability to completely change business operations and procedures. Big Data analytics is the answer to the current banking environment's challenges of growing competitiveness, fraud, and cyber security risks.

Boarding House Property Market Trends and Investor Preferences in Boarding House Development: A Comparative Study with Web Scraping

The development of the education industry and other supporting sectors scattered in the Sleman Regency - Indonesia has influenced the mushrooming of boarding houses. It is essential to use business intelligence for boarding house investors to analyze market trends to produce a boarding property business that is not inferior to its competitors. This study aims to contribute to the boarding house investors to obtain information on the characteristics, relationships, and effects of boarding house facilities on the rental prices set to remain competitive in the market. A total of 658 boarding house data from an e-commerce platform was extracted using business intelligence web scraping to determine the characteristics of the boarding house market trend in Sleman Regency. Multinomial Logistic Regression was used to find out the facilities for rooms, bathrooms, shared services and equipment, and common rooms, making the difference in rental prices for superior, exclusive, intermediate, and standard boarding house classes. The Multinomial Logistic Regression analysis results revealed that boarding houses with high rental prices were more concerned with things directly felt by tenants (everything about their room needs). Meanwhile, boarding houses with affordable rental prices prioritized things that general tenants could use/utilized.