Enhancing Business Intelligence Research Articles

Empowering big data analytics through machine learning: Applications, challenges, and future directions

Abstract. This paper explores the transformative role of machine learning (ML) methodologies in big data analytics, highlighting supervised learning, unsupervised learning, and deep neural networks' contributions to diverse sectors. Supervised learning, with regression analysis at its core, provides accurate forecasting in finance and healthcare by modeling relationships between variables. Unsupervised learning, through techniques like k-means and hierarchical clustering, uncovers patterns within data, offering insights for retail and biological analysis. Deep learning, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM), excel in complex tasks like image recognition and sequential data processing, driving advances in fields from autonomous driving to language translation. The application of ML in enhancing business intelligence, innovating fintech, and advancing healthcare analytics is discussed, alongside the challenges of data privacy, security, ethical considerations, and the skills gap. This paper underscores the need for advanced cryptographic techniques, bias mitigation strategies, and education to address these challenges. It concludes by emphasizing the importance of interdisciplinary education and AI advancements in bridging the skills gap, ensuring the ethical use of ML, and making these technologies accessible for future innovations.

The impact of business intelligence on promoting decision-making in Jordanian business companies

This research aims to investigate the impact of business intelligence on promoting decision-making in Jordanian business companies. The research Methodology is quantitative. It adopts an analytical case study approach, employing the analytical descriptive method, which is deemed appropriate for investigating social phenomena. The construction of the scale for this study relies on two variables that align with its objectives. This scale includes ten items that cover the main research question. The study’s target population includes all Jordanian business companies, with a sample size of 30 managers randomly selected from these companies. Several guidelines are adhered to when selecting a research instrument. The findings show that there is a positive impact of using business intelligence on promoting decision-making in Jordanian business companies. The employees of the companies have sufficient skills in the field of enhancing business intelligence since they actually use it in their workplace.

Integration of Big Data Analytics in Management Information Systems for Business Intelligence

In the era of big data, organizations are increasingly leveraging advanced analytics to extract valuable insights from vast and complex datasets. Management Information Systems (MIS) play a crucial role in collecting, processing, and analyzing data to support decision-making. Integrating big data analytics into MIS can enhance business intelligence and improve organizational performance. This study aims to investigate the integration of big data analytics into MIS and its impact on business intelligence. Specifically, the study seeks to identify the challenges and opportunities associated with this integration and explore best practices for implementation. A qualitative research approach was adopted for this study. Data was collected through semi-structured interviews based on a survey of over 312 information technology (IT) professionals from 21 industries with IT managers and business analysts from January 2022 to December 2023. Thematic analysis was used to analyze the data and identify key themes related to integrating big data analytics into MIS. The findings indicate that integrating big data analytics into MIS can significantly improve business intelligence. According to the respondents, on average, there was a 30% increase in the accuracy of decision-making processes after the integration. Additionally, organizations reported a 25% reduction in operational costs and a 20% increase in revenue as a result of the integration. Moreover, 70% of the respondents agreed that integrating big data analytics into MIS improved their organization's overall performance. Integrating big data analytics into MIS offers numerous benefits, including improved decision-making, cost savings, and revenue growth. However, organizations must overcome challenges such as data privacy and security concerns and the need for skilled personnel to manage and analyze big data. Overall, this study highlights the importance of integrating big data analytics into MIS for enhancing business intelligence and achieving organizational success.

Enhancing business intelligence in e-commerce: Utilizing advanced data integration for real-time insights

This paper explores how advanced data integration techniques and real-time insights can significantly enhance BI in the e-commerce sector. By leveraging methods such as ETL, ELT, data virtualization, API integration, and streaming data integration, e-commerce businesses can achieve a comprehensive, unified view of their data. The integration of real-time data enables dynamic pricing, personalized customer experiences, efficient inventory management, proactive fraud detection, and improved customer support. Despite challenges such as data silos, data quality issues, scalability, security, and technical complexity, solutions like data governance, cloud computing, and advanced analytics tools can address these obstacles. Future trends, including artificial intelligence, machine learning, edge computing, block chains, and data fabric, promise to further transform BI in e-commerce. Ultimately, advanced data integration and real-time insights are essential for e-commerce companies to stay competitive, optimize operations, and enhance customer satisfaction. In the fast-paced and competitive world of e-commerce, the ability to make quick, informed decisions is crucial for success. Business Intelligence (BI) plays a vital role by transforming vast amounts of data into actionable insights that drive strategic decisions and operational efficiency. This paper explores how advanced data integration techniques and real-time insights can significantly enhance BI in the e-commerce sector. By leveraging methods such as ETL, ELT, data virtualization, API integration, and streaming data integration, e-commerce businesses can achieve a comprehensive, unified view of their data. The integration of real-time data enables dynamic pricing, personalized customer experiences, efficient inventory management, proactive fraud detection, and improved customer support. Despite challenges such as data silos, data quality issues, scalability, security, and technical complexity, solutions like data governance, cloud computing, and advanced analytics tools can address these obstacles. Future trends, including artificial intelligence, machine learning, edge computing, block chain, and data fabric, promise to further transform BI in e-commerce. Case studies from leading e-commerce companies illustrate the practical benefits of implementing these advanced techniques, showcasing significant improvements in operational efficiency and customer satisfaction. Keywords: Business Intelligence, E-commerce Analytics, Data Integration, Real-Time Insights.

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.

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.

Identification and Ranking of Business Intelligence Components Using the Fuzzy TOPSIS Technique

Objective: The primary objective of this research is to identify and prioritize the components of business intelligence within subsidiaries of the National Bank by employing the fuzzy TOPSIS technique. This endeavor aims to establish a foundation for presenting a scientific approach to enhance business intelligence capabilities. Methodology: This study adopts a mixed-methods approach, initially gathering qualitative data through semi-structured interviews with senior managers from the bank's subsidiaries, followed by quantitative data collection using questionnaires. The fuzzy TOPSIS method was employed to handle the inherent ambiguity and uncertainty in the data, with linguistic variables used to represent the acceptability of each business intelligence component. Findings: Final model was including the dimensions of technical factors (components of information tools, characteristics of information and data, and analysis), business factors (components of stakeholders, economic and financial factors, and customer orientation), and intangible factors (including components of human capital and organizational capital) and 27 indicators. Subsequently, the identified components were ranked using the fuzzy TOPSIS technique, ultimately selecting "information confidentiality", "data reliability", and "high profit margin" as the most important indicators. Conclusion: It can be concluded that the dimensions of innovation in online advertising and its implications in digital marketing are interpretable, and legal supports for innovation in online advertising through the enforcement of laws, enactment and amendment of specific laws and regulations for digital marketing, removal of legal barriers to investment, revenue generation, and clarification of legal ambiguities and definitions in digital marketing are effective.

The Role of Artificial Intelligence in Enhancing Business Intelligence Capabilities for E-Commerce Platforms

The Role of Artificial Intelligence in Enhancing Business Intelligence Capabilities for E-Commerce Platforms

A Review Paper on Data Mining Methods and their Applications

This research paper provides a comprehensive review of data mining methods and their applications. Over the past decade, data mining has emerged as a crucial tool in extracting useful patterns and insights from large datasets across various domains. The paper begins by outlining the foundational principles and techniques of data mining, including classification, clustering, association rule mining, and anomaly detection. It then proceeds to explore the evolution and advancements of these techniques throughout the specified timeframe, highlighting key developments, methodologies, and challenges encountered in their application. Furthermore, the paper discusses notable applications of data mining in real-world scenarios such as healthcare, finance, telecommunications, and e-commerce. These applications demonstrate the versatility and effectiveness of data mining in solving complex problems, improving decision-making processes, and enhancing business intelligence. Moreover, the review addresses the impact of technological advancements and the proliferation of digital data on the field of data mining during the specified period. By synthesizing insights from existing literature and research studies, this review aims to provide researchers, practitioners, and stakeholders with a comprehensive understanding of the landscape of data mining methods and their practical implications.

A Review Paper on Data Mining Methods and Their Applications

This research paper provides a comprehensive review of data mining methods and their applications. Over the past decade, data mining has emerged as a crucial tool in extracting useful patterns and insights from large datasets across various domains. The paper begins by outlining the foundational principles and techniques of data mining, including classification, clustering, association rule mining, and anomaly detection. It then proceeds to explore the evolution and advancements of these techniques throughout the specified timeframe, highlighting key developments, methodologies, and challenges encountered in their application. Furthermore, the paper discusses notable applications of data mining in real-world scenarios such as healthcare, finance, telecommunications, and e-commerce. These applications demonstrate the versatility and effectiveness of data mining in solving complex problems, improving decision-making processes, and enhancing business intelligence. Moreover, the review addresses the impact of technological advancements and the proliferation of digital data on the field of data mining during the specified period. By synthesizing insights from existing literature and research studies, this review aims to provide researchers, practitioners, and stakeholders with a comprehensive understanding of the landscape of data mining methods and their practical implications.

The Role of Artificial Intelligence in Enhancing Business Intelligence Tools

The Role of Artificial Intelligence in Enhancing Business Intelligence Tools

Using deep learning to enhance business intelligence in organizational management

<abstract> <p>Business intelligence (BI) is crucial in organizational management, providing insights that enable informed decision-making. Traditional BI approaches, however, are limited in handling the vast amounts of data generated by organizations today. Deep learning, a subfield of machine learning, has shown great potential in improving BI through automated analysis of complex and large data sets. In this study, we explore the effectiveness of deep learning in enhancing BI for organizational management. We evaluate the accuracy and F-score of our proposed deep learning model against traditional BI methods in a real-world scenario. Our dataset contains a large volume of unstructured text data from customer feedback forms, which presents significant challenges for traditional BI approaches. Our deep learning model is trained using a convolution neural network (CNN) architecture to classify customer feedback into positive and negative sentiment categories. The model achieved an accuracy of 88% and an F-score of 0.86, outperforming traditional BI methods such as rule-based systems and sentiment analysis algorithms. Furthermore, our model's ability to handle unstructured data highlights its potential for processing diverse data types beyond structured data, commonly used in traditional BI.</p> </abstract>

The Role of Information Systems in Enhancing

this study aimed to identify the role of information systems in enhancing business intelligence at King Abdulaziz University. In order to achieve the research objectives, the researchers used the descriptive approach and data collection.

An Approach to Enhance Business Intelligence and Operations by Sentimental Analysis

Sentimental analysis is rapidly getting inducted into businesses as a direct result of the technology growth in every sector owing to globalization and industry 4.0. Sentimental analysis which is also known as opinion mining is used in identifying and analyzing text based on the tone that was conveyed by the person which can be categorized broadly into positive, negative and neutral. Businesses can utilize sentimental analysis to tap insight important insights regarding companies, organizations, people, trends and services. With the vast amount of Big Data increasing every day, especially from social media such as Twitter, Facebook etc. businesses can utilize sentimental analysis. This paper thus focuses on implementing machine learning models in Python to perform sentimental analysis from twitter tweets as a viable approach to enhance business intelligence, improve decision marking and target effective operations. The data used in this analysis is obtained from Kaggle collections of COVID-19 twitter dataset. This paper also discusses the various types of applications for sentimental analysis in business and their benefits. The findings from this paper will help improve understanding sentimental analysis for businesses and their practicality in real world scenarios as Big Data advances whilst business intelligence of companies rigorously demands outshining competitive advantage. © 2021, Success Culture Press. All rights reserved.

A social media analytic framework for improving operations and service management: A study of the retail pharmacy industry

The revolution in the digital economy is forcing the retail pharmacy industry to develop new business models to achieve operational excellence. A large amount of user-generated content on social media can be captured and analysed to help organisations gain insights into market requirements and enhance business intelligence. Accordingly, this study proposes an analytic framework for retail pharmacy organisations to: a) use social media and highlight the most-discussed topics by consumers, b) to identify the key areas for improvement based on the most negative comments received, and c) to determine the connections amongst the important concepts and enhance customer loyalty by adding values to consumers. We conduct an in-depth analysis on the Twitter platforms of the three largest retail pharmacy organisations in the UK: Boots, Lloyds and Superdrug. The findings show that issues with marketing, customer service and product are the key improvement areas for the retail pharmacies. Particularly, Boots received an overall better sentiment performance than Lloyds and Superdrug. We also determine the relationships amongst the important concepts discussed by consumers. The analysis generates insights into the use of social media for supporting pharmacy organisations in developing their social media strategies as well as improving their operations and service quality.

NCF: A Neural Context Fusion Approach to Raw Mobility Annotation

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e. distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct extensive experiments and find that our approach consistently outperforms the state-of-the-art baselines by at least 32% in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.

Data Science: A New Paradigm in the Age of Big-Data Science and Analytics

As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science’s terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.

Big Data Analytics Services for Enhancing Business Intelligence

ABSTRACTThis article examines how to use big data analytics services to enhance business intelligence (BI). More specifically, this article proposes an ontology of big data analytics and presents a big data analytics service-oriented architecture (BASOA), and then applies BASOA to BI, where our surveyed data analysis shows that the proposed BASOA is viable for enhancing BI and enterprise information systems. This article also explores temporality, expectability, and relativity as the characteristics of intelligence in BI. These characteristics are what customers and decision makers expect from BI in terms of systems, products, and services of organizations. The proposed approach in this article might facilitate the research and development of business analytics, big data analytics, and BI as well as big data science and big data computing.

Big Data in industry

The amount of data at the global level has grown exponentially. Along with this phenomena, we have a need for a new unit of measure like exabyte, zettabyte, and yottabyte as the last unit measures the amount of data. The growth of data gives a situation where the classic systems for the collection, storage, processing, and visualization of data losing the battle with a large amount, speed, and variety of data that is generated continuously. Many of data that is created by the Internet of Things, IoT (cameras, satellites, cars, GPS navigation, etc.). It is our challenge to come up with new technologies and tools for the management and exploitation of these large amounts of data. Big Data is a hot topic in recent years in IT circles. However, Big Data is recognized in the business world, and increasingly in the public administration. This paper proposes an ontology of big data analytics and examines how to enhance business intelligence through big data analytics as a service by presenting a big data analytics services-oriented architecture. This paper also discusses the interrelationship between business intelligence and big data analytics. The proposed approach in this paper might facilitate the research and development of business analytics, big data analytics, and business intelligence as well as intelligent agents.

From General to Specific

Neurophysiological methods and recording techniques are increasingly being embraced to enhance business intelligence about consumers' behavior. Researchers have found evidence linking individual characteristics with variations in mental processing and user literacy for neurally-controlling computer interfaces. The work presented here seeks to better understand the relationship between individual characteristics and neural activations as recorded by electroencephalography (EEG) while participants viewed certain types of media online. A study conducted with 21 right-handed individuals demonstrates that the individual characteristics of smoking, hand dexterity, and experience playing certain types of video games correlate with neural activations in the frontal lobe, reflecting arousal and engagement. These correlations indicate the need to control for particular participant characteristics when conducting studies using neurophysiological recording techniques and expand considerations for incorporating such novel, yet insightful tools into the business intelligence practice.