Future of Business Intelligence in Cloud Computing

As a new computational paradigm, cloud computing is attracting a lot of interest from researchers in the field of the business community and information technology sector and it can integrate with several heterogeneous resources makes it distinguished and unique to fulfill the demands of different types of users. The rapid increase in data volume and fixed access to online resources, which is related to all departments need to mine data for the discovery of knowledge. Its principal peculiarities incorporate a versatile asset design and along this line a suitable system for tending to be comprehended in an ideal mode. From the specific situations where cloud computing can be integrated, its use in business information and intelligence also conveys the highest aspirations from data mining to updates. This study gives an outline of the recent condition of the arrangement of Cloud Computing and elaborates, its implications in Business Intelligence and Data Mining. This study defines multiple layers that are expected to create such a framework in distinctive levels of deliberation, from the fundamental equipment stages to the product assets accessible to actualize the applications. At the end of this study, a few cases related to Data mining methodologies have been relocated to the Cloud Computing paradigm.

Business intelligence system (BIS) has gained a high momentum and played a significant role in enhancing the business environment especially with the help of cloud. The BIS with cloud computing is the solution to overcome complex problems where large amounts of data (market analysis, customer’s view, feedback and organization response time) are required to be processed. The objective of this paper is to show the realistic perspective of the possibilities for maximizing benefits and minimizing risks in Indian business. The authors have highlighted the importance of cloud computing in business and tried to develop a framework for integrating BIS with cloud computing, and an attempt has also been made to analyse the efficiency of the proposed model which improves performance over the cloud. Some of the challenging issues as well as the future research directions have also been discussed.

Current market is dynamic. Customer needs change, too. Global competition is also developing and market subjects must be able to interpret information correctly and make decisions instantly. Business Intelligence (BI) tools are based on the ultimate exploitation of data ownership and their transformation into information and knowledge. The increasing amount of data makes it necessary to develop linking business intelligence and cloud computing. This phenomenon brings about new opportunities as well as risks. This contribution is to analyse suitability of linking BI and cloud computing with respect to risks and benefits of cloud technologies. It should result in a group of criteria enabling to provide relevant information about suitability of this technology for organizations. An analysis of accessible studies on decision-making models and cloud computing and an analysis of companies’ attitudes to cloud computing and information technologies both served as starting points of the proposal.

Supply chains are increasingly becoming more complex, making collaboration progressively difficult to establish and maintain. It is imperative to understand not only the consequences, but also the drivers of effective and efficient collaboration. In this study, we attempt to show how varying levels of collaboration impact service level and how cloud computing fosters these levels of collaboration. We introduce a framework detailing how cloud computing impacts three levels of collaboration: (1) information centralisation, (2) vendor managed inventory and continuous replenishment programmes and (3) business intelligence (BI) collaborative planning, forecasting and replenishment. In addition, we use multi-agent-based simulation to analyse how each level of collaboration (enhanced through cloud computing) impacts service level as measured by fill rate. Obtained results show that cloud computing can enhance all three levels of collaboration. Further, our results demonstrate that BI collaborative planning, forecasting and replenishment have significantly greater service level benefits in comparison to other collaboration levels.

The traditional way to manage Information Technologies (IT) in the companies is having a data center, and licensing monolithic applications based on the number of CPUs, allowed connections, etc. This also holds for Business Intelligence environments. Nevertheless, technologies have evolved and today other approaches are possible. Specifically, the service paradigm allows to outsource hardware as well as software in a pay-as-you-go model. In this work, we will introduce the concepts related to this paradigm and analyze how they affect Business Intelligence (BI). We will analyze the specificity of services and present specific techniques to engineering service systems (e.g., Cloud Computing, Service-Oriented Architectures -SOA- and Business Process Modeling -BPM-). Then, we will also analyze to which extent it is possible to consider Business Intelligence just a service and use these same techniques on it. Finally, we store the other way round. Since service companies represent around 70% of the Gross Domestic Product (GDP) in the world, special attention must be paid to their characteristics and how to adapt BI techniques to enhance services.

The integration of Artificial Intelligence (AI) and cloud computing has revolutionized enterprise systems, particularly in predictive marketing. AI-powered enterprise solutions enable businesses to analyze vast amounts of data in real-time, enhancing decision-making, customer engagement, and operational efficiency. Predictive analytics allows companies to anticipate consumer behavior, refine marketing strategies, and optimize customer interactions. Cloud computing further supports AI-driven predictive marketing by providing scalable and cost-effective solutions that enhance data processing capabilities and business intelligence. AI-integrated enterprise resource planning (ERP) and customer relationship management (CRM) systems facilitate automated decision-making, improving supply chain management and personalized marketing campaigns. Despite its advantages, AI adoption in enterprise systems and predictive marketing presents challenges such as data privacy concerns, cybersecurity risks, and ethical considerations. The complexity of AI integration requires substantial investment in infrastructure and regulatory compliance to mitigate biases and ensure transparency in AI-driven decisions. Explainable AI (XAI) is increasingly necessary to build trust and accountability in enterprise applications. Future advancements in AI, including blockchain, augmented reality (AR), and quantum computing, will enhance predictive analytics and business intelligence, further transforming marketing automation and decision-making processes. The convergence of AI and blockchain is particularly promising in securing digital transactions and improving data transparency in enterprise operations. As AI continues to reshape enterprise systems and predictive marketing, businesses must adopt responsible AI practices, strengthen cybersecurity measures, and comply with global regulations to maximize its benefits. Companies that leverage AI-driven insights will gain a competitive edge by improving customer engagement, optimizing marketing strategies, and driving sustainable growth in the evolving digital economy.

Effective use of business intelligence has become essential for small and medium-sized organizations (SMEs) in the era of digitalization due to the introduction of new technologies. Therefore, this study aims to measure the influence of the Technology Acceptance Model (TAM) and other factors, for example, the quality of information, organization readiness and technology infrastructure, on business intelligence. A quantitative research methodology was used, with a sample size of 281 participants who were owners, managers and information system staff in Iraqi SMEs who had experience using business intelligence. The findings of this study indicated that the quality of information has a significant impact on perceived usefulness (PU) and perceived ease of use (PEOU). Similarly, PU, PEOU, organization readiness and technology infrastructure positively and significantly impact business intelligence adoption. This study offers a comprehensive analysis of the crucial aspects that contribute to the successful deployment of business intelligence, thereby influencing the outcomes of SMEs. The results of this study will help entrepreneurs, SME owners managers, and academics develop a business intelligence system that can enhance overall organizational efficiency in a dynamic economic setting. Putting in place a good business intelligence system will help managers make better decisions, boost economic growth for businesses, support new ideas in businesses, and improve their overall performance and output.

The paper aims to identify how Industry 4.0 technologies affect the quality and speed of the managers’ decision-making process across the different stages of the value chain, based on the example of the manufacturing sector. The paper adopts qualitative research, based on nine in-depth interviews with key informants, to capture senior executives’ experiences with implementing Industry 4.0 technologies in their organisations. The research is focused on three manufacturing industries: the automotive, food and furniture industries. The research shows that depending on the stage of the value chain, different Industry 4.0 technologies are more suitable for the support of managers’ decisions. Various Industry 4.0 technologies support decision-making at different stages of the manufacturing value chain. In the Design stage, 3D printing and scanning technologies play a crucial role. In the case of Inbound Logistics, robotisation, automation, Big Data analysis, and Business Intelligence are most useful. During the Manufacturing stage, robotisation, automation, 3D printing, scanning, Business Intelligence, cloud computing, and machine-to-machine (M2M) integration enable quick decision-making and speed up production. Sensors and the Internet of Things (IoT) optimise distribution in the Outbound Logistics stage. And finally, Business Intelligence supports decisions within the Sales and Marketing stage. It is also the most versatile technology among all particular stages. The paper provides empirical evidence on the Industry 4.0 technology support in decision-making at different stages of the manufacturing value chain, which leads to more effective value chain management, ensuring faster and more accurate decisions at each value-chain stage. When using properly selected Industry 4.0 technologies, managers can optimise their production processes, reduce costs, avoid errors and improve customer satisfaction. Simultaneously, Industry 4.0 technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks. This knowledge allows organisations to make better decisions and take proactive actions to prevent problems.

Today, more than ever, organizations face daily environmental changes. The source of change may be a new regulatory law, competitor, business model, hype in technology, new service trend, or the lack of skilled human resources. In order to understand the factors of change and their impacts on business strategies, organizations need to have in place a set of tools, methods and procedures to allow an instantaneous reaction, and foresee proactively the highest number of variables that will have impact in the organization’s competitive advantages. One of the central tools that managers have at their disposal to face these challenges come from the use of Information and Communication Technology (ICT), which are entering into a new stage. The maturity of some Internet technologies, business intelligence systems and outsourcing business models allows the availability of new services, based on cloud computing. The rise of new service trends are taking place, and if an organization is able of combining the advantages of those trends, it might gain competitive advantage, throughout Business Intelligence as a Service (BIaaS), and what began as a way to achieve operating savings, will eminently reveal its true disruptive potential throughout the Internet.

In the recent era of data science, AI, and machine learning the amount of data has increased exponentially, and future growth is predicted to be even greater. For the last 4 decades RDBMS is way for data storage, but with the rise of cloud computing online applications have taken place of the stand-alone applications, while distributed servers have taken the place of traditional dedicated servers. Cloud computing has become very efficient because of the lower cost, ability to scale up and down, flexibility, and pay-as-you-go model. This is the major change after the birth of the Internet. Cloud computing has revolutionized the way data is stored and accessed. Cloud databases are an integral part of the cloud computing paradigm, offering scalability, flexibility, and cost-efficiency for managing large volumes of data. Cloud databases such as Amazon RDS, IBM DB2 and MongoDB are becoming popular. Cloud databases find the restrictions of older relational databases. Data storage, retrieval, modification, and analysis are essentially the functions of cloud databases. Cloud databases are mostly used by data-intensive applications including data warehousing, data mining, and business intelligence. Applications for supply chain management, online e-commerce, and transactional data management, including banking and airline reservations, also benefit from cloud storage. This research paper reviews the concept of cloud databases, exploring their features, it examines the architecture of cloud databases, the comparison between traditional Relational Database Management Systems (RDBMS) and NoSQL databases, and popular implementations in the cloud environment. Additionally, the challenges faced in their development and deployment.

The term ‘cloud computing’ was initially coined to refer to on-demand computing services that were offered by commercial service providers such as Amazon, Google, and Microsoft. Cloud computing is an emerging computation model that shares resources over the Internet and provides on-demand facilities to its customers. In other words, it can be seen as a computation platform combined with shared computer processing resources over the Internet. Computing resources can be shared from anywhere using Internet connectivity. Thus, cloud computing enables ubiquitous and on-demand access to a shared pool of resources to meet users’ requirements [1]. Figure 2.1 depicts an overview of the cloud computing technology. As depicted in Figure 2.1, cloud computing consists of several shared computer resources, such as computing devices, software, and applications, which are accessed in an on-demand basis. Additionally, different consumers, who access the cloud platform through mobile phones, laptops, desktops, and other peripheral devices, are also present in the cloud computing technological framework. All the resources and consumers are linked through Internet connectivity. The entire framework is part of a cloud platform. Therefore, a cloud platform helps its users to store and process their data in data centers. The important feature of cloud computing is that a central management entity controls all operations associated with different users situated in different geographical regions. Thus, multiple agents can participate in a single platform to share/procure resources in an on-demand basis. Figure 2.2 shows an abstracted view of the cloud computing technology. The cloud manager maps the incoming requests with the available resources, which are shared with the cloud manager. So, it is evident that the cloud manager only manages the available resources and incoming requests in an optimum manner, so that the users of the cloud-services can get seamless and ubiquitous services. However, the cloud-service subscribers do not know from which hardware/software resources, the requested services are provided. Similarly, the owners of the shared resources also do not know to whom their resources are mapped. Therefore, although a common platform is used for multiple parties, an abstraction level is always used for security and privacy concerns. As evident from Figure 2.2, there are multiple parties that participate in the resource sharing/procuring/managing process – publisher, subscriber, and manager. The publishers publish their shared resources to the cloud-service providers.

Business Intelligence as a platform is of great assistance to software engineers for analyzing and amassing data to search for information through queries. Object oriented business engineering advances reusability by considering reusable components at the business level. Systematic reuse is the major factor governing the success of an object-oriented software business. Software mining or mining of software engineering data is the successful mining of software engineering data. The integration of mining of software engineering data and Business Intelligence tools gives rise to automation of software services at business level. Automation is the key to Industry 4.0 that we witness today. Therefore, automation of software intelligence is the need of the hour for service delivery. Today's cloud-based applications are working on delivery of Anything as a Service (XaaS) that accounts for provision of anything as a service like database, software, microservices, security on the cloud platform. Service-oriented Architecture (SoA) is based on Software as a Service (SaaS) of the cloud computing. Software intelligence is the form of artificial intelligence which automates the process of mining software engineering data for useful business applications. Not only it integrates the amalgamation of the above fields but at the same time associates the field with industry standards. The aim of this paper is to analyze the various Business Intelligence tools available for promoting intelligence at the business level with benefits to the SoA services particularly microservices for the cloud platform. With the analysis the goal of Business Oriented Software Engineering is enhanced with the analysis and proper usage of business intelligence tools at all the levels of software development process.

Cloud computing is an emerging information technology (IT) paradigm which undoubtedly has received significant attention from different spheres of influence. The cloud computing concept entails the provisioning of computing resources from remote locations to individuals or organizations over the internet at anytime. The technology is associated with several benefits but the reduction in capital and operational expenditure is the major one. The importance of IT innovations to Small and Medium Enterprises (SMEs) and the role of SMEs in economic growth have been demonstrated empirically. However, literature indicate that SMEs in developing economies are not taking the advantages of emerging technologies like cloud computing to improve operational excellence within their organizations. An in-depth review of academic publications, technical reports, and industry white papers on cloud computing has confirmed security and privacy is-sues as some of the key inhibitors to cloud computing adoption among SMEs. This is probably because the customer data is released to remote locations, which are invisible, and data is accessed over the internet, which is already over-whelmed with malicious attackers. This study explores the reasons for the lack of cloud computing adoption among SMEs in developing economies. When the rea-sons for this lag are identified, cloud computing service providers, technology policy makers, entrepreneurs and SMEs executives will be provided with the opportunity to develop appropriate solutions and strategies that will specifically meet the needs of SMEs in developing economies. This will promote and accelerate the acceptance rate of cloud services. When SMEs utilize cloud computing as an IT strategy, business growth becomes inevitable and the country's economic growth is witnessed.

This paper is based on the review of cloud computing in educational field. The technology provides IT-level facilities with services-on-demand-based feature, i.e., cloud computing, one of the growing technologies in today’s era. The reason why all IT and educational societies are moving toward cloud computing is it offers such kinds of services which help to reduce the initial investment and take back the maximum output in terms of processing power, storage, and networking. But, for using cloud computing in educational society, there are two ways: First is taking services from third parties like Amazon, IBM, and so on. Second is developing our own cloud platform within the educational society by using commodity hardware or by current working systems in that society. Many educational hubs invested already on their own infrastructure instead of buying from third parties and using same services and same features without paying too much money. This paper also focuses on the how some educational societies are currently working on the cloud platform and why other schools, colleges, and universities must move toward cloud platform with proper data security assurance. To use this cloud services freely, there are some open-source platforms currently present in the market. This research is based on finding better private cloud platform for educational societies based on their performance.

Organizations are increasingly using Business Intelligence (BI) solutions to achieve success via data driven insights in today's fast paced business climate. Businesses have been able to make informed and strategic choices thanks to traditional BI systems like SAP BusinessObjects (BO), Cognos, and other legacy technologies. Nonetheless, the growing demand for real-time data analysis and foresight has driven businesses to include complex analytics into their business intelligence platforms. This paper examines the role of BI in driving business growth, focusing on the integration of legacy tools with advanced analytics. It demonstrates how this integration speeds up decision-making, increases operational efficiency, and reveals new income prospects. This paper reviews many case studies and business intelligence deployments to demonstrate how firms may use data insights to influence strategic goals, improve operational efficiency, and maintain a competitive advantage in market trends.