A Proposed Model to Integrate Business Intelligence System in Cloud Environment to Improve Business Function

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 (BI) systems are applied by increasing numbers of organizations in a wide range of sectors. Despite the growing numbers of BI solutions and the experience augmented in many implementation projects worldwide, as well as the value that BI systems provide to organizations, IT literature lacks a coherent set of definitions through which BI systems can be classified, categorized, and assessed. Furthermore, BI implementation projects often do not succeed or do not fully accomplish the degrees of value and performance as expected, when firms fail to complete the system implementation, to satisfy the needs of users or when the benefits BI systems produce are lower than anticipated. This paper presents an analytical framework through which BI systems are defined and classified. On the basis of this framework, possible technical, organizational, and personal factors that affect the failure, partial or full success of BI system implementations are discussed. These factors are followed by a case study and empirical data analyses that exemplify and assess the extent to which various organizational attributes and properties of users influence the success or failure of BI implementation projects. Finally, implications regarding the management of BI system implementation projects and the organizations that apply them are derived.

Cloud business intelligence can solve numerous management issues that are faced by many businesses. If it is used in a correct manner, it can substitute seamless utilization of crucial information in the growth of business. In the self-hosted environment, business intelligence will face resource crisis situation on the never-ending expansion of warehouses and OLAP's demands on the primary network. Today, cloud computing has instigated optimism for the prospects of future business intelligence. But thing to focus here is, how will business intelligence be implemented on cloud platform, and further, how will the traffic be managed and what will the demand profile look like? Moreover, in today's world, data generated on a daily basis from many different sources are numerous and valuable information for making effective decisions. This chapter focuses and tries to attempt these questions related to taking business intelligence to the cloud.

A Business Intelligence (BI) system can provide information in a reliable, instantaneous and automatic way in order to support crucial decisions of companies as well as of governments. The first step for the implementation of a BI system is the development of the ontology, which can be performed via an institutional glossary. However, usually the link between the ontology and the BI system is not direct, and therefore the implementation of the BI system may be slow. In this paper, we show the application of concept maps for BI system implementation. We exemplify this application using the study case of the implementation of the BI system for the Federal Property Department (SPU), which belongs to the Ministry of Planning, Budget and Management (MP) in Brazil. Besides the efficient implementation via concept maps, the impacts of our proposed BI system are also highlighted in this paper.

As a tool and system, business intelligence plays a vital role in the strategic planning process of a business company. Business intelligence provides companies with functions such as collecting, storing, accessing, and analyzing company data. In the traditional business model, most companies use human resources to track these large amounts of data and information. And business intelligence provides an automated data processing method that significantly improves efficiency. During a customer relationship management service system, a large amount of data will be generated and stored. With this natural data advantage, data analysis has more possibilities in customer relationship management service systems. Based on the business intelligence system, this paper analyzes the data of the customer system. It puts forward the K-means clustering method to analyze the customer quality, the fuzzy set clustering algorithm to examine whether the customer is active. The rough set algorithm to judge to improve the user activity decision analysis. The proposed intelligent business system brings new technical solutions to the maintenance of current corporate customer relationships.

There is currently a trend for companies to invest in business intelligence (BI) systems and integrate them with their original enterprise resource planning (ERP) systems in order to enhance their management decision-making capability. Even though, much attention has been paid to decision-making benefits of integrated systems in practice, limited academic research has investigated the issue of decision-making improvement. Therefore, the motivation for this paper is to examine the impact of integrated ERP and BI (abbreviated as ERPBI) systems on decision making performance (DMP) and introduce a comprehensive measurement instrument for assessing that impact. A survey was conducted involving 108 companies in the semiconductor industry in Taiwan. The results of the survey indicate firstly, that ERPBI system usage in organisations is positively related to DMP. Secondly, organisations using ERPBI systems achieve higher levels of decision-making performance compared to organisations which only use ERP systems.

This article was focused on establishing whether Business Intelligence (BI) systems provide sustainability to commercial banks by influencing their financial condition. As part of the search for a solution to the research problem, a hypothesis was formulated which assumes that the use of the Business Intelligence management system improves the financial condition of commercial banks. To assess this impact, a novel comparative method was used, which assumed comparing financial condition indicators in three aspects: before and after the implementation of the Business Intelligence system (comparison over time), with average indicators of a group of banks (comparison to the industry), with reference to changes in the overall economic situation. As a result of the method used, a synthetic indicator of the impact of using Business Intelligence (ABI) was calculated. This study was conducted in relation to six out of the thirteen largest commercial banks listed on the Warsaw Stock Exchange in 2020, which have implemented the Business Intelligence system since 2001. The assets of the examined banks cover 60% of the assets of commercial banks in Poland. As a result of the study, a positive impact of using the BI system on selected areas of the financial condition of commercial banks was identified. In particular, this impact relates to areas of productivity, the quality of assets and liabilities, profitability and debt. The generalized results of this study allow for the determination of cause and effect relationships between the use of the BI system in commercial banks and the improvement of the financial condition indicators as well as sustainability banking.

In the era of the information economic development of society, which is based on the digitalization of communication technologies and software for business administration algorithms, business analytics occupies an important place, a tool for obtaining high-quality, unique, and expensive information, maintaining the global competitive advantages of projects in the face of long-term force majeure risks of the viability of the economy. Since the middle of the last century, information technologies have been in constant development, gradually being introduced into all spheres and strata of society: production, accounting and analytical support, management, analysis, forecasts, risks. This paper considers the tools of digital business transformation and trends within the new knowledge paradigm based on a controlled understanding of the value properties of an information product. The successful use of business intelligence can indicate an increase in the success of the company and the country's competitiveness in the global market. Business analytics of companies is currently undergoing a stage of reengineering due to testing various programs, understanding, and analyzing the database, improving communication interaction at all levels of business processes, and making decisions in conditions of interactivity and transparency. The state of traceability and transparency of information, evolution and dynamics of conclusions and proposals over a long period of the company's activity in the sectoral and global aspect is of value. The features, characteristics, and functions of business intelligence (BI) systems as a tool for increasing the efficiency of the company's activities are analyzed. Business intelligence systems were considered as a tool for data analysis and processing, a comparative analysis of modern business intelligence systems and market trends of business intelligence systems was conducted, because of which the role of BI systems in increasing the efficiency of the company's activities was revealed. Keywords: business analytics, business data analysis, data integration, data presentation, IT sphere, company performance.

Possibility of Improving Efficiency within Business Intelligence Systems in Companies

The paper proposes a new approach of data warehouse minimization by fuzzy-based ETL filter for ETL processes in business intelligence (BI) systems and discusses possibilities to tune up the process. First part introduces common company systems and possible data sources in the company. Second part states the problem with interpreting information in BI systems and explains a data representation in the BI systems. Third part of the paper defines a rule base and input and output values of the expert system. Last part of the paper proposes a two ways to minimize a data - modification border of the fuzzy set and omitting useless combinations of the linguistic variables and modifiers.

The use of Business Intelligence (BI) and Business Analytics for supporting decision-making is widespread in the world of praxis and their relevance for Management Accounting (MA) has been outlined in non-academic literature. Nonetheless, current research on Business Intelligence systems’ implications for the Management Accounting System is still limited. The purpose of this study is to contribute to understanding how BI system implementation and use affect MA techniques and Management Accountants’ role. An explorative field study, which involved BI consultants from Italian consulting companies, was carried out. We used the qualitative field study method since it permits dealing with complex “how” questions and, at the same time, taking into consideration multiple sites thus offering a comprehensive picture of the phenomenon. We found that BI implementation can affect Management Accountants’ expertise and can bring about not only incremental changes in existing Management Accounting techniques but also more relevant ones, by supporting the introduction of new and advanced MA techniques. By identifying changes in the Management Accounting System as well as factors which can prevent or favor a virtuous relationship between BI and Management Accounting Systems this research can be useful both for consultants and for client-companies in effectively managing BI projects.

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.

This paper aims at highlighting the increasing role of artificial intelligence in business and making familiar its various aspects vis-a-vis its immediate requirement in the present Indian business scenario. The paper takes into account the aspects of blockchain and deep learning components with regard to the business as future of artificial intelligence in business scenario. The study also includes the benefits and challenges of the use of artificial intelligence in business with influence of blockchain and deep learning. The relation between blockchain and deep learning and artificial intelligence has been discussed in this paper. The research collates findings from the use and implementation of components of blockchain and deep learning. The model is recommended in regard to future of artificial intelligence in business. The algorithm is written used for the implementation of artificial intelligence in business. The study concludes with the observations, future, and recommendations with respect to artificial intelligence in business with the implementation of blockchain and deep learning.

Increasing amount of data and the need for analysis of this data have made the concept of Business Intelligence System (BIS) more important to plan the future of businesses. BIS is a set of technologies, processes, methodologies, and architectures that enable the processing of large amounts of data and their transformation into high-quality information. Companies implement BIS for monitoring business processes, receiving reports on systems operation, distributing the right information in the right way at the right time and analyzing business indicators. BIS has a mixed structure with many different and conflicting criteria. Nevertheless, it is difficult to assess and decide on alternatives if information is not clear. In this study, the hesitant fuzzy linguistic term set (HFLTS) methodology overcomes the uncertainty related difficulties of this multi-criteria decision-making (MCDM) problem. This methodology facilitates decision-making processes of experts in hesitate situations. The integrated hesitant fuzzy linguistic (HFL) MCDM methodology is presented to determine the most appropriate BIS. HFL Analytic Hierarchy Process (AHP) method is implemented to find the criteria weights. Then, the most important BIS alternative is determined with HFL Complex Proportional Assessment (COPRAS) method. Lastly, an application is given to demonstrate the potential of this methodology.

This chapter introduces the implementation of Business Intelligence (BI), thus explaining the application overview of BI, the components of BI, the practical implementation of BI, the business value of BI, the trends in implementing BI, and the guidelines for implementing BI. BI is a broad category of business applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping business enterprise users make better business decisions. BI enlarges business performance, thus leading to higher level of efficiency, better quality outputs, better marketing decisions, and lessened risk of business failure in order to gain a competitive advantage in the global business environments. It is important to create and develop a BI system to enable the useful transformation of information into the valuable knowledge for enhancing BI in organizations. Implementing BI will increase organizational performance and achieve business goals in modern business.