determinant of business intelligence systems quality on Indonesian higher education information center

PDDIKTI Feeder as a business intelligence application is used as an information center in higher education, containing master data of each student and lecturer, learning process data, reporting graduate data and lecturer activities in teaching for decision making. Paradoxically, through the empirical data there are many problems in implementing business intelligence systems in private universities, related to the maturity of information technology, data quality and information culture. Addressing this gap, we present a descriptive verification analysis research on 40 private universities in Bandung city, Indonesia, using the Partial Least Square Model. We conclude there is a positive influence of information technology maturity, data quality and information culture on the quality of the business intelligence system.

Examining the effect of user satisfaction and business intelligence system usage on individual job performance

Business intelligence systems are in widespread use today due to the many business benefits. Users are one of the key stakeholders in the business intelligence process. For optimal system adaptation, the user should be able to interact with the application in order to improve its capacity to contribute to decision-making. For the business intelligence process itself to be effective, it is necessary to define the user needs regardless of the type of work they do. If the user is satisfied and thinks that the system improves his/her performance or the quality of decisions made, they will want to use it even more. System usage has sometimes been viewed as a direct reflection of system performance; however, this is difficult to define in organizations where system usage is mandatory. Business intelligence systems are especially mandatory to use, as they are used in large organizations and require greater investment than other systems. This is why it is important to investigate the nature of system usage and its impact on individual performance. This research model deals with determinants that represent dimensions of the information system's success theory. Those determinants are: user satisfaction, intention to use, system usage, and individual performance. Obtained results show that increased user satisfaction and intention to use, lead to increased system usage and that both the increase in user satisfaction and system usage lead to a rise in individual user performance.

Smart Inventory Management System and Predictive Analysis

The idea of a smarter inventory management and business intelligence systems is a challenge in its implementation for various organization and businesses especially small and medium sized retailers. The concept of the paper is based on the business aspects while it merges various technological features to make a Predictive Analytical Model. Very few organizations and companies implement this which amount to loss of business, redundancies and errors that can be largely solved by system that uses data science, machine learning and visualization. This paper aims to present various aspects that can be infused with technology to aid Business Techniques and enable automation.

MAINTAINING FINANCIAL DATA QUALITY FOR BUSINESS INTELLIGENCE

Only when the input data is reliable can mathematicalmodels and business intelligence systems for decisionmaking produce accurate and effective outputs. However,data taken from primary sources and gathered in a datamart may contain several anomalies that analysts mustidentify and correct. This research covers the activitiesinvolved in creating a high-quality dataset for businessintelligence and data mining. Three techniques areaddressed to achieve this goal: data validation, whichdetects and reduce anomalies and inconsistencies; datamodification, which enhances the precision and robustnessof learning algorithms; and data reduction, whichproduces a set of data with fewer characteristics andrecords but is just as insightful as the original dataset.

Capabilities of business intelligence for improving the efficiency of economic analysis in stressful economic conditions caused by pandemic

From the perspective of economic analysis, the phenomenon of business intelligence (BI), which is the main subject of this research, is described as a version of artificial intelligence oriented towards comprehensive analysis of high data volume and its transformation into applied knowledge on the economic activity of organizations. Emphasis is placed on the feasibility and peculiarity of implementation of business intelligence systems in stressful economic conditions caused by pandemic on the global and national scale. The empirical part of research lies in the development and testing of the intelligent solution in the area of information and analytical support for economic analysis. The acquired conclusions prove that the current situation on the global economic market promotes further development of methodology for implementing information technologies, and business intelligence in particular, in the economic analysis of business activity. In order to meet the specific needs of end-users of business intelligence systems, was developed the tool for calculating the economic efficiency of medical insurance for health improvement of the personnel. It is substantiate that innovative BI- technologies help to integrate and successfully apply the solutions of business analysis at any level of corporate management. The authors outline the leading parameters of the ideal version of business intelligence, as well as justify that the use of business intelligence systems in the area of information support for economic analysis gives a multiplicative effect: - contributes to the improvement of economic analysis, efficiency of implementation and accuracy of data; - ensures the effectiveness of economic analysis for achieving  best result with relatively low cost and maximum utilization time; - provides acceptable relevance of the data that underlie the management decisions, leaving out subjectivism.