Design of the Business Intelligence Dashboard for Sales Decision Making

2020 ◽  
Vol 24 (02) ◽  
pp. 3498-3513
Author(s):  
Murnawan ◽  
Rosalin Samihardjo ◽  
Ucu Nugraha
2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Abdul Hamid Arribathi ◽  
Maimunah Maimunah ◽  
Devi Nurfitriani

This study aims to determine the stages that must be implemented in building a Business Intelligence System structured and appropriate in building Business Intelligence Systems in an organization, and understand the important aspects that must be considered for investment development Business Intelligence System is increasing. Business must be based on the conditions and needs of the organization in achieving the desired goals. If these conditions occur, then the decision-making process will be better and more accurate. The purpose of this study is to determine the important aspects that must be understood and prepared in using the Business Intelligence System in an organization. The method used is the explanation as well as the research library of several books, articles and other literature.


2018 ◽  
Vol 28 (5) ◽  
pp. 1489-1496
Author(s):  
Branislav Stanisavljević

Research carried out in the last few years as the example of companies belonging to the category of medium-size enterprises has shown that, for example, typical enterprises, of the total number of data processed in information of importance for its business, seriously takes into consideration and process only 10% of the observed firms. It is justifiable to ask whether these 10% of the processed and analyzed business information can have an adequate potential or motive power to direct the organization to success that is measured by competitive advantages and on a sustainable basis? Or, the question can be formulated: what happens to the rest, mostly 90% of the information that the enterprise does not transform into a form suitable for business analysis and decision-making. It is precisely the task of business intelligence to find a way to utilize all the data collected and processed in the business decision-making process. In this regard, we can conclude that Business Intelligence is, in fact, the framework title for all tools and / or applications that will enable the collection, processing, analysis, distribution to decision-making bodies in the business system in order to derivate from this information valid business decisions - as the most important and / or most important task of the manager. Of course, from an economic point of view, the best decisions are management decisions that provide a lasting competitive advantage and achieve maximum financial performance. This means that business intelligence actually allows a more complete and / or comprehensive view of the overall business performance of all its parts and subsystems. But the system functions can be measured essential and positive economic and financial performance, as well as the position in the branch of the business to which it belongs, and wider, within the national economy. (Of course, today the boundaries of the national economy have become too crowded for many companies, bearing in mind globalization and competitiveness in the light of organization of work and business function). The advantage of business intelligence as a model, if accepted at the organization level, ensures that each subsystem in the organization receives precisely the information needed to make development decisions, but also decisions regarding operational activities. So, it should be born in mind that business intelligence does not imply that information is shared on some key words, on the contrary, the goal is to look at the context of the business, or in general, and that anyone in the further decision hierarchy can manage exactly the same information that is necessary for achieving excellent business performance. Because, if the insight into the information is not complete, the analysis is based on the description of individual parts, i.e. proving partial performance in the realization of individual information, which can certainly create a space for the loss of the expensive time and energy. Illustratively, if the view, or insight into the information, is not 100%, then all business decision-making is like the song of J.J. Zmaj "Elephant", about an elephant and a blindmen, where everyone feels and act only on the base of the experienced work, and brings judgment on what is what or what can be. As in this song for children, everyone thinks that he touches different animals and when they make claims about what they feel, everyone describes a completely different life. Therefore, business intelligence implies that information is fully considered and it is basically the basis or knowledge base, and therefore the basis of business excellence. In doing so, the main problem is how information is transformed into knowledge and based on it in business decision making. It is precisely in this segment that the main advantage of business intelligence is its contribution to the knowledge and business of the company based on power of knowledge. Therefore, for modern business conditions, it is characteristic that the management of the company is realized on the basis of partial knowledge about stakeholders (buyers, suppliers, competitors, shareholders, governments, institutional framework, legislation), and only a complete overview of managers at the highest level in all these partial interest groups allows managers to have a “boat” called the organization of labor leading a safe hand through the storm, Scile and Haribde threatens to endanger business, towards a calm sea and a safe harbor - called a sustainable competitive advantage based on power and knowledge.


Author(s):  
Vivek N. Bhatt

The article focuses on the study of prevailing decision making styles of Small Scale Industrial (SSI) Units. It presents data collected from 200 SSI units from Bhavnagar – a coastal city of Gujarat, India. The objective of writing the article is to depict heuristic decision patterns of small and medium enterprises, and the rare use of analytical or statistical business intelligence tools in decision making processes. It would be interesting to study the design of decision taken on routine basis in small units, poorly equipped with technology and technical know-how. The paper is descriptive in terms, and lays a lucid picture of present decision making processes.


2016 ◽  
Vol 16 (3) ◽  
pp. 219-229 ◽  
Author(s):  
Daniela Borissova ◽  
Ivan Mustakerov ◽  
Dilian Korsemov

Abstract In the paper a business intelligence tool based on group decision making is proposed. The group decision making uses a combinatorial optimization modeling technique. It takes into account weighted coefficients for evaluation criteria assigned by decision makers together with their scores for the alternatives in respect of these criteria. The proposed optimization model for group decision making considers also the knowledge level of the group members involved as decision makers. This optimization model is implemented in three-layer architecture of Web application for business intelligence by group decision making. Developed Web application is numerically tested for a representative problem for software choice considering six decision makers, three alternatives and 19 evaluation criteria. The obtained results show the practical applicability and effectiveness of the proposed approach.


2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Sekar Sari Wiradarma ◽  
Ken Dhita Tania ◽  
Dinna Yunika Hardiyanti

AbstractBusiness Intelligence (BI) is a collection of theories, methodologies, processes, architectures, and technologies that convert raw data into quality information for business purposes. BI can handle a large amount of information that can help in identifying problems and developing new opportunities. In designing and implementing Business Intelligence (BI) concept for monitoring banking product service using reference business intelligence roadmap approach. Business intelligence roadmap is one example of BI development that can be emulated because of its agile and adaptive nature and is intended to support the development of BI. By utilizing Business Intelligence application on transaction history of banking product data, it is hoped able to produce information that can support in giving recommendation and decision making appropriately. The data and information generated also become more accessible and easier to understand (user friendly).Keywords: business intelligence, business intelligence roadmap, OLAP, banking products


Author(s):  
Aleš Popovič ◽  
Jurij Jaklič

The IS literature has long highlighted the positive impact of information provided by Business Intelligence Systems (BIS) on decision-making, particularly when organizations operate in highly competitive environments. The primary purpose of implementing BIS is to utilize diverse mechanisms to increase the levels of the two Information Quality (IQ) dimensions, namely information access quality and information content quality. While researchers have traditionally focused on assessing IQ criteria, they have largely ignored the mechanisms to boost IQ dimensions. Drawing on extant literature of BIS and IQ, the research sought to understand how, at its present level of development, BIS maturity affects IQ dimensions, as well as the role that business knowledge may exert in mobilizing this link. The authors test the hypotheses across 181 medium and large organizations. Interestingly, the data describe a more complex picture than might have been anticipated.


Data Mining ◽  
2013 ◽  
pp. 550-566 ◽  
Author(s):  
Zaidoun Alzoabi ◽  
Faek Diko ◽  
Saiid Hanna

BI is playing a major role in achieving competitive advantage in almost every sector of the market, and the higher education sector is no exception. Universities, in general, maintain huge databases comprising data of students, human resources, researches, facilities, and others. Data in these databases may contain decisive information for decision making. In this chapter we will describe a data mining approach as one of the business intelligence methodologies for possible use in higher education. The importance of the model arises from the reality that it starts from a system approach to university management, looking at the university as input, processing, output, and feedback, and then applies different business intelligence tools and methods to every part of the system in order to enhance the business decision making process. The chapter also shows an application of the suggested model on a real case study at the Arab International University.


Author(s):  
Beixin ("Betsy") Lin ◽  
Yu Hong ◽  
Zu-Hsu Lee

A data warehouse is a large electronic repository of information that is generated and updated in a structured manner by an enterprise over time to aid business intelligence and to support decision making. Data stored in a data warehouse is non-volatile and time variant and is organized by subjects in a manner to support decision making (Inmon et al., 2001). Data warehousing has been increasingly adopted by enterprises as the backbone technology for business intelligence reporting and query performance has become the key to the successful implementation of data warehouses. According to a survey of 358 businesses on reporting and end-user query tools, conducted by Appfluent Technology, data warehouse performance significantly affects the Return on Investment (ROI) on Business Intelligence (BI) systems and directly impacts the bottom line of the systems (Appfluent Technology, 2002). Even though in some circumstances it is very difficult to measure the benefits of BI projects in terms of ROI or dollar figures, management teams are still eager to have a “single version of the truth,” better information for strategic and tactical decision making, and more efficient business processes by using BI solutions (Eckerson, 2003). Dramatic increases in data volumes over time and the mixed quality of data can adversely affect the performance of a data warehouse. Some data may become outdated over time and can be mixed with data that are still valid for decision making. In addition, data are often collected to meet potential requirements, but may never be used. Data warehouses also contain external data (e.g. demographic, psychographic, etc.) to support a variety of predictive data mining activities. All these factors contribute to the massive growth of data volume. As a result, even a simple query may become burdensome to process and cause overflowing system indices (Inmon et al., 1998). Thus, exploring the techniques of performance tuning becomes an important subject in data warehouse management.


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