scholarly journals Data-Driven Decision Making

2021 ◽  
Author(s):  
Hernán Darío Rojas Gutiérrez ◽  
José Alejandro Morales Bobadilla ◽  
Jayden Ortiz Diaz ◽  
Jorge Enrique Portella Cleves
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Business Intelligence ◽  
Third Party ◽  
Private Company ◽  
Business World ◽  
Industrial Sectors ◽  
Fast Pace ◽  
Global Economic Growth ◽  
Search For Information

Data is the most valuable asset of a company or organisation nowadays, for the management of such data there are several techniques used for the storage and analysis of these data, if the organisation chooses wrongly among the alternatives it could face a very costly problem. Based on the above information we will study a very important issue today in the business world, with the global economic growth has also grown the world of technology and therefore organizations or companies also need to move forward with firm and fast pace how business evolves; Its weaknesses, strengths and the need to always be at the forefront of technological tools we will dive into the subject of business intelligence or also known as Business Intelligence (BI), Datawarehouse and Data Mining which is nothing more than a group of applications and tools that allow you to extract, transform and load some data to get to get information and knowledge in order to make a quick decision, accurate and efficient in the organization to achieve the objectives outlined. Due to the fact that there are still companies or organisations that make blind decisions in their customer or strategic processes. In order to solve this problem we rely on data mining as an alternative to minimise risks, which can lead to major and valuable losses within an organisation. In the case of a private company where data mining is applied to study patterns of customer behaviour on their own parameters of location, consumption, etc.. And third party data. The search for information is profitable for the business administration. Data mining is applied as a tool for the development of marketing tactics in competitive production and industrial sectors. This technology attempts to help perceive the attachment of databases. Data mining works on a preferential level looking for patterns, behaviours, orders or groupings that can create a model that allows us to better understand the concept and help in decision making so organisations rely on different systems such as CRM, ERP and many others, but to move from just information to generate business intelligence must be centralised in a single place where you can run data analysis of all types to discover trends that help decision making that place can be mainly a Lake or a Warehouse.

Expanding Business Intelligence Power with System Dynamics

2011 ◽  
pp. 126-140 ◽  
Author(s):  
Edilberto Casado
Keyword(s):  
Data Mining ◽  
System Dynamics ◽  
Real World ◽  
Business Intelligence ◽  
Simulation And Modeling ◽  
Business World ◽  
Data Mining Techniques ◽  
Business Dynamics ◽  
Using Data ◽  
Systems Thinking Approach

This chapter explores the opportunities to expand the forecasting and business understanding capabilities of Business Intelligence (BI) tools with the support of the system dynamics approach. System dynamics tools can enhance the insights provided by BI applications — specifically by using data-mining techniques, through simulation and modeling of real world under a “systems thinking” approach, improving forecasts, and contributing to a better understanding of the business dynamics of any organization. Since there is not enough diffusion and understanding in the business world about system dynamics concepts and advantages, this chapter is intended to motivate further research and the development of better and more powerful applications for BI.

scholarly journals Business Intelligence and competition ability of enterprise

2005 ◽  
Vol 53 (6) ◽  
pp. 85-92
Author(s):  
Vladimír Konečný ◽  
Ivana Rábová
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information And Communication Technologies ◽  
Business Intelligence ◽  
Information Technologies ◽  
Transformation Process ◽  
Strategic Decision ◽  
New Information ◽  
Information And Communication ◽  
Olap Analysis

As far as the current state of the information and communication technologies usage is concerned, the information systems of the companies cover the major part of the transaction processes and the large amount of the processes at the level of the tactical decision-making.Intensive implementation of the information technologies in many areas of the human activities cause gathering of the large amount of the data. The volume of the internal and external databases grows rapidly and the problem is to take advantage of the data they contain. But the problem is not only the growing volume of the databases but also the different and database structures. To get the new information from the large and incompatible database sources is possible but very inefficient. A manager often needs the information very fast to achieve competitive advantage and to solve problems at the level of strategic decision-making. Another problem is the fact that the databases often contain information that is hidden there and there is no way known how to get this information out of the database. In this case, the user needs at least suitable tools in order to perform experiments and to explore and identify patterns and relationships in the data.The transformation process of the data to information and to knowledge that is used in the process of decision-making is called Business Intelligence. Modern database tools offer wide support for building the data warehouse, OLAP analysis and data mining.Our contribution focuses on the application of one of the data mining techniques such as neural networks and artificial intelligence. The application of those methods will be based on the assessment of the food quality and composing of the corresponding trend indicator.

Closing the Gap between Data Mining and Business Users of Business Intelligence Systems

2012 ◽  
Vol 3 (4) ◽  
pp. 14-53 ◽  
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Easy Access ◽  
Complete Knowledge ◽  
Business Intelligence Systems ◽  
Closing The Gap

Since Lunh first used the term Business Intelligence (BI) in 1958, major transformations happened in the field of information systems and technologies, especially in the area of decision support systems. BI systems are widely used in organizations and their importance is recognized. These systems present themselves as essential parts of a complete knowledge of business and an irreplaceable tool in the support to decision making. The dissemination of data mining (DM) tools is increasing in the BI field, as well as the acknowledgment of the relevance of its usage in enterprise BI systems. BI tools are friendly, iterative, and interactive, allowing business users an easy access. The user can manipulate directly data, having the ability to extract all the value contained into that business data. Problems noted in the use of DM in the field of BI is related to the fact that DM models are complex in order to be directly manipulated by business users, not including BI tools. The nonexistence of BI tools allowing business users the direct manipulation of DM models was identified as the problem. More of these issues, possible solutions and conclusions are presented in this article.

scholarly journals Optimal customer relationship management in telecalling industry by using data mining and business intelligence

2017 ◽  
Vol 7 (1.1) ◽  
pp. 12
Author(s):  
T. Kamalakannan ◽  
P. Mayilvaghnan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Business Intelligence ◽  
Research Method ◽  
Support Vector ◽  
Telecommunication Industry ◽  
Churn Prediction ◽  
Customer Churn ◽  
Positive Rate ◽  
Customer Churn Prediction

Decision making system in telecommunication industries plays a more important role where it is required to find customer churn. Customer churn prediction requires finding out and analyzing the information about the business data intelligence techniques which can be done efficiently by adapting the business intelligence techniques. Business intelligence provides tools to predict and analyze the historical, current and predictive views of business operations. However, this would be more complex task with high volume of data which are gathered from million of telephone users for the time being. It can be handled effectively by introducing the data mining techniques which select the most useful information from the gathered data set from which decision making can be done efficiently. In this research method, telecommunication industry is considered in which customer churn prediction application is focused. The main goal of this research method is to introduce the data mining technique which can select the most useful information from the telecommunication industry dataset. This is done by introducing the Hybrid Genetic Algorithm with Particle Swarm Optimization (HGAPSO) method which can select the most useful information. In this research, the hybrid HGAPSO combines the advantages of PSO and GA optimally. From the selected information, decision making about the customer churn prediction can be done accurately. Finally decision making is done by predicting the customer behaviour using Support Vector Machine classification approach. The performance metrics are considered such as precision, recall, f-measure, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), time complexity and ROC. Experimental results demonstrated that HGAPSO provides highly scalable which is used for prediction examination in the business intelligence.

Big Data Mining and Business Intelligence Trends

2017 ◽  
Vol 7 (1) ◽  
pp. 23-33
Author(s):  
Harun Bayer ◽  
Mustafa Aksogan ◽  
Enes Celik ◽  
Adil Kondiloglu
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence ◽  
High Capacity ◽  
Efficient Solutions ◽  
Strategic Decisions ◽  
Business World ◽  
Large Size ◽  
Capacity Data ◽  
Operational Processes

The conventional databases are not capable of coping with the high capacity data due to different forms of these data’s and fast production speed. In this context, The Big Data structure comes into the scene. The Big Data has been stated as the gold of our age by many authorities. Today, large sizes of data can be analyzed and this led to changes in the lives of people, companies, states, and researchers. The companies develop effective and efficient solutions by analyzing large size of data through big data solutions for their strategic decisions, operational processes, campaign management and marketing techniques. In this research, the introduction has been made to the Big Data architecture, along with daily increasing data mining techniques and methods which will be a solution for accumulating data and current advancements in big data solutions have been addressed. In addition, some well-known companies’ tendency to implement business intelligence systems have been examined. The effects of potential threads which are the results of the big data in the business world are analyzed and a couple of suggestions for the future have been presented.

Modelling in Support of Decision Making in Business Intelligence

2021 ◽  
pp. 115-144
Author(s):  
Roumiana Ilieva ◽  
Malinka Ivanova ◽  
Tzvetilina Peycheva ◽  
Yoto Nikolov
Keyword(s):  
Decision Making ◽  
Predictive Modeling ◽  
Business Intelligence ◽  
Business Processes ◽  
Future Research ◽  
Business World ◽  
Future Research Directions ◽  
Self Service ◽  
Key Steps

Modelling in support of decision making in business intelligence (BI) starts with exploring the BI systems, driven by artificial intelligence (AI). The purpose why AI will be the core of next-gen analytics and why BI will be empowered by it are determined. The role of AI and machine learning (ML) in business processes automation is analyzed. The benefits from AI integration in BI platforms are summarized. Then analysis goes through predictive modeling in the domain of e-commerce. The use of ML for predictive modeling is overviewed. Construction of predictive and clustering models is proposed. After that the importance of self-services in BI platforms is outlined. In this context the self-service BI is defined and what are the key steps to create successful self-service BI model are sketched. The effects of potential threads which are the results of the big data in the business world are examined and some suggestions for the future have been made. Lastly, game-changer trends in BI and future research directions are traced.

scholarly journals Peran Business Intelligence dalam Solusi Bisnis

2013 ◽  
Vol 4 (2) ◽  
pp. 812
Author(s):  
Siswono Siswono
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Statistical Analysis ◽  
Business Intelligence ◽  
Analytical Skills ◽  
Critical Technology ◽  
Access To Data ◽  
Intelligence Application

The purpose of this study is to give examine the use of Business Intelligence as a critical technology solutions in the decision making by management. Business Intelligence application is able to address the needs of organizations in improving problem analytical skills encountered in making decisions with the ability tocollect, store, analyze and provide access to data, as well as dovarious activities such as statistical analysis, forecasting, and data mining.

DATA MINING APPROACHES IN BUSINESS INTELLIGENCE: POSTGRADUATE DATA ANALYTIC

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Mohd Shahizan Othman ◽  
Shamini Raja Kumaran ◽  
Lizawati Mi Yusuf
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Business Intelligence ◽  
Support Vector ◽  
Svm Classifier ◽  
Higher Education Management ◽  
Education Management ◽  
Layer 4 ◽  
Using Data

Over recent years, there has been tremendous growth of interest in business intelligence (BI) for higher education. BI analysis solutions are operated to extract useful information from a multi-dimensional datasets. However, higher education-based business intelligence is complex to build, maintain and it faces the knowledge constraints. Therefore, data mining techniques provide an effective computational methods for higher education-based business intelligence. The main purpose of using data mining approaches in business intelligence is to provide decision making solution to higher education management. This paper presents the implementation of data mining approaches in business intelligence using a total of 13508 postgraduates (PG) data. These PG data are to allow the research to identify the postgraduates who Graduate On Time (GOT) via business intelligence process integrating data mining approaches. There are four layers will be discussed in this paper: data source layer (Layer 1), data integration layer (Layer 2), logic layer (Layer 3), and reporting layer (Layer 4). The main scope of this paper is to identify suitable data mining which is to allow decision making on GOT so as to an appropriate analysis to education management on GOT. The results show that Support Vector Machine (SVM) classifier is with better accuracy of 99%. Hence, the contribution of data mining in business intelligence allows an accurate decision making in higher education.

scholarly journals Business Intelligence and Nosocomial Infection Decision Making

2015 ◽  
pp. 193-215 ◽  
Author(s):  
Eva Silva ◽  
Ana Alpuim ◽  
Luciana Cardoso ◽  
Fernando Marins ◽  
César Quintas ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Data Analysis ◽  
Nosocomial Infection ◽  
Business Intelligence ◽  
Clinical Decision Support System ◽  
Healthcare Professionals ◽  
Clinical Decision ◽  
Healthcare Organizations

The implementation of Business Intelligence tools in healthcare organizations helps the managers and the healthcare professionals in their decision making process through data manipulation and data analysis. The main goal of this chapter is to evaluate the applicability of the Business Intelligence tools and concepts to healthcare and their performance as a Clinical Decision Support System, analyzing the evolution of nosocomial infection in the Centro Hospitalar do Porto, by defining a set of indicators that can help the nosocomial infection management and inducing Data Mining models to predict the occurrence of nosocomial infections (sensitivity of 91%). The knowledge obtained with the analysis of the indicators and the knowledge obtained with the nosocomial infection prediction can be applied by healthcare professionals in their decision making. Through the analysis of the data collected, Business Intelligence tools help overcome the problems associated with the complexity, heterogeneity, and distributiveness present in the healthcare environment.

Penilaian Kinerja Dosen Dengan Menggunakan Metode AHP Studi Kasus di STIE Ahmad Dahlan Jakarta

Liquidity ◽  
2018 ◽  
Vol 2 (1) ◽  
pp. 100-109
Author(s):  
Ellya Sestri
Keyword(s):  
Decision Making ◽  
Performance Appraisal ◽  
Technological Progress ◽  
Pairwise Comparison ◽  
Relative Importance ◽  
Faculty Performance ◽  
Business World ◽  
The Face ◽  
Hierarchy Process ◽  
Rule Out

An increasingly rapid technological progress in the era of globalization in the business world, so do not rule out the possibility that a decision-making is something that is very vital in determining the decisions to be taken in the face of competitive business world. Decision making can be influenced by several aspects, this can affect the speed of decision making by the decision maker in which decisions must be quick and accurate. Lecturer Performance Assessment Using the Analytical Hierarchy Process is a decision support system that aims to assess faculty performance according to certain criteria. This system of faculty performance appraisal criteria to map a hierarchy, where each hierarchy will be performed pairwise comparison, the pairwise comparisons between criteria, so to get a comparison of the relative importance of criteria with each other. The results of this comparison is then analyzed to obtain the priority of each criterion. Once completed and performed an assessment of alternative options to be compared and calculated to obtain the best alternatives according to established criteria.

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