scholarly journals MAINTAINING FINANCIAL DATA QUALITY FOR BUSINESS INTELLIGENCE

2021 ◽  
Author(s):  
Naveen Kunnathuvalappil Hariharan
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Data Reduction ◽  
Input Data ◽  
Financial Data ◽  
Primary Sources ◽  
Data Validation ◽  
High Quality ◽  
Original Dataset ◽  
Business Intelligence Systems

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.

ASD-BI

2015 ◽  
pp. 61-82
Author(s):  
Mouhib Alnoukari
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Tacit Knowledge ◽  
Business Intelligence ◽  
The Other ◽  
Agile Methods ◽  
Discovery Process ◽  
Agile Method ◽  
Agile Methodologies ◽  
Business Intelligence Systems

ASD-BI is an agile “marriage” between business intelligence and data mining. It is one of the first attempts to apply an Adaptive Software Development (ASD) agile method to business intelligence systems. The ASD-BI methodology's main characteristics are adaptive to environment changes, enhance knowledge capturing and sharing, and help in implementing and achieving an organization's strategy. The focus of the chapter is to demonstrate how agile methods would enhance the integration of data mining in business intelligence systems. The chapter presents ASD-BI main characteristics and provides two case studies, one on higher education and the other on (Bibliomining). The main result of the chapter is that applying agile methodologies for integrating business intelligence and data mining systems would increase transfer of tacit knowledge and raise the strategic dimension of using the knowledge discovery process.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2009 ◽  
pp. 223-229
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

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.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2011 ◽  
pp. 1013-1020
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

scholarly journals Evaluasi Program Pembelajaran Tematik Di MI Mitra PGMI STAIN Ponorogo

MADRASAH ◽  
2016 ◽  
Vol 7 (1) ◽  
pp. 26
Author(s):  
Athok Fu'adi
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Data Reduction ◽  
Input Data ◽  
Lower Class ◽  
Data Validation ◽  
School Teachers ◽  
Inhibiting Factors ◽  
The Subject ◽  
Thematic Learning

<span><em>The purpose of this research is to evaluate thematic learning in Islamic </em><span><em>School that becomes partner of PGMI (Education of Islamic Elementary </em><span><em>School Teachers) that includes: 1) teachers’ context in thematic learning, 2) </em><span><em>implementation of thematic learning, 3) supporting and inhibiting factors</em><br /><span><em>of thematic learning.</em><br /><span><em>This research is a qualitative research and the subject of research is teacher </em><span><em>in the lower class. The research instrument in this research is the researcher </em><span><em>itself. The data is collected through conducting interview, observation,</em><br /><span><em>and documentation. Data validation is collected through conducting </em><span><em>triangulation and observation continuously. Data analysis is done since </em><span><em>collected data using interactive model that consist of three steps, such as </em><span><em>data reduction, input data, and make conclusion.</em><br /><span><em>The result of this research shows that the teachers’ context in thematic </em><span><em>learning for beginner class is already appropriate, whereas for their teaching </em><span><em>experience is still less. However, from the result of training, it can be </em><span><em>concluded that teachers actually could implement thematic learning well. </em><span><em>The implementation of thematic learning is already good; it can be seen from </em><span><em>the existence of lesson plans, display of students’ tasks, and also portfolio</em><br /><span><em>assessment which includes a lot of students’ practices and discussions. The </em><span><em>supporting and inhibiting factors in the thematic learning is resulted from </em><span><em>the teachers, the students, and the environment but those factors could be</em><br /><span><em>solved and fially the result is the thematic learning could be done well.</em><br /><span><strong>Keywords: </strong><span><em>Evaluation, thematic learning</em></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span></span>

Implementation System of Business Intelligence System In The Company

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Abdul Hamid Arribathi ◽  
Maimunah Maimunah ◽  
Devi Nurfitriani
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Research Library ◽  
Decision Making Process ◽  
Intelligence System ◽  
Business Intelligence Systems

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.

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

2019 ◽  
Vol 1 (1) ◽  
pp. 121-131
Author(s):  
Ali Fauzi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Direct Investment ◽  
Capital Investment ◽  
Export Market ◽  
Added Value ◽  
Knowledge Generation ◽  
Management Scenarios ◽  
Data Mining Techniques ◽  
Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

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