scholarly journals PENTAHO SEBAGAI SOLUSI MASALAH PENGOLAHAN DATABASE

2012 ◽  
Vol 9 (2) ◽  
pp. 86
Author(s):  
Nurtriana Hidayati
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Integration ◽  
Data Processing ◽  
Functional Organization ◽  
Decision Making Process ◽  
Complex Data ◽  
Business Decisions ◽  
Access To Data

<p>Information is the result of data processing plays an important role in anorganization, especially in decision-making process. Pentaho application of Intelligent Business Products is one of the technologies for collecting, storing, analyzing, and providing access to data to help enterprise users make better business decisions. Pentaho has a function as reporting, analysis, dashboards, data integration (ETL) and data mining. Pentaho is better to manage large and complex data and be able to complete the functional organization.</p>

Organizational Data Mining (ODM)

2011 ◽  
pp. 1-8
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Managerial Decision-Making Process in the Modern Business Conditions in the EU

2020 ◽  
pp. 324-348
Author(s):  
Boban Melovic ◽  
Slavica Mitrovic Veljkovic ◽  
Dragana Cirovic ◽  
Ivana Djakovic Radojicic
Keyword(s):  
Decision Making ◽  
Cultural Dimensions ◽  
Decision Making Process ◽  
Managerial Decision ◽  
Business Decisions ◽  
Eu Countries ◽  
Content Analysis Method ◽  
Modern Business ◽  
The Eu

This chapter analyzes the differences of decision-making process in the EU member countries, caused by differences in main dimensions of national culture of each of them. The influence of different cultural dimensions on decision-making process is explained. Thanks to the application of qualitative research method and deductive approach, there are conclusions about specificities of decision-making process, in particular EU countries. Using the inductive approach, content analysis method and method of synthesis, the EU countries were grouped regarding to the decision-making styles that are the most appropriate in each of them, based on the characteristics of the cultural framework that exist within them. Obtained results may help managers to better understand their decision-maker role in different cultural environment and it would enable them to apply the appropriate decision-making style, which would increase the quality of business decisions that are being made.

Data Mining Fundamental Concepts and Critical Issues

2011 ◽  
pp. 418-423
Author(s):  
John Wang ◽  
Qiyang Chen ◽  
James Yao
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Classification Model ◽  
Data Warehouses ◽  
Business Decisions ◽  
Large Databases ◽  
Critical Issues ◽  
Mining Tools

Data mining is the process of extracting previously unknown information from large databases or data warehouses and using it to make crucial business decisions. Data mining tools find patterns in the data and infer rules from them. The extracted information can be used to form a prediction or classification model, identify relations between database records, or provide a summary of the databases being mined. Those patterns and rules can be used to guide decision making and forecast the effect of those decisions, and data mining can speed analysis by focusing attention on the most important variables.

Data Mining Technology Applications in Tobacco Commercial Enterprise

2012 ◽  
Vol 461 ◽  
pp. 418-420
Author(s):  
Yi Min Mo ◽  
Xin Shun Tong ◽  
Li Hua Yang
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Demand Forecasting ◽  
Customer Relationship ◽  
Complex Data ◽  
Market Demand ◽  
Mining Technology ◽  
Technology Applications ◽  
Commercial Enterprise ◽  
Key Issues

The wide application of information technology has greatly improve the work efficiency but also caused a large and complex data accumulation. How to get the valuable information from vast amounts of data are the key issues in data processing. This paper studied the application of data mining technology in tobacco commercial enterprise from three aspects: market demand forecasting, customer relationship management and historical data processing. Analysis of how to use data mining technology to make full use of large amounts of data to provide a basis for tobacco commercial enterprise’s decision-making.

Organizational Data Mining (ODM)

2008 ◽  
pp. 2289-2295 ◽  
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Symbolic Data Clustering

2011 ◽  
pp. 1087-1091 ◽  
Author(s):  
Edwin Diday ◽  
M. Narasimha Murthy
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Internal Structure ◽  
Data Clustering ◽  
Large Datasets ◽  
Complex Data ◽  
Symbolic Data Analysis ◽  
New Techniques ◽  
Symbolic Data

In data mining, we generate class/cluster models from large datasets. Symbolic Data Analysis (SDA) is a powerful tool that permits dealing with complex data (Diday, 1988) where a combination of variables and logical and hierarchical relationships among them are used. Such a view permits us to deal with data at a conceptual level, and as a consequence, SDA is ideally suited for data mining. Symbolic data have their own internal structure that necessitates the need for new techniques that generally differ from the ones used on conventional data (Billard & Diday, 2003). Clustering generates abstractions that can be used in a variety of decision-making applications (Jain, Murty, & Flynn, 1999). In this article, we deal with the application of clustering to SDA.

Data Mining technology as a tool for supporting analytical decision making process in Health Information Management System (HIMS)

2021 ◽  
Vol 5 (2) ◽  
pp. 139-147
Author(s):  
Gbenga Femi Asere ◽  
Dung Emmanuel Botson
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information System ◽  
Strategic Management ◽  
Information Management ◽  
Management System ◽  
Clinical Decision Making ◽  
Information Management System ◽  
Decision Making Process ◽  
Mining Technology

Wide spread use of information system in the delivery of managed healthcare system and the challenges of identifying and disseminating relevant healthcare information, complex and diverse data and knowledge forms and tasks coupled with the prevalence of legacy systems require automated approaches for effective and efficient utilization of massive amount of data to support in strategic planning and decision-making and assist the strategic management mechanisms. Despite the fact that data mining is progressively used in information systems as a technology to support analytical decision making, it is however still barely used in hospital information system to support analytical decision making process. Hence, this paper presents the usefulness of data mining technology in Hospital Information Management System (HIMS). Data mining technology offered capabilities to increase the productivity of medical personnel, analyze care outcomes, lower healthcare costs, improve healthcare quality by using fast and better clinical decision making and generally assist the strategic management mechanisms.

scholarly journals Aplicação de Mineração de Dados para o Auxílio da Tomada de Decisão em Gestão de Pessoas

2020 ◽  
Author(s):  
Samuel Brati Favarin ◽  
Rafael Ballottin Martins
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Human Resources ◽  
Human Resource ◽  
Decision Making Process ◽  
Software Company ◽  
New Knowledge ◽  
Resource Specialists

People are the foundation of organizations. For companies remain competitive, they need to develop and maintain their human resources. Professionals of the area, must rely on data to make their decisions, otherwise, it can generate bad decisions, taken only by intuition or experience. In this context, this project aimed to help the future decision making process made by human resource specialists of a People Management Software Company using the KDD process to generate new knowledge. In the data mining stage were used The Decision Tree, Neural Network, APRIORI and K-Means algorithms, generating patterns to be analysed with human resource specialists. Preliminary results demonstrate that it is possible to observe standards that classify employees as highly engaged, engaged, neutral and disengaged.

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