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.

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.

Creating Competitive Advantage by Using Data Mining Technique as an Innovative Method for Decision Making Process in Business

2013 ◽  
pp. 205-213
Author(s):  
Mert Bal ◽  
Yasemin Bal ◽  
Ayse Demirhan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Competitive Advantage ◽  
Decision Making Process ◽  
Success Factor ◽  
Data Sets ◽  
Data Mining Technique ◽  
Critical Success ◽  
Using Data

Competitive advantage is at the heart of a firm’s performance in today’s challenging and rapidly changing environment. One of the central bases for achieving competitive advantage is the organizational capability to create new knowledge and transfer it across various levels of the organization. Traditional methods of data analysis, based mainly on human dealing directly with the data, simply do not scale to handle with large data sets. This explosive growth in data and databases has generated an urgent need for new techniques and tools that can intelligently and automatically transform the processed data into useful information and knowledge. Consequently, data mining has become a research area with increasing importance. Organizations of all sizes have started to develop and deploy data mining technologies to leverage data resources to enhance their decision making capabilities. Business information received from data analysis and data mining is a critical success factor for companies wishing to maximize competitive advantage. In this study, the importance of gaining knowledge for organizations in today’s competitive environment are discussed and data mining method in decision making process is analyzed as an innovative technique for organizations.

Creating Competitive Advantage by Using Data Mining Technique as an Innovative Method for Decision Making Process in Business

2011 ◽  
Vol 1 (3) ◽  
pp. 38-45 ◽  
Author(s):  
Mert Bal ◽  
Yasemin Bal ◽  
Ayse Demirhan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Competitive Advantage ◽  
Large Data ◽  
Research Area ◽  
Decision Making Process ◽  
Success Factor ◽  
Organizational Capability ◽  
Data Mining Technique

Competitive advantage is at the heart of a firm’s performance in today’s challenging and rapidly changing environment. One of the central bases for achieving competitive advantage is the organizational capability to create new knowledge and transfer it across various levels of the organization. Traditional methods of data analysis, based mainly on human dealing directly with the data, simply do not scale to handle with large data sets. This explosive growth in data and databases has generated an urgent need for new techniques and tools that can intelligently and automatically transform the processed data into useful information and knowledge. Consequently, data mining has become a research area with increasing importance. Organizations of all sizes have started to develop and deploy data mining technologies to leverage data resources to enhance their decision making capabilities. Business information received from data analysis and data mining is a critical success factor for companies wishing to maximize competitive advantage. In this study, the importance of gaining knowledge for organizations in today’s competitive environment are discussed and data mining method in decision making process is analyzed as an innovative technique for organizations.

Knowledge Structuring for Sustainable Development and the Hozo Tool

2017 ◽  
pp. 195-221 ◽  
Author(s):  
Jenny S. Huang ◽  
Kouji Kozaki ◽  
Terukazu Kumazawa
Keyword(s):  
Decision Making ◽  
Sustainable Development ◽  
Open Source ◽  
Case Studies ◽  
Practical Approach ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
Balance Of Nature ◽  
The Core ◽  
Knowledge Structuring

The search for more actionable knowledge lies at the core of Sustainability Science and its implicit desire to improve the lives of various stakeholders without disrupting the balance of Nature and efficient use of all available resources. In this chapter, the authors have examined current shortfalls in knowledge-centric research and proposed the creation of an Ontology-based open-source tool to create a more practical approach for researchers to facilitate both thought and decision-making process in order to solve pressing issues with place-based actions. The effectiveness of the Hozo Tool is then examined and validated using four case studies in an attempt to both refine the current models and propose the necessary steps to create a more holistic knowledge ecosystem – one that might ultimately facilitate broader collaboration worldwide.

scholarly journals Exploring Factors Affecting the Emergency Specialists’ Decision-Making in Case of Emergencies in Patients

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Abbasali Ebrahimian ◽  
Seyed-Hossein Hashemi-Amrei ◽  
Mohammadreza Monesan
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Qualitative Content Analysis ◽  
Decision Makers ◽  
Decision Making Process ◽  
Factors Affecting ◽  
Emergency Situations ◽  
The Core ◽  
Semistructured Interviews ◽  
Emergency Decision

Introduction. Appropriate decision-making is essential in emergency situations; however, little information is available on how emergency decision-makers decide on the emergency status of the patients shifted to the emergency department of the hospital. This study aimed at explaining the factors that influence the emergency specialists’ decision-making in case of emergency conditions in patients. Methods. This study was carried out with a qualitative content analysis approach. The participants were selected based on purposive sampling by the emergency specialists. The data were collected through semistructured interviews and were analyzed using the method proposed by Graneheim and Lundman. Results. The core theme of the study was “efforts to perceive the acute health threats of the patient.” This theme was derived from the main classes, including “the identification of the acute threats based on the patient’s condition” and “the identification of the acute threats based on peripheral conditions.” Conclusions. The conditions governing the decision-making process about patients in the emergency department differ from the conditions in other health-care departments at hospitals. Emergency specialists may have several approaches to decide about the patients’ emergency conditions. Therefore, notably, the emergency specialists’ working conditions and the others’ expectations from these specialists should be considered.

Achieving Global Competitive Human Resources in the Industrial Revolution

2021 ◽  
Author(s):  
Pang william panggantara
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Human Resources ◽  
Competitive Advantage ◽  
Industrial Revolution ◽  
Global Market ◽  
Massive Transformation ◽  
Business Decision ◽  
Fourth Wave

Fourth wave of industrial revolution is marked by the use of information technology, artificial inteligence (A.I), and automatic engines. Competitive advantage has become a necessity for every business actor when they wants to competing in the global market. The current condition definitely encouraging the occurence of massive transformation at all business levels and units this condition happens because every business actor can enter from and any other countries markets easily. this condition making professionalism of every business actor is highly prioritized like many case in the business decision making and continous innovation.

scholarly journals International Student Mobility

2018 ◽  
pp. 15-30
Author(s):  
Rashim Wadhwa
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
International Students ◽  
International Student ◽  
Student Mobility ◽  
Internationalization Of Higher Education ◽  
Decision Making Process ◽  
Core Element ◽  
International Student Mobility ◽  
The Core

International student mobility is the core element of the internationalization of higher education. In recent years, a significant change has been observed in the outlook of individuals which is giving a boost to this phenomenon. Within this context, the present chapter analyzed the phenomenon of international student mobility through different approaches by providing critical outlook. An attempt has been made to list the important determinants which influence the decision-making process of international students.

scholarly journals Predicting Academic Course Preference using Hadoop

2019 ◽  
Vol 8 (4) ◽  
pp. 2527-2530
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
New Technologies ◽  
Parallel Applications ◽  
Distributed File System ◽  
Industrial Training ◽  
Effective Learning ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Mining Tools

These days new technologies have been introduced by this new academic trends also have been came into existence into the education system. And this leads to huge amounts of data which makes a big challenge for the students to store the preferred course. For this many data mining tools have been invented to convert the unregulated data into structured format to understand the meaningful information. As we know that Hadoop is a distributed file system which is used to hold huge amounts of data this stores the files in a redundant fashion across multiple machines. Due to this it leads to failure and parallel applications do not work. To avoid this problem we are using Mapreduce for decision making of students in order to choose their preferred course for industrial training purpose for their effective learning techniques to increase their knowledge and capability.

scholarly journals Expert System for Diagnosis of Uterine Myomas using the Certainty Factor Method

2021 ◽  
Vol 1 (4) ◽  
pp. 103-108
Author(s):  
Syahrizal Dwi Putra ◽  
M Bahrul Ulum ◽  
Diah Aryani
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Computer System ◽  
Decision Making Process ◽  
Certainty Factor ◽  
Uterine Myomas ◽  
Factor Method ◽  
Two Factors

An expert system which is part of artificial intelligence is a computer system that is able to imitate the reasoning of an expert with certain expertise. An expert system in the form of software can replace the role of an expert (human) in the decision-making process based on the symptoms given to a certain level of certainty. This study raises the problem that many women experience, namely not understanding that they have uterine myomas. Many women do not understand and are not aware that there are already symptoms that are felt and these symptoms are symptoms of the presence of uterine myomas in their bodies. Therefore, it is necessary for women to be able to diagnose independently so that they can take treatment as quickly as possible. In this study, the expert will first provide the expert CF values. Then the user / respondent gives an assessment of his condition with the CF User values. In the end, the values obtained from these two factors will be processed using the certainty factor formula. Users must provide answers to all questions given by the system in accordance with their current conditions. After all the conditions asked are answered, the system will display the results to identify that the user is suffering from uterine myoma disease or not. The Expert System with the certainty factor method was tested with a patient who entered the symptoms experienced and got the percentage of confidence in uterine myomas/fibroids of 98.70%. These results indicate that an expert system with the certainty factor method can be used to assist in diagnosing uterine myomas as early as possible.

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