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.

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.

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.

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.

scholarly journals Study of The ID3 and C4.5 Learning Algorithms

2020 ◽  
Vol 1 (2) ◽  
pp. 29-43
Author(s):  
Y. Fakir ◽  
M. Azalmad ◽  
R. Elaychi
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Decision Tree ◽  
Decision Trees ◽  
Information Gain ◽  
Learning Algorithms ◽  
Large Data ◽  
Classification Algorithms ◽  
Important Data

Data Mining is a process of exploring against large data to find patterns in decision-making. One of the techniques in decision-making is classification. Data classification is a form of data analysis used to extract models describing important data classes. There are many classification algorithms. Each classifier encompasses some algorithms in order to classify object into predefined classes. Decision Tree is one such important technique, which builds a tree structure by incrementally breaking down the datasets in smaller subsets. Decision Trees can be implemented by using popular algorithms such as ID3, C4.5 and CART etc. The present study considers ID3 and C4.5 algorithms to build a decision tree by using the “entropy” and “information gain” measures that are the basics components behind the construction of a classifier model

Big Data Myth

2019 ◽  
pp. 223-237
Author(s):  
Agata Mardosz-Grabowska
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Qualitative Data ◽  
New Technologies ◽  
Marketing Management ◽  
Individual Experience ◽  
Decision Making Process

Organizations are expected to act rationally; however, mythical thinking is often present among their members. It refers also to myths related to technology. New inventions and technologies are often mythologized in organizations. People do not understand how new technologies work and usually overestimate their possibilities. Also, myths are useful in dealing with ambivalent feelings, such as fears and hopes. The text focuses on the so-called “big data myth” and its impact on the decision-making process in modern marketing management. Mythical thinking related to big data in organizations has been observed both by scholars and practitioners. The aim of the chapter is to discuss the foundation of the myth, its components, and its impact on the decision-making process. Among others, a presence of a “big data myth” may be manifested by over-reliance on data, neglecting biases in the process of data analysis, and undermining the role of other factors, including intuition and individual experience of marketing professionals or qualitative data.

Convergence Marketing

2012 ◽  
pp. 274-290
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Competitive Advantage ◽  
Relationship Marketing ◽  
Customer Value ◽  
Emerging Technologies ◽  
Market Competition ◽  
Customer Management ◽  
Decision Making Process ◽  
Customer Knowledge

The convergence marketing has emerged as an outgrowth of relationship marketing. Many firms in the competitive marketplace prefer on developing strategies by convergence of technology with customer value in promoting products and services. It is argued in this chapter that such convergence among the firms involve consumers, social networks, and technology that influence the decision making process for gaining competitive advantage. This chapter discusses the attributes of convergence of technology with customer value that provides higher competitive advantage to the business through frequent interactions among the market players and customers. The convergence marketing emphasizes on strategic, reflective, and organizational look at relationship marketing and contributes to existing knowledge within three fundamental elements including organizational customer focus, customer management focus, and customer knowledge focus. This chapter also examines how convergence of technology and customer value drives value co-creation in the firms and discuss that in an increasing market competition, customers interact with emerging technologies, and consumer communities co-create value with firms.

Application of Statistical Analysis Tools and Concepts to Big Data and Predictive Analytics to New Product Development

2020 ◽  
Vol 3 (1) ◽  
pp. 17-35
Author(s):  
Brian J. Galli
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Product Development ◽  
Competitive Advantage ◽  
New Product Development ◽  
New Product ◽  
Big Data Analysis ◽  
Life Cycles ◽  
The Impact

In today's fiercely competitive environment, most companies face the pressure of shorter product life cycles. Therefore, if companies want to maintain a competitive advantage in the market, they need to keep innovating and developing new products. If not, then they will face difficulties in developing and expanding markets and may go out of business. New product development is the key content of enterprise research and development, and it is also one of the strategic cores for enterprise survival and development. The success of new product development plays a decisive role both in the development of the company and in maintaining a competitive advantage in the industry. Since the beginning of the 21st century, with the continuous innovation and development of Internet technology, the era of big data has arrived. In the era of big data, enterprises' decision-making for new product development no longer solely relies on the experience of decision-makers; it is based on the results of big data analysis for more accurate and effective decisions. In this thesis, the case analysis is mainly carried out with Company A as an example. Also, it mainly introduces the decision made by Company A in the actual operation of new product development, which is based on the results of big data analysis from decision-making to decision-making innovation. The choice of decision-making is described in detail. Through the introduction of the case, the impact of big data on the decision-making process for new product development was explored. In the era of big data, it provides a new theoretical approach to new product development decision-making.

Integrating OLAP/SOLAP in E-Business Domains

2011 ◽  
Vol 24 (3) ◽  
pp. 45-60
Author(s):  
Ben Ali ◽  
Samar Mouakket
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
United Arab Emirates ◽  
Decision Makers ◽  
Job Seekers ◽  
Job Market ◽  
Analysis Techniques ◽  
Analytical Processing ◽  
User Friendly

E-business domains have been considered killer domains for different data analysis techniques. Most researchers have examined data mining (DM) techniques to analyze the databases behind E-business websites. DM has shown interesting results, but this technique presents some restrictions concerning the content of the database and the level of expertise of the users interpreting the results. In this paper, the authors show that successful and more sophisticated results can be obtained using other analysis techniques, such as Online Analytical Processing (OLAP) and Spatial OLAP (SOLAP). Thus, the authors propose a framework that fuses or integrates OLAP with SOLAP techniques in an E-business domain to perform easier and more user-friendly data analysis (non-spatial and spatial) and improve decision making. In addition, the authors apply the framework to an E-business website related to online job seekers in the United Arab Emirates (UAE). The results can be used effectively by decision makers to make crucial decisions in the job market of the UAE.

scholarly journals Personalized web search on e-commerce using ontology based association mining

2017 ◽  
Vol 7 (1.1) ◽  
pp. 286
Author(s):  
B. Sekhar Babu ◽  
P. Lakshmi Prasanna ◽  
P. Vidyullatha
Keyword(s):  
Data Mining ◽  
Web Search ◽  
Large Data ◽  
Association Mining ◽  
Data Sets ◽  
Data Mining Algorithm ◽  
Web Data ◽  
Data Mining Technique ◽  
Web Data Mining ◽  
The Web

 In current days, World Wide Web has grown into a familiar medium to investigate the new information, Business trends, trading strategies so on. Several organizations and companies are also contracting the web in order to present their products or services across the world. E-commerce is a kind of business or saleable transaction that comprises the transfer of statistics across the web or internet. In this situation huge amount of data is obtained and dumped into the web services. This data overhead tends to arise difficulties in determining the accurate and valuable information, hence the web data mining is used as a tool to determine and mine the knowledge from the web. Web data mining technology can be applied by the E-commerce organizations to offer personalized E-commerce solutions and better meet the desires of customers. By using data mining algorithm such as ontology based association rule mining using apriori algorithms extracts the various useful information from the large data sets .We are implementing the above data mining technique in JAVA and data sets are dynamically generated while transaction is processing and extracting various patterns.

A Hybrid Method for Prediction and Assessment Efficiency of Decision Making Units

2013 ◽  
Vol 5 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Iman Rahimi ◽  
Reza Behmanesh ◽  
Rosnah Mohd. Yusuff
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Rules ◽  
Large Data ◽  
Poultry Meat ◽  
Small Data ◽  
Data Set ◽  
Data Mining Techniques ◽  
Decision Making Units

The objective of this article is an evaluation and assessment efficiency of the poultry meat farm as a case study with the new method. As it is clear poultry farm industry is one of the most important sub- sectors in comparison to other ones. The purpose of this study is the prediction and assessment efficiency of poultry farms as decision making units (DMUs). Although, several methods have been proposed for solving this problem, the authors strongly need a methodology to discriminate performance powerfully. Their methodology is comprised of data envelopment analysis and some data mining techniques same as artificial neural network (ANN), decision tree (DT), and cluster analysis (CA). As a case study, data for the analysis were collected from 22 poultry companies in Iran. Moreover, due to a small data set and because of the fact that the authors must use large data set for applying data mining techniques, they employed k-fold cross validation method to validate the authors’ model. After assessing efficiency for each DMU and clustering them, followed by applied model and after presenting decision rules, results in precise and accurate optimizing technique.

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