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

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.

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Organizational Data Mining (ODM)

Organizational Data Mining ◽

10.4018/978-1-59140-134-6.ch001 ◽

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.

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Data analysis for gathering and analysis of no structured and semi-structured information using data mining technique

IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications ◽

10.1109/iisa.2014.6878725 ◽

2014 ◽

Author(s):

Sozon Papavlasopoulos

Keyword(s):

Data Mining ◽

Data Analysis ◽

Data Mining Technique ◽

Mining Technique ◽

Structured Information ◽

Using Data

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Organizational Data Mining (ODM)

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch136 ◽

2008 ◽

pp. 2289-2295 ◽

Cited By ~ 1

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.

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Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit195119 ◽

2019 ◽

pp. 131-136

Author(s):

Umar Sidiq ◽

Syed Mutahar Aaqib ◽

Rafi Ahmad Khan

Keyword(s):

Data Mining ◽

Decision Tree ◽

Research Work ◽

Support Vector ◽

Data Sets ◽

Data Mining Technique ◽

K Nearest Neighbors ◽

Data Set ◽

Classification Techniques ◽

Using Data

Classification is one of the most considerable supervised learning data mining technique used to classify predefined data sets the classification is mainly used in healthcare sectors for making decisions, diagnosis system and giving better treatment to the patients. In this work, the data set used is taken from one of recognized lab of Kashmir. The entire research work is to be carried out with ANACONDA3-5.2.0 an open source platform under Windows 10 environment. An experimental study is to be carried out using classification techniques such as k nearest neighbors, Support vector machine, Decision tree and Naïve bayes. The Decision Tree obtained highest accuracy of 98.89% over other classification techniques.

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Copyright Exceptions Reform and AI Data Analysis in ChinaA Modest Proposal

Artificial Intelligence and Intellectual Property ◽

10.1093/oso/9780198870944.003.0010 ◽

2021 ◽

pp. 196-220

Author(s):

Tianxiang He

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Big Data ◽

Data Analysis ◽

Data Collection ◽

Incomplete Data ◽

Copyright Law ◽

Data Sets ◽

Copyright Exception ◽

Using Data

The development of artificial intelligence (AI) technology is firmly connected to the availability of big data. However, using data sets involving copyrighted works for AI analysis or data mining without authorization will incur risks of copyright infringement. Considering the fact that incomplete data collection may lead to data bias, and since it is impossible for the user of AI technology to obtain a copyright licence from each and every right owner of the copyrighted works used, a mechanism that can free the data from copyright restrictions under certain conditions is needed. In the case of China, it is crucial to check whether China’s current copyright exception model can take on the role and offer that kind of function. This chapter suggests that a special AI analysis and data mining copyright exception that follows a semi-open style should be added to the current exceptions list under the Copyright Law of China.

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Critical Success Factors for Implementing CRM Using Data Mining

Interscience Management Review ◽

10.47893/imr.2008.1006 ◽

2008 ◽

pp. 50-55

Author(s):

Jayanti Ranjan ◽

Vishal Bhatnagar

Keyword(s):

Data Mining ◽

Critical Success Factors ◽

Success Factors ◽

Customer Relationship ◽

Innovative Technology ◽

Success Factor ◽

General Factor ◽

Related Data ◽

Critical Success ◽

Using Data

The paper presents the Critical success factors for implementing the Customer Relationship Management (CRM) in a firm using the Data mining (DM). The use of the data mining in CRM is widely accepted by the firms. The success of proper implementation of CRM using Data mining in firms is mixed. This is due to the fact that investment involve in this implementation requires planning regarding the factors which need to be considered before going for the new innovative technology. These factors may vary from firm to firm but the general factor for effective implementation of the CRM using data mining is essential. This factor termed as Critical success factor (CSF) decides the failure or success of the implementation. The paper demonstrates the key factors which need to be considered before automating the process of searching the mountain of customer’s related data using Data mining to find patterns that are good predictors of behaviors of the customer which help achieve successful CRM. The paper gives an idea of how proper planning and effective management can lead to increased customer satisfaction and profit for the firms.

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An approach of electric power demand forecasting using data-mining method: a case study of application of data-mining technique to improve decision making

International Journal of Management and Decision Making ◽

10.1504/ijmdm.2006.008173 ◽

2006 ◽

Vol 7 (1) ◽

pp. 88 ◽

Cited By ~ 3

Author(s):

Toshio Sugihara

Keyword(s):

Data Mining ◽

Decision Making ◽

Electric Power ◽

Demand Forecasting ◽

Mining Method ◽

Power Demand ◽

Data Mining Technique ◽

Mining Technique ◽

Using Data

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Small independent retail firms and locational decision‐making: outdoor leisure retailing by the crags

Journal of Small Business and Enterprise Development ◽

10.1108/14626000410537164 ◽

2004 ◽

Vol 11 (2) ◽

pp. 222-232 ◽

Cited By ~ 19

Author(s):

Elke Pioch ◽

John Byrom

Keyword(s):

Decision Making ◽

Great Britain ◽

Decision Making Process ◽

Success Factor ◽

Critical Success Factor ◽

Retail Operations ◽

Critical Success ◽

Methods Of Evaluation ◽

Locational Decision ◽

Significant Factors

The importance of location to retail organisations has long been recognised in the geography and retail marketing literatures, with subjective and “gut feel” methods of evaluation emerging as highly significant factors in the decision‐making process. Through the application of existing frameworks we seek to highlight the importance of location to small independent retailers in the context of outdoor leisure retailing. The case of “UpFront”, a pseudonym for a retailer operating four outlets in Great Britain, is presented. It is shown that, although based largely on luck and opportunism, the firm's locational “strategy” has been crucial to its success as a leading player in the sector. Based on detailed interviews with the managing director and employees, the role and importance of location as a critical success factor to the organisation is presented. In conclusion, a call is made for greater engagement with the nuances of location to small retail organisations, given its impact on a large number of retail operations.

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Finding Patterns in Class-Labeled Data Using Data Visualization

Data Mining Patterns ◽

10.4018/978-1-59904-162-9.ch005 ◽

2011 ◽

pp. 106-123

Author(s):

Gregor Leban ◽

Minca Mramor ◽

Blaž Zupan ◽

Janez Demšar ◽

Ivan Bratko

Keyword(s):

Gene Expression ◽

Data Mining ◽

Data Analysis ◽

Data Visualization ◽

Gene Expression Data ◽

Data Sets ◽

Cancer Gene ◽

Manual Search ◽

Degree Of Separation ◽

Using Data

Data visualization plays a crucial role in data mining and knowledge discovery. Its use is, however, often difficult due to the large number of possible data projections. Manual search through such sets of projections can be prohibitively timely or even impossible, especially in the data analysis problems that comprise many data features. The chapter describes a method called VizRank, which can be used to automatically identify interesting data projections for multivariate visualizations of class-labeled data. VizRank assigns a score of interestingness to each considered projection based on the degree of separation of data instances with different class label. We demonstrate the usefulness of this approach on six cancer gene expression data sets, showing that the method can reveal interesting data patterns and can further be used for data classification and outlier detection.

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