Ethics of Data Mining

2011 ◽  
pp. 783-788 ◽  
Author(s):  
Jack Cook
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Financial Services ◽  
Ethical Issues ◽  
Price Change ◽  
Mining Operation ◽  
Decision Makers ◽  
Customer Relationship ◽  
Good Decision ◽  
Customer Data

Decision makers thirst for answers to questions. As more data is gathered, more questions are posed: Which customers are most likely to respond positively to a marketing campaign, product price change or new product offering? How will the competition react? Which loan applicants are most likely or least likely to default? The ability to raise questions, even those that currently cannot be answered, is a characteristic of a good decision maker. Decision makers no longer have the luxury of making decisions based on gut feeling or intuition. Decisions must be supported by data; otherwise decision makers can expect to be questioned by stockholders, reporters, or attorneys in a court of law. Data mining can support and often direct decision makers in ways that are often counterintuitive. Although data mining can provide considerable insight, there is an “inherent risk that what might be inferred may be private or ethically sensitive” (Fule & Roddick, 2004, p. 159). Extensively used in telecommunications, financial services, insurance, customer relationship management (CRM), retail, and utilities, data mining more recently has been used by educators, government officials, intelligence agencies, and law enforcement. It helps alleviate data overload by extracting value from volume. However, data analysis is not data mining. Query-driven data analysis, perhaps guided by an idea or hypothesis, that tries to deduce a pattern, verify a hypothesis, or generalize information in order to predict future behavior is not data mining (Edelstein, 2003). It may be a first step, but it is not data mining. Data mining is the process of discovering and interpreting meaningful, previously hidden patterns in the data. It is not a set of descriptive statistics. Description is not prediction. Furthermore, the focus of data mining is on the process, not a particular technique, used to make reasonably accurate predictions. It is iterative in nature and generically can be decomposed into the following steps: (1) data acquisition through translating, cleansing, and transforming data from numerous sources, (2) goal setting or hypotheses construction, (3) data mining, and (4) validating or interpreting results. The process of generating rules through a mining operation becomes an ethical issue, when the results are used in decision-making processes that affect people or when mining customer data unwittingly compromises the privacy of those customers (Fule & Roddick, 2004). Data miners and decision makers must contemplate ethical issues before encountering one. Otherwise, they risk not identifying when a dilemma exists or making poor choices, since all aspects of the problem have not been identified.

Ethics Of Data Mining

2011 ◽  
pp. 454-458
Author(s):  
Jack Cook
Keyword(s):  
Data Mining ◽  
Decision Maker ◽  
Price Change ◽  
New Product ◽  
Decision Makers ◽  
Product Price ◽  
Good Decision ◽  
Marketing Campaign ◽  
Inherent Risk ◽  
Considerable Insight

Decision makers thirst for answers to questions. As more data is gathered, more questions are posed: Which customers are most likely to respond positively to a marketing campaign, product price change or new product offering? How will the competition react? Which loan applicants are most likely or least likely to default? The ability to raise questions, even those that currently cannot be answered, is a characteristic of a good decision maker. Decision makers no longer have the luxury of making decisions based on gut feeling or intuition. Decisions must be supported by data; otherwise decision makers can expect to be questioned by stockholders, reporters, or attorneys in a court of law. Data mining can support and often direct decision makers in ways that are often counterintuitive. Although data mining can provide considerable insight, there is an “inherent risk that what might be inferred may be private or ethically sensitive” (Fule & Roddick, 2004, p. 159).

Ethics Of Data Mining

2008 ◽  
pp. 2834-2840
Author(s):  
Jack Cook
Keyword(s):  
Data Mining ◽  
Decision Maker ◽  
Price Change ◽  
New Product ◽  
Decision Makers ◽  
Product Price ◽  
Good Decision ◽  
Marketing Campaign ◽  
Inherent Risk ◽  
Considerable Insight

Decision makers thirst for answers to questions. As more data is gathered, more questions are posed: Which customers are most likely to respond positively to a marketing campaign, product price change or new product offering? How will the competition react? Which loan applicants are most likely or least likely to default? The ability to raise questions, even those that currently cannot be answered, is a characteristic of a good decision maker. Decision makers no longer have the luxury of making decisions based on gut feeling or intuition. Decisions must be supported by data; otherwise decision makers can expect to be questioned by stockholders, reporters, or attorneys in a court of law. Data mining can support and often direct decision makers in ways that are often counterintuitive. Although data mining can provide considerable insight, there is an “inherent risk that what might be inferred may be private or ethically sensitive” (Fule & Roddick, 2004, p. 159).

Ethics Of Data Mining

2008 ◽  
pp. 211-217
Author(s):  
Jack Cook
Keyword(s):  
Data Mining ◽  
Decision Maker ◽  
Price Change ◽  
New Product ◽  
Decision Makers ◽  
Product Price ◽  
Good Decision ◽  
Marketing Campaign ◽  
Inherent Risk ◽  
Considerable Insight

Decision makers thirst for answers to questions. As more data is gathered, more questions are posed: Which customers are most likely to respond positively to a marketing campaign, product price change or new product offering? How will the competition react? Which loan applicants are most likely or least likely to default? The ability to raise questions, even those that currently cannot be answered, is a characteristic of a good decision maker. Decision makers no longer have the luxury of making decisions based on gut feeling or intuition. Decisions must be supported by data; otherwise decision makers can expect to be questioned by stockholders, reporters, or attorneys in a court of law. Data mining can support and often direct decision makers in ways that are often counterintuitive. Although data mining can provide considerable insight, there is an “inherent risk that what might be inferred may be private or ethically sensitive” (Fule & Roddick, 2004, p. 159).

Marketing customer response scoring model based on machine learning data analysis

2020 ◽  
pp. 1-11
Author(s):  
Tang Yan ◽  
Li Pengfei
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Extraction ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Customer Data ◽  
Modeling And Analysis ◽  
Scoring Model ◽  
Model Based ◽  
Learning Data

In marketing, problems such as the increase in customer data, the increase in the difficulty of data extraction and access, the lack of reliability and accuracy of data analysis, the slow efficiency of data processing, and the inability to effectively transform massive amounts of data into valuable information have become increasingly prominent. In order to study the effect of customer response, based on machine learning algorithms, this paper constructs a marketing customer response scoring model based on machine learning data analysis. In the context of supplier customer relationship management, this article analyzes the supplier’s precision marketing status and existing problems and uses its own development and management characteristics to improve marketing strategies. Moreover, this article uses a combination of database and statistical modeling and analysis to try to establish a customer response scoring model suitable for supplier precision marketing. In addition, this article conducts research and analysis with examples. From the research results, it can be seen that the performance of the model constructed in this article is good.

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.

Legal and Technical Issues of Privacy Preservation in Data Mining

2011 ◽  
pp. 1158-1163 ◽  
Author(s):  
Kirsten Wahlstrom ◽  
John F. Roddick ◽  
Rick Sarre ◽  
Vladimir Estivill-Castro ◽  
Denise de Vries
Keyword(s):  
Data Mining ◽  
Social Responsibility ◽  
Private Information ◽  
New Technologies ◽  
Ethical Issues ◽  
Sensitive Information ◽  
Personal Privacy ◽  
Customer Data ◽  
Moral Principles ◽  
Very Large Databases

To paraphrase Winograd (1992), we bring to our communities a tacit comprehension of right and wrong that makes social responsibility an intrinsic part of our culture. Our ethics are the moral principles we use to assert social responsibility and to perpetuate safe and just societies. Moreover, the introduction of new technologies can have a profound effect on our ethical principles. The emergence of very large databases, and the associated automated data analysis tools, present yet another set of ethical challenges to consider. Socio-ethical issues have been identified as pertinent to data mining and there is a growing concern regarding the (ab)use of sensitive information (Clarke, 1999; Clifton et al., 2002; Clifton and Estivill-Castro, 2002; Gehrke, 2002). Estivill-Castro et al., discuss surveys regarding public opinion on personal privacy that show a raised level of concern about the use of private information (Estivill-Castro et al., 1999). There is some justification for this concern; a 2001 survey in InfoWeek found that over 20% of companies store customer data with information about medical profile and/or customer demographics with salary and credit information, and over 15% store information about customers’ legal histories.

scholarly journals Penerapan Data Mining untuk Mendukung Pemasaran Produk Pinjaman Bank

CAHAYAtech ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 161
Author(s):  
Sanusi Amir ◽  
Heri Abijono
Keyword(s):  
Data Mining ◽  
Banking Sector ◽  
Confusion Matrix ◽  
Customer Relationship ◽  
Bank Loan ◽  
Customer Data ◽  
C4.5 Algorithm ◽  
Good Order ◽  
Selection Of

Competition in the banking sector requires every bank to compete to make types of credit products that are able to attract customers to want to transact with banks. Marketing with a product centric strategy needs to be combined with a customer centric to keep Customer Relationship Management in good order with customers. Customer identity can be used to realize the concept of a customer centric marketing strategy. This study describes the role of Data Mining on customer data accompanied by the application of Decision Tree techniques with the C4.5 algorithm to identify the most influential fields in predicting loan products. The customer data fields used in this study include total balance, date of birth, occupation, and status. Selection of fields based on the terms and conditions that apply to each type of loan. The results of applying the C4.5 algorithm to customer data in the formation of loan product prediction decision trees show that the work attribute is the dominant attribute (highest Gain value) among the other attributes. Prediction results are validated with Confusion Matrix with an accuracy value for loan product prediction of 82%. The results of this study concluded that the customer database is a source of data in the prediction process of loan products to be offered to customers, which can be used to support the marketing of bank loan products with a customer centric orientation.

scholarly journals Customer Churn Prediction using Logistic Regression with Regularization and Optimization Technique

2020 ◽  
Vol 9 (9) ◽  
pp. 334-339
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Optimization Technique ◽  
Customer Relationship ◽  
Communication Process ◽  
Prediction Algorithm ◽  
Churn Prediction ◽  
Customer Data ◽  
Customer Churn ◽  
Customer Churn Prediction

Customer Relationship Management (CRM) is a challenging issue in marketing to better understand the customers and maintaining long-term relationships with them to increase the profitability. It plays a vital role in customer centered marketing domain which provides a better service and satisfies the customer requirements based on their characteristics in consuming patterns and smoothes the relationship where various representatives communicate and collaborate. Customer Churn prediction is one of the area in CRM that explores the transaction and communication process and analyze the customer loyalty. Data mining ease this process with classification techniques to explore pattern from large datasets. It provides a good technical support to analyze large amounts of complex customer data. This research paper applies data mining classification technique to predict churn customers in three variant sectors Banking, Ecommerce and Telecom. For Classification, enhanced logistic regression with regularization and optimization technique is applied. The work is implemented in Rapid miner tool and the performance of the prediction algorithm is assessed for three variant sectors with suitable evaluation metrics.

Spatial Clustering Analysis of K-Means Algorithm in the Classification of Bank Card Customers

2014 ◽  
Vol 687-691 ◽  
pp. 1274-1277
Author(s):  
Kang Lv
Keyword(s):  
Data Mining ◽  
Financial Services ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Spatial Relationship ◽  
Data Bank ◽  
Customer Relationship ◽  
Current Status ◽  
Clustering Methods ◽  
Bank Card

K-means algorithm is a simple and efficient data mining clustering algorithm. For the current status of the bank card customer relationship management, based on data mining technology, design based on K-means clustering algorithm banking customer classification system. Data mining techniques can extract vast amounts of customer information data bank card implicit knowledge and spatial relationship model will represent the bank customers feature set of data objects automatically classified into each composed of clusters of similar objects, bank card customers in the banking system classification. This paper analyzes the existing spatial clustering methods summary and conclusion, based on the combined data bank card customers, according to the volatility of funds used to different customer groups, the use of K-means analysis to study characteristics of client groups, providing appropriate financial services.

Algorithms and the Social Organization of Work

2020 ◽  
pp. 803-822
Author(s):  
Ifeoma Ajunwa ◽  
Rachel Schlund
Keyword(s):  
Ethical Issues ◽  
Machine Learning Algorithms ◽  
Customer Relationships ◽  
Customer Relationship ◽  
Customer Data ◽  
Hiring Process ◽  
Customer Interaction ◽  
The Social ◽  
Automated Algorithms ◽  
A Company

This chapter argues that the proliferation of automated algorithms in the workplace raises questions as to how they might be used in service of the control of workers. In particular, scholars have noted machine learning algorithms as prompting a data-centric reorganization of the workplace and a quantification of the worker. The chapter then considers ethical issues implicated by three emergent algorithmic-driven work technologies: automated hiring platforms (AHPs), wearable workplace technologies, and customer relationship management (CRM). AHPs are “digital intermediaries that invite submission of data from one party through preset interfaces and structured protocols, process that data via proprietary algorithms, and deliver the sorted data to a second party.” The use of AHPs involves every stage of the hiring process, from the initial sourcing of candidates to the eventual selection of candidates from the applicant pool. Meanwhile, wearable workplace technologies exist in a variety of forms that vary in terms of design and use, from wristbands used to track employee location and productivity to exoskeletons used to assist employees performing strenuous labor. Finally, CRM is an approach to managing current and potential customer interaction and experience with a company using technology. CRM practices typically involve the use of customer data to develop customer insight to build customer relationships.

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