The Role Of Data Mining In Business Optimization: Techniques And Applications Essay.

Definition of Data Mining

Data mining was introduced in the 1990’s. Data mining roots are traced back along classical statistics, artificial intelligence and machine learning. Traditional techniques may be unsuitable due to enormity of data, high dimensionality of data and heterogeneous distributed nature of data. Data mining is basically done for the extraction of hidden information. Data mining is all about data quality, privacy and security measure. It enhances the competitiveness of an organization. Data mining helps in Decision making, Data Presentation, Data Mining, Data Exploring, Data Preprocessing, Warehousing and Data sources which are discussed later in the report (Shmueli & Lichtendahl, 2017). The potential use of data mining techniques refers to the way the data can be used to conceive a better outlook on the technology collection.

In this report the Data Mining in Business has been summarized using recent article, the use of data analysis and the security, privacy and ethics regarding data mining in business intelligence. Identifying the business requirements and patterns, and analyzing the importance of current and future aspects of data mining and visualization.

Data Mining is basically the technique by which a company turns raw data into useful information. To looks for patterns in larger batches of raw information by using software, business can know more about their customer an develop more effective marketing strategy. This increases sales and decreases costs as well (Witten et al, 2016). Data mining generally depends on effectiveness of data collection, computer processing and data warehousing. Data mining keeps track of customer’s buying records in super markets, which helps to decide when to put items on sale and when to sell them in full price. This is an example of effective use of Data mining in business (Shmueli & Lichtendahl, 2017). Data mining can be a cause for concern to prove certain hypothesis.

In Businesses, Data Mining is used to discover relationships and patterns in order to make better business decisions. Data Mining helps in sorting sales, trends, for development of smart marketing campaigns and predict accurate a customer’s loyalty (Larose, 2014).

The Role of Data Mining in Business Optimization is that Data Mining helps to provide competitive advantages in business (Wu et al., 2014). There are six primary techniques of Data Mining to analyze data: Classification, Regression, Clustering, Association Rule Learning, Anomaly Detection and Summarization.

Data Mining is defined as a business process for exploring large amount of data to discover meaningful patterns and rules (Shmueli & Lichtendahl, 2017). Applying data mining is done to improve the business and gain advantages over the competitors. The most important areas of business that applies data mining successfully are: Retail, Banking and Insurance. (1) Retail data mining can identify customer buying behavior, shopping pattern and trends, improving the quality of services, achieve better customer retention and satisfaction, enhancing good consumption ratios, designing more effective goods, transport and distributive policies hence reducing the costing in business as well (Kasemsap, 2015). These retail industry thus have many applications like Customer segmentation, Establishing customer shopping behavior, Customer Retention and Sales campaigns improvement. (2) Banking includes customer segmentation, profitability, credit analysis, predicting payment defaults, marketing, transaction, ranking investments, optimizing portfolios, cash management and forecasting operations. The main examples of the applications of data mining techniques in the banking industries are Credit scoring, Customer segmentation and Customer profitability. (3) Data mining helps in business firm practices that acquire new customers, retain existing customers (Hofmann & Klinkenberg, 2013). In insurance the data mining techniques have applications like Risk factor identification, fraud detection and Customer Segmentation and retaining. Data mining techniques can be improved by analyzing larger amount of data available for companies.

Potential Use of Data Mining Techniques

A very usual example of Data Mining and Business Intelligence comes from Service Providers in the mobile phone and utility industry. Companies collate billing information, service interactions, visiting websites and other services to give customer a probability score and the target offers incentives to customers those who are predicted to be a higher risk of losing.

The article has been taken from SIM University [WniSIM], Singapore by Pui Mun Lee on ‘Use of Data Mining in Business Analytics to support Business Competitiveness’ Vol. 17, No. 2(https://www.cluteinstitute.com/ojs/index.php/RBIS/article/viewFile/7843/7904 ). In this paper, use of Data Ming in Business Analytics are been explained. Along with how these are used to support the use of Business Intelligence in e-businesses (George, Kumar & Kumar, 2015). The difference between data mining, business analytics and business intelligence is compared and contrasted in the article. And suggesting on how electronic businesses can data mining on enhancing the competitiveness in the market. The article discusses about the Business conduction in the information age. Data mining promises to harness the potential value of data present in the organization. The application of data mining and analytic tools helps in managing the insightful information. Here, Data Mining is used to describe different analysis techniques like statistics, artificial intelligence and machine learning that are programmed to scan a large amount of data in any database system. Business analytics are used to describe the entire function of applying skills and algorithms related to data mining and data collection methods (Wixom, 2014). It is said that for e-business the ability to have real-time identification of customer segments, credit risks and other fraudulent is critical for business competitiveness, increasing business risks. Technologies and Increasing data resources are expected to drive a growth in business analytics and data mining. With increase of applications of data mining in an exponential manner, some competitive advantages are indulged and the ability to sieve through massive amount of data is initiated by data mining. Identification of relevant patterns becomes a strategic tool to improve the key areas like customers, operations and supply chains.

Database mining is the process of mining for implicit, formerly unidentified and potentially essential information from large amount of data. The privacy and security of user information has become significant policy for public anxiety from decades. However, rapid technological changes and the rapid growth of internet electronic commerce and the development of sophisticated techniques of collecting, analyzing and use of personal information is now a major factor for both public and government (Rusu, Triantafyllidis & Kremers, 2015). The field of data mining is gaining determined reorganization on the availability of large amount of data that are easily collected and stored via various channels which contains personal information. Ethics in Data mining has serious implications and the experts of data mining techniques must act responsibly  for making aware of the ethical issues that may affect their particular applications.

Importance of Data Quality, Privacy, and Security Measures

The enormous potential to revolutionize people’s aspect by the predictive power of Big Data has made possibilities for data breaching of security and privacy threats on big data. With increase in the involvement of people, big data security is in great concerns. If a sophisticated software component ignores the security then the infrastructure is further targeted for the potential attack (Willis III, 2013). Consumers here demand a better level of security and privacy approach through the services applied in terms and conditions. But one should be aware of this heightened security because such securities can provide legitimate excuses to collect more private data and may hurt the ethical value of a person. Big data could be helpful for enhancement of one’s privacy by allowing more information to the leveraged and improving the quality of intelligence on the potential attack. There are several other security concerns about Big Data (Swan, 2013). Some organizations eagerly wait for the target deliverance of the personal information and track down the online move. Big Data makes easier access to the data and makes it easier to track the data with lesser expense and more advanced analyzing techniques (Ryoo, 2017).

Digitalization has radically transformed our lives. The mobile services, internet, massive data collection and the analytics applied to these as to propel digital revolution. There are inter-related facets to this digital revolution (Provost & Fawcett, 2013). But the main interest is on the increased capabilities and storage of data and the analytical models. Big Data rapidly advances, increases the area of human existence from life insurance to the sentencing of criminal. Organization the policies that can be adopted is the SANS Codes of ethics. And not compromising in data classification policies, data protection standards, social media and reputation of the organization. The ethical consideration centrally includes human rights and other rights too like: The Right to privacy and science; Understanding human rights; Rights in conflict (Sekaran & Bougie, 2016). There is a dynamic relationship between Big Data and Human rights for an example the preliminary purpose of EHRs is to store patient information that are used in clinical purpose. But ERD data can be pooled together linking to another database (Ryoo, 2017). As data mining tends to be one of the most rapidly changing disciplines with various new technologies and concepts which are still under development, researchers, academicians and professionals in the discipline, hence needs access to the most current information about the issues, concepts and technologies trending in the field (Kedar et al., 2013).

Applications of Data Mining in Business Optimization

Social implications of Data Mining and Information Privacy: Interdisciplinary Framework and Solutions gives a critical source of information related to the issues emerged and solutions in data mining, influencing the political and socioeconomic factors.

The crucial establishment of safe and secure network is as follows:

1. To Protect Company’s Assets- For development of procedure and policies the organizations addresses security requirements. Protection of organization’s assets and liability addresses the ethical responsibilities (Tasioulas, 2017).
2. For Competitive advantage- Development of an effective security system for network and giving the organization a competitive edge (Imtiyaj, 2015).
3. Attract customers to the firm’s products, which means boosting sales and profits
4. Make employees want to stay with the business, reduce labor turnover and therefore increase productivity
5. Attract more employees wanting to work for the business, reduce recruitment costs and enable the company to get the most talented employees
6. Attract investors and keep the company’s share price high, thereby protecting the business from takeover (Tasioulas, 2017).

Example for Difficulty with access control: Hadoop is a collection of software components. Allows the processing of large amount of data in distributive computing infrastructure. Hadoop has a basic security features suitable for systems although its access control mechanism was not designed for large scale adoption (Ryoo, 2017).

Example for Consumer demand drives security and privacy: Big Data analytics engine picks out malicious emails with pinpoint accuracy hence ideal world does not have to get worries about fraudulent emails like phishing.

Example for Big Data-used against company’s privacy: Big Data drastically improves the effectiveness of fraud detection and insurance company starts questioning coverage to consumers on basis of big-data profiles.

Conclusion

Increase in data resources tends to drive a growth in business analytics and thus data mining. Businesses are getting to realize the application of data mining with the competitive edge. The emergence of Big Data plays a vital role on scientific and technological innovation generations which are both potential benefits and grave risk. In order to secure these benefits and minimizing the risks, organizations engage in some ethical thinking. The ability to use of data mining to sieve through massive amount of data and identification pattern hence becomes a strategic tool for improving key areas of the business such as customers, operations and supply chains. Thus, Business Intelligence and Data Mining works hand in hand for the development of knowledge based industry.

References

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