Data Mining Applications in Business Intelligence: A Report

This report delves into the application of data mining and visualization within the context of business intelligence. It begins by defining data mining and its crucial role in transforming raw data into valuable business insights, emphasizing its significance in protecting sensitive customer information. The report outlines the core uses of data mining, including improved decision-making, effective marketing strategies, and customer behavior analysis. It highlights key data mining techniques such as classification, regression, clustering, and association rule learning, and demonstrates their application across various sectors, including retail, banking, and insurance. The report also references an article on an advanced inventory data mining system, illustrating practical implementations and the evolution towards data-driven management. Ultimately, the report concludes by underscoring the growing importance of data mining in driving business analytics and fostering a knowledge-based industry, emphasizing its symbiotic relationship with business intelligence.

Running head: DATA MINING AND VISUALIZATION FOR BUSINESS INTELLIGENCE
DATA MINING AND VISUALIZATION FOR BUSINESS INTELLIGENCE
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1DATA MINING AND VISUALIZATION FOR BUSINESS INTELLIGENCE
Introduction
In Business, privacy goes to the extent of protecting the data and information of a customer,
which are both sensitive and confidential. It makes sure that the IT systems are in compliance
with present privacy policy of the organization. The most crucial part in business is privacy and
thus the organizations must understand and know the means of this privacy concern. Data mining
delivers a vital role in the competitive advantages of an organization. In the data storage system
the data mining techniques has become crucial and there has been a potential use of data mining
in this field. Data Mining was introduced in the year 1990. It was 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.
Task 1: Data Mining in Business
Data Mining is the technique in which the company turns raw data and information into
useful information. By data mining in business, the organization can know more about their
customer and develop more effective marketing strategy. It basically depends on the computer
processing, data collection and data warehousing (George, Kumar & Kumar 2015). This helps in
keeping track of the customer’s buying record in the retail shops or in super markets, which
helps in decision making process of the customer’s liking and disliking and the marketing
technique of the shop. Data mining can be a cause for concern to prove certain hypothesis.

2DATA MINING AND VISUALIZATION FOR BUSINESS INTELLIGENCE
1. Summarizing in brief - Use of Data Mining in Business
The Data Mining is generally used to find the relationship and pattern that helps in
making better decisions in Business. This helps in determining and sorting the sales, trends and
for develops campaigns for smart marketing and hence predicts the loyalty toward the product of
the customers.
The Role of Data Mining in Business Optimization is that Data Mining helps to provide
competitive advantages in business. There are six primary techniques of Data Mining to analyze
data: Classification, Regression, Clustering, Association Rule Learning, Anomaly Detection and
Summarization. There are three main areas where data mining is applied successfully: Retail,
Banking and Insurance (Imtiyaj 2015). In Retail helps in improving the quality and costing of the
services, achieves better customer retention and satisfaction, enhancing good consumption ratios,
designing effective products, distributive policies and transporting. In Banking the data mining
includes segmentation of customers, profitability, and credit analysis, marketing, predicting
payment defaults, and transaction, even investment ranking, portfolios optimizing, cash
management and forecasting operations. Credit scoring, Customer segmentation and Customer
profitability are the main examples of this sector. In Insurance there are applications like fraud
detecting, retaining, risk factors identification and many more (Kasemsap 2015).
2. Article related to Data Mining in Business
The article has been chosen “An Advanced Inventory Data Mining System for Business
Intelligence.” By Q Zhou, B Xia, W Xue, C Zeng, R Han and T Li explaining the iMiners, that
have been developed for intelligent management system. From demand-drive the inventory
management system is improved to data-driven and hence addresses the challenges for complex
transaction process and Big Data.

3DATA MINING AND VISUALIZATION FOR BUSINESS INTELLIGENCE
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. Business Intelligence and Data Mining works hand in hand for the development of
knowledge based industry.

4DATA MINING AND VISUALIZATION FOR BUSINESS INTELLIGENCE
References
George, J., Kumar, V., & Kumar, S. (2015). Data Warehouse Design Considerations for a
Healthcare Business Intelligence System. In World Congress on Engineering.
Imtiyaj, S. (2015). Privacy Preserving Data Mining. transactions, 2(2).
Kasemsap, K. (2015). The role of data mining for business intelligence in knowledge
management. Integration of data mining in business intelligence systems, 12-33.
Zhou, Q., Xia, B., Xue, W., Zeng, C., Han, R., & Li, T. An Advanced Inventory Data Mining
System for Business Intelligence.