Data Mining Assignment Example | Topics and Well Written Essays - 1000 words

Data Mining - Assignment Example Raw data that is stored in the business corporate database increase day by day and as time passes. This is from various numerous credit and cash transactions in the company which is measured in gigabytes or terabytes. This data is usually stored in the centralized database; the raw data does not provide much information. Data warehousing Companies have decided to store their data and invest in a tremendous resource. The information and data on their potential and current customers is stored in this data houses as they are becoming part of the technology. These warehouses are used in consolidation of data which is located in the desperate databases. This data houses store stores usually stores large quantities of data on categories for easier and faster retrieval and interpretation by users. They also enable business managers and executives to store and retrieve large amounts of transactions, and the data required in responding to markets and make more informed business ideas and deci sions. Better decision making When the best and available data are collected, data analysis are performed and the most appropriate predictive model is created which results in better understanding on the customers reactions and behaviors towards the marketing programs and reasons for leaving the business. To add on this, various models may results in increased funds success, late payments and reduced bad loans. The good predictive analytics aids businesses in the use of information of previous events to project on new future projects and a good outcome (Olson & Delen, 2008). These are pattern based predictions which are based, on the interrelations between elements of data that cannot be seen on a spreadsheet analysis which leads to a good decision and accurate information. Data mining is a powerful tool which makes it good for business analytics, and the models utilizing procedures to bring about deserving results in customer service. It is also easy to determine which good have be en sold and the resulting reactions from customers with increased abundance of data and information, and the growing interrelationship in departmental functions. The processing of customers response can also be time consuming and demanding, labor intensive and expensive in terms of the company staff and this makes its predictive analytic activity to enhance the discovery of products sold to customers. Web mining This technique involves data mining processes such clustering, prediction and the modeling of the differences that analyzes the results of intermediate action, in addition to this, apart from data mining, web mining is a dependent of a real time system that invokes targeted offers on behalf of a process which can be up selling and customer retention and requirement analysis from the customers. This also supports individual marketing of customers based on horizontally collected data in numerous data sources as various transactions occurs. In web mining, real time data process es are identified across all transactions with customers and hence an instance feedback is obtained and hence is the best tool to prevent anomalies and fraud. Clustering This is the method of which data items are grouped in data mining according to their logical relationships on consumer preferences this data is mined to prove market segments or consumer affinities (Han & Kamber, 2006). The most important