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CASE STUDY OF DATA MINING
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A case study in Data Warehousing and Data mining Using the SAS System. Data Warehouses
The drop in price of data storage has given companies willing to make the investment a tremendous resource: Data about their customers and potential customers stored in "Data Warehouses." Data warehouses are becoming part of the technology. Data warehouses are used to consolidate data located in disparate databases. A data warehouse stores large quantities of data by specific categories so it can be more easily retrieved, interpreted, and sorted by users. Warehouses enable executives and managers to work with vast stores of transactional or other data to respond faster to markets and make more informed business decisions. It has been predicted that every business will have a data warehouse within ten years. But merely storing data in a data warehouse does a company little good. Companies will want to learn more about that data to improve knowledge of customers and markets. The company benefits when meaningful trends and patterns are extracted from the data. What is Data Mining?
Data mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.
Data mining derives its name from the similarities between searching for valuable information in a large database and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find where the value resides.
What Can Data Mining Do?
Although data mining is still in its infancy, companies in a wide range of industries - including retail, finance, health care, manufacturing transportation, and aerospace - are already using data mining tools and techniques to take advantage of historical data. By using pattern recognition technologies and statistical and mathematical techniques to sift through warehoused information, data mining helps analysts recognize significant facts, relationships, trends, patterns, exceptions and anomalies that might otherwise go unnoticed.
For businesses, data mining is used to discover patterns and relationships in the data in order to help make better business decisions. Data mining can help spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty.
Specific uses of data mining include:
• Market segmentation - Identify the common characteristics of customers who buy the same products from your company.
• Customer churn - Predict which customers are likely to leave your company and go to a competitor.
• Fraud detection - Identify which transactions are most likely to be fraudulent.
• Direct marketing - Identify which prospects should be included in a mailing list to obtain the highest response rate.
• Interactive marketing - Predict what each individual accessing a Web site is most likely interested in seeing.
• Market basket analysis - Understand what products or services are commonly purchased together; e.g., beer and diapers.
• Trend analysis - Reveal the difference between typical customers this month and last.
• Data mining technology can generate new business opportunities by:
Automated prediction of trends and behaviors:...
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