Data warehouse mining involves analyzing information from databases to extract useful insights. Companies use data warehouses to collect customer data and make informed business decisions. Retailers can predict customer behavior and estimate the impact of pricing decisions using data mining techniques like regression. Choosing the right data mining tool is crucial for effective analysis.
Data warehouse mining is the analysis of information held in one or more databases in order to make the information useful. These databases, or data warehouses, are a central repository for data. Companies aggregate the information they collect about their customers in a data warehouse. Once the information has been gathered, it is “mined” and from it useful information is extracted to produce information that can help the company make business decisions that will increase profits or reduce costs. Retailers often use data warehouse mining to analyze and predict the behavior of their customers.
For example, when a shopper goes to the supermarket and hands his frequent shopping card to the cashier, information about his purchases is collected and stored in the company’s data warehouse. A supermarket chain will have millions of data points on what people buy, when, how much and at what price. A store might know that 50,000 packages of frozen peas were sold last year, but that information alone isn’t particularly helpful. If the data warehouse extraction reveals, however, that 75% of those frozen peas were sold during the months that fresh peas weren’t available, or that 10% of the peas were sold in the two weeks leading up to the Thanksgiving, the company may be able to use that information to increase annual sales of frozen peas.
Companies can use data warehouse mining techniques to predict future sales. Data mining can also help them estimate the impact of storage and pricing decisions. At the supermarket, data mining could prevent stores from running out of frozen peas in the event of a low harvest of fresh peas in a given year.
Data mining regression is a data mining technique that is used to show what is likely to happen to a data value if something in the equation changes. Using the supermarket example, the regression predicts the level of sales of frozen peas if fresh peas increase in price. Regression takes historical data and applies a formula to it that predicts future behavior.
Businesses often use a data warehouse mining software application to collect and mine their data. The correct application is determined by how much data they have and what kind of analysis they want to do. Choosing the right data mining tool is critical to collecting and interpreting useful data.
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