Data mining is a process used by companies to turn raw data into useful information. The term is used a lot in computer science and comes from the English language, which translated into Portuguese means data mining.
Generally, data mining sometimes called data or knowledge discovery, is the process of analyzing data from different perspectives and the summary of them into useful information. Information that can be used to increase yields, reduce costs, or both.
When you use the software to look for patterns in large amounts of data, businesses can learn more about your customers and develop marketing strategies more effective, as well as increase sales and decrease costs.
The software of data mining is one of a series of analytical tools that aggregates and analyzes data. It allows users to analyze data from different dimensions and different angles, categorize it, and summarize the relationships identified.
Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
However, for the process to be successful, the data mining will depend on the gathering effective data and storage, as well as the processing of the computer.
Real-world examples of Data Mining
The supermarkets are well-known users of techniques of data mining. Many of them offer loyalty cards free of charge for the customers, which give them access to reduced prices, which are not available to non-clients.
These cards make it easier for the stores to crawl who is buying what, when they are buying and at what price.
The shops can then use these data, after you analyze them, for various purposes, such as: offering discounts targeted to the purchasing habits of the clients, to decide when to put items on sale and when to sell them at full price.
Another hypothetical example would be:
A chain of supermarkets which uses the ability of the software data mining to analyze patterns of local purchasing. And through that to discover that when men buy diapers, Thursdays, and Saturdays, for example, they also tend to buy beer.
Other analyses show that these buyers typically do their weekly grocery shopping on Saturdays and Thursdays, buy only a few items. So through this, the retailer can conclude that these customers buy the beer for the next weekend.
The supermarket chain can use this information in various ways, in order to increase their sales. For example, changing the local exposure of the beer to near the location of the diapers.
This way, they could tell certainly with the sale of diapers for the price without discounts on Thursdays.
Data are any facts, numbers or texts that can be processed by a computer. Currently, organizations accumulate a vast and growing amount of data in different formats and different databases. This includes:
- Operational data or transactional, such as: sales, costs, inventories, payroll and accounting;
- Data is not operating, such as industry sales, forecast data and macro economic data;
- Meta data: data about data itself, such as the database design or logical definitions of the data dictionary.
The patterns, associations, or relationships among all this data can provide information. For example, the analysis of data on point-of-sale and sell transactions may yield information about when and where the products are being sold.
The information can be converted into knowledge about historical patterns and future trends. For example, summary information on retail sales of supermarkets can be analyzed in light of promotional efforts to provide knowledge of the buying behavior of the consumer.
Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts.
Advances drastic in data capture, processing power, transmission, and storage capabilities are enabling organizations to integrate their various databases into the “data warehouses”.
The data store is defined as a process of centralized management and data recovery.
Storage of data, such as data mining, is a relatively new term although the concept has existed for years. The data store represents a vision of maintaining a central repository of all organizational data.
The centralization of data is needed to maximize user access and analysis. Impressive technological advances are making this vision a reality for many companies.
In addition, dramatic advances in data analysis software are allowing users to access these data freely. The data analysis software is what supports data mining.
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