Covering the essentials of business intelligence, explore the features & functions for an overview.
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Data Mining is the process of uncovering hidden information or patterns from large data sets. Data is extracted from large databases using statistics, artificial intelligence, decision trees, machine learning, etc.
The primary goal of the data mining process is to transform the extracted data into an understandable structure. Data mining finds relationships within data, often which are difficult or impossible to see without it.
The relationships discovered can be used to analyze past trends within a business and ultimately be used to make predictions for the future. Data mining turns raw data into readable and useful information. Data mining processes provide quick solutions to questions that are too complex without it. Data mining techniques work well with already existing systems and new products as well.
The data mining process predominantly involves 6 phases: problem definition, data exploration, data preparation, modelling, evaluation, and deployment. The selected data goes through these 6 phases and only relevant information is produced as the outcome.
An example of data mining is how sales data is analyzed for a store. If all the transactions for each customer were stored in a database, simply looking at that data would be meaningless. With data mining, however, that data can be analyzed and segmented to show the sales trends over time, by age range and region, and be used to predict how the company will grow, helping the store decide which items to promote or put on sale.
Though data mining is the buzz in the market, it presents certain privacy issues. For example, when a system wants to predict the future trend of a product, it might intrude into the privacy of individuals using that product and gather their sensitive and personal information.