Traditionally, analysts have performed the task of extracting useful information from recorded data, but the increasing volume of data in modern business and science calls for computer-based approaches. As data sets have grown in size and complexity, there has been a shift away from direct hands-on data analysis toward indirect, automatic data analysis using more complex and sophisticated tools. The modern technologies of computers, networks, and sensors have made data collection and organization much easier. However, the captured data needs to be converted into information and knowledge to become useful. Data mining is the entire process of applying computer-based methodology, including new techniques for knowledge discovery, from data.[4]
Data mining identifies trends within data that go beyond simple analysis. Through the use of sophisticated algorithms, users have the ability to identify key attributes of business processes and target opportunities.
Although data mining is a relatively new term, the technology is not. Companies for a long time have used powerful computers to sift through volumes of data such as supermarket scanner data to produce market research reports. Continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy and usefulness of analysis.
The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user. Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g. rule based systems) and opaque in others such as neural networks. Moreover, some data mining systems such as neural networks are inherently geared towards prediction and pattern recognition, rather than knowledge discovery.
Metadata, or data about a given data set, are often expressed in a condensed data mine-able format, or one that facilitates the practice of data mining. Common examples include executive summaries and scientific abstracts.
Data mining relies on the use of real world data. This data is extremely vulnerable to collinearity precisely because data from the real world may have unknown interrelations. An unavoidable weakness of data mining is that the critical data that may explain the relationships is never observed. Alternative approaches using an experiment based approach such as Choice Modelling for human generated data may be used. Inherent correlations are either controlled for or removed altogether through the construction of an experimental design.
Recently, there were some efforts to define a standard for data mining, for example the CRISP-DM standard for analysis processes or the Java Data Mining Standard. Independent of these standardization efforts, freely available open-source software systems like RapidMiner and Weka have become an informal standard for defining data mining processes.
It used to be a lot more difficult to obtain information of what customers were buying and what the trends were but now with new ERP technology all business processes are integrated and it has become very easy to track and analyze exactly what and when consumers are doing. ERP systems make information very easily accessible because so much data is stored within one massive database. Web-based marketers make use of technology known as “cookies” which are small data files stored on your computer that tells them who accessed the site and when.