“Data mining” is the term commonly used for sifting through a database (internet, records or files) to find data which would be useful in identifying the purchase-related behavioural patterns of the consumers of a particular company. To make it even simpler, data mining involves drawing up patterns in information variables and units, organising them into coherent forms, analysing and drawing inferences from them. Sometimes, tasks such as data mining and data entry services are outsourced. Data entry services, especially, are sent to India. Anyway, to get back to the topic at hand, the techniques of data mining can vastly vary from complex to simple. And, not all the techniques are uniformly geared towards discovering or realising similar things. Through data mining, it is possible for people to analyse and process huge amounts of data and establish them into patterns with respect to groups and demographics. Types of data mining are as follows:
1. Cluster mining: Cluster-style data mining or data detection is a kind of pattern-recognition tool which can be used to detect patterns hidden in big data supersets. With the help of this technique, common data characteristics emerge, which are then used to group the data into categories and cross-categories.
2. Anomaly Detection: The second one in the list is “anomaly detection”. This one mainly aims to discover any abnormalities or irregularities in data. It is mostly heavily used in fields related to forensics or weather forecasting.
3. Regression: This one is a technique which chiefly aims to accurately forecast the outcomes of particular sets of analyses. Huge sets of existing variables are mostly used for this purpose. Future patterns of user engagement and prices of property can be predicted using this technique.
Reading up to this point, the benefits of data mining would be more or less clear to the reader. But to sum them up in clear-cut points, below is the list:
Data mining is used in almost every kind of industry, and especially in the banking, forensic, consumer service and finance-related industries. In these places, data mining is put to use in order to create risk models for mortgages and loans.
In the field of marketing, data mining is mainly used to assist in conversions, striving to maximise consumer satisfaction and also to create well-researched and smart advertising campaigns. Basically, studying customer needs based on demographics is what data mining is all about. This is achieved by recalling past data and looking at the consistency/irregularity of records over time.
In case of governmental organisations (for example, tax bodies), data mining is used to detect irregularities in citizen behaviour.
In fields like manufacturing, data mining is basically geared at improving and enhancing the comfort quotient of the products. For example, if you’re engaged in the furniture business, then you’re going to utilise data mining to find out what kind of padding on your sofa your customers prefer.
So these are some of the uses of data mining.