What is data mining

The techniques of data mining answer different types of questions and needs.

Among the main data mining techniques are descriptive analyses and predictive analyses.

Descriptive analyses allow for the examination of data by identifying the reasons that have led to the current state of a company, which can be either success or failure:

• Clustering: a statistical analysis technique used to identify similar objects, dividing data into different groups. They are divided based on characteristics they share, such as purchasing behavior, preferences for one product over another. This type of technique allows the company to better define its target audience.

As for predictive analytics, it is used with the aim of predicting possible future outcomes, estimating a potential response of a group of consumers to an advertising campaign:

• Regression: a technique that measures the relationship between one variable and a set of data variables that have been detected based on various tests, for example in the field of medicine, it measures the probability of survival of a patient based on their diagnostic data;

Neural networks: software capable of identifying patterns or schemas and, therefore, making predictions.

Thanks to these analyses, data mining is able to trace information about consumers regarding their needs, thanks to data tracking and analysis software.

Data mining tools

For an effective marketing strategy, data mining follows a path with various characteristics, namely:

• Identify the goal you want to achieve;
• Select useful data;
• Clean data and processing: separation between valid and useless data and, therefore, final selection of essential information;
• Transformation: understanding well if the format in which the data is represented is compatible with various software or if it needs to be converted first;
• Data mining: Choosing the best software to obtain suitable information;
• Evaluation of Results: it is assessed whether the goal has been achieved;
• Evaluation of Results: it is assessed whether the results are in line with one's objectives.

The information obtained from software reveals the usefulness of data mining in identifying hidden associations, useful for adopting targeted marketing strategies and effectively finding one’s ideal target.

In which sector is data mining applied?

The sectors where data mining is applied are numerous and can be grouped into macro-categories, namely: 

• Marketing;
• Economics and finance;
• Science;
• Information and communication technologies (ICT);
• Statistics;
• Industry.

As far as marketing is concerned, the main applications of data mining are:

• Clustering: identification of buyer types who share purchasing habits and socio-demographic characteristics;
• Customer retriever: analyzing customer behavior;
• Market basket analysis: the products and services that are commonly purchased together.

In the financial sector, data mining is applied to:

• Fraud detection: being able to trace fraudulent behaviors;
• trend forecasting;
• analysis on interactions between financial markets.

In the scientific field , data mining is used in:

• medicine and biology;
• meteorology;
• astronomy.

In the statistical field, however, data mining speeds up demographic analyses by deriving information.

Thanks to the efficiency of data mining, detecting anomalies and discrepancies in a dataset will be a piece of cake.

Data mining can be useful not only for data protection but also to identify malware that could compromise a customer database.