Data mining essentially is the art of analysing data from various different perspectives with the aim of extracting the right information. Data mining is mainly used to extract information which in turn can aid in improved output and better results..

Data mining involves six common classes of tasks:

Anomaly detection (Outlier/change/deviation detection) –The identification of unusual data records, that might be interesting or data errors that require further investigation.
Association rule learning (Dependency modeling) – Searches for relationships between variables. This is sometimes referred to as market basket analysis.
Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
Classification – is the task of generalizing known structure to apply to new data.
Regression – attempts to find a function which models the data with the least error.
Summarization – providing a more compact representation of the data set, including visualization and report generation.

Why do we need data mining?

Any business will always deal with huge amount of data and it is important to be sure that the data can be extracted for apt purposes. At PATEL, we have special data experts on board who are aware of the best processes by which they can extract data out for different uses.