As the borders of data science get bigger, conducting a substantial analyzes becomes tiresome. Recent times of data science was full of new concepts and elements. Samples sets getting bigger, tools becoming more comprehensive, and result variables are getting multivariate; alas, all that complexity brings inefficient estimators, noisy sampling, and all kinds of non-normal statistical problems. However, there is a brand-new tool coined to data science in order to cure the problems of the data: data mining. Although the pre-processed data is complex, which is the reason of data mining, the processed data also has its humble share of complexity.

What is Data Mining?

As the name suggests (not literally), subjected data goes under some
processes to reach the core of the data i.e. the most useful thing in dataset. One
way of doing this, for example, is to erase the noisy parts of the data set.
However, the procedure itself is not as easy as it sounds. One must answer some
initial questions in order to be freed from the noisy part of data set such as
*what
is important in this data set? Would I use this segment later? What was the
pattern
I discovered before?* etc. Unfortunately, questions are limitless and some of
them
are beyond the human capacity to proceed. Therefore, the mining itself is done
through **algorithms, machine learning,** and **complex statistical
modelling.** In fact,
these three pillars are what comprises the **data mining** i.e. *using
algorithms,
machine learning, and statistics to make a data more useful. *

Why is it important?

The importance of data mining comes from its explanation. As mentioned
here (Buraya LİNK), ** the less is more now.** That is, having more data in
the first
hand may look like it will be more beneficial; but, if you don’t have the tools to
analyze or even process them, then it is only noise. However, if you have the
abovementioned tools for data mining, then you can:

**Cure all the noise of the data set****Understand the relevant point of the sample****Take data-driven decisions easily**

Hence, even if you own the full data on any subject, **you cannot
have
the knowledge if it is unstructured.** Having knowledge starts from the
**processed** and
**structured data.**

Benefits of Data Mining

The sole benefit of data mining comes from the ability to differentiate the useful parts and noisy part of the data set. In fact, the numerous benefits can be listed as the process became widespread. Specific modelling on data mining may uncover or at least give a clue on historical patterns, the conversion rates of the sales, and all kinds of behavioral variables.

A more specific model may give insights on future patterns as machine
learning and algorithms endorse the formation of a **predictive modelling.**
Thus, the
data mining would collect the most concentrated data to prepare it for the
information formation process. The further iterations to the process would help a
** prediction model** to emerge where from

**fraud detection to improving operational efficiency**can be done.

Data Mining and Predictive Analysis

While data mining is a tool for finding trends and patterns that are
invisible to human eye through machine learning and algorithms, the predictive
analysis is the implication that arose from that refined data, in which business
knowledge is used together to **discover future patterns.** Those estimation are
leaning
on the patterns and trends that are concentrated in data mining process; thus, what
is going to be next would be answered easily. Therefore, the data mining and
predictive analysis are co-existing in the data science in order to reveal the
patterns in the data and make use of those patterns to make estimation about future.
Those two will absolutely yield a good ground for any examination, where **data
mining
creates the best foundation for predictive analytics. **