By Britney Pereira | 4th March 2021

Data Mining VS Data Analysis

As time passes by we generate more and more data daily, considering the last two years we generated around 90% of data, and it is growing rapidly.

Data takes care of most aspects of our life. It is important to know how to properly analyze and then categorize your data to know how to use the data properly.

Correctly categorizing your data will give you a better understanding of how and where you can use it.

To give you a better perspective, when you access the internet, visit websites, the time you spend on these websites is the data that you generate. Using relevant tools can help companies and organizations process this data and get better insights about who is visiting their websites and how they can enhance their profits.

What is Data Mining?

Data mining is a very systematic process of identifying, digging, and discovering hidden information and patterns from huge data sets. The process does not provide insights.

What is Data Analysis?

Data analysis is a statistical process that helps the analyst to conclude the data. The process is generally used to provide insights that can be used to improve their business decisions. Because of this process businesses identify the core area they need to work on and need to improve.

How Does Data Mining and Data Analysis Come Together?

Data mining concentrates on precise patterns from large databases.

Data analysis will organize the unprepared data for useful insights and business decisions.

Most data mining studies are done on structured data whereas data analysis is done on both structured, semi-structured, or unstructured data.

One of the goals of data mining is to make data more usable.

Data analysis helps to prove a hypothesis or in taking business decisions.

In data mining, the area of expertise includes machine learning, database, and statistics.

The area of expertise in data analysis includes statistics, mathematics, and subject knowledge.

A data mining specialist is a data analyst but he has extensive knowledge of hands-on coding. They build algorithms to identify structure and patterns in the data.

Since data analysis involves preparing the raw data, cleansing the data, transforming, and then finally presenting it in the form of based visualizations. This is not a job of a single person and hence an entire team has to do it.

Data mining does not need visualizations such as bar charts or graphs, whereas all these visualizations are the bread and butter of data analysis because without a good representation of data every effort that is put into the analysis will not be satisfactory.


Data mining and data analysis have been around for a long time. Some people term them as one while most of them have understood the difference between them. Both data mining and data analysis are essential to be done perfectly.

People have also learned the difference between the two fields and appreciate the areas and also respect the boundaries of the two fields.

Data mining and data analysis both are equally important in this data-driven world. Both these fields also require people with a good skillset.

While the data analysis tools are becoming simpler, data mining will require expertise because results can be difficult to describe and you will need to verify them using other methods.

To function data mining and data analysis needs a strong data warehouse. This just means that more attention needs to be given to the aspects of ETL as well as the analytic capacity.

The final result of the data is good as long as the data that is being fed to the system is good.

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