Data Mining Steps

What is Data Mining?

Data Mining can be defined as the process that enables a user to find or discover various patterns and trends in a vast and large amount of data from different data sources. Those data sources can be data warehouse, the databases, the web and other online repositories where data can be found.

Why do Businesses need Data Mining?

In today’s world, Big Data has become a household name. Big data refers to a big volume of data that can be structured and unstructured. Big data can be examined for insights that helps businesses to make better decisions and plan strategic business moves.

Because of Big Data, the process of data mining has become more significant and crucial. Therefore, for the large amounts of data, the usage of data mining process becomes inevitable or unavoidable. The data mining process is beneficial because it helps in obtaining the important and relevant data from raw data which enables the businesses to keep record of the data and take action accordingly.

The data mining process is a key element for businesses. This enables them to make better decisions and obtain relevant information with the help of observing different patterns and trends in various datasets.

Data Mining Steps:

Data Mining is a vast and extensively used process in today’s world. Nonetheless, it involves different steps. The precise data mining steps are difficult to obtain as it mainly depends upon the scope of the problem and on the practitioner that how they aggregate the steps.

However, there are some steps that are commonly used in every data mining process. Following are those steps:

Identify the business goals:

The first and foremost step is the identification of the business goal. This is one of the most important steps in Data Mining. There are some specific things that a user needs to keep in mind such as the problem in business that needs to be solved, the costs i.e., reduce maintenance or operational costs, retention, customer acquisition and so on.

Once these points are taken care of, then the users should move on to the next step.

Identify the data mining goals:

This is also a very important step even though it does not involve any technical aspects of data mining. In this step, the user has to transform the Business Goals obtained in the first step to certain Data Mining goals. In simpler words, the user will now need to discover the data sets needed for the process, the specification of those data sets and so on.

Prepare and Preprocess:

In the previous step, the data was collected and the process of identification was done. Now, in this step, the technical aspects of the process will start. In this step, the collected data will be cleansed by using the process of data cleaning and integrated using multiple data sources. After integration, the data will be ready.

Modeling:

In this step, the appropriate algorithm for the given task has to be chosen. In addition to that, the tools for the process will be chosen in order to increase the productivity. Those tools will help to create the model and assess initial results. Modeling consists of various other steps also.

Train and Test:

In this step, the results are evaluated and the models are tested on various sample datasets. After this, the results are reviewed. The process can continue iterating until consistency of the results are satisfactory for the users.

Verify and Deploy:

This is the last step in the data mining steps. In this step, the final model is verified and deployed. This step from the process resolves the information into values, by using basic numerical counts, direct value comparison, group comparison and so on to pick out the specific elements and then interpret and report the results.