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  • Apoorva Pachori

Data Mining vs. Data Analysis

“It matters not what the data says, but what it is that you do with the data.” - Master Oogway, probably.



Data holds a zillion possibilities. But, you can only realize the potential of these letters and numbers once you process them and turn them into something meaningful. This is where data mining, data analysis and even data exploration come into place.


Are data mining and data analysis the same thing? And what is data exploration? They all sound similar.


In this article we will explore data mining vs data analysis and even explore a dataset. Let's get started!


Data analysis vs Data Mining

Data Mining and Data Analysis


What Is Data Mining?


When you read the term ‘mining’, you probably think of someone in a hard hat, chipping away at rocks in a mine, searching for something. Data mining isn’t that different. When you mine data, you aim to ‘discover’ hidden patterns and data within a large dataset. Data mining is the process of collecting raw data and turning it into something useful.


Today, data mining is an essential part of business decision-making. Several industries such as retail, finance, healthcare, transportation, telecommunication, and e-commerce, use automated data mining techniques to generate insights from heaps of data. The data is stored in bulk and then processed with different data mining techniques in order to gather insights.


Data is analyzed and metadata - data about data - is created in classification analysis. Strange values are identified in outlier detection. Similar data is grouped in a process called cluster analysis. Relationships between data are detected in associate rule learning. Finally, statistical methodologies such as regression analysis can be applied to determine the relationship between data fields.


Examples of Data Mining


In the retail industry, data mining helps in customer segmentation. Data mining tools can identify the characteristics of target customers and segment them into distinct groups. Then, companies can devise sales and marketing strategies for each segment.


Data mining also helps in building predictive intelligence models for fraud detection in the banking sector. Businesses can run data mining algorithms through vast samples of fraudulent and non-fraudulent reports, and build models that can identify fraudulent and non-fraudulent transactions.



Data Mining Vs Data Analysis


Isn’t data analysis the same as data mining? While they are similar and the terms are sometimes used interchangeably, data analysis is not the same as data mining. Data mining can be considered a precursor to data analysis. Once all of the data is mined, then it can be a source for a data analysis study.


Data analysis is an extensive process that moves through several iterative phases.


  • First, data analysts identify a problem statement or a question they want to answer. Then, they start collecting data and building up datasets.

  • Before these datasets can be analyzed, they need to be cleaned. Empty and incomplete fields are removed, data is verified and validated, and the data structure is formatted and standardized.

  • Then, analysts perform a data exploration before starting the actual data analysis process. Within this stage, data mining tools come into the picture. They are used to discover patterns within databases.

  • Data visualization software is also used to transform the processed data into an easy-to-understand graphical format.

Based on the business needs and the initial problem statement, data analysis can be:


  1. Descriptive analysis: To understand what happened.

  2. Diagnostic analysis: To understand what happened, and why.

  3. Predictive analysis: To predict what is likely to happen in the future.

  4. Prescriptive analysis: To decide what to do in order to achieve a specific outcome.


Data Exploration vs Data Analysis


Data exploration, data exploring, or exploratory data analysis is the first step in data analysis.


Before data analysts can make a deep dive and understand patterns, trends, and anomalies in data, they perform data exploring as an ‘initial review.’ Data exploration is more superficial than data analysis, and can be done manually, or with simple tools like MS Excel or Gigasheet. Analysts may even conduct data exploration in data mining operations.


Exploring Data With Gigasheet


Everyone can benefit from analyzing data. However, not everyone knows how to code or use sophisticated tools.


If you are not a coder, it doesn’t mean you should miss out on the power of data.


Gigasheet is an easy-to-use, no-code data analysis solution. If you know how to use MS Excel or Google Sheets, you can start analyzing datasets with Gigasheet right now! All you need is a free account and some data.


Let Us Explore A Marketing Dataset


This dataset from Kaggle is perfect for data exploration. It contains records of 2206 customers of a company with data on their customer profiles, product preferences, and campaign performance.