Difference between Data Profiling and Data Mining

 Data profiling and data mining sound similar but they are actually different. This article will explain the difference between data profiling and data mining that you should know.

A collection of data in the database is called a dataset. These data sets are in tabular format with rows and columns. The columns represent variables whereas rows represent values. For choosing datasets for application. It is important to understand what is a dataset and its related metadata. 

Two processes for this are Data profiling and Data mining. These are some heavy terms used in the world of data analytics and machine learning. But these two terms are often confused by people. Data profiling and data mining sound similar but they are actually different. This article will explain the difference between data profiling and data mining that you should know.

What is Data Mining?

Data Mining is a process in which patterns and relations within datasets are identified.  Then useful knowledge is derived from those datasets. This knowledge is then used for business intelligence. It is an interdisciplinary process which is based on data extraction, machine learning, statistics and database systems. It is used by scientists working on cancerous cells or sales teams working towards their goals. 

Data Mining is the process of applying computer-based methods and technologies to extract information hidden in the data. A larger number of statistical techniques are used to examine data to find answers to the questions in various fields.  

A large amount of raw data is evaluated and turned into knowledgeable information. Data mining is the search for valuable information from datasets. Then this information is used to discover patterns and relationships in the datasets. This process consists of many steps like data discovery, regression, clustering, visualization and much more. 

You should know that data mining and data analysis are two different things. Data mining uses statistical models and machine learning to discover hidden patterns. Whereas, Data analysis is used to test hypotheses and models on datasets. 

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What is Data Profiling?

Data Profiling is the process in which raw data is analyzed from data sets. Then statistics and data summaries are gathered from it. It is like extracting metadata from a given dataset. These metadata like statistics or relations among the columns of data can help understand the datasets. Data profiling can be done to any data whereas some profiling is type-specific. 

It’s a process of creating and examining data summaries. Data profiling results in deep insights into data. It examines the data to determine its legitimacy and quality. Companies can then benefit from this data. 

Data profiling is very different from data analysis. Data analysis is used to derive business information from the datasets. Data profiling derives information from the data to assess and discover relations in the datasets. It is helpful in understanding and preparing the data for integration and inspection.  

Let’s understand the difference between data mining and profiling more deeply. 

Difference between Data Profiling and Data Mining

Data Profiling Vs Data Mining 

Let’s start with understanding what Data Profiling and Data mining is.


In data profiling, the gathered information is analyzed. Then insights and statistics of the data are collected. It is very helpful for companies because this process assesses the quality of data and identifies problems in the data sets. Data profiling can be conducted using mean, mode, frequency, minima, maxima etc

Whereas, data mining is the process in which data patterns are extracted from a database. In this process, the raw data in the database is evaluated and transformed into useful knowledge. 

Both of these have different sets of processes as mentioned below.

Process: Data Profiling Vs Data Mining

Data Profiling uses analytical and discovery techniques for collecting summaries of data or statistics. A business analyst then analyzes this informative data. His focus is to determine if the data is matching the business intent or not.

In profiling, the data is prepared for cleansing, integration and inspection. 

Talking about data mining has two categories. Descriptive Data Mining and Predictive Data mining.

Descriptive data mining is in which new information is produced from the existing dataset. Predictive data mining uses variables in the dataset to predict unknown future values of other related variables. 

Purpose of Data Mining And Data Profiling

Data mining analyzes data for finding knowledgeable information. It includes a collection of effective data. Processing and segmenting that data using mathematical algorithms. This will predict future variables and datasets. The data is classified, clustered and summarized. These ideas can then be used for Business Intelligence.

In data, profiling information is derived from the data. The information quality is assessed and anomalies in the datasets are discovered. It will be helpful in distinguishing accurate information from the data. This process is very important since having accurate information is the key to success.


Yes, some techniques of data mining can be used for data profiling. In data profiling, you collect statistics and informative data in a summary. Whereas Data mining helps identify patterns in massive data sets. Data mining focuses on large datasets and data profiling supports it.

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