Data Mining vs Data Profiling: Difference and Comparison

 A collection of data in a database is known as a dataset. They are in a tabular format consisting of columns and rows. Every column constitutes a variable, while each row represents a value.

One of the basic requirements before picking datasets for any application is- to understand the dataset and its metadata. Two processes for this are- Data mining and Data profiling. 

Key Takeaways

  1. Data Mining is discovering patterns and relationships in large datasets, whereas Data Profiling is analyzing and assessing data quality, completeness, and consistency.
  2. Data Mining is used to extract useful insights and knowledge from data, while Data Profiling is used to identify data quality issues and potential data sources for analysis.
  3. Data Mining is an exploratory process, while Data Profiling is a preparatory process before data analysis.

Data Mining vs Data Profiling

The difference between data mining and data profiling is that- data mining is a process of collecting patterns from any given data. On the other hand, data profiling is the process of locating metadata from a dataset. In data mining, you apply a wide range of methodologies to extract information. While in data profiling, you analyze data to collect summaries. 

Data Mining vs Data Profiling

Data mining is the procedure of analyzing massive amounts of data to locate business intelligence. It helps companies to mitigate risks, seize opportunities and solve problems.

Data mining helps in finding answers for those questions in business that consume a lot of time manually. It uses a large number of statistical techniques to examine data.  

The process of creating and examining summaries of data is known as data profiling. It produces critical insights into any data. Companies can leverage this data to their advantage.

Data profiling looks through the data to determine its quality and legitimacy. Algorithms discover characteristics in a dataset, such as minimum, maximum, mean, and frequency. 

Comparison Table

Parameters of ComparisonData MiningData Profiling
DefinitionIt is a process of collecting patterns from any data. It is a process of finding metadata in any given dataset.
PurposeTo mine the data for solving problems. To form a base of information.
Task Classification, summarization, regression, estimation, and description.Picking statistics or summaries.
ToolsApache SAMOA and Rapid miner. Aggregate profiler and Talend open studio
WorkingExtraction of information through methodologies. Examining raw data.

What is Data Mining?

Data mining is the task of identifying correlations and patterns in large datasets to derive bits of knowledge. You can use this helpful information in several areas of Business Intelligence.

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The purpose of understanding complex datasets is similar in every science, business, and engineering field. In simple words, data mining is mining knowledge from data. 

You can use data mining in several areas of business. Some of the sectors are marketing and sales, healthcare, education, and product development. You can gain a profound advantage over your competitors if you use it correctly.

It enables you to learn about customers, increase your revenue, think of new marketing strategies and reduce costs. 

A data mining project starts by collecting and preparing the correct data for analysis. If the quality of data is poor, then do not expect any good results. Data miners must ensure that the quality of information is satisfactory.

They follow the basic steps to achieve reliable results-

  1. Understanding the business
  2. Understanding data
  3. Preparation of data
  4. Evaluation
  5. Deployment

An ample amount of data is pouring into businesses in several formats at unprecedented volumes. The success of a business depends on how effectively you discover insights and include them in processes and decisions.

Data mining authorizes a company to have a better future by understanding the present and past. 

What is Data Profiling?

Data profiling is the task of extracting raw data from any given dataset. The purpose of doing this is to collect statistics or summaries about the data. It is a set of activities that are there to determine the metadata of a dataset.

Metadata includes statistics or dependencies among columns which helps in understanding new datasets. 

You can use data profiling to derive useful information about the data and evaluate its quality. Through this, you can also discover anomalies in a dataset. It sifts through the information to determine its legitimacy and quality.

Analytical algorithms detect characteristics in a dataset, such as frequency, mean, maximum, and minimum. 

The applications in data profiling analyze a database by collecting information about it. There are three types of data profiling-

  1. Structure discovery – It helps in determining whether the data has a correct format and is consistent. To check the validity of the data, it uses basic statistics. 
  2. Content discovery – It mainly focuses on the quality of the data. You should process the data for formatting. 
  3. Relationship discovery – It identifies connections among datasets. 
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Nowadays, companies store a large amount of data in the cloud. So effective data profiling is the need of the hour. Cloud-based data allows businesses to keep petabytes of data. It is crucial to maintain standards. 

Main Differences Between Data Mining and Data Profiling

  1. The task of identifying correlations and patterns within datasets is known as data mining. On the other hand, the process of analyzing information from any dataset is called data profiling. 
  2. Data mining includes methodologies that are computer-based to extract some useful information. But data profiling involves examining raw data from any given dataset. 
  3. Data mining is there to mine the data for crucial information to solve problems. On the other hand, data profiling aims to form a knowledge base of information. 
  4. The tasks in data mining include regression, classification, summarization, description, and estimation. But the jobs in data profiling are analytical techniques and discovery for collecting statistics or summaries. 
  5. Some tools for data mining are Apache SAMOA and Rapid Miner. On the other hand, Aggregate profiler and Talend open studio are some tools for data profiling. 
References
  1. https://books.google.com/books?hl=en&lr=&id=vIqqDwAAQBAJ&oi=fnd&pg=PR1&dq=data+mining&ots=rrMiHNoZgo&sig=Ye_cPNBMden9NpA1YzsK9hQk7ws
  2. https://dl.acm.org/doi/abs/10.1145/2590989.2590995

Last Updated : 11 June, 2023

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16 thoughts on “Data Mining vs Data Profiling: Difference and Comparison”

  1. Cloud-based data storage has brought new challenges, and effective data profiling is indeed crucial in maintaining data standards and quality.

    Reply
  2. The way data mining and data profiling contribute to understanding complex datasets in a variety of sectors, including marketing and sales, is very interesting.

    Reply
  3. It’s fascinating to see how data mining and data profiling have become integral to various business sectors. The potential they offer is immense.

    Reply
  4. The step-by-step description of data mining and data profiling procedures is very insightful. It’s important to ensure data quality before further analysis.

    Reply

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