Difference Between Data Mining and Data Profiling

 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. 

Data Mining vs Data Profiling

The main 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 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 Between Data Mining and Data Profiling

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. The purpose of understanding complex datasets is similar in every field of science, business, and engineering. 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 the correct data and preparing it 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-

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-

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

• 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. 
• Data mining includes methodologies that are computer-based to extract some useful information. But data profiling involves examining raw data from any given dataset. 
• Data mining is there to mine the data for crucial information to solve problems. On the other hand, the goal of data profiling is to form a knowledge base of information. 
• 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. 
• 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. 

Conclusion 

Data privacy is one of the crucial tasks that everyone should do every time. Nowadays, people keep their data on- either laptops or mobile phones as they have to share everything online. A single company keeps information on hundreds of its customers while making sure that their identities are safe. 

They do this so that people can trust them and the reputation of the company does not fall. If some private information leaks, then get ready for some bad things coming your way. Many government corporations spend thousands of dollars every year to keep their data safe and secure. 

An average person does not have a large amount of money to spend, but he can follow some steps to protect his data. Use a mail slot to avoid letting thieves steal anything. Also, use strong passwords for all your accounts. 

References

2. https://dl.acm.org/doi/abs/10.1145/2590989.2590995

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