Difference between Data Mining and Big Data


Big Data represents the vast amount of data that can be structured, semi−structured, and unstructured sets of data ranging in terms of terabytes. In contrast, Data Mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through a large amount of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. Data mining utilizes tools like machine learning, visualization, statistical models, etc. to extract the useful data from the Big Data.

Read this article to find out more about Data Mining and Big Data and how they are different from each other.

What is Data Mining?

Data mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through a large amount of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. It is the analysis of observational datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner.

Data mining can include the use of several types of software packages including analytics tools. It can be automated, or it can be largely labor−intensive, where individual workers send specific queries for information to an archive or database.

Generally, data mining defines operations that contain relatively sophisticated search operations that return focused and definite results. For instance, a data mining tool can view through dozens of years of accounting data to find a definite column of expenses or accounts receivable for a specific operating year.

What is Big Data?

Big Data refers to the vast amount that can be structured, semi−structured, and unstructured sets of data ranging in terms of terabytes. It is complex to process a large amount of data on an individual system that's why the RAM of this computer saves the interim computation during the processing and analyzing. When we try to process such a huge amount of data, it takes much time to do these processing steps on a single system. Also, our computer system doesn't work correctly due to overload.

Big data sets are those that outgrow the simple type of database and data handling structure that were used in previous times when big data was more highly−priced and less feasible. For instance, sets of data that are too high to be simply handled in a Microsoft Excel spreadsheet can be defined as big data sets.

Difference between Data Mining and Big Data

The following table highlights all the major differences between Data Mining and Big Data −

Data Mining Big Data
Data mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through a large amount of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. Big Data is an all−inclusive term that defines the collection and subsequent analysis of significantly huge data sets that can include hidden data or insights that could not be found using traditional methods and tools. The amount of data is quite a lot for traditional computing systems to handle and analyze.
The purpose is to find patterns, anomalies, and correlations in a large store of data. The purpose is to discover insights from data sets that are diverse, complex, and of massive scale.
Use cases include financial services, airlines and trucking companies, the healthcare sector, telecommunications and utilities, media and entertainment, e−commerce, education, IoT, etc. It acts as a base to machine learning and artificial intelligence applications worldwide.
Data Mining is the closest view of the data because its answers 'what' about the data. Big Data expresses 'why' of the data.
Data mining contains both large voltage and low volume data. Big data contains only large volume data.
Data mining is used for analyzing the data to extract some meaningful information. Big data is used for identifying the relationship among data.

Conclusion

From the above comparison, we may conclude the most significant difference between Data Mining and Big Data that is Data Mining is a tool used for data analysis, whereas Big Data is a whole concept that acts as a base for machine learning and artificial intelligence.

Updated on: 20-Dec-2022

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