Data Mining and Big data are not the same terms, but both of them linked to each other in some ways. Both Big Data and Data mining are in trend these days. Many people opting their career in these fields. Moreover, they are earning a pretty good amount. Big data is means a collection of large datasets that can’t be handled easily. Here specific tools are required to handle this kind of data. On the other hand, Data Mining means to find important and relevant information from huge chunks of data. In this blog, we will discuss the top differences between Big data and data mining.
What are Big data and Big data analytics?
Big data is in simple terms is extremely large chunks of the data. This kind of data can be structured and unstructured. It is completely dependent on these terminologies
- Volume- It includes the quantity of the data
- Variety-It includes data types- either structured or unstructured
- Value- It includes how much valuable data is extracted.
- Veracity- Quality and trustworthiness of the data
- Velocity- It refers to data that is growing at fast speed.
Big data analytics- The process of analyzing larger data sets to extract useful information.
Various Examples of this information include:-
- Market trends,
- Customer preferences,
- Hidden patterns and unknown correlations.
The analytics findings mainly lead to innovative revenue opportunities, operational efficiency, various marketing benefits.
What is Data Mining?
Data mining is also known as data discovery or knowledge discovery. In simple terms it is the process of examining data from different perspectives and summarizing it into useful information.Different businesses used this application to increase their revenue and reduce operational expenses.
Also Read- Big Data Vs Data warehouse | Differences between big data and data warehouse
Check out the differences between Big data and data mining
| Features | Big Data | Data Mining |
| 1. Definition | The data sets which are too difficult to handle, which are in large chunks is defined as big data | Data mining is the process of discovering information from hidden data |
| 2. Objective | The main objective is to targets the data relationship. | It mainly targets an analysis of data to extract useful information. |
| 3. Volume | It involves a large volume of data | It can take data of any volume either less or more |
| 4. Business factors | It works on business factors in large terms | It involves business factors in small terms |
| 5. Main focus | Here the main focus is on relationships between data | Here the main focus is on details of the data |
| 6. View | It is the biggest picture | It is the closest view |
| 7. Data Types | It includes structured, semi-structured and unstructured data | It includes relational, structural and dimensional data |
| 8. Analysis | It is mainly used for data analysis | It is mainly used for statistical analysis |
| 9. Expression of data | It explains why data | It explains what of data |
| 10. Dependency | Big data is dependent on data mining | Data is not dependent on the Big data |
| 11. Used for | It is primarily used for Dashboards and predictive measures. | It is primarily used for strategic decision-making purposes. |
| 12. Storing | Storing of data in big data is the challenge | Here data can be small or large it is not tricky here |
| 13. Data extraction | The Data Extraction process is time-consuming | The data extraction process is not time-consuming |
| 14. Outcomes | Dashboards and predictive measures | Mainly used for strategic decision making |
| 15. Database techniques | Distributed database type techniques | Centralized database techniques |
| 16. Properties | Big data is an asset | Data mining is the handler |
| 17. Uses | Helps in identifying the root causes of failures and issues in real-time,
Fully understanding the potential of data-driven marketing. Market basket and Improving customer engagement and increasing customer loyalty.
|
Helpful in predicting future, moreover it is used to identify customer habits, helps in making decisions and for
· Quick fraud detection:
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Conclusion
As we saw, Big data simply means a large amount of data. All the big data solutions depend on the presence of data. It can be considered as a mixture of Business Intelligence and Data Mining. Data mining makes use of various kinds of tools and software on Big data to get particular outcomes. In short, big data is the asset and data mining is the controller or we can say the manager of that is used to provide profitable results. In this blog we have discussed the top 17 differences between Big data and data mining. If you are having any doubt, feel free to ask me in the comment box.
Also Read- Differences between RDBMS and Hadoop | RDBMS vs Hadoop
