Big Data vs Data Mining

Data mining and big data are known to be two different things. While it is true that both of them relate to the use of large datasets for handling data that serves our purpose and yet they are Data mining vs big data two different terms in the aspect of operation they are used for. We can say that big data refers to a collection of large datasets and the best example to explain that is datasets in Excel sheets which are too large to be handled easily. On the other hand, data mining is referred to as an activity of going through a very large chunk of data with the aim of looking for relevant or pertinent information. You can also checkout best online Data science & Big Data courses.

• Difference between Big Data and Data Mining
  • What is Big Data?
  • Importance of Big Data
  • Challenges faced in Big Data
  • What is Data Mining?
  • Importance of Data Mining
  • Challenges faced in Data Mining
• Head to Head Comparison between the Two
• Key Difference between the Two
• Final Thoughts

Difference between Big Data and Data Mining:

What is Big Data?

In easy words, all such data that is equal to or greater than 1 TB is known as big data. All the analysts predict that there will be 5,200 GBs of data on every person in the world by 2020.

Importance of Big Data:

Here the importance of big data does not refer to how much data there is but what we are getting out of it. We can analyze such data to reduce cost and time and make smart decisions out of it. If you’re looking to advance your data skills, these Top certifications in Big Data can help you get started.

Challenges faced in Big Data:

Below are the challenges that can be faced while dealing with big data.

• Storing such a huge amount of data in an effective manner is a little difficult.
• How are we able to process and extract valuable information from these huge piles of data in a given timeframe?

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What is Data Mining?

Data mining or we can also say “Knowledge Discovery of Data” refers to the extraction of knowledge from a very large amount of data that is, big data. Data mining is mostly used in statistics, machine learning, and artificial intelligence. Thus, considered as the step of the “Knowledge discovery in databases”.

Importance of Data Mining:

Data mining helps greatly in credit ratings, fraud detection, and targeted marketing where we find out which types of transactions are likely to be a fraud. This is done by checking the past transactions of a user and checking customer relationship like which customers are loyal from the start. And which customers are most likely to leave for other companies. You can also find out learn Artificial Intelligence & Machine Learning.

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Challenges faced in Data Mining:

Many challenges are expected in data mining and some of them are mentioned below.

• How to mine different types of knowledge in databases?
• Handling noise and incomplete data.
• Handling complex types of data.
• Protection of data-security.
• Integrity and privacy.

Head to Head Comparison between the Two

1. Focus

Data Mining:

• Big data usually focuses on lots of details of a data.

Big Data:

• On the other hand, data mining focuses on the different relationships between data.

2. View

Data Mining:

• Data mining is considered to be a close up view of data.

Big Data:

• Contrary, big data is known to be the bigger picture of data.

3. Data

Data Mining:

• Data mining aims to express what the data is all about.

Big Data:

• If we talk about big data, then it tends to express the “WHY” of data.

4. Volume

Data Mining:

• Can be used in small and big data as well.

Big Data:

• Strictly refers to large amount of data sets.

5. Data Types

Data Mining:

• It deals with structured data, relational and dimensional database.

Big Data:

• Whereas big data deals with structured, semi-structured, and unstructured data.

6. Analysis

Data Mining:

• In data mining, statistical analysis is done and it focuses on prediction and discovery of business factors on small scale.

Big Data:

• Whereas in data mining, data analysis is usually done and it focuses on prediction and discovery of business factors on a very large scale. You can also checkout data analysis for social scientists.

7. Results

Data Mining:

• Data mining is mainly for strategic decision making.

Big Data:

• Big data is for dashboards and predictive measures.

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Key Difference between the Two

Here are the key differences between big data and data mining.

• Big data is a term that is usually referred to a large amount of data whereas if we talk about data mining then it refers to deep drive into the data for extracting the key knowledge from a small or large amount of data. You might also be interested in Spark Certification.
• The main concept that is behind data mining is to dig deep into analyzing the different patterns and relationships of data which can be then used in Artificial Intelligence and predictive analysis. But know that, the main concept in big data is the source, variety, and volume of data and how it can be stored and then process the amount of data. Analyzing the big data properly in making business decisions play a crucial part in determining the organization’s growth.

Final Thoughts

By reading all the above explanation on data mining and big data, we can say that data mining is not depended on big data at all. It is because it can be done on the small and large amount of data. But on the other hand, big data surely depends on data mining. It is because if we are not able to find the importance of large amount of data then know that this data is of no use to us. So if you have any confusion regarding what big data and data mining is and what are the key differences between them, then this article is what you need to read.