Difference Between Big Data and Data Mining
What is Big Data?
Big Data refers to a huge volume of data that can be structured, semi-structured and unstructured. It comprises of 5 Vs i.e.
- Volume: It refers to an amount of data or size of data that can be in quintillion when comes to big data.
- Variety: It refers to different types of data like social media, web server logs etc.
- Velocity: It refers to how fast data is growing, data is exponentially growing and at a very fast rate.
- Veracity: It refers to an uncertainty of data like social media means if the data can be trusted or not.
- Value: It refers to the data which we are storing and processing is worth and how we are getting benefit from this huge amount of data.
Big data can be analyzed for insights that lead to better decisions and strategic business moves.
How much data does it take to be called Big Data?
Usually, data which is equal to or greater than 1 Tb known as Big Data. Analysts predict that by 2020, there will be 5,200 Gbs of data on every person in the world.
Example: On average, people spend about 50 million tweets per day, Walmart processes 1 million customer transaction per hour.
Why is Big Data Important?
The importance of Big Data does not mean how much data we have but what would you get out of that data. We can analyze data to reduce cost and time, smart decision making etc.
- Storing such a huge amount of data efficiently.
- How do we process and extract valuable information from this huge amount of data within a given timeframe?
What is Data Mining(KDD)?
Data Mining also known as Knowledge Discovery of Data refers to extracting knowledge from a large amount of data i.e. Big Data. It is mainly used in statistics, machine learning and artificial intelligence. It is the step of the “Knowledge discovery in databases”.
Business and government share information that they have collected with the purpose of cross-referencing it to find out more information about the people tracked in their databases.
The components of data mining mainly consist of 5 levels, those are: –
- Extract, transform and load data into warehouse
- Store and manage
- Provide data access (Communication)
- Analyze (Process)
- User Interface (Present data to user)
Need for Data Mining
Analyze relationship and patterns in stored transaction data to get information which will help for better business decisions.
Data mining helps in Credit ratings, targeted marketing, Fraud detection like which types of transactions are like to be a fraud by checking the past transactions of a user, checking customer relationship like which customers are loyal and which will leave for other company.
We can do 4 relationships using data mining:
- Classes: It is used to locate the target
- Clusters: It will group the data items to logical relation
- Association: Relationship between data
- Sequential Pattern: To anticipate behavioral patterns and trends.
Challenges in Data Mining
- Mining different types of Knowledge in databases
- Handling noise and incomplete data
- Efficiency and scaling of data mining algorithms
- Handling relational and complex types of data
- Protection of data security, integrity, and privacy
Head To Head Comparison Between Big Data vs Data Mining (Infographics)
Below is the Top 8 Comparision between Big Data vs Data Mining
Key Difference Between Big Data vs Data Mining
Below is the difference between Big Data and Data Mining are as follows
Big Data and Data Mining are two different concepts, Big data is a term which refers to a large amount of data whereas data mining refers to deep drive into the data to extract the key knowledge/Pattern/Information from a small or large amount of data.
The main concept in Data Mining is to dig deep into analyzing the patterns and relationships of data that can be used further in Artificial Intelligence, Predictive Analysis etc. But the main concept in Big Data is the source, variety, volume of data and how to store and process this amount of data.
Analyzing of Big data to give a business solution or to make a business definition plays a crucial role to determine growth.
We can say that Data Mining need not be depended on Big Data as it can be done on the small or large amount of data but big data surely depends on Data Mining because if we are not able to find the value/importance of a large amount of data then that data is of no use.
Big Data vs Data Mining Comparision Table
|Feature||Data Mining||Big Data|
|Focus||It mainly focusses on lots of details of a data||It mainly focusses on lots of relationships between data|
|View||It is a close-up view of data||It is the Big Picture of data|
|Data||It expresses what about the data||It expresses Why of the data|
|Volume||It can be used for small data or big data||It refers to a large amount of data sets|
|Definition||It is a technique for analyzing data||It is a concept than a precise term|
|Data Types||Structured data, relational and dimensional database.||Structured, Semi-Structured and Unstructured data (in NoSQL)|
|Analysis||Mainly Statistical Analysis, focus on prediction and discovery of business factors on small scale.||Mainly data analysis, focus on prediction and discovery of business factors on large scale.|
|Results||Mainly for strategic decision making||Dashboards and predictive measures|
Conclusion – Big Data vs Data Mining
As we saw, Big data only refers to only a large amount of data and all the big data solutions depends on the availability of data. It can be considered as the combination of Business Intelligence and Data Mining.
Data mining uses different kinds of tools and software on Big data to return specific results. It is mainly “looking for a needle in a haystack”
In short, big data is the asset and data mining is the manager of that is used to provide beneficial results.
This has been a guide to Big Data vs Data Mining, their Meaning, Head to Head Comparison, Key Differences, Comparision Table, and Conclusion. You may also look at the following articles to learn more –