Big Data Vs Data Mining – An Easy Guide In Just 4 Points


Big Data vs Data Mining – Is this the first time you hear these two words “data mining” and “big data”? If you are not familiar with Executive Information System (EIS) it is highly probably you have rarely come across these two terms. Executive Information System is also known as an executive support system is a category of management support system that supports and facilitates decision-making needs and senior executive information. Big data and data mining are related to the use of large data sets to handle the reporting of the database that serves different businesses. Read on further to know what is big data, what does data mining mean, and the differences between big data and data mining. 

  1. What is big data?
  2. What is data mining?
  3. Difference between big data and data mining
  4. Big Data Vs Data Mining

1) What is big data?

Big data is an assortment of data that is huge and voluminous and growing exponentially with time. None of the traditional data management tools can store or process big data efficiently due to its large size and complexity. Big Data can be of three types – structured, unstructured, and semi-structured. Structured big data refers to any easily storable data, accessible and processed in a fixed format form. Any data whose structure and form are unknown is termed unstructured data while semi-structured data is data containing characteristics of both structured and unstructured data. 

2) What is data mining?

Data mining is the process and practice of searching a large amount of stored data to discover trends and patterns going beyond simple analysis. Another name for data mining is knowledge discovery in data (KDD). In simpler words, data mining can be termed as a process of extracting useable data from a larger set of any raw database. This is done to establish relationships and identify patterns to solve problems through data analysis. It is mainly used in machine learning, statistics, and artificial intelligence.

3) Difference between big data and data mining

Data mining and big data differ from each other as two separate concepts describing interactions with expanded data sources. Yet both big data and data mining are related and fall under the kingdom of business intelligence. Business intelligence encompasses data analysis intending to reveal trends, insights, and patterns. Big data is generally referred to as a concept or as an item, but on the other hand, data mining is contemplated as more of an action. 

4) Big Data Vs Data Mining

  • Meaning: Data mining is the process of discovering useful and interesting patterns and relationships in large databases. Big data refers to large and diverse sets of information from several sources growing rapidly.
  • Focus: Data mining mainly emphasises lots of details of data while big data emphases on lots of relations between data.
  • View: Data mining is a close-up view of the database, while big data is a big picture of the database.
  • Nature: Data mining expresses what is the data about; on the other hand, big data asks the questions of why in the data.
  • Volume: Data mining can be used for both – big and small data. Big data itself refers to a large number of datasets.
  • Definition: Data mining is a technique to analyse the data, while big data is a whole notion than a precise term.
  • Categories/ data types: Data mining can be classified into structured, relational, and dimensional data. Big data can be categorised into structured, unstructured, and semi-structured data.
  • Analysis: Data mining focuses mainly on predictions, statistical analysis, and discovery of business factors on a small measure while big data mainly focuses on predictions in data analysis, and discovery of business factors on large measure.
  • Purpose: Data mining is mainly used for strategic decision making, while big data is used for predictive measures and dashboards.
  • Scope: Data mining is manual, along with being automated in nature, while big data is only automated because the composition of huge data is difficult.
  • Set: Data mining is a subset of big data, while big data is the superset.


Both the concepts of big data and data mining exist in the same sphere under business intelligence. Big data is analysed, reviewed, and mined resulting is the gain of business intelligence. Both concepts serve the same data-driven insights. These tools help you understand your business procedures, decision-making processes, and further increase productivity and financial yield. While big data is a powerful asset for the business data mining is the manager that provides beneficial results.

Some examples of real-world big data include discovering consumer shopping patterns and habits, personalized marketing, fuel optimisation for the transport industry, predictive inventory ordering. Examples of data mining include E-commerce sites. Data mining is not exactly dependent on big data; it can be done on any database, while big data is completely dependent on data mining. After reading the above article, we hope you understand what is big data, what data mining means, the differences between big data and data mining, and increasingly use it in your business.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 


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