The following table highlights the main differences between Data Mining and Big Data:
Data Mining
Data mining is the process of discovering meaningful new patterns and relationships by analyzing a large amount of data using pattern recognition technologies and statistical techniques.
Big Data
Big Data refers to the collection and analysis of extremely large data sets that cannot be handled and analyzed using traditional methods. It aims to uncover hidden insights and information that traditional approaches may miss.
Purpose: Data mining focuses on finding patterns, anomalies, and correlations within a large data store, while Big Data aims to discover valuable insights from diverse, complex, and massive data sets.
Use Cases: Data mining finds applications in various industries such as finance, airlines, healthcare, telecommunications, media, e-commerce, education, and IoT. Big Data serves as the foundation for machine learning and artificial intelligence applications worldwide.
Data Perspective: Data mining provides a close view of the data, answering the “what” about the data. On the other hand, Big Data focuses on understanding the “why” behind the data, exploring the reasons and relationships.
Data Volume: Data mining deals with both large and small data volumes, while Big Data specifically refers to extremely large-scale data sets.
Data Analysis: Data mining focuses on analyzing the data to extract meaningful information, while Big Data is used to identify relationships and connections among the data.