Introduction
Data mining and Data Analytics has become an integral part of modern economy. Advancement in technology has revolutionized nearly every aspect of human life. We live in the most fascinating era of technology where we are experiencing rapid technological advancement, global connectivity, ubiquitous computing and mobile revolution, automation and artificial intelligence and disruptive innovations and startups. In all these advances, the most common and powerful thread is ‘data’.
Data, now , has become one of the most significant tools to excel in the modern business environment. Every organization is going after the data which are exclusive and unique. The reason behind is that data is power and data makes you a step ahead of your competitors. Despite such great significance of data, individuals and businesses often get confused in understanding two very important concepts i.e. data mining and data analytics. In this article, we will explore the differences between these two topics which are often considered the same.
Definition
Data mining is simply the activity of extracting knowledge, patterns or information from a larger cluster of data using various techniques such as statistical analysis, machine learning, clustering, decision trees, neural networks, text mining and pattern recognition.
Data analytics is exploration, analysis, and interpretation of data to derive meaningful insights and support decision-making. It is more focused towards implementation and execution of data rather than extraction. Data mining is one of the components in data analytics.
Differences between Data Mining and Data Analytics
Data mining and data analytics are two very common and distinct concepts that are closely related but are different. Mining deals with activities for uncovering hidden patterns and associations with bulk data sets whereas data analytics involves activities for interpreting data for informed decision and meaningful insights. Data mining and data analytics have some basis of differences, Some of most prominent differences are as below:
Difference Table
| Basis | Data Mining | Data Analytics |
| Definition | It basically deals with the process of sorting large data, finding correlation and relationships, discovering patterns etc. | Data Analytics is a broader term. It involves the entire process from collecting data to deriving meaningful insights to support decisions. It includes data mining. |
| Objectives | The main objective of data mining is to extract knowledge from data that may not be easily evident. | The main objective of data analytics is to explore and analyze the data for understanding behavior, trend, and approach to support some kind of decision making. |
| Data Sources | Both data mining and data analytics share common data sources. The application of data source could be different. For instance, data mining uses the ‘transaction data’ to uncover patterns and associations for identifying recurring items in customer purchase, relationship between product purchases and customer demographics. Data analytics uses ‘transactional data’ for sales forecasting, product pricing and customer segmentation. | |
| Techniques | Data mining uses statistical analysis, machine learning algorithms, clustering, classification and association rules for uncovering patterns and relationships. | Data analytics techniques involve data visualization, descriptive statistics. Predictive modeling, and prescriptive analytics. |
| Exploration | Data mining emphasizes the exploration on discovering the hidden patterns and relationships | Data analytics go beyond hidden patterns and apply the insights to derive business decisions, problem solving and other business actions. |
| Context | Data mining is used with academic research, scientific discovery, and specialized application in anomaly detection. | Data analytics is used across industries for business intelligence performance analysis, behavior prediction and optimization. |
| Output | Data mining outputs patterns, relationships or rules. | Data analytics outputs insights, visualizations, reports and recommendations. |