Data Mining vs Data Analytics

Data mining and data analytics are related but distinct concepts within the broader field of data science. Here are the key differences between the two:

1. Purpose: Data Mining: Data mining is primarily focused on discovering patterns, trends, and relationships within large datasets. Its main goal is to extract useful knowledge from data. Data Analytics: Data analytics, on the other hand, involves analyzing data to gain insights, answer specific questions, and make data-driven decisions. It focuses on interpreting data to derive actionable insights.

2. Process: Data Mining: Data mining involves the process of exploring and analyzing large datasets to uncover hidden patterns and relationships. It often includes techniques such as clustering, classification, association rule mining, and anomaly detection. Data Analytics: Data analytics encompasses a broader range of activities, including data collection, cleaning, analysis, interpretation, and visualization. It involves using statistical and analytical methods to extract insights from data and present them in a meaningful way.

3. Scope: Data Mining: Data mining is primarily concerned with discovering patterns and relationships within data, often with the goal of making predictions or identifying trends for future use. Data Analytics: Data analytics has a broader scope and can include various types of analysis, such as descriptive analytics (understanding what happened), diagnostic analytics (why it happened), predictive analytics (what will happen), and prescriptive analytics (what actions to take).

4. Application: Data Mining: Data mining is commonly used in fields such as marketing, finance, healthcare, and retail for tasks like customer segmentation, fraud detection, market basket analysis, and churn prediction. Data Analytics: Data analytics is applied across various industries and functions, including business intelligence, operations management, marketing, human resources, finance, and supply chain management, to support decision-making and improve business performance.

In summary, data mining focuses on discovering patterns and relationships within data, while data analytics involves analyzing data to gain insights and make data-driven decisions. While data mining is a subset of data analytics, data analytics encompasses a broader range of activities and applications beyond just mining data. In a world that is now overwhelmed by technical buzzwords; it is important to know the difference, lest leaders make decisions on half-baked analyses (data mining) as opposed to giving enough time and resources to complete the data due diligence.