Data profiling is usually done to assess a dataset for its uniqueness, consistency and logic. It cannot identify incorrect or inaccurate data values.

Data mining is the process of finding relevant information which has not been found before. It is the way in which raw data is turned into valuable information.

Data mining and data profiling are two distinct but related concepts in the field of data analytics. Here’s a breakdown of the key differences between them:

Objective:
- Data mining: The primary objective of data mining is to discover patterns, trends, correlations, or insights from large datasets to make predictions or uncover hidden relationships. It involves using various algorithms and techniques to analyze data and extract useful information.
- Data profiling: Data profiling, on the other hand, focuses on assessing the quality, structure, and content of data. Its goal is to understand the characteristics of the data, identify anomalies, errors, or inconsistencies, and assess data quality to ensure it is suitable for analysis or other purposes.
Techniques:
- Data mining: Data mining involves using techniques such as clustering, classification, regression, association rule mining, and anomaly detection to extract patterns and insights from data.
- Data profiling: Data profiling techniques include statistical analysis, data visualization, data summarization, and data quality assessment to understand the structure, content, and quality of the data.
Output:
- Data mining: The output of data mining is typically patterns, trends, models, or insights that can be used for decision-making, prediction, or optimization.
- Data profiling: The output of data profiling is a report or summary describing the characteristics of the data, including statistics, data quality metrics, and identified issues or anomalies.
Purpose:
- Data mining: Data mining is often used for predictive analytics, pattern recognition, customer segmentation, fraud detection, and other advanced analytics tasks.
- Data profiling: Data profiling is used to assess data quality, understand data structure, prepare data for analysis, identify data cleansing or transformation needs, and ensure data compliance with regulatory requirements.

In summary, while data mining aims to extract actionable insights and patterns from data, data profiling focuses on understanding and assessing the quality of the data itself. Both are essential components of the data analytics process, but they serve different purposes and utilize different techniques.