Important Differences between Data Mining and Data Processing

Data Processing

Also known as Data Warehousing is a technology that aggregates structured data from one or more sources in order to compare and analyze rather than transaction processing.

Benefits of Data Processing:

  • Consistent and quality data
  • Reduce in cost
  • Timely access of data
  • Improved performance and productivity

Data Mining

It is the process of extracting useful information, finding patterns and correlations within large data sets to identify relationships between data. Data mining tools also allow businesses to predict customer behavior.

Benefits of Data Mining

  • Direct marketing
  • Analyzing trends
  • Fraud Detection
  • Forecasting in financial markets

Data Mining

Data Processing

Definition It is the process of extracting important pattern from large datasets. It is the process of analysing and organizing raw data in order to determine useful information’s and decisions
Function It is used in discovering hidden patterns in raw data sets. In this all operations are involved in examining data sets to fine conclusions.
Data set In this data set are generally large and structured. Dataset can be large, medium or small, Also structured, semi structured, unstructured.
Visualization It generally does not require visualization Surely requires Data visualization.
Goal Prime goal is to make data useable. It is used to make data driven decisions.
Required Knowledge It involves the intersection of machine learning, statistics, and databases. It requires the knowledge of computer science, statistics, mathematics, subject knowledge Al/Machine Learning.
Models Often require mathematical and statistical models Analytical and business intelligence models
Known as It is also known as Knowledge discovery in databases. Data analysis can be divided into descriptive statistics, exploratory data analysis, and confirmatory data analysis
Output It shows the data tends and patterns. The output is verified or discarded hypothesis

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