| Purpose |
Stores and manages structured data from various sources for reporting and analysis. |
Extracts hidden patterns, trends, and knowledge from data, often for predictive analysis. |
| Focus |
Focuses on data storage, organization, and retrieval for business intelligence. |
Focuses on the analysis and discovery of valuable insights and knowledge from data. |
| Data Type |
Typically deals with structured and historical data. |
Works with structured, semi-structured, or unstructured data, including historical and real-time data. |
| Activities |
Involves data consolidation, integration, and transformation. |
Involves data exploration, pattern recognition, clustering, and prediction. |
| User Interaction |
Provides a platform for querying and reporting on data for decision-making. |
Employs algorithms and models to uncover hidden patterns and make predictions. |
| Tools |
Uses tools like SQL, ETL processes, and reporting tools for data management. |
Uses machine learning algorithms, data mining software, and statistical tools for analysis. |
| Output |
Generates reports, dashboards, and visualizations for business users. |
Generates patterns, insights, and predictive models for data-driven decision-making. |
| Goal |
Supports historical analysis, summarization, and decision support. |
Aims to discover new knowledge, trends, and predictive insights from data. |
| Process |
Involves data extraction, transformation, loading (ETL), and querying. |
Involves data preprocessing, modeling, evaluation, and interpretation. |
| Timeframe |
Focuses on long-term data storage and retrieval. |
Focuses on real-time or batch processing for immediate or future insights. |
