A3.4.2 Primary Objectives of Data Warehouses in Data Management and Business Intelligence Notes

Primary Objectives of Data Warehouses in Data Management and Business Intelligence

1. Data warehouses play a critical role in modern data management and business intelligence.
2. They are designed to store, organize, and analyze large volumes of historical data, enabling organizations to make informed decisions.

Key Objectives of Data Warehouses

1. Efficient Data Storage and Retrieval
2. Support for Business Intelligence and Analytics
3. Integration of Diverse Data Sources
4. Historical Data Preservation

Core Features of Data Warehouses

1. Append-Only Data
2. Subject-Oriented Data
3. Integrated Data
4. Time-Variant Data
5. Non-Volatile Data
6. Optimized for Query Performance

Append-Only Data

1. Data warehouses are designed to be append-only, meaning data is added but not deleted or modified.
2. Purpose:
   1. Preserve Historical Data: Ensures a complete record of past transactions and events.
   2. Enable Trend Analysis: Allows organizations to analyze changes over time.

Subject-Oriented Data

1. Data in a warehouse is organized around specific subjects or domains, such as sales, customers, or products.
2. Purpose:
   1. Focused Analysis: Enables targeted analysis of specific business areas.
   2. Simplified Queries: Reduces complexity by eliminating the need for complex joins across multiple tables.

Integrated Data

1. Data warehouses consolidate data from multiple sources into a unified format.
2. Purpose:
   1. Consistency: Ensures data is consistent and free from duplicates or errors.
   2. Comprehensive Analysis: Provides a holistic view of the organization by combining data from various systems.

ETL Process (Extract, Transform, Load)

1. Extract: Data is collected from various sources.
2. Transform: Data is cleaned, filtered, and formatted to match warehouse structure.