What is the main purpose of Slowly Changing Dimensions (SCD) in data management?

What is the main purpose of Slowly Changing Dimensions (SCD) in data management?

• A. To increase data storage capacity
• B. To manage changes in dimensional tables based on rate and type of change
• C. To improve data redundancy
• D. To create permanent data records

Answer: (B)

The main purpose of Slowly Changing Dimensions (SCD) in data management is to manage changes in dimensional tables based on the rate and type of change. As organizations analyze historical data and track changes over time, they often need to keep a record of how dimension data changes, such as customer information or product details. SCD provides methodologies to handle different types of changes: 1. **Type 1**: Simply overwrites the existing data with the new values. This is useful when you do not need to track the history of changes. 2. **Type 2**: Creates a new record for every change, allowing for a complete history of changes to be maintained. This is essential for analytics that require historical data insights. 3. **Type 3**: Stores the previous value as well as the current value in the same record. This method allows tracking of limited historical data without the complexity of a full history. By implementing Slowly Changing Dimensions, organizations can ensure they have accurate and historical data representations that reflect the real-world changes in the data over time, which is vital for reporting, analytics, and decision-making processes. This method effectively supports business intelligence efforts by providing the necessary context for understanding how dimensions evolve.

The main purpose of Slowly Changing Dimensions (SCD) in data management is to manage changes in dimensional tables based on the rate and type of change. As organizations analyze historical data and track changes over time, they often need to keep a record of how dimension data changes, such as customer information or product details.

SCD provides methodologies to handle different types of changes:

1. Type 1: Simply overwrites the existing data with the new values. This is useful when you do not need to track the history of changes.

2. Type 2: Creates a new record for every change, allowing for a complete history of changes to be maintained. This is essential for analytics that require historical data insights.

3. Type 3: Stores the previous value as well as the current value in the same record. This method allows tracking of limited historical data without the complexity of a full history.

By implementing Slowly Changing Dimensions, organizations can ensure they have accurate and historical data representations that reflect the real-world changes in the data over time, which is vital for reporting, analytics, and decision-making processes. This method effectively supports business intelligence efforts by providing the necessary context for understanding how dimensions evolve.