Who needs SCDs, anyway?

Not implementing slowly changing dimensions (SCDs) in a data warehouse can lead to several risks, including:

• Loss of Historical Accuracy: Without SCDs, changes in dimension attributes may be overwritten, resulting in the loss of historical data. This can affect the ability to perform accurate trend analysis over time¹.
• Inaccurate Reporting: If historical changes are not tracked, reports generated from the data warehouse may not reflect the true state of the data at previous points in time, leading to potentially misleading business insights².
• Inability to Track Changes: SCDs allow for tracking the evolution of dimension attributes. Without them, it’s impossible to see how changes in data affect key performance indicators, which is crucial for strategic decision-making².
• Compromised Data Analysis: The lack of historical context can compromise the depth and reliability of data analysis, as analysts won’t have access to a complete picture of the data’s evolution¹.
• Reduced Data Quality: Over time, the quality of data can degrade if changes are not properly managed and recorded, making it difficult to rely on the data for accurate analysis¹.

Implementing SCDs is crucial for maintaining the integrity and usefulness of a data warehouse, especially for businesses that rely on historical data for analytics and decision-making processes¹².

(1) Implementing Slowly Changing Dimensions (SCDs) in Data Warehouses. https://www.sqlshack.com/implementing-slowly-changing-dimensions-scds-in-data-warehouses/.
(2) Slowly Changing Dimensions (SCD): 4 Types & How to Implement – ThoughtSpot. https://www.thoughtspot.com/data-trends/data-modeling/slowly-changing-dimensions-in-data-warehouse.
(3) Mastering Slowly Changing Dimensions (SCD) | DataCamp. https://www.datacamp.com/tutorial/mastering-slowly-changing-dimensions-scd.