Understanding Slowly Changing Dimensions (SCD) for Data Analysts

As a data analyst, dealing with evolving datasets is a common challenge, especially in the world of data warehousing and business intelligence. Slowly Changing Dimensions (SCD) help manage changes in dimensional data over time. SCDs address the question: How do you handle changes in attributes like customer addresses, product prices, or employee roles without losing historical data?

This blog will explain what SCDs are, why they are essential, and how you can manage them with practical examples.

What are Slowly Changing Dimensions?

A dimension in data warehouse context, is a structure that categorizes facts and measures to help users answer business questions. For instance, in a sales database, the "Customer" dimension contains attributes like customer name, address, and contact details. Over time, these attributes might change (e.g., a customer might move to a new city).

Slowly Changing Dimensions (SCDs) refer to tracking and managing changes in dimension data over time without compromising the integrity of historical data.

Types of Slowly Changing Dimensions

There are several methods for handling changes in dimensional data, with the most common being SCD Types 0, 1, 2, and 3—each offering its own advantages and trade-offs. Let's talk about each one of them now.

1. SCD Type 0: Retain Original

In SCD Type 0, the data never changes once it's entered. This method is used when historical accuracy is crucial, and the data should not be altered once entered.

Example:

You have a product dimension table where once the product launch date is entered, it’s never updated or changed, even if errors or changes occur later.

2. SCD Type 1: Overwrite

In SCD Type 1, the old data is overwritten with new data. This is suitable when historical changes are irrelevant, and only the latest information is required.

Example:

Consider a customer address. If a customer moves, the address in the database is updated to the new one, and the old address is lost.

3. SCD Type 2: Track Historical Changes

In SCD Type 2, both the current and previous states of data are stored, creating a complete history of changes. This method is useful when you need to track changes over time and maintain historical records.

Example:

When a customer changes their address, the old address is kept as a historical record, and a new row is created for the updated address with effective start and end dates.

In this method:

• We insert a new row for the updated address.

• The old row is marked as historical by updating the enddate and setting iscurrent to 0.

4. SCD Type 3: Store Previous and Current Values

SCD Type 3 stores both the current and one previous state of the data. It’s a limited historical record, suitable for tracking only the most recent change.

Example:

In a scenario where you only need to store the current and previous salary of an employee, a separate column can be used to store the historical value.

In this example, the currentsalary is updated, and the old value is moved to previoussalary.

Why Should a Data Analyst Care About SCDs?

For a data analyst, it’s critical to ensure that reports and insights are accurate, especially when dealing with data that changes over time. Understanding SCDs allows you to:

• Maintain historical accuracy.

• Track changes in customer behaviors or product performance over time.

• Answer questions related to trends and historical analysis effectively.

Real-World Use Cases of Slowly Changing Dimensions for Data Analysts

Imagine you’re analyzing customer retention. You want to know how many customers placed an order after moving to a new address. If you use SCD Type 1, you might lose track of customers who moved. But with SCD Type 2, you can analyze how customer behavior changed before and after their relocation, which could provide valuable business insights.

As a data analyst, effectively managing Slowly Changing Dimensions (SCDs) is crucial for preserving both the accuracy and depth of your analyses. By mastering the different SCD types (Type 0, 1, 2, and 3), one can make informed decisions on how to track changes in key data, ensuring that your reports maintain historical integrity without sacrificing relevance. This approach empowers analysts to deliver insights that are not only precise but also adaptable to evolving data, providing a more complete picture to drive better decision-making.