Select Distinct Logo Clear Background

Business Analytics Blog

A Deep Dive into Slowly Changing Dimensions (SCDs)

Explore Slowly Changing Dimensions

Source systems are a treasure trove of historical information. Importing data in a data warehouse is a a key part of the gathering data for your business. But what happens when the very information within changes over time? How do you maintain historic data integrity and keep accurate analysis over time?

The answer is Slowly Changing Dimensions (SCDs). They are the data wranglers that not only capture the present state but also meticulously track every historical shift. This allows you to embark on a journey through your data’s past. Typically implemented in your ETL tool or data warehouse.

In the realm of data warehousing and business intelligence they are a crucial concept. In this blog post, we’ll dive into what Slowly Changing Dimensions are, their types, and how they are implemented in data warehousing.

What are Slowly Changing Dimensions?

In a data warehouse, dimensions represent the descriptive attributes of business entities. These include as customers, products, or locations. Slowly Changing Dimensions refer to those dimensions whose attributes change gradually over time. Managing these changes effectively is essential for a well designed data warehouse. Doing so will preserve historical data accuracy and optimise database performance. These will enable meaningful analysis.

Enter the SCD: Your Historical Compass:

Historic Compass

SCDs are specialized tables designed to tackle this very challenge. They act as historical guardians, recording every change that occurs within a dimension. This ensures you don’t lose sight of valuable insights hidden in the past. This allows you to:

  • Analyse trends: Witness how customer preferences morphed over time. Identifying shifts in demand or the impact of marketing campaigns.

  • Understand change: Dive deeper into product name changes. Uncover how they affected sales figures or customer sentiment.

  • Accurate Analysis: How do you ensure analysis is accurate. You want to know the data at a snapshot in time. That could be address, cost or even name.

  • Audit your data: Track who made changes to a customer record, when, and why, ensuring data integrity and accountability.

Choosing Your SCD Path:

What are the Slowly Changing Dimensions types? They are typically categorized into three main types:

  1. SCD Type 1 (Overwrite; SCD1): The “simple and efficient”. In this approach, when a change occurs in a dimension attribute, the original value is simply overwritten with the new value. This means that historical data is not preserved, and the dimension reflects only the most recent state. While this method is simple and efficient, it lacks historical tracking.

  2. SCD Type 2 (New Row; SCD2): SCD2 addresses the limitation of SCD1 by preserving historical data. When a change occurs, instead of overwriting the existing record, a new record is added to the dimension table with the updated attributes. Each record is assigned a surrogate key, and effective date ranges are maintained to track when each version of the dimension was valid. This allows for historical analysis and comparison of data over time. Whilst it preserves every change in a new row, it will create a detailed log but potentially will bloat your data warehouse.

  3. SCD Type 3 (Current Flag – SCD3): SCD3 strikes a balance between SCD1 and SCD2. It maintains some historical data but in a more limited fashion compared to SCD2. In this approach, a separate attribute or column is added to the dimension table to store the most recent historical value as well as the current value. This allows for some level of historical tracking without creating multiple records for each change. By maintaining both current and historical values, this offers a balance between comprehensiveness and efficiency.

The best type for you depends on your specific needs and data volume. Remember, choosing the right SCD is like selecting your time travel machine. You need to consider the trade-offs between speed, detail, and storage capacity.

Unlocking the Power of the Past:

Implementing SCDs unlocks a treasure trove of benefits:

  • Accurate historical analysis: No longer limited to the present, you can analyse how data has changed and why, gaining a deeper understanding of trends and patterns.

  • Enhanced data quality: By tracking changes, you ensure data consistency and integrity, boosting your confidence in your analysis.

  • Powerful reporting: With historical context at your fingertips, you can craft reports that reveal deeper insights and tell richer stories.

Implementation of Slowly Changing Dimensions:

Implementation

Implementing Slowly Changing Dimensions involves careful design and consideration of the specific requirements. Some common steps involved in implementing SCDs include:

  1. Identifying Slowly Changing Dimensions: Analyse the data and identify which dimensions are subject to slow changes over time.

  2. Choosing the SCD Type: Decide whether SCD1, SCD2, or SCD3 is the most suitable for each dimension based on business requirements.

  3. Designing Dimension Tables: Design the dimension tables to accommodate historical data. This should include the surrogate keys, and effective date ranges, as necessary for the chosen SCD type.

  4. Loading Data: Develop ETL (Extract, Transform, Load) processes to handle the loading of data into the dimension tables. This needs to adhere to the chosen SCD strategy.

  5. Querying and Reporting: Develop queries and reports that leverage the historical data stored in the dimension tables. This will enable trend analysis, comparison, and other historical reporting requirements.

Implementation Challenges:

Challenges

Like any time travel adventure, navigating SCDs comes with its own set of hurdles:

  • Complexity: Designing and implementing effective SCDs requires careful planning and understanding of your data landscape.

  • Performance: Large volumes of historical data can impact query performance, demanding optimization strategies.

  • Data governance: Clear rules and processes are crucial to manage data changes effectively, ensuring the accuracy and reliability of your historical record.

Charting Your Course:

SCDs are powerful tools for navigating the ever-changing dimensions of your data warehouse. By understanding their types, benefits, and challenges, you can embark on a journey through time, unlocking valuable insights from your historical data and gaining a deeper understanding of your business. Remember, SCDs are just one piece of the data warehousing puzzle. Use them wisely, and your data will become a powerful time machine, revealing the secrets of your past and guiding you towards a brighter future.

 

Contact us if you want to find out more or discuss references from our clients.

Find out about our Business Intelligence Consultancy Service.

Or find other useful SQL, Power BI or other business analytics timesavers in our Blog

Our Business Analytics Timesavers are selected from our day to day analytics consultancy work. They are the everyday things we see that really help analysts, SQL developers, BI Developers and many more people. Our blog has something for everyone, from tips for improving your SQL skills to posts about BI tools and techniques. We hope that you find these helpful!

Blog

Blog Posted by David Laws

 

David Laws Principal Consultant

LinkedIn