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How do you manage historical and current data in dimension tables?

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Dimension tables are essential components of a data warehouse that store descriptive attributes of business entities, such as customers, products, or locations. They help to provide context and meaning to the facts and measures stored in fact tables. However, managing dimension tables can be challenging, especially when the attributes change over time. How do you keep track of the historical and current values of your dimensions without compromising the accuracy and integrity of your data warehouse? In this article, we will explore some common techniques and best practices for handling dimension changes in dimension tables.

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1 Type 1: Overwrite

One simple way to deal with dimension changes is to overwrite the old values with the new ones in the dimension table. This approach is called Type 1 or current value. It is suitable for dimensions that do not require historical tracking, such as spelling corrections, minor updates, or low-impact changes. For example, if you want to change the name of a product category from "Books" to "Literature", you can simply update the dimension table with the new name and leave the fact table unchanged. This way, you can avoid creating duplicate records or confusing users with inconsistent labels.

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2 Type 2: Create new row

Another way to handle dimension changes is to create a new row in the dimension table for the new values and keep the old row as well. This approach is called Type 2 or historical value. It is suitable for dimensions that require historical tracking, such as changes in customer status, product price, or location hierarchy. For example, if a customer moves from one city to another, you can insert a new row in the customer dimension table with the new city and assign a new surrogate key to it. Then, you can update the fact table with the new surrogate key for the transactions that occurred after the change. This way, you can preserve the history of the customer's location and analyze the impact of the change on your business.

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Naga Lanka

Data Modeling and Data Architecture at Early Warning?
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This is very effective when we need historical and current data maintained in the same table. We have to be very careful with these type of dimensions when data gets purged as per the regulatory rules. It becomes very tricky to handle these table, not impossible but get challenging.

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3 Type 3: Add new column

A third way to deal with dimension changes is to add a new column in the dimension table for the new values and keep the old column as well. This approach is called Type 3 or current and previous value. It is suitable for dimensions that require tracking only the most recent change, such as changes in sales territory, employee manager, or product category. For example, if a product moves from one category to another, you can add a new column in the product dimension table for the new category and update it with the new value. Then, you can use the old column for the previous category and leave it unchanged. This way, you can compare the performance of the product before and after the change and see how it affects your sales.

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4 Type 4: Create separate table

A fourth way to deal with dimension changes is to create a separate table for the changing attributes and link it to the main dimension table. This approach is called Type 4 or mini-dimension. It is suitable for dimensions that have a large number of frequently changing attributes, such as customer demographics, product features, or location details. For example, if you have a customer dimension table with many columns for age, gender, income, education, and so on, you can create a separate table for these attributes and assign a surrogate key to each combination of values. Then, you can link the main customer dimension table to the mini-dimension table with a foreign key and update it whenever there is a change. This way, you can reduce the size and complexity of your dimension table and improve the performance and flexibility of your data warehouse.

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5 Type 6: Combine methods

A fifth way to deal with dimension changes is to combine two or more of the previous methods and apply them to different attributes of the same dimension. This approach is called Type 6 or hybrid. It is suitable for dimensions that have a mix of attributes that require different levels of historical tracking, such as customer behavior, product life cycle, or location evolution. For example, if you have a product dimension table with attributes such as name, price, category, status, and launch date, you can use Type 1 for name, Type 2 for price, Type 3 for category, Type 4 for status, and Type 6 for launch date. This way, you can customize your dimension table to meet your specific business needs and analytical goals.

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6 Best practices

Managing dimension changes in a data warehouse is not an easy task. It requires careful planning and design of your dimension tables and their relationships with your fact tables. To ensure successful dimension changes, you should select the appropriate method for each dimension based on business requirements, data quality, and data volume. Additionally, you should use surrogate keys to link your dimension tables to your fact tables instead of natural or business keys that may change over time. It’s important to document your dimension changes and their impact on the data warehouse and reports/dashboards, as well as communicate these changes to users and stakeholders with clear and consistent definitions and labels. Lastly, it’s important to monitor your dimension changes and evaluate their effectiveness and efficiency on the data warehouse performance and usability.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Krishna Ghanta

DCIO | Data Analytics | Digital & Cloud Transformation | Finance Transformation | Advisor | B2B & B2C Solutions
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Effective dated, Partitioned by date, top of stack row as per as of dates data needed from dimension table in relation to the transaction date from Fact tables.

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How do you manage historical and current data in dimension tables?

1

2

3

4

5

6

7

1 Type 1: Overwrite

2 Type 2: Create new row

3 Type 3: Add new column

4 Type 4: Create separate table

5 Type 6: Combine methods

6 Best practices

7 Here’s what else to consider

Data Warehousing

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How do you manage historical and current data in dimension tables?

1

2

3

4

5

6

7

1 Type 1: Overwrite

2 Type 2: Create new row

3 Type 3: Add new column

4 Type 4: Create separate table

5 Type 6: Combine methods

6 Best practices

7 Here’s what else to consider

Data Warehousing

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