How do you balance the trade-offs between conformed dimensions and customized dimensions?

1 What are conformed dimensions?

Conformed dimensions are dimensions that have the same meaning and values across different fact tables or data marts. For example, a product dimension that contains the same attributes and hierarchies for sales, inventory, and marketing facts is a conformed dimension. Conformed dimensions enable consistent and integrated analysis of data from different sources and perspectives. They also reduce the complexity and redundancy of the data warehouse, as they can be reused and shared by multiple facts and data marts.

2 What are customized dimensions?

Customized dimensions are dimensions that are tailored to the specific needs of a fact table or a data mart. For example, a customer dimension that contains different attributes and levels of detail for CRM and loyalty facts is a customized dimension. Customized dimensions allow more flexibility and granularity in the analysis of data from a particular domain or application. They also enable faster query performance and loading, as they can be optimized and partitioned for a specific fact or data mart.

3 What are the trade-offs between conformed and customized dimensions?

When considering the trade-offs between conformed and customized dimensions, it is important to consider the business requirements, data quality, and technical constraints of the data warehouse. Conformed dimensions can enhance data consistency and integration, but may limit flexibility and granularity. On the other hand, customized dimensions can increase flexibility and granularity, but create inconsistency and duplication. Additionally, conformed dimensions simplify design and maintenance of the data warehouse, but may increase its size and complexity; whereas customized dimensions reduce size and complexity, but complicate design and maintenance. Finally, conformed dimensions improve cross-functional and enterprise-wide analysis, yet can degrade query performance and loading; while customized dimensions improve query performance and loading, but hinder cross-functional and enterprise-wide analysis.

4 How to balance conformed and customized dimensions?

Finding the right balance between conformed and customized dimensions in a data warehouse project depends on context and objectives. Generally, conformed dimensions should be favored, as it ensures data quality, consistency, and integration. However, customized dimensions may be necessary for higher granularity, faster performance, or lower storage costs. Documentation of the rationale and scope of each conformed and customized dimension is essential for users and developers to understand the data model. Furthermore, conformed and customized dimensions should be reviewed and updated periodically to meet changing business needs and data sources.

5 How to implement conformed and customized dimensions?

Implementing conformed and customized dimensions in a data warehouse depends on the architecture and tools used. A single conformed dimension table is the simplest method, where a single dimension table is used for all facts and data marts sharing the same dimension. This is easy to maintain, but may lead to large, complex dimension tables that affect performance and loading. Multiple conformed dimension tables offer more flexibility, where multiple tables are used for different facts and data marts sharing the same dimension; however, this requires more joins and mappings to ensure consistency and integration. A role-playing dimension table is a hybrid approach, where a single dimension table is used for multiple facts and data marts with different roles or perspectives. Lastly, a customized dimension table allows for unique customization and optimization, but can create data isolation and duplication.

6 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?