What is a conformed dimension?

1 Conformed Dimensions

One of the challenges of data warehousing is to ensure that data from different sources and subject areas is consistent and compatible, so that it can be integrated and analyzed together. This is where conformed dimensions come in. A conformed dimension is a dimension that has the same meaning and values across different fact tables, regardless of the business process or domain they belong to. For example, a date dimension is a conformed dimension if it has the same attributes and levels of granularity in all fact tables that use it, such as sales, inventory, and orders. A conformed dimension enables cross-functional analysis and comparison of data from different perspectives and sources.

2 Benefits of Conformed Dimensions

Using conformed dimensions in data warehousing has several advantages, such as improving data quality and accuracy by eliminating discrepancies and conflicts among sources, reducing data redundancy and maintenance costs by reusing the same dimension tables for multiple fact tables, and enhancing data usability and accessibility by providing a common language and framework for business users and analysts to query and report on data. Additionally, conformed dimensions can support data governance and standardization by enforcing consistent definitions and rules for data elements and attributes.

3 Types of Conformed Dimensions

There are two main types of conformed dimensions: fully conformed dimensions and partially conformed dimensions. A fully conformed dimension is a dimension that is identical in all fact tables that use it, both in terms of structure and content. For example, a product dimension is a fully conformed dimension if it has the same attributes and values in all fact tables that relate to products, such as sales, inventory, and returns. A partially conformed dimension is a dimension that has some common attributes and values across different fact tables, but also some unique or specific attributes and values for each fact table. For example, a customer dimension is a partially conformed dimension if it has some common attributes such as name, address, and phone number, but also some specific attributes such as loyalty status, segment, and preferences for each fact table that relates to customers, such as sales, marketing, and service.

4 How to Create Conformed Dimensions

To create conformed dimensions, data warehouse designers need to take several steps. They should identify the key business processes and subject areas that generate data for the data warehouse, analyze the data sources and their schemas, and define the business requirements and objectives for the data warehouse. Additionally, they should design a dimensional model for each business process or subject area using a star or snowflake schema, where each fact table is linked to one or more dimension tables. Furthermore, they should review and compare the dimensional models for different business processes or subject areas, identify the common and unique dimensions, create conformed dimensions by harmonizing attributes, values, and hierarchies of the common dimensions, and add specific attributes and values of the unique dimensions. Lastly, they should implement the conformed dimensions by creating dimension tables in the data warehouse database and loading data from the data sources using ETL (extract, transform, and load) processes.

5 Best Practices for Conformed Dimensions

Data warehouse designers need to adhere to some best practices to ensure the effectiveness and efficiency of conformed dimensions. This includes using a data dictionary or metadata repository to document and manage the definitions, descriptions, and rules of the conformed dimensions and their attributes. Additionally, surrogate keys should be used to identify and link the dimension records, instead of natural keys, to prevent conflicts between data sources. Slow changing dimensions techniques should also be used to handle changes and updates in the dimension records over time, such as type 1, type 2, or type 3. Degenerate dimensions can be used to store attributes that are part of the fact table's primary key, but not part of any dimension table. Lastly, junk dimensions can be employed to store attributes that have low cardinality and high variability, and do not belong to any logical dimension.

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?