Modern data warehouse design assumes that business transactions such as sales, orders, shipments, fulfillments, and receivables can occur at a rapid rate and each the details of each transaction needs to be recorded.  Hence a fact table with a dimensional model contains a separate record for each business transaction.  While in contrast, the describing or text-based values of the transaction or dimension often remain fairly constant.  Often, dimensional tables within the dimensional model do not take changes into account.

But in reality, dimensional values can and do change over time and numerous fields of a given row within a dimension table will need to be updated. This phenomenon in data modeling is known as “slowly changing dimensions” and it can be applied to any dimension table within a data warehouse schema.  Moreover, both simple and advanced modeling techniques have been established and can be implemented for handling updates and changes within a dimension table.  In addition, slowly changing dimensions assist the data warehouse in precisely recording the past values, providing an efficient method for tracking history, and allowing for the ability to respond to changes to descriptive values of transactions.

Examples of slowly changing dimensions include:

–  account name

–  customer phone number

–  vendor address

–  product description

These are good examples as they are text-based values that remain relatively constant, but can change and commonly do change over time.  Names, phone numbers, addresses are fairly intuitive and it is easy to see how these values can change slowly over time.  But let’s see how a product description could change…  A simple ingredient change or a packaging change in a product may be so trivial that the organization does not decide to give the product a new product id.  Rather the source system provides the data warehouse with a revised description of the product.  Hence the data warehouse needs to track both the old and new descriptions of the product.

Other good examples of common slowly changing dimensions are the region and territory names for a sales force. Many organizations have management that rename their region and territories on a regular basis or the management realigns their regions and territories along customer purchase patterns.   Typically the requirement of a data warehouse is to keep a record of the names of the regions and territories and the dates they were active.

Originally pioneered by Ralph Kimball, PhD, four main data modeling techniques have been established for managing dimension tables that contain slowly changing dimensions:

Type One – Overwrite the Record

Type Two – Update Record to Inactive / Create an Active Record

Type Three – Leverage Previous and Current Value Fields

Type Four – Insert Into a History Table

These four data modeling techniques range from the complete loss of historical data to an elegant but technically complex method of saving almost all historical data.  Choice of the appropriate technique by the database designer can ensure that the data warehouse contains required historical values and allows for comparisons of current data or data from other time periods.