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Suppose you are designing a star to track orders. This time, the customers are corporate entities, not individuals. One of the attributes of the customer dimension will be company_ name. Companies change their names from time to time. You need to determine how the dimension table should respond when a name changes. You ask a user about how to handle changes in company names and receive the reply, We always use the current name of the company for all our analysis. If the story stops there, the situation clearly calls for a type 1 response to name changes. When a company s name changes, overwrite the old value. This makes sense; companies tend to be known by their current names. You wouldn t want a change in name to get in the way of this year versus last comparisons. As you reach for your pad to note this requirement, the user has an additional thought. Oh, but sometimes we need to know the company name that was in effect at the time of an order. That s mainly for statutory reports. With this afterthought, the situation has become more complicated. A type 1 response won t associate a historically accurate company name with each order. That requires a type 2 response. Unfortunately, the type 2 response conflicts with the initial requirement to use the current name to study all history.

When neither a type 1 response nor a type 2 response seems to meet all the requirements, your first reaction might be to consider a type 3 response. A type 3 response captures two versions of the attribute, which sounds promising. Recall, however, that neither version of the attribute can be used to segregate facts recorded before the change from facts recorded after the change. Either value can be used to study all facts, but there is no way to tell which version was in effect at the time of any individual transaction.

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When requirements call for changed data to result in both type 1 and type 2 behavior, the solution is disarmingly simple: provide for both. This cannot be achieved with a single attribute, but it can be achieved by providing a pair of attributes. Each attribute has the same source, but one will react to changes as a type 1 response and the other as type 2. This solution is known as a type 1/2 hybrid. It s a good idea to name these attributes clearly, since each serves a different function. Use the suffix _current to identify the type 1 attribute, and _historic to identify the type 2 attribute. When the source for the pair of attributes changes, the ETL process must do two things: Update the _current value for all records that share the same natural key. Insert a new row, initializing both _current and _historic values to the new value. When reporting on the data, developers choose which column to use based on the requirements for the report. Use the _current column to group all facts under the current values. Use the _historic column to group all facts under the historic values. TIP A hybrid design allows for type 1 and type 2 treatment of the same source attribute by providing two separate dimension columns. One is designated as a type 1 attribute and can be used to group all facts under the latest value. The other is designated as a type 2 attribute and can be used to group facts with the historic values. You may be a bit confused at this point. To understand better how a hybrid change works, look at the following specific example.

In fact Andersen had been a leader in creating and maintaining high standards in the accounting profession But times had clearly changed at Andersen as it went from an auditing company to a consulting firm as well Technology companies had granted huge numbers of options and were therefore vehemently opposed to the proposed FASB rule Tech firms were also a growing constituency among Andersen s clients When they let their opposition to the accounting rule be known, Andersen bowed to the pressure Sadder still was the apparent quid pro quo in Andersen s sudden shift of position In exchange for a change of position on the FASB rule tech companies had apparently pledged to lobby Congress for tort reform for the accounting profession This reform would limit the liability of individual partners in cases of inaccurate audits and other actions for which the firm was found liable.

Most people need to spend some time exploring dimension content before formulating a query. This allows them to choose which attributes should appear on a report and which should be used as filters. As you learned in 1, the act of exploring a dimension table is called browsing. Unfortunately, this is an alien concept to most business intelligence tools, which treat the browse as they would any other query. Although most lack a browser interface, business intelligence tools work just fine when building reports that include data from a single dimension table. This is a reasonable stand-in for the browse, particularly if the user interface allows the user to interactively explore the results. However, there are still some design features that may lead to trouble. Mini-dimensions and split dimensions both pose problems when users wish to browse them.

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