Serious DBMSs are capable of browsing amongst the textual attributes such as flavor and package type in a 500,000-row table and giving the user good interactive performance. The biggest customer dimensions, however, are at least ten times bigger than the biggest product dimensions. All large companies that deal with individual human being customers have customer lists ranging from a few million to as many as 100 million records. This dimension cannot be compressed or summarized. The customer list dimension drives just about every interesting fact table in these businesses and needs to be available at the lowest level of granularity in order to subset the business based on detailed customer characteristics.
Retailers are anxious to periodically update their customer information.
Insurance companies must update their information about their customers, their insured automobiles, and their insured homes, because it is critical to have an accurate description of these items at the time a policy is approved and when a claim is made. However, it would seem like you are between a rock and a hard place. You must track the slowly changing nature of the customer list, but you don't dare use either of the techniques appropriate for medium-size dimensions because of the table size. You gain the advantage of being able to track the changing nature of the customer descriptions, but you make some compromises as well. First, you need to make a subtle change in order to successfully create this new demographics dimension.