CONCLUSION TO THE REVIEW OF FIRST-GENERATION METHODS

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In this chapter we focused on the subject of time. Time has a profound effect on data warehouses because data warehouses are temporal applications. The temporal property of data warehouses was never really acknowledged in first-generation data warehouses and, consequently, the representation of time was, in most cases, not adequate. In dimensional models the representation of time is restricted largely to the provision of a time dimension. This enables the fact tables to be accurately partitioned with respect to time, but it does not provide much assistance with the representation of time in the dimensional hierarchies.

The advent of CRM has highlighted the problem and reinforced the need for a systematic approach to dealing with time. In order to design and build a truly customer centric data warehouse that is capable of assisting business people in the management of serious business problems such as customer churn, we absolutely have to make sure that the problems posed by the representation of time are properly considered and appropriately addressed. We saw how the first-generation data warehouse design struggles to answer the most basic of questions, such as How many customers do we have? With this kind of limited capability it is impossible to calculate churn metrics or any other measures that are influenced by changes in circumstances.

We also examined how the traditional approach to solving the problem of slowly changing dimensions can cause the database to have to generate hundreds or thousands of extraneous new records to be inserted when dealing with simple hierarchies that exist in all organizations.

We then went on to explore some of the different types of temporal query that we need to be able to ask of our data. Temporal selection, transition detection, and state duration queries will enable us to analyze changes in the circumstances of our customers so that we might gain some insight into their subsequent behavior. None of these types of query can be accommodated in the traditional first-generation data warehouse.

It is reasonable to conclude that, in first-generation data warehouse design, the attributes of the dimensions are really regarded as further attributes of the facts. When we perform a join of the fact table to any dimension we are really just extending the fact attributes with those of the dimension. The main purpose of the type two solution appears to be that we must ensure that each fact table entry joins up with the correct dimension table entry. In this respect it is entirely successful. However, it cannot be regarded as the solution to the problem of the proper representation of time in data warehouses. The next generation, the customer-centric solution that supports CRM, has to be capable of far more.

This concludes our exploration of the issues surrounding the representation of time in data warehouses. We now move on to the development of our general conceptual model. In doing so we will reintroduce the traditional conceptual, logical, and physical stages. At the same time, we will attempt to overcome the problems we have so far described.

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