Like other accumulating snapshots we’ve discussed, there are multiple dates in the fact table corresponding to the standard process milestones. We want to analyze the prospect’s progress by these dates to determine the pace of move- ment through the pipeline, and we also want to spot bottlenecks. This is espe- cially important if we see a significant lag involving a candidate whom we’re interested in attracting. Each of these dates is treated as a role-playing dimen- sion, using surrogate keys to handle the inevitable unknown dates when we first load the row.
The applicant dimension contains many interesting attributes about our prospective students. Admissions analysts are interested in slicing and dicing these applicant characteristics by geography, incoming credentials (grade point average, college admissions test scores, advanced placement credits, and high school), gender, date of birth, ethnicity, and preliminary major. Analyzing these characteristics at various stages of the pipeline will help admissions per- sonnel adjust their strategies to encourage more (or fewer) students to proceed to the next mile marker.
As we saw previously, accumulating snapshots are appropriate for short-lived processes, such as the applicant pipeline, that have a defined start and end, as well as standard intermediate milestones. This type of fact table allows us to see an updated status and ultimately final disposition of each prospective applicant. We could include a fact for the estimated probability that the prospect will become a student. By adding all these probabilities together, we would see an instantaneous prediction of the following year’s enrollment.
Another education-based example of an accumulating snapshot focuses on research proposal activities. Some user constituencies may be interested in view- ing the lifecycle of a research grant proposal as it progresses through the grant pipeline from preliminary proposal to grant approval and award receipt. This would support analysis of the number of outstanding proposals in each stage of the pipeline by faculty, department, research topic area, or research funding source. Likewise, we could see success rates by the various dimensions. Having this information in a common repository such as the data warehouse would allow it to be leveraged more readily by a broader university population.