The display is a three-line dot-matrix display with 2 lines for data and one larger line for the time.
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The sample database, queries, and reports used for this case are available for download upon request.
The case also demonstrates solutions for typical difficulties encountered when deriving age and experience measures.
The case can be used by BI instructors to demonstrate the implementation and value of VA. This is then followed by a case study of applying VA to typical sales data. The first section of this paper provides examples of VA. Rather than addressing advanced statistical and methodological issues, this paper aims to promote VA through improved awareness and data preparation methods. 9) discusses “censoring” (missing data cases that are common in survival analysis) and “time-varying explanatory variables” as two typical features that “create major problems for standard statistical methods.” Rahimi (2011) provides a good example of how age, period, and cohort effects are analyzed to show that the Nazi occupation of Norway during World War II was followed by reduced risks for some types of cancer. Furthermore, the percent of reports with age calculations varied widely across these seven organizations, ranging from a low of 0.2 percent to a high of 5.5 percent.Īnother source of difficulty in using VA, is methodological complexity. A convenience sample of 8,992 reports gathered from seven organizations with advanced BI skills identified age-related calculations in only 2.4 percent of the reports. Perhaps due to lack of awareness, VA might be underutilized even among experienced business intelligence (BI) practitioners. For example, to support data mining of factors influencing online procurement auctions, Millet, Parente, Fizel, & Venkataraman (2004) suggest that bid times “could be stored in terms of elapsed time since the start of the auction or before its end, instead of clock time” (p. Therefore, to facilitate VA we should consider alternative approaches for preparing the data for analysis. VA is difficult because experience and age change with time and require calculations. (Kimball, 1997)Īccording to Kimball, even well-designed data marts are not suited to this type of analysis, and he knows of “no application development or decision-support environment that handles this kind of application automatically” (1997). You may want to ask what is the average time until these customers have a credit default (if they do). You want to measure their purchases and their payments as a function of the time after the granting of credit. Once this triggering event occurs, you want to study the behavior of this cohort group. When discussing “Time Issues” in data mart design, Kimball (1997) highlights it as an advanced application need that is “seen repeatedly in data warehouse environments.” As a case in point, Kimball describes the need to analyze all customers who had their credit limit raised to $1,000: Vintage Analytics (VA) is a useful but challenging technique. While each domain may use a different name, let us define Vintage Analytics as the analysis of how age and experience influence performance.
While empirical evidence (Weil, 2011) casts doubts about the reliability of wine charts, the same approach of tracking performance by age and origination date has proven valuable for other domains such as credit risk (Siarka, 2011) and healthcare (Collett, 2003).
Vintage charts have long been used to select wines, and to consume them at their optimal age.
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