Building an Effective Data Warehouse
This case study provides insight into how a data warehouse was built for a firm in the financial sector using its existing Microsoft technology. It touches on the current form of "static reports" currently used within the company, which we have identified as problematic. This case study showcases a step-by-step method of how this DW is built. After reading, you should understand the theory and practical application of the DW approach. How would you apply a similar framework to a large department store chain's supply chain?
6. Discussion
Looking at the results section, obtaining information in both use cases was shown to be faster through the use of an OLAP cube when compared with the method previously used by the holding company. Looking for the first use case, the hypothesis testing for the difference between the two means (OLAP cube vs. traditional method) is higher than the t-value (2-tailored) for a significant level of 0,05 and 0,01. Therefore, this difference can be considered extremely statistically significant, which means that for use case I the use of OLAP cube offers better performance. This conclusion is valid for both scenarios (estimation account balances in next month and next year). In the second use case, the hypothesis testing for the difference between the two means (OLAP cube vs. traditional method) is also higher than the t-value (2-tailored) for a significant level of 0,05 and 0,01. Consequently, this difference can be also considered extremely statistically significant, which means that the OLAP cube offers better performance. This conclusion is valid for the three considered scenarios, when we get the average balance from all accounts and for all months since last year, last 2 years and last 3 years.
We also estimated the time benefits differences by the adoption of OLAP cubes. In use case I, the saving time by using OLAP cubes is around 5,5 hours after one year of use, which is equivalent to almost one working day (considering 7,5H/day). In use case 2, the saving time by adopting OLAP cubes is 26,2 hours after one year of use, which is equivalent to more than three business working days. If we consider that these use cases have a daily recurrence, the sum of the time savings of these two cases by employing OLAP cubes is more than 4 working days per day. Additionally, the growth of the number of records involved in the analyzed data (number of years) was shown to have a greater impact in use case I than in use case II. In other words, the greater the amount of information to be examined, the greater is the advantage of adopting OLAP cubes, which also confirms the findings obtained by Chouhan 24.
Finally, the increase of the number of records involved in the analyzed data (number of years) can be observed as having a greater impact in embodiment 2 than in embodiment 1. Simply put, the greater the amount of information to be examined, the greater is the advantage mode 1.