The data mart provided in Microsoft Access format was exported to Microsoft Excel and its PivotTable functionality and the entries in the fact table 3 were analyzed. Though it is not possible to perform comprehensive data mining on the data using Excel tools, the data was converted to a customer versus product pivot table displaying total sales amount and total sales quantity…
The data was analyzed with respect to the time dimension - week. Sales were analyzed against the customers weekly. It was identified that the sales amount graph for the six weeks of 2005 for ALL the customers generated the following trend:
The above graph displays that there was a drop in sales amount in the third week of 2005. Since this is a graph of cumulative sales, we can assume it to be a fair representation (normalized) of the entire dataset. After the third week, the sales picked up again in the fourth week, however, this was not sustained: there was a consistent drop in the fifth and sixth week of 2005. This pattern is an interesting one from an analytical perspective. It shows the cumulative pattern of the sales of the company for the six weeks. The pivot table capabilities of Excel can allow drilling down to a specific customer too, however, this pattern represented in the graph is an important one for the company to analyze the potential reasons for the rise or drop. Comparing the trend with changes in ther variables, for example, the firms strategies at those times the company can understand the best practices that led to the changes in their sales.
There are several other trends in the comp...
The analysis provides ample opportunity for the company to analyze its customers' purchases too. In a formal OLAP tool, the customers can be drilled down according to the concept hierarchy and various other aspects of weekly sales can be unearthed. For example, the customer group can be used as an important field to analyze the customers' purchases week-wise. The customer dimension does not have enough fields for a triple concept hierarchy, otherwise, it could lead to a detailed group-wise analysis for sales. The trend could also be analyzed product-wise. It is important to understand again, that a time-based analysis of sales is the most beneficial one as it leads to comparison of all trends of sales with other actions that have been taken in the past.
The following graph shows the trend of product sales, detailed against customers, week-wise:
This shows again the importance of analysis of data through pictorial representations: the outlier lines in the data depict extreme values. Using a more user-friendly OLAP tool, an analyzer can drill down to the extreme value and find out a possible reason for that value. It is necessary to understand the dimensions of a graph when analyzing the results. A manager needs to be able to pull down and across the dimensions of a data warehouse in order to analyze the data from different perspectives and get the best results.
Answer 1 - b
The use of a data warehouse or a mart is to facilitate the analytical requirements of a manager interested in finding trends in data or correlating the actions of a company in the past with changes in business profits or sales. Thus, there are several forms of presentation that can be considered suitable to present ...
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The content of the paper comes from four books reviewed by the author and primarily serves the purpose of delivering fundamental knowledge of the topics which are gaining immense importance today (Gray 2001). The target audience of this paper are mostly scholars and researchers who could get basic information and clear definitions of topics mentioned above.
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