Facts table present real data stored while dimension tables describes each row in a fact table. Any data mart must present business changing trends and the user requirements in an organization. The database for MEGA SAVE was analyzed using OLAP Statistics and Reporting. The main advantage of data mart is that it can be used to analyze both small and large data of an organization because data marts are response to real business needs. This are some of the key guidelines that the designers of the database should base in coming up with the data mart. Charts and reports are used to describe the data set. Table 1.0 The above table shows group customer against group product from the sales. Table 1.2 From the schema the main aim of the developers of the schema is to evaluate the sales according to different groups of the buyers. The group products and the customer group ID are illustrated in the table above. For instance, customer group ID 1 is for Young Rich Women while 2 for Young Poor Women. The total sales for each product is given in the columns. Graph 1.0 The graph above shows the sales of group products sold overall in all counties. Fresh meat, soft drink and dairy were the least sold items while the highest sales was fruit vegetable followed by beer. The schema designed was to outline the group sales that has the highest score. The graph below shows the sales grouped by the product. The group customers are also categorized in the graph this graph is aimed at identifying the favorite precuts customers buy frequently. By different type of buyers. Decision makers can use this information in determining the type of product to be focused a given group of the consumers. Graph 2.0 Graph 3.0 The graph above shows sales of group product over the county. The decision makers may want to know which county has the highest sales and which product is the best sales. West Yorkshire has the largest sales above 1200 sales for fruit vegetables while east Yorkshire has the least sales below 800 sales for fruits vegetables. The decision maker may want to identify the sales in each county based on the scores. For instance, which store is in which county and how is it performing? The table below shows these data. Table 1.3 The company’s main trend or patterns are to the target population on the market and the kind of products it is selling to these regions. After looking at the design principles about data mart, there should be a single access point to the data mart hence the information from all the stores should be accessed at a given point. The schema omitted the most important question about the sales in each region. From a business perspective, the two main questions that emerge are: What is the specific business application of the data being loaded to the data mart from the operating system files. Therefore loading legacy data during the loading of the data mart should be done. The first law in data mart designing is the law of loading complexity in this case having multiple sources where data will be extracted from is a complexity. Some sources may not be loaded or the administrator may present information to the users in the format that they did not expect, hence to users data will not be available. Feedback mechanism should be design early in the designing stage of the data mart. I used bar graphs in representing my information because the values in the database are of frequency
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