Data Tables Evaluated The Judder Foods current data table has been sufficient but the company is suggested some changes to make it easier to understand and even more descriptive as well. First thing Judder Foods could do is change the table to a schematic database design. A schematic database will combine a few of the tables that already exist and explain each table a bit more in detail with less words and these tables are known as relationship tables. The Judder Foods need to design an entity relationship diagram to illustrate hat needs to be on the table.
A few of the tables could be combined to make reading the tables much easier. Judder foods could combine inventory/ order relationship, for example; order/sale relationship table, inventory/sale relationship table, and sale/receive payment relationship table. By combining any one of these tables Judder Foods could process orders faster, find information faster by having the data in order and labeled and overall making the company’s system more efficient. One table Judder Foods does not have that the management should consider which would benefit the business is an “employee table”.
This way; Judder could have personal information, sales numbers, last date he/she was reviewed, and just able to keep up with the employee’s performance (Beggaring, 2008). The management should evaluate Judder Food’s inventory data and see if it can recommend changes that will improve the day-to- day operations at each store. Inventory Data Evaluated Judder Food’s inventory data is well kept but there are some recommendations that will help improve management’s decision making. The first thing to remember is to create a pivot table for the inventory data.
Pivot tables are ‘two- dimensional statistical summaries of database information” (Beggaring, 2008). By creating this type of table it will allow each store to keep the quantities of each item alongside the price which makes it easier to keep up with what is in stock and what items need to be reorder. The pivot table makes it easier to track customer demands by data mining, “which means using a set of data analysis and statistical tools to detect relationships, patterns, or trends among stored ATA” (Beggaring, 2008).