Management Information System Access Lab Examination 202

Management Information Systemaccess Lab Examination11 13 2018

Analyze and complete the tasks involving a Microsoft Access database related to Management Information Systems. You are required to work on a provided Access file “MIS Lab Exam” to implement validation rules, create lookup tables, design queries for specific data analysis, and generate a crosstab. Once completed, submit the updated Access file via email within 24 hours of receipt to avoid point deductions.

Paper For Above instruction

The purpose of this examination is to assess proficiency in designing and implementing data validation, lookup tables, and queries within Microsoft Access, focusing on real-world business scenarios. The tasks involve setting validation rules for data integrity, creating lookup tables for meaningful data presentation, aggregating data through sum functions, filtering data based on specific date ranges, and utilizing joins and relationship diagrams for comprehensive reporting.

First, setting validation rules within the SalesOrderHeader table is essential to maintain data integrity. For example, ensuring that the OrderDate is earlier than the DueDate prevents logical errors during data entry. This can be achieved by utilizing the Validation Rule property of the OrderDate field with an expression such as [OrderDate]

Next, establishing a lookup table for the StateProvince table involves creating a table that displays full country names instead of abbreviations for the CountryRegionCode field. By setting a lookup field that references the CountryRegion table, users can select from a list of country names instead of code abbreviations, enhancing usability and reducing data entry errors. This is achieved by configuring the lookup properties to reference the foreign table and display the field with country names.

In creating summary data, a query that calculates the total of the TotalDue field across all sales orders is necessary. To sum TotalDue, the data type of the field may need alteration to ensure it can handle aggregate operations effectively, especially if it was previously set as a text type. The query will include an aggregate function, Sum([TotalDue]), added in the Total row of the query design. Additionally, the sum can be displayed at the bottom of the datasheet view for quick assessment of total sales.

Designing specific queries for targeted analysis involves filtering data based on date ranges. For example, by creating a query named SalesQ4_query, which selects OrderID, OrderDate, and TotalDue for orders placed between October 1, 2001, and December 31, 2001, inclusive, using the criteria under the OrderDate field as: Between #10/1/2001# And #12/31/2001#. This helps analyze quarterly sales performance during the last quarter of 2001.

Furthermore, analyzing sales performance per salesperson during 2001 involves aggregating data by SalesPersonID. The query, SalesPerformance_query, sums the SubTotal field for all orders within that year, with appropriate criteria placed on the OrderDate field (Year([OrderDate])=2001). Grouping by SalesPersonID allows a clear comparison of individual contributions, aiding management in performance evaluations.

For a comprehensive overview of product sales, create a ProductSales_query that displays all products along with their total sales amounts, including products that had no sales. This necessitates a Left Join between the Product and SalesOrderDetail tables to include all products from the Product table, regardless of whether they have associated sales. Calculating LineTotal as [UnitPrice]*[OrderQty] in the query and sorting in descending order highlights best-selling products, while null values indicate products without sales.

Analyzing sales by geographic location, specifically states and provinces, involves creating the SalesByStateProvince_query. This query aggregates TotalDue values grouped by StateProvinceCode, with the total formatted as currency and ordered from highest to lowest. The relationship diagram guides the linking of SalesOrderHeader with StateProvince via the foreign key, enabling accurate aggregation of regional sales data.

Finally, creating a crosstab query, SaleByCustomerAndProductCategory_Crosstab, offers insights into sales performance across customers and product categories. Using the OrderedItems query as a source, the crosstab displays the sum of sales amounts, calculated as [UnitPrice]*[OrderQty], grouped by CustomerID and ProductCategoryID. This provides a comparative view of customer preferences and product popularity, instrumental for targeted marketing strategies.

In conclusion, this examination demonstrates core access skills—validation, lookup setup, data summarization, filtering, joining, and crosstab creation—crucial for managing and analyzing business data effectively. Accurate execution of these tasks ensures data integrity, facilitates insightful reporting, and enhances decision-making processes within the organization.

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