Grader Instructions Access 2019 Project Exp19 Ch05 Cap

Grader Instructionsaccess 2019 Projectexp19 Access Ch05 Capassessme

Analyze and modify a Microsoft Access database for a credit union by creating validation rules, lookup fields, input masks, and advanced queries. The project includes tasks such as creating tables, setting required fields, establishing validation rules with input messages, converting fields to lookup wizards, adding parameter criteria, and designing queries with functions like Round and DatePart. The goal is to improve data integrity, facilitate data entry, and generate insightful summaries relevant to loan and customer management.

Sample Paper For Above instruction

The management of financial institutions heavily relies on accurate, consistent, and easily accessible data. In the context of a credit union like Paterson Credit Union, the integrity and usability of database systems directly impact customer service and operational efficiency. This paper explores a comprehensive approach to enhancing an Access database through the application of validation rules, lookup field configurations, input masks, and advanced query designs, ultimately aiming to improve data quality and reporting capabilities.

The first step involves establishing a well-structured reference table, "AccountTypes," to catalog all possible account types, such as Platinum, Silver, and Gold. Using Design view, this table is created with "AccountType" as the primary key, ensuring each entry is unique and standardized. The primary purpose of this is to maintain data consistency when assigning account types to customer records. After populating "AccountTypes" with the relevant types, it serves as the source for lookup fields, simplifying data entry and reducing errors.

Next, the focus shifts to enforcing data completeness by setting critical fields as required. In the "Customers" table, the "PhoneNumber" and "AccountType" fields are designated as required in Design view. This guarantees that any new customer record must include contact information and account classification before saving, thus minimizing incomplete data entries that could hinder communication or analysis.

The validation of numerical input is similarly crucial, especially for financial data such as loan interest rates. In the "Loans" table, a validation rule is added to the "InterestRate" field, constraining it within the acceptable range of 2.0% to 10.25%. An accompanying validation message provides clear guidance to the user when inputting data. When attempting to enter a value like 1.9, the message prompts correction, reinforcing adherence to business rules regarding loan interest rates which directly impact the bank's lending policies and profitability.

To facilitate consistent and user-friendly data entry, input masks are applied to the "PhoneNumber" field. Such masks enforce a specific format, ensuring that all phone numbers adhere to a standardized appearance, like (XXX) XXX-XXXX. This standardization simplifies data management, improves readability, and supports effective communication processes.

The database's usability is further enhanced by converting the "AccountType" field into a lookup wizard. By sourcing data from the "AccountTypes" table, users can easily select an account type from a dropdown list, reducing manual entry errors and streamlining data input. This configuration is especially vital when working with large datasets or multiple users.

Advanced query design techniques are used to make data analysis more dynamic. One such query, "Customer Loans Parameter," incorporates a parameter prompt "Enter Minimum Loan Amount." This allows users to retrieve loans exceeding a specified threshold dynamically, aiding in targeted analysis. Summing and averaging loan amounts provide further insights into the loan portfolio, essential for risk assessment and strategic planning.

Another query, "Missing Addresses," uses calculated fields to identify customer records lacking address data. By adding a conditional field "AddressPresent," which displays "Missing" when the address is null, the database enables targeted updates to incomplete records, ensuring data completeness and operational efficiency in communication.

For analytical purposes, rounding interest rates is performed using the "Round" function in the "Loans by Interest Rate" query. This simplifies numerical analysis, allowing for straightforward interpretation of interest rates as whole numbers, which can aid in classifying loans into categories such as high or low interest.

The "Payment By Month" query leverages "DatePart" to extract the month from "PaymentDate," enabling grouping of payment data by month. Summarizing total and average payments monthly provides critical insights into cash flow trends, aiding managerial decision-making and forecasting.

Finally, the "Refinance Candidates" query classifies loans into high or low priority based on the interest rate, with a cutoff point at 7.9%. This classification helps prioritize loan reviews and refinancing efforts, optimizing resource allocation and risk management.

Throughout these modifications, the overarching goal is to ensure data accuracy, facilitate user-friendly data entry, and produce meaningful reports that support decision-making processes within the credit union. Proper implementation of validation rules, lookup fields, input masks, and advanced queries ensures the database remains a reliable and efficient tool for managing customer and loan data, ultimately contributing to the institution’s operational success and customer satisfaction.

References

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