As SeaForest's network for farmer customers grows, the company sets up a relational database to replace the existing spreadsheet record of the farmer's information and the sales transactions. This change allows Sea Forest to address the emerging issues about their "Quality of Data" as the amount of data grows. In class, you were introduced to 6 dimensions of the Quality of Data: a. Suggest TWO such potential Quality of Data dimensions that are likely to be violated by using a spreadsheet to manage a large amount of transactional data. Justify your suggestions with a brief explanation and a specific example. Note that your suggestions must be related to the nature of spreadsheets for this specific purpose. (9 marks) b. For each of the two violations given in your answer to part a, explain how the use of a relational database instead of a spreadsheet can avoid such violations. Support each explanation with a specific example of relational database design (eg. How the data is organised to minimise the specific violation). Note that your answer must be about how relational databases may address the short-comings of spreadsheets for managing large amounts of data, rather than the properties of relational databases that are not related to such short-comings. (6 marks) Copy and paste the following answer template onto the answer field. Fill in your answer accordingly. a. Dimension 1: Justification: Example: Dimension 2: Justification: < > Example:

a. Dimension 1: Consistency Justification: Spreadsheets do not enforce data consistency. Different users may enter data in different formats or use different terms for the same thing, leading to inconsistencies. Example: One user might enter a date as "DD-MM-YYYY" while another might enter it as "MM/DD/YYYY". This inconsistency can lead to confusion and errors in data interpretation.

Dimension 2: Accuracy Justification: Spreadsheets are prone to human error. Users can easily make mistakes when entering or updating data, leading to inaccuracies. Example: A user might accidentally enter a sales transaction amount as $1000 instead of$100, leading to inaccurate financial records.

b. Dimension 1: Consistency How a relational database can avoid the violation: Relational databases enforce data consistency through the use of predefined data types and formats. They also support the use of constraints to ensure that data entered into the database meets certain conditions. Example: In a relational database, a date field would be defined to accept only dates in a specific format (e.g., "YYYY-MM-DD"). This ensures that all dates are entered consistently.

Dimension 2: Accuracy How a relational database can avoid the violation: Relational databases reduce the risk of human error by automating data entry and update processes wherever possible. They also support the use of transactions, which ensure that all data modifications are done in a controlled and secure manner. Example: In a relational database, a sales transaction record might be automatically created whenever a sale is made, using the exact sale amount entered into the system. This reduces the risk of human error in data entry.