Evaluation
● Within this database that I’ve created with this case study I have managed to reduce data
duplication by normalising the given flat file.
● First thing I made sure was all the data was atomic.
● Every field had its own unique name.
● Every table has its own primary key e.g. for my customer table the primary key is
customer ID.
● No 2 customers have the same ID reducing data duplication.
● My events table has an event ID as a primary key.
● My sales table has a seat sale ID as a primary key.
● In the seats sale table I have an event ID and Customer ID and these are foreign keys
which helped me link the data.
● Within this table structure I also added validation to reduce errors. For example, a
surname has a presence check whereby a surname cannot be left blank. The rule i used
for that is: is, not ,null
● The postcode field has another validation called format check which is LL0 0LL. This will
not let the user type in a number where there should be a letter.
● The seat type field i have set up a lookup wizard e.g. seatWithTable SeatWithoutTable.
This will prevent errors when the user is typing or selecting seat type. This will also
reduce spelling errors. This will also speed up data entry as users are selecting and not
typing.
● There are further validation for example number of tickets. This field has a range check
set. For example a customer can only buy a ticket between 1-8. Again this is reducing
errors.
● I also got a validation text for all the validated fields. This will help the user understand if
they make an error what to do.
● I also sorted data to ascending data to make my data more organised and efficient to
read. I've increased productivity in my database. For example I've created a query that
searches for events that are held between 20th and 21st. This has increased productivity
because the users don't need to search for data.
● I've also increased productivity in terms of calculating the income generated by using
calculated fields. For example number of tickets * event ticket price.
● I have used various functions to calculate values. For example the total number ticket
sold I used a sum function equal to sum. =sum(),=max(),=min(),=count(). I have used the
count function to see how many customers bought tickets in this case 6.
● I have also used criteria where I can search for seat with table and seat without table.
● I have created a report on the total income generated for these events. Reports are
suitably laid out for users to read easily.
● The reports also have calculated fields.
● I think my database is user friendly because of the lookup values and validation rules that
have been applied in this database.
● Finally my database has been thoroughly tested to make sure that it is efficient, reliable.
● I believe ive met all the requirements by creating the 3 tables: customer, events, and set
sale. I've managed to do the 2 queries and furthermore, I've managed to successfully do
the report. Overall I believe I have managed to achieve the requirement for this case
study.
● Within this database that I’ve created with this case study I have managed to reduce data
duplication by normalising the given flat file.
● First thing I made sure was all the data was atomic.
● Every field had its own unique name.
● Every table has its own primary key e.g. for my customer table the primary key is
customer ID.
● No 2 customers have the same ID reducing data duplication.
● My events table has an event ID as a primary key.
● My sales table has a seat sale ID as a primary key.
● In the seats sale table I have an event ID and Customer ID and these are foreign keys
which helped me link the data.
● Within this table structure I also added validation to reduce errors. For example, a
surname has a presence check whereby a surname cannot be left blank. The rule i used
for that is: is, not ,null
● The postcode field has another validation called format check which is LL0 0LL. This will
not let the user type in a number where there should be a letter.
● The seat type field i have set up a lookup wizard e.g. seatWithTable SeatWithoutTable.
This will prevent errors when the user is typing or selecting seat type. This will also
reduce spelling errors. This will also speed up data entry as users are selecting and not
typing.
● There are further validation for example number of tickets. This field has a range check
set. For example a customer can only buy a ticket between 1-8. Again this is reducing
errors.
● I also got a validation text for all the validated fields. This will help the user understand if
they make an error what to do.
● I also sorted data to ascending data to make my data more organised and efficient to
read. I've increased productivity in my database. For example I've created a query that
searches for events that are held between 20th and 21st. This has increased productivity
because the users don't need to search for data.
● I've also increased productivity in terms of calculating the income generated by using
calculated fields. For example number of tickets * event ticket price.
● I have used various functions to calculate values. For example the total number ticket
sold I used a sum function equal to sum. =sum(),=max(),=min(),=count(). I have used the
count function to see how many customers bought tickets in this case 6.
● I have also used criteria where I can search for seat with table and seat without table.
● I have created a report on the total income generated for these events. Reports are
suitably laid out for users to read easily.
● The reports also have calculated fields.
● I think my database is user friendly because of the lookup values and validation rules that
have been applied in this database.
● Finally my database has been thoroughly tested to make sure that it is efficient, reliable.
● I believe ive met all the requirements by creating the 3 tables: customer, events, and set
sale. I've managed to do the 2 queries and furthermore, I've managed to successfully do
the report. Overall I believe I have managed to achieve the requirement for this case
study.
