Evaluation
By normalizing the provided flat file, I was able to minimize data duplication
within the database that I built for this case study.
First thing I made sure was that all the data was atomic. Every field had its
own unique name. Every table has its own primary key,for my customer table
the primary key is customer ID, for my party table the primary key is partyID,
my party sales table has the primary key partySalesID. In the party sale table I
have a party ID and Customer ID and these are foreign keys which helped me
link the data.
For my tables I also added validations to reduce errors. A surname has a
presence check whereby a surname cannot be left blank. The rule I used for
this presence check is : Is Not Null.
For the postcode field I’ve used another validation that is called format check,
which looks like LL0 0LL. This will not let the user type anything he wants in
the postcode field. He can’t type a number where a letter should be.
For the lounge type field I have set up a lookup wizard which is going to let the
user choose between 2 Lounge types : Standard , Vip. This will prevent errors
when the user is typing or selecting lounge type. This will also help to
minimize spelling mistakes. Users would be able to enter data faster since
they will be choosing rather than typing.
For the number of tickets field I have been using a range check set. The
customer can buy at least one ticket and a maximum of eight tickets.
To make my data more organized and efficient to read, I also sorted it
ascending. In my database, I've improved productivity. For example, I've built
a query that looks for events on the 25th and 27th of the month. Since users
no longer have to search for data, efficiency has improved.
Through using calculated fields, I've also improved my efficiency when it
comes to estimating the income generated. For example number of tickets *
party ticket price. I have also used criteria where I can search for standard and
vip lounge types.
I created a report on the total income generated by these parties. Reports
have a clear layout so it’s user friendly. The report has calculated fields.
By normalizing the provided flat file, I was able to minimize data duplication
within the database that I built for this case study.
First thing I made sure was that all the data was atomic. Every field had its
own unique name. Every table has its own primary key,for my customer table
the primary key is customer ID, for my party table the primary key is partyID,
my party sales table has the primary key partySalesID. In the party sale table I
have a party ID and Customer ID and these are foreign keys which helped me
link the data.
For my tables I also added validations to reduce errors. A surname has a
presence check whereby a surname cannot be left blank. The rule I used for
this presence check is : Is Not Null.
For the postcode field I’ve used another validation that is called format check,
which looks like LL0 0LL. This will not let the user type anything he wants in
the postcode field. He can’t type a number where a letter should be.
For the lounge type field I have set up a lookup wizard which is going to let the
user choose between 2 Lounge types : Standard , Vip. This will prevent errors
when the user is typing or selecting lounge type. This will also help to
minimize spelling mistakes. Users would be able to enter data faster since
they will be choosing rather than typing.
For the number of tickets field I have been using a range check set. The
customer can buy at least one ticket and a maximum of eight tickets.
To make my data more organized and efficient to read, I also sorted it
ascending. In my database, I've improved productivity. For example, I've built
a query that looks for events on the 25th and 27th of the month. Since users
no longer have to search for data, efficiency has improved.
Through using calculated fields, I've also improved my efficiency when it
comes to estimating the income generated. For example number of tickets *
party ticket price. I have also used criteria where I can search for standard and
vip lounge types.
I created a report on the total income generated by these parties. Reports
have a clear layout so it’s user friendly. The report has calculated fields.
