Information and Communication Technology | Data Organisation and Data Control

Data Organisation and Data Control: Your Guide to Tidy and Trustworthy Data

Hey everyone! Welcome to your study notes for "Data Organisation and Data Control". Don't worry, this topic sounds more complicated than it is. We're going to break it down together.

Think about all the information on your phone, in your school's records, or on a shopping website. How is it all kept tidy and accurate? That's what this chapter is all about! We'll learn how data is structured, like organising books in a library, and how we make sure it's correct and reliable. This is a super important skill in our digital world!

1. The Hierarchy of Data: Building Blocks of Information

To understand how computers manage huge amounts of information, we need to know how they organise it. Imagine building with LEGOs. You start with the smallest brick and build up to a giant castle. Data is organised in a similar way, from the smallest piece to the largest collection. This structure is called the hierarchical organisation of data.

Let's use a school's student information system as our example:

Imagine your school has a digital filing cabinet to store information about every student.

The Smallest Pieces: Data and Fields

• Data: These are the raw, individual facts and figures. Examples: "Chan Tai Man", "17", "Class 6A". By itself, "17" doesn't mean much.
• Field: A field is a category or a single piece of information about a person or thing. It's like a labeled box that holds one type of data. The field gives context to the data.
  Examples: The field 'Student Name' holds the data "Chan Tai Man". The field 'Age' holds the data "17".

Putting the Pieces Together: Records

• Record: A record is a complete set of fields about ONE specific person, item, or event. It's like a single index card for one student, containing all their details.
  Example: The record for Chan Tai Man would contain his name, age, class, student ID, address, etc.

Chan's Record: [Field: Student Name, Data: Chan Tai Man], [Field: Age, Data: 17], [Field: Class, Data: 6A]

Grouping Them Up: Files

• File: A file is a collection of related records. Think of it as a whole drawer in the filing cabinet, containing all the index cards for every student in the school.
  Example: A 'Students.dat' file would contain the records of Chan Tai Man, Peter Wong, Mary Lee, and every other student.

The Big Picture: Databases

• Database: A database is an organised collection of related files. It's the entire filing cabinet! It might have a file for students, another for teachers, another for courses, and another for exam results, all linked together.
  Example: The School Database would contain the 'Students' file, the 'Teachers' file, and the 'Courses' file.

Visualising the Hierarchy:

Database (The whole school system)
   └── File (e.g., The 'Students' file)
       └── Record (e.g., All info about Chan Tai Man)
           └── Field (e.g., His 'Age')
               └── Data (e.g., '17')

Key Takeaway for Data Hierarchy

Remember this order, from largest to smallest: Database -> File -> Record -> Field.
Memory Trick: "Do Fine Restaurants Fry?" - a silly sentence to help you remember the order!

2. File Access Methods: Finding Your Data

Okay, so we have our data neatly organised in files. But how does a computer actually read the information? There are two main ways, called file access methods.

Sequential Access: The Cassette Tape Method

With sequential access, records are stored and read one after another, in a specific order (sequentially). To get to a record in the middle, you MUST go past all the records before it.

Analogy: Think of a music cassette tape. To listen to the 5th song, you have to fast-forward through the first four songs. You can't just jump straight to it.

• Advantages:
  • Simple to implement and understand.
  • Very efficient if you need to process EVERY record in the file (e.g., calculating the monthly electricity bill for every customer).
  • Can use simple, inexpensive storage media like magnetic tape.
• Disadvantages:
  • Very slow if you only need one specific record from the middle or end of the file.
  • Updating or deleting a record in the middle is difficult; you often have to rewrite the entire file.
• Applications:
  • Payroll systems: Processing the salary for every employee one by one.
  • Billing systems: Generating utility bills for all customers.
  • Backup systems: Where data is simply written out in order.

Direct Access (or Random Access): The MP3 Player Method

With direct access, the computer can jump straight to any record in a file without reading the ones before it. Each record has a unique address (like a house number) that the system can use to find it instantly.

Analogy: Think of an MP3 player or Spotify. You can instantly select and play any song you want, no matter where it is on the playlist. You don't need to listen to the songs before it.

• Advantages:
  • Very fast retrieval of individual records.
  • Easy to update, add, or delete single records without affecting the rest of the file.
• Disadvantages:
  • More complex to set up.
  • May be less efficient if you need to process all the records, as the system might be jumping all over the storage device.
  • Requires more complex storage devices like hard disks or SSDs.
• Applications:
  • ATM systems: Instantly finding your bank account information.
  • Airline reservation systems: Quickly looking up flight availability or a specific booking.
  • Online databases: Finding a specific product on a shopping website.

Key Takeaway for File Access

Sequential = In Order, One by One. (Slow for one, good for all).
Direct = Jump Straight There. (Fast for one, maybe not for all).

3. The Need for Data Control: Why Bother?

If data is wrong, it's useless! Imagine an ATM giving out the wrong amount of money or the school recording your exam marks incorrectly. That would be a disaster. Data control refers to the processes and procedures put in place to ensure that data is:

• Accurate: The data is correct and free from errors.
• Up-to-date: The data is current. (e.g., your home address is your new one, not your old one).
• Secure: The data is protected from unauthorised access or changes.
• Consistent: The same piece of data is the same everywhere it's stored.

Without data control, decisions made using the data would be unreliable, leading to mistakes, financial loss, and chaos. This is why we have methods to check and protect our data.

Key Takeaway for Data Control

Data control is all about maintaining the quality and integrity of our data, so we can trust it. It's like quality control for information.

4. Error Detection and Prevention: The Data Police

So, how do we actually control the data and keep it accurate? We use several clever techniques to find (detect) and stop (prevent) errors. Don't worry if this seems tricky at first, we'll use simple examples.

Method 1: Verification (Are you sure?)

Verification is a process to prevent errors by checking that the data entered into the system matches the original source. It's usually a human-focused check to catch typos.

• How it works: A person checks the data, or the system asks the user to enter it twice.
• Goal: To ensure the data was copied or typed correctly.
• Example 1: When you sign up for a new account, the form asks you to "Confirm your password" by typing it a second time. This verifies that you didn't make a typo the first time.
• Example 2: An office clerk reads the data from a paper form and then asks a colleague to double-check the entered information on the screen. This is also verification.

Method 2: Validation (Does this make sense?)

Validation is an automatic computer check to ensure that the data entered is reasonable, sensible, and follows a set of rules. The computer can't know if your name is *really* Chan Tai Man, but it can check if the age you entered is something crazy like 200.

• How it works: The computer program runs checks on the data as it's entered.
• Goal: To ensure the data is acceptable and logical.
• Common Validation Checks:
  • Range Check: Checks if a number is within a certain range. (e.g., Exam marks must be between 0 and 100).
  • Type Check: Checks if the data is the correct type. (e.g., An 'Age' field should only accept numbers, not letters).
  • Format Check: Checks if the data follows the correct pattern. (e.g., A Hong Kong ID card number must be in the format of 1 letter followed by 6 digits and a check digit in brackets, like 'A123456(7)').
  • Presence Check: Checks that a field has not been left empty. (e.g., The 'Email Address' field cannot be blank).

Quick Review: The BIG Difference!

This is a very common exam topic, so let's make it clear!
Verification: Did I type it in correctly? (Checks for typos)
Validation: Is what I typed in sensible? (Checks for logical errors)

Example: Imagine you are entering a student's exam mark. The original mark on the paper is 85.
- If you accidentally type 58, verification (like double-checking) would catch this error. Validation would NOT, because 58 is a perfectly valid mark between 0 and 100.
- If you accidentally type 850, validation (a range check) would catch this error because it's outside the 0-100 range.

Method 3: Parity Checking (A Quick Check for Digital Messengers)

Parity checking is a simple method used to detect errors when data is being transmitted from one place to another (e.g., over a network or from the keyboard to the computer). It checks if a single bit (a 1 or 0) has been flipped by mistake during transmission.

How it works - A Step-by-Step Guide:

Computers agree beforehand to use either even parity or odd parity.

1. The computer counts the number of '1's in a byte of data (usually 7 or 8 bits).
2. It adds an extra bit, called a parity bit, to the end.
3. • Under even parity, the parity bit is set to 1 or 0 to make the total number of '1's (including the parity bit) an even number.
   • Under odd parity, the parity bit is set to make the total number of '1's an odd number.
4. The receiving computer counts the '1's. If the count doesn't match the agreed-upon parity (even or odd), it knows an error occurred!

Let's see an example with the data `1011001` and EVEN parity:

1. Count the '1's in `1011001`: There are four '1's.
2. Is four an even number? Yes.
3. Set the parity bit: Since we already have an even number of '1's, we add a `0` as the parity bit to keep it even.
4. Data sent: `10110010` (Total '1's = 4, which is even. All good!)

What if the data was `1111001` with EVEN parity?

1. Count the '1's in `1111001`: There are five '1's.
2. Is five an even number? No.
3. Set the parity bit: We need to make the total number of '1's even, so we add a `1` as the parity bit.
4. Data sent: `11110011` (Total '1's = 6, which is even. Perfect!)

Did you know? Parity checking is a basic error detection method. It's fast but has a weakness: it can't detect an error if two bits are flipped, because the parity would still look correct! (e.g., an even number of errors).

Final Key Takeaway for Error Control

Verification = Human check for typos.
Validation = Computer check for sense.
Parity Check = A simple bit-counting check for transmission errors.