Information Technology (9626) | Data processing and information

Welcome to AS Level IT: Data Processing and Information!

Hello! This chapter, "Data Processing and Information," is the foundation of everything we do in IT. Think of it as learning the difference between raw ingredients and a delicious, finished meal. We'll explore how simple facts turn into useful knowledge, how we keep that knowledge safe, and the different ways computers handle massive amounts of data every day.

Don't worry if some terms look new—we will break down complex concepts like encryption and validation into easy steps. Let's get started!

1.1 Understanding Data and Information

What is the Difference Between Data and Information?

This is a classic exam question, so pay close attention to this distinction!

• Data: These are raw, unstructured facts, figures, or symbols. They have no inherent meaning or context by themselves.
  Example: 25.04.2024, 45, USD, A123.


• Information: This is data that has been processed, organized, and given context, making it useful and meaningful.
  Example: On 25.04.2024, the product with code A123 sold 45 units for USD 200.

Key Takeaway: Data becomes Information through context and meaning.

Sources and Uses of Data

We gather data in two primary ways: directly (we collect it ourselves) or indirectly (we use data collected by someone else).

Direct Data (Primary Source)

This is data collected specifically for the current purpose. It is often more accurate and tailored, but usually more time-consuming and expensive to acquire.

• Sources:
  • Questionnaires and Surveys (Asking customers directly).
  • Interviews (Talking to experts or users).
  • Data Logging (Sensors automatically recording temperature, pressure, etc.).
  • Observation (Watching and recording behaviour).
• Suitability: Ideal when you need specific, highly relevant data that is up-to-date and customized to your exact study.

Indirect Data (Secondary Source)

This is data that already exists, often collected for a different purpose but useful for yours. It is usually quicker and cheaper to acquire, but may be outdated or less relevant.

• Sources:
  • Weather data (from a meteorological office).
  • Census data or electoral registers.
  • Research from textbooks, journals, and websites.
  • Personal information collected by businesses and sold to third parties (e.g., marketing lists).
• Suitability: Ideal for large-scale analysis, historical comparison, or when budget and time are limited.

1.2 Quality of Information

Even if data is processed, it isn't useful unless it's high quality. Factors affect the reliability of the resulting information:

• Accuracy: Is the data correct and free from errors? Inaccurate data leads to poor decisions.
• Relevance: Does the information actually relate to the task or question being asked?
• Age (Timeliness): Is the information current? Information that is too old may be irrelevant (e.g., yesterday's stock price).
• Level of Detail: Is there enough detail for the purpose? (Too little detail means missing context; too much detail can overwhelm the user).
• Completeness: Is all the necessary data present? Missing fields or gaps reduce the reliability.

Analogy: If you are calculating monthly sales, knowing the total figure (low detail) might be fine for the CEO, but the Sales Manager needs sales broken down by product and region (high detail).

1.3 Encryption: Keeping Data Safe

The need for encryption is critical because data often travels across networks or is stored in accessible locations. Encryption ensures that even if unauthorized people gain access, the data remains unreadable.

Encryption is the process of converting information or data into a code (ciphertext) to prevent unauthorized access.

Methods of Encryption

Encryption relies on a mathematical process (an algorithm) and a key (a secret value) to scramble and unscramble the data.

1. Symmetric Encryption (Private Key Only):
   

   Uses the same key for both encrypting and decrypting data. It is fast, but the challenge is securely sharing the private key with the recipient.
   Example: Locking a diary with a single, shared key.

2. Asymmetric Encryption (Private and Public Keys):
   

   Uses two mathematically related keys: a Public Key (shared widely) and a Private Key (kept secret by the owner). Data encrypted with the public key can only be decrypted with the matching private key (and vice versa). This solves the secure key-sharing problem.
   Example: Sending a locked box (public key) that only the recipient has the master key (private key) to open.

Encryption Protocols

These are standard sets of rules used to manage secure communication, especially in client-server communication (like browsing a website).

• TLS/SSL (Transport Layer Security / Secure Socket Layer):
  • Purpose: Used to secure communication over a network, most commonly seen when you browse a secure website (HTTPS).
  • How it works: Ensures data exchanged between your web browser (client) and the website server is private and tamper-proof. It uses asymmetric encryption initially to securely agree on a symmetric key for the bulk of the data transfer.
• IPsec (Internet Protocol Security):
  • Purpose: A suite of protocols used to secure IP communication. It is often used to set up Virtual Private Networks (VPNs).
  • How it works: It operates at the network level, securing data packets as they travel across the internet, protecting entire communication flows.

Uses of Encryption

Encryption is vital for:

• Protection of Data: Securing files stored on hard drives (disk encryption).
• Systems Encryption: Protecting login credentials and communication channels within large systems (like banking or government systems).

1.4 Checking the Accuracy of Data

Before processing data, we must ensure it is correct. We use two main methods: Validation and Verification.

Validation (Making sure data is REASONABLE)

Validation checks if data entered falls within acceptable limits or matches defined rules. It does not guarantee the data is factually correct, only that it is suitable for the system.

• Types of Validation Checks:
  • Presence Check: Ensures a mandatory field has been filled (e.g., a customer must enter their name).
  • Range Check (Limit Check): Checks if the data is within a specified minimum and maximum value (e.g., age must be between 18 and 99).
  • Type Check: Ensures the data is of the correct data type (e.g., a phone number field contains only numbers, not letters).
  • Length Check: Checks the number of characters (e.g., a product code must be exactly 6 characters long).
  • Format Check: Checks if the data conforms to a specific pattern or structure (e.g., an email address must contain an '@' symbol).
  • Lookup Check: Compares input against a list of acceptable values stored elsewhere (e.g., comparing a country code against a database list).
  • Consistency Check: Checks if data in different fields is logically consistent (e.g., a delivery date cannot be earlier than the order date).
  • Check Digit: An extra digit calculated from the rest of the code, used to detect miskeying errors (common for ISBNs or barcodes).

Verification (Making sure data is CORRECT)

Verification checks if the data entered into the system exactly matches the original source data.

• Types of Verification Methods:
  • Visual Checking: A person manually looks at the screen input and compares it to the source document. (Slow but effective for catching obvious mistakes).
  • Double Data Entry: The data is entered twice by two different people or systems. The system then compares the two inputs and flags any discrepancies. (Highly effective for accuracy, but doubles the workload).
  • Parity Check/Checksum/Hash Total/Control Total: These are technical checks used primarily in transmission or batch processing to ensure data hasn't been corrupted or lost during transfer.
  • Control Total/Hash Total: A non-meaningful number derived from the data (e.g., summing up all customer ID numbers). This total is checked after processing to ensure all records were included.

1.5 Data Processing Methods

Once we have quality data, we need to process it. The method chosen depends entirely on the application's requirements, especially how quickly the output is needed and whether the input affects the process itself.

1. Batch Processing

Data is collected over a period of time and processed together in a group (a "batch") at a scheduled time, usually when computer use is low (e.g., overnight).

• Characteristics:
  • No immediate interaction needed.
  • Ideal for large volumes of data that require extensive processing power.
• Uses:
  • Calculating utility bills (electricity, water).
  • Updating credit card and debit card accounts (transactions are batched up and processed nightly).
  • Payroll processing and customer accounts updates.
• Sequential Master File Update Process:
  1. Data is collected into a Transaction File (all the new changes).
  2. The Master File (the main record) and the Transaction File are sorted into the same order (usually by primary key).
  3. The system reads both files sequentially, comparing records.
  4. The Master File is updated based on the Transaction File records, creating a new, updated Master File.

2. Online Processing

Data is processed immediately upon entry, typically involving direct interaction with a user or system, but the action *does not* necessarily affect the physical environment in real-time.

• Characteristics:
  • Requires immediate feedback or transaction completion.
  • Involves real-time input by a user.
• Uses and Steps Involved:
  • Electronic Funds Transfer (EFT): When you use a debit card, the transaction is immediately processed to check funds and update your bank balance.
  • Online Shopping: Placing an order updates stock levels immediately.
  • Automatic Stock Control: Systems update inventory as items are sold.
  • Electronic Data Interchange (EDI): Automatic transfer of documents (like invoices or orders) between businesses.
  • Business-to-Business (B2B) buying and selling.

3. Real-Time Processing (Crucial Link: Output Affects Input)

This is a specific type of online processing where the output of the process directly influences or controls the next input. The response time is instantaneous, as delays can be critical.

• Characteristics:
  • Usually controlled by a microprocessor or specialized computer system.
  • Continuous feedback loop: Sense (Input) -> Process -> Actuate (Output) -> Sense...
• Uses (Microprocessor-controlled Systems):
  • Greenhouses/Central Heating/Air Conditioning: Sensors measure temperature (input); if too cold, the system turns on a heater (output).
  • Burglar Alarms: Motion sensors (input) trigger a siren (output).
  • Control of Traffic/Pedestrian Flow: Traffic lights adjust based on vehicle presence detected by sensors.
  • Car Park Barriers: Sensors detect tickets, prompting the barrier actuator to lift or remain closed.
• Wireless Sensor and Actuator Networks (WSANs): These are groups of devices that sense conditions and then act upon them wirelessly.
  • Smart Homes: Controlling lighting or security based on ambient conditions.
  • Autonomous Vehicles (Cars, Drones, Ships): Constantly sensing environment (distance, speed, obstacles) and immediately adjusting steering, speed, or altitude (output).
  • Guidance Systems (Space Rockets): Sensing current position and making immediate trajectory corrections.

Chapter 1: Final Summary

We have established the essential relationship between Data (raw facts) and Information (processed, contextualized data). We learned that data must be of high Quality (accurate, relevant, timely) and protected through Encryption (symmetric and asymmetric protocols like TLS/SSL). Finally, we saw how data integrity is maintained using Validation (checks for reasonableness) and Verification (checks for accuracy against the source), and explored the different scenarios where Batch, Online, and Real-Time Processing are applied.