CA

Data and Data Processing Cycle - Vocabulary

Data and Information

  • Data: Raw facts/figures without inherent meaning.
  • Information: Processed, arranged data that is meaningful and relevant.
  • Relationship: The data processing cycle transforms raw data into information.
  • GIGO (Garbage In, Garbage Out): Poor data quality leads to poor information quality.

The Data Processing Cycle: Overview

  • A five-stage cycle transforming data into information.
  • Stages: Collection, Preparation, Input, Processing, Output.
  • Model: I = P(D), where I is Information, P() is processing, and D is Data.
Stage 1 — Collection
  • Purpose: Gather raw data.
  • GIGO principle applies (data quality impacts output).
Stage 2 — Preparation
  • Purpose: Clean and organize data for processing (e.g., standardizing units like ^ ext{\circ} \text{C}, defining time periods).
  • Prevents misleading results from low-quality data.
Stage 3 — Input
  • Purpose: Enter prepared data into a computer system for storage and access by software.
Stage 4 — Processing
  • Purpose: Transform raw data into information (e.g., arranging, sorting, combining, applying mathematical operations like averages).
Stage 5 — Output
  • Purpose: Present the processed information to the user in various forms (e.g., graphs, documents, audio).

Data Flow Diagram (Simplified)

  • Linear flow: Data (\rightarrow) COLLECTION (\rightarrow) PREPARATION (\rightarrow) INPUT (\rightarrow) PROCESSING (\rightarrow) OUTPUT (\rightarrow) Information.

Example: Weather Data Calculation

  • Data: Temperature readings like T = [35^\circ \text{C}, \, 34^\circ \text{C}, \dots] for Mon-Fri.
  • Processing: Calculating the average daily temperature for the week.
  • Output: Summarized information, e.g., the average temperature for the week is 34^\circ \text{C}.

Foundational Principles and Real-World Relevance

  • Data vs. Information: Raw facts vs. meaningful processed data.
  • Data Quality: Crucial; GIGO ("Garbage In, Garbage Out").
  • Relevance: Underpins systems in weather, business analytics, etc., emphasizing data integrity for reliable results and ethical decision-making.