OCR F200 Topic Area 1: Understanding Data

Vocabulary flashcards covering core definitions, file formats, data types, and data classifications from OCR Level 3 Cambridge Advanced National in IT Unit F200 (Topic Area 1).

Data

Raw, unprocessed facts, figures, observations or measurements that on their own may lack context or meaning.

Information

Data that has been processed, organised or presented so that it has context and meaning.

Knowledge

Understanding gained by interpreting information alongside experience, expertise or insight to help make decisions or solve problems.

Big Data

Extremely large data sets that are too complex to be processed using traditional data processing methods. Big data can include structured, semi-structured and unstructured data

Structured Data

data that is arranged using a predefined format in rows and columns

Semi-Structured Data

Semi-structured data is data that lacks a rigid schema but contains tags or markers, like XML, to organize and separate information.

Unstructured Data

data that lacks a predefined format, like text documents, emails, social media posts, videos, audio files

Data Format

The way data is represented, encoded or organised in a file, affecting compatibility, storage size, quality, readability and which applications can use it.

ASCII

A character-encoding standard that assigns numeric codes to characters, where standard ASCII has $128$ codes (for example, uppercase A is code $65$).

AIFF (Audio Interchange File Format)

An audio format associated with Apple, commonly used to store high-quality, often uncompressed audio.

WAV / WAVE (Waveform Audio File Format)

An audio format developed by Microsoft and IBM that commonly stores high-quality, often uncompressed audio.

XML (Extensible Markup Language)

A markup language that uses tags to structure and label data, commonly used for data exchange and configuration.

JSON (JavaScript Object Notation)

A lightweight text format that represents data using key–value pairs and arrays, commonly used in web/mobile applications and APIs.

JPEG / JPG

An image format commonly used for photographs and web images that typically relies on lossy compression, reducing file size by discarding some image detail.

PNG

An image format that supports lossless storage and transparency, suitable for logos, icons and graphics with sharp edges.

TIFF

A high-quality image format used in scanning, publishing and professional workflows that preserves detail and supports rich image data.

Numeric Format

A format designed to store numerical values so software can execute mathematical calculations, sorting and statistical comparisons.

Plain Text (TXT)

A text format without rich formatting that is simple, portable and easy to open, but does not inherently preserve complex structure.

CSV (Comma-Separated Values)

A lightweight text format that stores table-like data as rows, with values separated by commas.

Fixed-Width Text

A text format where each field occupies a defined character width so columns align by character position rather than separators.

Boolean

A data type that holds only two logical values, usually TRUE or FALSE, used for flags, eligibility, authentication states and filters.

Character

A data type representing a single letter, digit or symbol, such as a single grade or initial.

Date

A data type that represents a calendar date and sometimes time, enabling chronological sorting and date calculations.

Integer

A data type representing whole numbers without a fractional part, used for counts, quantities and indexes.

Real

A data type representing numbers that can include a fractional or decimal part, used for measurements, prices and statistical values.

String

A data type representing a sequence of characters, such as a name, address or sentence.

Qualitative Data

Non-numerical descriptions, categories, opinions or qualities gathered via open questions, observations and text that provide rich detail and context.

Quantitative Data

Numerical values that can be counted or measured to support calculations, statistics and trend comparisons.

1.1 Data, Information and Knowledge

Data

• Meaning: Raw, unprocessed facts, figures, symbols, or measurements that lack context and meaning on their own.

• Examples: Standalone numbers such as $18$, $21$, $25$; individual sensor readings; unlabelled till scan values.

1.1 Data, Information and Knowledge

Information

• Meaning: Data that has been processed, organised, or presented in context so that it becomes meaningful, structured, and useful.

• Examples: A weekly sales report; a line graph displaying daily temperature changes; a student report card showing subject averages.

1.1 Data, Information and Knowledge

Knowledge

• Meaning: The actionable understanding and insight gained by interpreting information using personal experience, skills, and human judgement to make decisions or solve problems.

• Examples: A store manager seeing falling sales figures and deciding to launch a discount promotion; a doctor reviewing lab test results to prescribe treatment.

1.1 Data, Information and Knowledge

Differences and Interaction (The DIK Flow)

• The Chain: $\text{Data} \rightarrow \text{Processing} \rightarrow \text{Information} \rightarrow \text{Human Insight} \rightarrow \text{Knowledge} \rightarrow \text{Action}$

• Key Differences:

  • Data consists of raw, unorganised facts with no context.

  • Information gives structure and meaning to data.

  • Knowledge applies human experience to information to guide practical decisions.

• Feedback Loop: Taking an action produces new real-world results and fresh data, repeating the cycle.

1.1 Data, Information and Knowledge

Sources of Data, Information and Knowledge

• Sources of Data: Digital sensors, till checkouts (EPOS), online questionnaires, web server click logs, satellite monitors, and social media posts.

• Sources of Information: Analytical dashboards, summary reports, spreadsheet charts, database queries, and published articles.

• Sources of Knowledge: Industry experts, personal work experience, training manuals, textbooks, and learned practical skills.

1.1 Data, Information and Knowledge

Stages to Convert Data into Information

1. Collect: Gather raw data from a relevant source (e.g. scanning barcodes at a till).

2. Organise: Sort, group, or filter the raw data (e.g. grouping sales records by department).

3. Process and Analyse: Calculate totals, averages, or identify patterns (e.g. calculating daily revenue).

4. Contextualise: Add meaningful labels, units, dates, or comparisons.

5. Present: Display the result in a clear format such as a chart, table, or dashboard.

1.1 Data, Information and Knowledge

Role of Data Quality in Information Conversion

• Core Principle: The quality and reliability of the resulting information depends completely on the quality of the input data ('Garbage In, Garbage Out').
• Accurate: Incorrect figures lead to false reports and poor business decisions.
• Complete: Missing values leave critical blind spots in analysis.
• Timely: Outdated records cause slow reactions to changing situations.

1.1 Data, Information and Knowledge

Benefits and Limitations of Data and Information for Organisations

• Benefits:
  • Enables evidence-based strategic decision-making rather than guesswork.
  • Identifies operational delays to improve workplace efficiency.
  • Highlights customer buying habits to maintain optimal stock levels.
• Limitations:
  • High financial costs to collect, store, and analyse data securely.
  • Poor-quality or biased data leads to flawed strategic decisions.
  • Information overload can overwhelm employees.

1.1 Data, Information and Knowledge

Benefits and Limitations of Data and Information for Individuals

• Benefits:
  • Tailored recommendations for entertainment, news, and shopping.
  • Everyday convenience, such as live GPS navigation and smart scheduling.
  • Easy tracking of personal health, fitness, and educational progress.
• Limitations:
  • Loss of personal privacy through continuous behavioral tracking.
  • Risk of identity theft or financial fraud following a data breach.
  • Potential for unfair profiling or automated exclusion.

1.1 Data, Information and Knowledge

Applications of Data and Information in Society

• Healthcare: Electronic patient records and health trends help plan treatments and track disease outbreaks.
• Education: Attendance and assessment records help teachers identify pupils requiring extra support.
• Public Services: Traffic sensor data and census records help councils plan public transit and budget allocations.
• Business: Sales transaction data helps shops minimise stock waste and offer targeted discounts.

1.1 Data, Information and Knowledge

Negative Societal Impacts of Data and How to Reduce Them

• Negative Impacts:

  • Algorithmic Bias: Automated models trained on historical data producing unfair or discriminatory decisions.

  • Privacy Infringement: Persistent monitoring of individual movements, online browsing, and messages.

  • Misinformation: False or manipulated data presented as factual information.

• Ways to Reduce Harm:

  • Implement strict cybersecurity and access controls.

  • Follow legal frameworks such as data protection legislation.

  • Conduct regular fairness audits on algorithms and improve public data literacy.

1.1 Data, Information and Knowledge

Scenario: Data, Information and Knowledge in a Weather App

• Scenario: Explaining how a mobile weather app applies data, information, and knowledge.

• Application:

  • Data: Unprocessed temperature and air pressure readings collected by weather sensors.

  • Information: An in-app forecast graph showing a $90\%$ probability of heavy rain this afternoon.

  • Knowledge: A commuter reads the forecast and uses past experience to carry an umbrella and depart $10$ minutes early.

1.1 Data, Information and Knowledge

Scenario: Customer Purchase Data for a Supermarket

• Scenario: Evaluating the use of loyalty card purchase records by a supermarket chain.

• Application:

  • Benefit: Identifying high-demand grocery items at weekends to order stock accurately and avoid food waste.

  • Limitation: Storing customer purchase histories creates privacy liabilities; an unauthorised breach could expose personal data and damage consumer trust.

1.2 Big Data

Big Data (Definition and Characteristics)

• Meaning: Datasets characterised by such massive volume, velocity, and variety that traditional data processing tools and standard databases cannot capture, manage, or analyse them efficiently.
• Key Characteristics:
  • High volume (vast quantities of data).
  • High velocity (generated and collected at high speed in real time).
  • High variety (contains structured, semi-structured, and unstructured forms).
  • The goal is extracting useful patterns and actionable value.

1.2 Big Data

Primary Sources of Big Data

• Online Activity: Web search queries, website navigation trails, and user clickstreams.
• Social Media: Live streams of textual posts, photo uploads, video shares, and comments.
• Internet of Things (IoT): Connected smart energy meters, environmental monitors, and road cameras.
• Mobile Telemetry: Real-time GPS location tracking and smartphone fitness monitors.
• Financial Systems: Millions of real-time card transactions and stock market trades.

1.2 Big Data

Data Structures Found in Big Data

• Structured: Organized in strict tabular formats with rows and columns (e.g. relational SQL databases).
• Semi-Structured: Contains tags or markers to separate values without a rigid table layout (e.g. XML documents, JSON payloads).
• Unstructured: Lacks any predefined data model or layout (e.g. free-text emails, audio recordings, video files).
• Key Fact: Big data environments regularly capture and analyse all three structures together.

1.2 Big Data

Uses and Applications of Big Data

• Healthcare: Tracking global epidemiological trends and analysing genetic data for personalized medicine.
• Transport: Using real-time vehicle GPS feeds to adjust traffic signals and optimize delivery routes.
• Retail and E-Commerce: Driving automated recommendation engines and forecasting warehouse demand.
• Financial Services: Identifying fraudulent credit card transactions within fractions of a second.
• Environmental Monitoring: Processing satellite images and ocean buoy data to forecast extreme weather.

1.2 Big Data

Benefits and Limitations of Big Data for Organisations

• Benefits:
  • Reveals subtle consumer trends and patterns across millions of records.
  • Improves forecasting accuracy for stock inventory, staffing, and revenue.
  • Enables automated, real-time personalization of digital services.
• Limitations:
  • Substantial financial investment required for cloud storage and compute power.
  • High technical difficulty of combining disparate data structures from different sources.
  • Poor-quality or unverified inputs can lead to flawed automated conclusions.

1.2 Big Data

Benefits and Limitations of Big Data for Individuals

• Benefits:
  • Highly relevant entertainment, search, and retail recommendations.
  • Shorter journey times through dynamic GPS routing and public transit updates.
  • Enhanced healthcare interventions informed by broad medical data analysis.
• Limitations:
  • Intrusive behavioral profiling and loss of individual privacy.
  • High risk of exposure in large-scale corporate data breaches.
  • Automated decisions (such as loan rejections or insurance pricing) made without transparent explanations.

1.2 Big Data

Scope Boundaries of Big Data (OCR F200 Specification)

• Included in Exam Scope:
  • Core concept, definitions, and defining characteristics of big data.
  • Typical sources and the data structures it contains (structured, semi-structured, unstructured).
  • Practical uses, advantages, and limitations for organisations and individuals.
• Excluded from Exam Scope:
  • Historical evolution and architectural timelines of big data.
  • Detailed ETL data preparation pipelines.
  • Advanced mathematical data mining algorithms.
  • Distributed server cluster hardware configurations.

1.2 Big Data

Scenario: Big Data in Urban Transport Systems

• Scenario: A city transport authority uses big data to reduce commuter congestion.
• Application:
  • Use: Ingest real-time transit card taps, bus GPS transponders, and road traffic camera feeds.
  • Advantage: Dynamically adjusts underground train frequency and traffic light timing during peak delays.
  • Limitation: High maintenance costs for real-time sensor networks and public concerns regarding constant location monitoring.

1.3 Data and File Formats

Data Format (Concept and Selection Criteria)

• Meaning: The specific method used to encode, organise, and store data inside a computer file.
• Why It Matters: Determines storage size, processing speed, visual or auditory quality, and software compatibility.
• Selection Criteria:
  • Intended purpose (visual display, audio mastering, relational storage, or web transfer).
  • Target software and operating system compatibility.
  • File size constraints versus quality requirements (lossy vs lossless).
  • Need for transparency, calculations, or relational table structures.

1.3 Data and File Formats

ASCII

• Full Name: American Standard Code for Information Interchange.
• Features: A $7$-bit character-encoding standard providing $128$ unique numeric codes (e.g. uppercase 'A' is code $65$).
• Uses: Basic text files, low-level terminal communications, and legacy configuration files.
• Advantages: Minimal storage overhead; universal compatibility across all operating systems.
• Disadvantages: Restricted to $128$ characters; cannot represent non-Latin alphabets, accented characters, or emojis.

1.3 Data and File Formats

AIFF

• Full Name: Audio Interchange File Format.
• Features: An uncompressed, lossless audio container format primarily associated with Apple that preserves original sound waveforms.
• Uses: Studio sound recording, professional audio editing, and mastering on macOS.
• Advantages: Pristine sound fidelity; no acoustic details are discarded; ideal for repetitive studio editing.
• Disadvantages: Very large file sizes; impractical for online streaming or mobile storage; less standard on Windows.

1.3 Data and File Formats

WAV / WAVE

• Full Name: Waveform Audio File Format.
• Features: A high-quality, uncompressed (lossless) audio format developed jointly by Microsoft and IBM.
• Uses: Professional audio editing, studio recording, and Windows system audio.
• Advantages: Bit-perfect sound reproduction; universal compatibility across Windows platforms and audio software.
• Disadvantages: Very large file sizes; demands substantial bandwidth, making it unsuited for web downloads or mobile streaming.

1.3 Data and File Formats

Comparison: AIFF versus WAV

• Similarities: Both store uncompressed, lossless audio, provide high fidelity, and generate large file sizes.
• Differences:
  • AIFF was developed by Apple and is native to macOS workflows.
  • WAV was developed by Microsoft/IBM and is the standard on Windows.
• Suitability: Use AIFF when working primarily within Apple production ecosystems; use WAV for Windows-based or general audio production.

1.3 Data and File Formats

XML

• Full Name: Extensible Markup Language.
• Features: A text-based markup language that uses custom hierarchical tags to structure and label data.
• Uses: Transferring data between incompatible enterprise systems, configuration files, and web services.
• Advantages: Self-describing and human-readable; highly flexible; supports strict schema validation rules.
• Disadvantages: Verbose syntax with repetitive opening and closing tags, creating large files that require more processing to parse.

1.3 Data and File Formats

JSON

• Full Name: JavaScript Object Notation.
• Features: A lightweight, text-based data format that structures data using key-value pairs and arrays.
• Uses: Web APIs, mobile app data transfer, and configuration files.
• Advantages: Compact and lightweight; fast for web browsers and apps to parse; easy for humans to read and write.
• Disadvantages: Lacks built-in mandatory schema validation; cannot store code comments in standard formats.

1.3 Data and File Formats

Comparison: XML versus JSON

• Structure: XML relies on opening and closing tags; JSON uses key-value pairs and brackets.
• File Size: JSON is more compact and produces smaller data transfer payloads than verbose XML.
• Parsing Speed: JSON is parsed faster and with less memory overhead by mobile devices and web browsers.
• Validation: XML provides more mature enterprise schema validation tools.
• Suitability: Use JSON for web and mobile APIs; use XML for complex enterprise architectures requiring formal validation schemas.

1.3 Data and File Formats

JPEG / JPG

• Full Name: Joint Photographic Experts Group.
• Features: A raster image format that uses lossy compression to discard image data not easily noticed by the human eye.
• Uses: Digital camera photographs and general website imagery.
• Advantages: High compression ratios resulting in small file sizes; fast to transmit; universal platform support.
• Disadvantages: Irreversible quality loss each time the file is re-saved; does not support transparent backgrounds.

1.3 Data and File Formats

PNG

• Full Name: Portable Network Graphics.
• Features: A raster image format that uses lossless compression, preserving all original pixel data.
• Uses: Website interface logos, icons, technical diagrams, and graphics requiring sharp edges.
• Advantages: Lossless compression maintains image quality; supports alpha-channel transparency.
• Disadvantages: Produces significantly larger file sizes than JPEG when storing continuous-tone photographs.

1.3 Data and File Formats

TIFF

• Full Name: Tagged Image File Format.
• Features: A professional graphics container format that stores uncompressed or losslessly compressed images with high colour depth.
• Uses: Commercial print publishing, document scanning, and professional photo archiving.
• Advantages: Uncompromised image fidelity with zero compression artifacts; supports professional CMYK print colours.
• Disadvantages: Extremely large file sizes; not natively supported by standard web browsers.

1.3 Data and File Formats

Comparison: JPEG versus PNG versus TIFF

• Compression Type: JPEG is lossy; PNG and TIFF are lossless.
• Transparency Support: PNG supports alpha transparency; standard JPEG and web TIFF do not.
• File Size: JPEG is smallest; PNG is intermediate; TIFF is very large.
• Best Use Cases:
  • JPEG: Online photographic galleries and responsive websites.
  • PNG: Website logos, icons, and graphics requiring transparent backgrounds.
  • TIFF: High-resolution document scanning, professional photography archiving, and commercial printing.

1.3 Data and File Formats

Numeric Data Format

• Features: Stores numerical data in binary representations specifically configured for mathematical operations.
• Uses: Spreadsheet calculation columns, accounting ledgers, and database metric fields.
• Advantages: Enables software to execute arithmetic calculations, sorting, and statistical comparisons directly.
• Disadvantages: Subject to precision and rounding errors in floating-point values; formatting mismatches can cause import failures.

1.3 Data and File Formats

Plain Text (TXT)

• Features: Unformatted character streams without font styles, colours, margins, or embedded media.
• Uses: Readme documentation, batch scripts, and system error logs.
• Advantages: Universally compatible across all platforms; very lightweight; cannot contain malicious embedded macros.
• Disadvantages: Does not support text formatting (no bold or italics); cannot represent structured tables natively.

1.3 Data and File Formats

CSV

• Full Name: Comma-Separated Values.
• Features: A plain text format that organises tabular records into lines, with column values separated by commas.
• Uses: Transferring tabular data between incompatible spreadsheets, databases, and reporting systems.
• Advantages: Lightweight, human-readable, and universally supported across data analysis tools.
• Disadvantages: Cannot store formulas, text formatting, or multi-sheet workbooks; data containing commas requires escaping.

1.3 Data and File Formats

Fixed-Width Text

• Features: A plain text format where every data column occupies an exact, predetermined number of characters, padded with spaces.
• Uses: Legacy banking mainframes, government payroll exports, and batch data processing.
• Advantages: Consistent layout where data aligns by column position without needing separator characters.
• Disadvantages: Inflexible; truncates data that exceeds the set width; inflates file size with unnecessary padding spaces.

1.3 Data and File Formats

Comparison: CSV versus Fixed-Width Text versus Plain Text (TXT)

• Field Separation: CSV uses commas; Fixed-Width uses character column positions; TXT has no field separation.
• Tabular Capability: CSV and Fixed-Width are designed for tabular data; TXT stores unstructured text.
• Flexibility: CSV easily handles variable-length fields; Fixed-Width cuts off text that exceeds the column width.
• Suitability: Use CSV for modern database exports; use Fixed-Width for legacy mainframe transfers; use TXT for simple unformatted notes.

1.3 Data and File Formats

Scenario: Choosing and Justifying File Formats

• Scenario 1 (Website Logo Over a Coloured Header): Select PNG because it offers lossless compression for crisp edges and supports alpha transparency.
• Scenario 2 (Mobile Weather Application Live Feed): Select JSON because its lightweight key-value structure minimises bandwidth and is parsed efficiently by mobile apps.
• Scenario 3 (High-Resolution Magazine Cover for Print): Select TIFF because it preserves uncompressed image detail and supports professional CMYK print profiles.

1.4 Data Types and Data Classifications

Boolean Data Type

• Meaning: A data type that stores only one of two possible logical states: $\text{TRUE}$ or $\text{FALSE}$ (or $1$ and $0$).
• Suitable Uses: Binary flags, yes/no responses, account active states, and user consent checkboxes.
• Example: $\text{EmailOptIn} = \text{TRUE}$
• Advantages: Extremely memory-efficient; ideal for controlling logical program flow.
• Disadvantages: Cannot represent intermediate states, degrees of certainty, or descriptive detail.

1.4 Data Types and Data Classifications

Character Data Type

• Meaning: A data type that stores a single letter, digit, punctuation mark, or symbol.
• Suitable Uses: Middle initials, single-letter exam grades, or single-character menu choices.
• Example: 'A' (for an exam grade) or 'M' (for a middle initial).
• Advantages: Highly memory-efficient ($1$ byte); simple to validate.
• Disadvantages: Cannot store words, phrases, or multi-character numbers.

1.4 Data Types and Data Classifications

Date Data Type

• Meaning: A dedicated data type that stores calendar dates and optionally timestamps in a structured format.
• Suitable Uses: Dates of birth, booking appointments, transaction records, and invoice deadlines.
• Example: 15/09/2024 or 2024-09-15
• Advantages: Enables chronological sorting and mathematical calculations (e.g. determining age or elapsed days).
• Disadvantages: Inconsistent regional formats (e.g. $\text{DD/MM/YYYY}$ vs $\text{MM/DD/YYYY}$) can lead to parsing errors.

1.4 Data Types and Data Classifications

Integer Data Type

• Meaning: A data type that stores whole positive or negative numbers without decimal or fractional parts.
• Suitable Uses: Discrete counts, stock quantities, attendance figures, and primary key database IDs.
• Example: $45$ items in stock, or customer ID $1042$.
• Advantages: Exact arithmetic without rounding errors; fast processing and low memory use.
• Disadvantages: Cannot store fractional values, percentages, or measurements (e.g. $2.5$ is rounded or truncated).

1.4 Data Types and Data Classifications

Real Data Type

• Meaning: A data type that stores numbers containing fractional or decimal components (floating-point numbers).
• Suitable Uses: Currency amounts, physical measurements, weights, temperatures, and exam percentages.
• Example: $19.99$, $36.6$, or $84.5\%$.
• Advantages: Accommodates continuous data scales and high-precision measurements.
• Disadvantages: Prone to slight floating-point rounding errors; uses more memory than integer storage.

1.4 Data Types and Data Classifications

String Data Type

• Meaning: A data type that stores an indexed sequence of alphanumeric characters, symbols, and spaces.
• Suitable Uses: Customer names, postal addresses, phone numbers with leading zeros, and user comments.
• Example: "Jane Smith", "10 High Street", or "07123456789".
• Advantages: Flexible; stores any combination of text, digits, and special characters.
• Disadvantages: Consumes more storage than numeric types; cannot be used directly in calculations without conversion.

1.4 Data Types and Data Classifications

Scenario: Selecting Data Types for a Student Database

• Student ID (4021): Integer (whole number count) or String (if formatted with leading zeros).
• Student First Name ("Amira"): String (sequence of text characters).
• Course Grade ('A'): Character (a single letter grade).
• Exam Percentage (87.5): Real (requires decimal precision).
• Enrolment Date (04/09/2024): Date (supports date calculations and chronological sorting).
• Tuition Fees Paid (TRUE): Boolean (binary true/false flag).

1.4 Data Types and Data Classifications

Qualitative Data

• Meaning: Non-numerical descriptive data that captures opinions, feelings, categories, or observational qualities.
• Examples: Open customer survey feedback, doctor's clinical notes, interview transcripts, and car colours.
• Advantages: Provides rich detail, context, and reasons behind human behaviors.
• Disadvantages: Subjective and difficult to summarise, compare, or evaluate using automated statistical tools.

1.4 Data Types and Data Classifications

Quantitative Data

• Meaning: Numerical data that can be counted or measured on a numerical scale.
• Examples: Test scores, daily temperature measurements, website visitor counts, and heights.
• Advantages: Objective, precise, and easy to analyse using mathematical averages, charts, and statistical tests.
• Disadvantages: Lacks contextual explanation; explains what happened but not why.

1.4 Data Types and Data Classifications

Comparison: Qualitative versus Quantitative Data

• Nature: Qualitative is descriptive and text-based; Quantitative is numerical and measurable.
• Collection Methods: Qualitative uses open interviews and observation; Quantitative uses sensors, counts, and closed survey scales.
• Analysis Methods: Qualitative is analysed by grouping into themes; Quantitative is analysed using mathematical formulas and charts.
• Core Role: Qualitative explains why; Quantitative measures how many or how much.

1.4 Data Types and Data Classifications

Structured Data

• Meaning: Data organised in a rigid, predefined tabular format with defined rows and columns.
• Examples: Relational database tables, spreadsheets, and CSV files.
• Advantages: Simple to search, filter, validate, and query using software such as SQL.
• Disadvantages: Inflexible; difficult to adapt when new or unexpected attributes need to be added quickly.

1.4 Data Types and Data Classifications

Unstructured Data

• Meaning: Data that lacks a predefined conceptual schema, data model, or tabular layout.
• Examples: Customer emails, social media videos, audio recordings, satellite imagery, and free-text documents.
• Advantages: Captures rich, authentic real-world detail and narrative context.
• Disadvantages: Difficult to search, index, and query without specialised natural language processing or AI tools.

1.4 Data Types and Data Classifications

Semi-Structured Data

• Meaning: Data that does not adhere to a rigid tabular schema but contains internal tags, keys, or markers to separate data elements.
• Examples: XML documents, JSON payloads, and web pages with HTML tags.
• Advantages: More flexible than rigid tables; easier to organise and parse than completely unstructured text.
• Disadvantages: Verbose compared to raw binary data; lacks the strict integrity constraints of relational databases.

1.4 Data Types and Data Classifications

Distinction: Nature of Data versus Organisation of Data

• Nature of Data (Qualitative vs Quantitative): Distinguishes what the data represents—descriptive qualities vs measurable numbers.

• Organisation of Data (Structured vs Unstructured): Distinguishes how the data is formatted—in a predefined table schema vs free-form layout.

• Cross-Classification Examples:

  • Database column of financial balances: Quantitative and Structured.

  • Numbers mentioned in the body of an email: Quantitative and Unstructured.

  • Database column of predefined customer categories: Qualitative and Structured.

  • Recorded audio interview: Qualitative and Unstructured.

1.4 Data Types and Data Classifications

Scenario: Classifying Patient Healthcare Data

• Patient Heart Rate (72\,\text{bpm}): Quantitative and Structured (a numerical measurement recorded in a defined database field).

• Doctor's Clinical Notes: Qualitative and Unstructured (descriptive free-text observations about patient recovery).

• Blood Group ('O+'): Qualitative and Structured (a categorical label stored in a predefined database field).

• Consultation Video Recording: Qualitative and Unstructured (rich video and audio containing observational dialogue).