Models, Information & Information Systems

The study of information: representation, processing, and communication

Informatics

The study of information: how it is represented, processed, and communicated.

Model

A representation of something in the world.

Abstraction

An abstract model is simpler than the real thing.

  • Represents a snapshot; becomes more inaccurate over time
  • Choices are made about what to include/exclude
  • Different models can exist of the same thing

Instantiation

Building something from a template model by adding detail.

  • Instantiations vary (no two exactly the same)
  • Model can be altered during instantiation
  • Model becomes dated over time

Data, knowledge, information

  • Knowledge: Captured by a model (rules/understanding)
  • Data: Describes a specific case
  • Information: Knowledge + data (applying knowledge to infer something)

Language

  • Terminology: Words/labels
  • Grammar: How terms relate
  • Helps label and quantify real-world data

System

A model with multiple entities/components.

Features: inputs/outputs, emergent behaviour, environmental impact, component parts, feedback, purposive and arbitrary

Information system

A system that transforms data into information; component parts are themselves models ("model of models").

Quiz

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❓ What is a model?
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❓ What is information?
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❓ What is an information system?
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❓ What is the correct order of the DIKW hierarchy?
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A model is a representation of something in the world. An abstract model is simpler than the real thing, built for a particular purpose. Models are informed by the world (abstraction) and realised in the world (instantiation).

Key concepts

🎯 Abstraction

Creating a model informed by the real world:

  • Simpler than reality
  • Captures a snapshot in time
  • Choices made about what to include/exclude
  • Different models can represent the same thing
  • Built for a specific purpose

Example: A map abstracts roads and landmarks, excluding building interiors.

⚙️ Instantiation

Realising a model in the world by adding detail:

  • Building from a template model
  • Each instance varies (no two exactly the same)
  • Model can be altered during instantiation
  • Template becomes dated over time
  • No general-purpose template model exists

Example: Building a house from blueprints—each house differs slightly.

1. Conceptual models

Abstract representations that describe concepts and their relationships.

  • Entity-Relationship (ER) diagrams
  • UML class diagrams
  • Business process models
Think of a library system. What are the main concepts? (Books, Members, Loans) How do they relate? Draw your own conceptual model.

2. Logical models

More detailed models that define structure without implementation specifics.

  • Normalised database schemas
  • Data flow diagrams
  • System architecture diagrams
For the library system, define the attributes: Book(ISBN, Title, Author), Member(ID, Name, Email), Loan(Book_ISBN, Member_ID, Date)

3. Physical models

Implementation-specific models showing actual system structure.

  • Database physical schemas
  • Network topology diagrams
  • Deployment diagrams
Consider: MySQL vs PostgreSQL, AWS vs Azure, indexes on ISBN and Member_ID for faster queries.

Why use models?

  • Simplification: models are simpler than reality, focusing on essential elements
  • Communication: provide a shared language for stakeholders
  • Purpose-driven: built for specific tasks or decisions
  • Temporal: capture a snapshot, but become outdated as reality changes

Build a model

A model is a computational abstraction that captures the structure, behaviour, and relationships of a real-world system in a form that can be processed, analysed, and simulated.

📚 Library system

Model how books, members, and loans interact

🏥 Healthcare system

Model patient records, appointments, and treatments

🛒 E-Commerce platform

Model products, customers, orders, and inventory

📱 Social network

Model users, posts, connections, and interactions

Information transformation is the process of converting raw data into actionable knowledge through computational processing, algorithms, and human interpretation.

Transform data > information > knowledge

Input your data

↓

Processing steps

    ↓

    Information generated

    ↓

    Knowledge and insights

    This demonstrates data processing, statistical analysis, and pattern recognition—core informatics techniques for extracting meaning from data.

    Information: from data to wisdom

    Knowledge is captured by a model (rules/understanding). Data describes a specific case. Information = knowledge + data (applying knowledge to infer something).

    📍 Data

    Describes a specific case

    Example: Patient temperature: 38.5°C

    📈 Information

    Applying knowledge to data to infer something

    Example: Applying the fever rule to 38.5°C → “Patient has a fever”

    🧠 Knowledge

    Rules and understanding captured by a model

    Example: Clinical guideline: “Temperature > 38°C indicates fever”

    💎 Wisdom

    Applied knowledge with judgement in context

    Example: Deciding whether to prescribe medication based on fever, patient history, and current symptoms

    Information quality

    Click each characteristic to reveal why it matters.

    Accurate information reduces risk of incorrect decisions.
    Incomplete information can lead to missed risk and wrong actions.
    Even accurate information is less useful if it arrives too late.
    Relevance avoids noise and supports better decisions.
    Right access at the right time supports safe workflows.
    Verifiability supports trust, audit, and accountability.
    Security protects privacy and reduces harm from breaches.
    Collecting data must be worth the effort and resource cost.

    Core informatics concepts

    ⚖️ Inference procedure

    A rule-based “if X then Y” procedure to apply knowledge to data.

    Example: IF temperature > 38°C THEN flag as fever

    📚 Knowledge base

    A set of statements defining how concepts in labelled data relate.

    Example: Collection of clinical rules about symptoms and diagnoses

    🏗️ Ontology

    A formal conceptualisation that keeps order in a knowledge base and constrains valid statements.

    Example: Medical ontology defining valid relationships between diseases and symptoms

    🤖 Automation

    Capturing knowledge formally enables computers to automate applying knowledge to data.

    Example: Automated alert system applying fever rules to patient temperatures

    🧠 Mental models and clinical guidelines

    Clinical guidelines are themselves a model. They may be held “in a clinician’s head” (mental model) or formalised in documentation. The process of creating formal guidelines involves capturing tacit knowledge and making it explicit.

    An information system is a system that transforms data into information. It has multiple components that are themselves models—a “model of models”. Systems have inputs/outputs, emergent behaviour, are impacted by environment, have component parts, feedback, and are both purposive and arbitrary.

    🖥️ 1. Hardware

    Physical technology

    • Computers and servers
    • Networks and routers
    • Mobile devices
    • Storage systems (SSD, cloud)

    Your laptop, phone, and WiFi router are all hardware.

    ⚙️ 2. Software

    Programs and applications

    • Operating systems (Windows, Linux)
    • Databases (MySQL, MongoDB)
    • Applications (Office, Teams)
    • User interfaces and APIs

    From your OS to the apps you use daily.

    📁 3. Data

    Raw facts and information

    • Structured databases
    • Documents and files
    • Multimedia content
    • Analytics datasets

    Records, transactions, documents, sensor streams.

    📋 4. Procedures

    Policies and rules

    • User guides and manuals
    • Business processes
    • Security protocols
    • Backup procedures

    How the system should be used safely and consistently.

    👥 5. People

    Human resources

    • End users
    • Developers and engineers
    • Managers and decision makers
    • IT support staff

    The system only works if it fits people and workflows.

    Types of information systems

    Click each card to reveal more (replaces pop-up alerts).

    🔄 Transaction Processing Systems (TPS)

    Handle day-to-day operations and transactions

    Examples: point-of-sale, payroll, order processing

    Open
    TPS process high-volume, routine transactions reliably (e.g. recording a sale or booking). They prioritise speed, accuracy, and consistency.

    📊 Management Information Systems (MIS)

    Provide reports and summaries for management decisions

    Examples: performance dashboards, inventory summaries

    Open
    MIS convert operational data into structured reports (weekly summaries, KPIs), supporting monitoring and planning.

    🎯 Decision Support Systems (DSS)

    Support analysis and decision-making

    Examples: forecasting, what-if analysis, modelling

    Open
    DSS help with complex, non-routine decisions by combining data, models, and assumptions (e.g. “what if demand rises 20%?”).

    👔 Executive Information Systems (EIS)

    Provide strategic information for senior management

    Examples: strategic dashboards, business intelligence

    Open
    EIS present high-level summaries for strategic decisions, often combining internal KPIs with external context (markets, policy, risk).

    Information systems as socio-technical systems

    Information systems are not just technology - they are socio-technical systems where technology, people, processes, and organisational context interact dynamically. Understanding this interaction is fundamental to informatics.

    Case study selector

    Choose a case study to explore how informatics principles apply:

    🏥 NHS electronic health records

    How technology, clinicians, patients, and policy interact

    📦 Amazon recommendation system

    Algorithms, user data, and customer experience

    🎵 Spotify music platform

    Data analytics, user behaviour, and personalisation

    🚗 Uber ride-sharing platform

    Real-time matching, pricing algorithms, and dynamics

    For any information system, ask these informatics questions:

    • What data is collected and how is it processed? (Data flow)
    • What algorithms or rules govern system behaviour? (Computational logic)
    • How do humans and technology interact? (Human-computer interaction)
    • What are the ethical implications? (Informatics ethics)
    • How does context affect system use? (Socio-technical perspective)

    Core informatics principles

    🎯 Abstraction

    Hiding complexity, showing only essential details

    Example: Google Maps hides route algorithms and traffic models and shows clear instructions such as “Turn left in 500m”.

    💾 Representation

    How information is encoded and stored digitally

    Example: A photo is stored as pixels with numeric values, and formats (JPG/PNG) represent the same image differently.

    🤖 Automation

    Using computation to perform tasks without human intervention

    Example: An alert system applies fever rules automatically to new observations.

    📈 Scalability

    Ability to handle growing amounts of work

    Example: Platforms scale using distributed systems, caching, and load balancing.

    🔗 Interoperability

    Different systems working together

    Example: Booking systems connect to payments and providers through APIs and standards.

    🔄 Feedback loops

    Outputs influencing future inputs

    Example: Personalisation can reinforce behaviour patterns by learning from engagement signals.

    Models

    • Representations of reality
    • Simpler than the real thing
    • Built for specific purposes
    • Abstraction and instantiation
    • Become dated over time

    Information

    • Knowledge + data
    • Inference from rules
    • Transforms data meaningfully
    • Supports decisions
    • Requires context

    Information systems

    • Model of models
    • Multiple components
    • Transform data to information
    • Feedback and emergent behaviour
    • Purpose-driven

    Key takeaways from this module:

    • Models are imperfect representations built for a purpose: they simplify reality, become dated over time, and involve choices about what to include or exclude
    • Models support transforming data into information: by capturing knowledge (rules), we can apply that knowledge to data to generate meaningful information
    • Systems are models with multiple components: they have inputs/outputs, emergent behaviour, feedback loops, and are influenced by their environment
    • Information systems transform data into information using component models: they are “models of models” that work together to process and communicate information

    Further learning

    Recommended topics for deeper study

    • Database design and normalisation
    • Business process modelling (BPMN)
    • Systems thinking and analysis
    • Enterprise architecture frameworks (e.g. TOGAF, Zachman)
    • Agile and DevOps methodologies
    • Data governance and information security
    • Cloud computing and modern architectures
    • Artificial intelligence and machine learning in information systems