Know and understand the use of live data

7. The Systems Life Cycle

Objective

Know and understand the use of live data within the systems life cycle.

What is Live Data?

Live data (also called real‑time data) is information that is generated, captured and updated continuously as events occur. It is available for immediate processing, analysis or display without delay.

Why Live Data Matters in a System

• Supports timely decision‑making.
• Enables monitoring of dynamic processes (e.g., stock levels, sensor readings).
• Improves user experience through up‑to‑date information.
• Allows automated responses to changing conditions.

Stages of the Systems Life Cycle and the Role of Live Data

1. Planning
   • Identify the need for real‑time information (e.g., “the system must display current weather”).
   • Assess data sources that can provide live feeds.
2. Analysis
   • Define functional requirements for live data handling (frequency, latency, accuracy).
   • Map data flow from source to user interface.
3. Design
   • Choose appropriate technologies (APIs, WebSockets, MQTT, streaming services).
   • Design database structures that can store time‑stamped records.
   • Plan for error handling and data validation.
4. Development
   • Implement code to retrieve, process and display live data.
   • Integrate security measures (authentication, encryption).
5. Testing
   • Simulate live data streams to verify system responsiveness.
   • Check for data loss, duplication, or latency issues.
6. Implementation
   • Deploy the system with live connections to production data sources.
   • Monitor performance and adjust bandwidth or polling intervals as needed.
7. Maintenance
   • Regularly review data source reliability.
   • Update APIs or protocols when providers change.
   • Optimize storage for growing historical live data.

Benefits of Using Live Data

• Immediate visibility of critical information.
• Enhanced automation (e.g., alerts, control systems).
• Improved accuracy of reports and dashboards.
• Competitive advantage through faster response times.

Challenges and Mitigation Strategies

• Latency: Use efficient protocols (WebSocket, UDP) and minimise network hops.
• Data \cdot olume: Implement buffering, compression, and selective sampling.
• Reliability: Include fallback mechanisms and redundancy for data sources.
• Security: Encrypt streams (TLS) and enforce strict access controls.
• Data Quality: Validate incoming data and handle missing or corrupt values.

Typical Real‑World Examples

• Banking: live transaction feeds for fraud detection.
• Manufacturing: sensor data from production lines for predictive maintenance.
• Retail: real‑time inventory levels displayed on e‑commerce sites.
• Transport: live GPS tracking of buses and trains.
• Weather services: continuously updated temperature and precipitation data.

Summary Table – Live Data Use Across the Life Cycle

Life‑Cycle Phase	Live Data Activity	Key Considerations
Planning	Identify need for real‑time information.	Source availability, cost, required update frequency.
Analysis	Define functional and non‑functional requirements.	Latency tolerance, data volume, security level.
Design	Select protocols, design data flow diagrams.	Scalability, fault tolerance, integration points.
Development	Code retrieval, processing, and display modules.	Error handling, API versioning, testing hooks.
Testing	Simulate streams, measure response times.	Load testing, stress testing, validation rules.
Implementation	Connect to live production feeds.	Monitoring tools, rollback plan, user training.
Maintenance	Monitor performance, update connections.	Change management, documentation, security patches.

Key Points to Remember

• Live data is dynamic and must be handled continuously throughout the life cycle.
• Each phase has specific tasks to ensure the system can reliably receive, process and present live information.
• Balancing speed, accuracy, security and cost is essential for successful live‑data systems.