The Foundations of System Analysis and Design
A short list of the key principles of SAD and best practices for system analysis.
👋 Hi, this is Thomas. Welcome to a new edition of Beyond Runtime, where I dive into the messy, fascinating world of distributed systems, debugging, AI, and system design. All through the lens of a CTO with 20+ years in the backend trenches.
System analysis and design (SAD) is built upon a set of core principles and methodologies that ensure the development of robust, scalable, and efficient information systems.
These foundational elements are essential for aligning the system with the organization's goals and requirements, and they should be integrated into every stage of the software development lifecycle.
Iterative Process
One of the key principles of SAD is the iterative nature of the process. System requirements and solutions are continuously refined, developed, and improved in an incremental manner.
This iterative approach is crucial during the initial phases of system design and throughout the software development lifecycle as the system evolves and undergoes maintenance and enhancements.
For example, an initial analysis of the software architecture, potential alternatives, and eventual trade-offs, can help with selecting the optimal solution. Afterwards, throughout the software lifecycle, regular system design reviews and architecture assessments (e.g. using collaborative reviews, performance and load testing, simulations, prototyping, monitoring, etc.) can help ensure that the architecture meets functional and quality requirements.
Stakeholder Engagement
Effective system design heavily relies on the active participation and feedback of all relevant stakeholders, including end-users, managers, and cross-departmental teams.
Engaging these groups is essential for understanding their needs, preferences, and pain points, which in turn helps to create a system that meets their expectations and supports their workflows.
Documentation and Tools
SAD employs a variety of tools and techniques to conceptualize, plan, and document system architectures. These tools include system architecture diagrams, data flow diagrams, use cases, jobs-to-be-done frameworks, and requirement specifications.
Proper documentation facilitates knowledge sharing because it makes it easier to communicate the design to stakeholders, onboard new team members, and maintain the system in the future.
When choosing your system design tools, make sure to prioritize those that offer:
Real-Time System Architecture Visualizations
Interactive Complexity Levels (i.e. you can select and customize the level of abstraction or detail you need)
A Single Source of Truth for all your System Information
Quality Metrics and Standards
Establishing clear and measurable quality goals and requirements is fundamental to practical architecture evaluation. By quantifying the system’s performance, reliability, and maintainability (among others), helps turn subjective judgments into objective assessments.
Examples include SLAs, uptime percentage expectations (e.g., five nines = 99.999%), and end-user time savings (e.g., three hours per day per user).
Best Practices in System Analysis
There are six best practices in system analysis that ensure a comprehensive and successful outcome:
Define Requirements Clearly: Document system requirements to align with organizational needs.
Ensure Stakeholder Engagement: Involve stakeholders to identify issues and gather diverse perspectives.
Take an Iterative and Agile Approach: Continuously adapt and refine requirements and solutions.
Use Prototyping and Proofs of Concept: Validate design decisions and gather early user feedback.
Engage in Risk Management: Identify and mitigate potential risks.
Emphasize Documentation and Communication: Maintain thorough documentation and clear communication.
These best practices help focus SAD on the ultimate goal of building effective software systems.
💜 This newsletter is sponsored by Multiplayer.app. AI-powered debugging for distributed systems.
I originally wrote about this topic in this article:
I explored these topics:
Foundations of system analysis and design
System analysis phases and steps
System analysis and design patterns
Architecture evaluation
The CAP theorem
Best practices in system analysis
📚 Interesting Articles & Resources
“Imaginary Problems Are the Root of Bad Software” - George at Cerebralab
There are many factors which can be a catalyst for bad software, but chief among them is imaginary problems: designing software to do something other than its intended purpose.
“Programming Is Mostly Thinking” - Tim Ottinger
Programming is 1/12th motion and 11/12ths thinking because code is just the residue of the all the work that goes into building a software system.
“Tacit Knowledge is Dangerous” - Ethan Rahn
Informal, undocumented knowledge can take the form of tacit or tribal knowledge, both can lead to loss of institutional knowledge within an org.