Business Analysis: Functional & Non-Functional Requirements

Transform ambiguous business requests into precise, actionable system specifications with proven requirement extraction techniques. This lesson bridges the gap between what stakeholders want and what developers need to build. You'll learn systematic methods to break down complex business goals into specific, measurable functional requirements that define exactly how a system should behave. Through practical examples, you'll master the art of identifying hidden requirements and articulating them clearly.

Master the art of creating structured, searchable functional requirements documentation that streamlines the development process. This lesson shows you how to build and maintain a robust FR list that eliminates duplicates while ensuring complete coverage of system functionality. Learn industry-proven techniques for implementing unique identifiers and establishing clear traceability between requirements. You'll discover practical strategies for organizing requirements in a format that both technical and non-technical stakeholders can easily navigate. By the end of this lesson, you'll be equipped to create documentation that serves as a reliable blueprint for development teams and project success.

Uncover and define essential data requirements that power effective software solutions. This lesson reveals systematic techniques for identifying both explicit and hidden data elements crucial to system functionality. You'll learn strategic questioning methods that ensure comprehensive data coverage and understand how to map data requirements to user perspectives and system functions. Through real-world examples, you'll develop expertise in spotting potential data gaps early, preventing expensive modifications during development. Perfect for analysts seeking to create robust specifications that stand up to scrutiny and support seamless system implementation.

Transform your functional requirements documentation by diving deep into information architecture and user-centric data components. This lesson equips you with practical techniques to build robust Informational Requirements (IR) indexes and define critical data attributes that drive software success. You'll learn to seamlessly integrate user perspectives and define essential data attributes such as usability, algorithms, and precision. By the end of this lesson, you'll confidently create detailed functional specifications that bridge the gap between business needs and technical implementation, ensuring your software projects hit their mark.

This lesson includes the topics:

• Usability Requirements Define User Views

• Defining Data Elements

• Algorithms for Derivable Data

• Data Element Accuracy

Summary: Volumes, Precision, Accuracy, Formula, and Source

Discover how to transform vague quality requirements into measurable specifications that drive project success. Learn why non-functional requirements are crucial companions to functional specifications, and master techniques for quantifying subjective needs like usability, reliability, and performance. Through real-world examples of system failures and successes, you'll understand how to define concrete metrics for availability, trainability, and flexibility. This lesson equips you to collaborate effectively with stakeholders to establish clear, testable performance criteria that prevent costly system failures and ensure your solutions truly meet business needs.

Learn a practical approach to discovering and defining performance requirements by examining each system function through two key lenses: how often it's used and how quickly it must respond. You'll discover how to convert vague timing needs into specific metrics, identify which functions need automated tracking, and determine when manual measurement makes more sense. By the end, you'll confidently spot hidden performance needs and create measurement plans that work in the real world without slowing down your systems.

Constraints set the non-negotiable limits for any solution, which must be met for success. Internal constraints stem from business policies or rules, while external ones are shaped by physical or regulatory environments. This lecture teaches you how to identify both types, understand their impact on IT projects, and document them effectively. You'll learn techniques to link constraints to project components, ensuring solutions comply with regulations and policies, ultimately resulting in stronger, more compliant applications.

This lecture also includes a downloadable resource that expands your NFR toolkit considerably. "The Complete “Ilities” Guide: 121 NFR Quality Attributes, Testing Criteria, and AI Integration Standards, Simply Put!" catalogs quality attributes with plain-language descriptions, testable acceptance criteria, and AI integration standards. Most teams work from a list of eight or ten. Now you'll have 121. Download it, save it somewhere safe, and keep it close. You'll want it openin during the AI demos in Section 4, where "it seemed complete" will not be an acceptable testing criterion.

The demos in this seciton were built when Jasper was a reasonable default for AI-assisted requirements work. The market had other plans. Before you watch a single demo in this section, this short lecture explains why that doesn't matter as much as you might think, and what to do when the tool on screen isn't the tool on your desk.

You'll walk away knowing exactly how to translate the prompt logic from any demo in this section to ChatGPT, Claude, Gemini, or whatever your organization has branded and rolled out with future-focused enthusiasm.

The thinking behind the techniques hasn't changed. Consider this your two-minute orientation before the real work begins.

Explore how generative AI tools are transforming the traditionally complex task of requirements decomposition. This introductory lesson explains how AI writing assistants can help break down user stories and stakeholder requirements into functional and non-functional components. You'll build a foundational understanding of how these tools work, what they do well, and - equally important - where they can quietly lead you astray. Think of it as knowing which end of the hammer to hold before you start swinging. This lesson sets the stage for the hands-on demonstrations ahead so you can leverage AI tools effectively while keeping a healthy dose of professional skepticism close at hand.

Discover why prompt engineering might be the most underrated skill in modern requirements analysis. This foundational lesson explains how AI tools interpret your prompts with uncomfortable literalness - they give you what you asked for, not necessarily what you meant. You'll learn why precise communication matters, why including examples in your prompts dramatically improves results, and why validating AI-generated content isn't optional - it's the job. Whether you're working with ChatGPT, Claude, Gemini, or whatever AI tool lands on your desk next month, the principles here transfer. This lesson prepares you for the demonstrations ahead while making the case for something refreshingly old-fashioned: human judgment.

Meet the Feed Me app - a mobile solution for dietary-conscious diners who are tired of arriving at a restaurant only to discover the menu has exactly one item they can eat. This foundational lesson introduces a concise vision statement for the app and demonstrates how the same core functionality can be expressed as stakeholder requirements, user stories, and lean features. Methodology matters, and so does knowing how to shift between them. This practical example serves as our running reference point throughout all the AI-assisted analysis demonstrations that follow, so it's worth understanding before we start putting AI to work on it.

Watch generative AI do the heavy lifting on functional requirements identification - and then watch us decide what's actually worth keeping. This hands-on demonstration walks you through crafting effective prompts to generate function lists from user requirements, critically evaluating what comes back, and refining the output into precise, development-ready functional requirements. Using our restaurant app case study, you'll see how to combine AI's brainstorming capacity with the human judgment that keeps requirements grounded in reality. The prompts demonstrated here work with any major AI tool, so you can apply them immediately regardless of what your organization is currently using.

Generative AI turns out to be surprisingly good at asking the data questions you forgot to ask. This lesson demonstrates how to use AI tools to identify user views, data elements, and data accuracy requirements for your application. You'll learn to craft prompts that surface the essential information each function depends on - from basic profile details to specific dietary restriction handling. Through practical examples, you'll see how AI generates the right questions about data currency and accuracy, helping you walk into your next SME conversation better prepared than you would have been otherwise. The prompts shown here are tool-agnostic and ready to use the moment this lesson ends.

Non-functional requirements have a long and undistinguished history of being either ignored until go-live or written so vaguely they're impossible to test. Generative AI can help with both problems - if you know how to prompt it. This practical lesson demonstrates how to craft increasingly refined prompts for generating NFRs, moving from broad system qualities like scalability to specific security requirements tied to individual functions. You'll learn to guide AI toward measurable, testable criteria while recognizing outputs that sound authoritative but mean nothing. You'll also understand when human expertise has to take the wheel, because AI has no idea what your organization's standards are, what your regulatory environment requires, or what keeps your security team up at night.

The most underused productivity tool in AI-assisted requirements work is the saved prompt template. This practical lesson shows you how to build reusable prompt templates that combine carefully engineered prompts with customizable variables - the equivalent of what some platforms call recipes, playbooks, or custom instructions. You'll learn to develop structured workflows for generating consistent requirements documentation, from vision statements to functional specifications, and how to share those templates across your team so everyone is working from the same starting point. Through hands-on examples, you'll discover how to build, refine, and maintain your own prompt library while avoiding the common trap of templates that produce confidently outdated results. Consistency in requirements work is a team sport, and this is how you make AI a reliable member of the team.

Let's be direct: generative AI tools offer some compelling advantages for requirements analysis, and this lesson makes that case without resorting to vendor hype. From measurable time savings in requirements workshops to significant reductions in rework caused by missing or misunderstood requirements, the benefits are real - as long as you use the tools with your eyes open. You'll learn how AI reduces certain categories of human error, accelerates early project phases, and functions as a tireless brainstorming partner that never gets tired of being asked "what did we miss?" You'll also see how AI helps surface the requirements that tend to hide in organizational blind spots - the ones nobody writes down because everybody assumes somebody else already knows them. This lesson gives you the honest, practitioner-level case for AI in requirements work.

Every tool that can make your work better can also make your mistakes bigger. This lesson is the honest counterweight to everything we've demonstrated in this section. You'll learn about over-reliance risks, the security vulnerabilities that come with pasting sensitive requirements into public AI tools, and the data privacy issues that can turn a productivity win into a compliance conversation you'd rather not have. We'll cover why "garbage in, garbage out" is not just a cliché in AI-assisted analysis, and why the human relationships that good requirements work depends on can quietly erode when analysts start treating AI output as a finished product. This isn't a lecture designed to scare you away from generative AI. It's designed to make sure you use it like a professional instead of a passenger.

Master a straightforward, no-frills approach to organizing requirements for small-scale projects using basic Excel spreadsheets. Learn how to structure different requirement types - from functional and informational to performance and constraints - across dedicated worksheet tabs. This pragmatic solution helps maintain relationships between requirements while avoiding common documentation pitfalls. While not suited for large projects, this simple spreadsheet approach offers an accessible starting point for basic requirements management. Perfect for analysts working on smaller initiatives who need an immediate, cost-effective way to track and organize requirements before considering more sophisticated tools.

Please download your job aid “A Simple Requirements Management Template”.

This lesson wraps up by summarizing the process of deriving functional and non-functional requirements. It emphasizes the importance of measurable, clear requirement statements to guide solution providers. Following the course’s structured approach ensures solutions that meet business needs and minimizes misinterpretation. To reinforce learning, practice applying these concepts by taking the final exam to flex your knowledge.

This bonus lecture lists other Udemy courses we offer for aspiring and practicing business analysts to improve your skills in a wide variety of areas you need to round out your toolkit.