Anastasiya Styatugina, Head of the Analysis Group at LeverX, ran an internal knowledge-sharing session on one deceptively simple topic: scalable requirements.
Insights from LeverX’s Internal BA Lab.
If you’ve been in business analysis long enough, you probably know the honeymoon period. A new project starts, you set up a clean structure, define terminology that finally makes sense, and, just for a moment, you enjoy the rare feeling that everything is under control.
Fast-forward a year, and the documentation has drifted into legacy territory, updates are scattered across half a dozen pages, and you catch yourself thinking: “How did we get here again?”
This article pulls together the practices that helped our team at LeverX avoid that slow documentation decay. The insights come from an internal knowledge-sharing session with Anastasiya Styatugina, Head of the Analysis Group (with 10+ years in the field), who has led the full spectrum of custom product builds and multi-year enterprise programs.
What she shared below is battle-tested on projects where requirements lived for years, not sprints.
There are many reasons why a software project might fail. But one thing we hear a lot is, “We never really agreed on what success meant,” or “The project kept growing a little more in every meeting.”
Underneath those complaints sits the same root cause: we didn’t get the requirements right.
The Head of the Analysis Group at LeverX, Anastasiya Styatugina, summed it up during our session:
“A good requirement is one you can understand and test without having to call the analyst for clarification.”
Most teams can recite the SMART acronym on command. But in practice, requirements writing demands attention to finer details. Let’s break that into concrete traits with requirement examples from real tech work.
Bad requirements sound vague. Who does what, when, and why? Nobody knows.
Passive voice hides responsibility. The active voice forces you to name the actor.
Some documents read like a novel: one requirement covers half the application. As a result, readers scroll and lose their place. Developers implement half of it. Testers report three bugs caused by the same misunderstanding.
Anastasiya Styatugina explains how to avoid this:
“One requirement ID = one discrete, testable behavior or rule. If you see five different flows inside one spec (or if it takes a lot of pages to read), it’s time to split it into smaller, linked pieces.”
Document what is out of scope — it often matters more than describing what you will deliver. For example: “The ability to filter the list of users is out of scope.”
That one line can save hours of arguments in User Acceptance Testing (UAT).
When someone reads your requirement, they should know exactly how to check it and answer a simple question: Does it pass? Yes or no.
Vague words like “fast,” “user-friendly,” or “convenient” collapse the second you try to test them.
One effective way to express testable requirements is the Gherkin format (Given–When–Then). It helps establish a shared understanding between stakeholders and can support automated testing when adopted by the delivery team.
Some ideas look great on a whiteboard and die at the moment they touch a real system. The truth is, not every clever idea belongs in this release or this product.
Relevant requirements are:
When an IT business analyst refers to a feature as a “User Profile” and a developer calls the same thing an “Account Card,” misunderstandings are almost inevitable.
Your team is already balancing deadlines, tickets, and ongoing priorities. Adding terminology confusion to that mix can make things more challenging than they need to be.
Pick one term, define it once, and stick to it. Don’t invent synonyms (“client” vs “customer,” “bonus” vs “reward”).
When requirements follow a consistent format, the team quickly understands how to read, interpret, and work with them.
How to Maintain Consistency
Example
On one long-running project, analysts rotated over several years: Analyst A → B → C → D. Because everyone used the same templates, names, and glossary, the documentation stayed uniform despite turnover. Developers worked without interruption, and new analysts needed minimal ramp-up.
We’ve now covered what makes a good requirement. But projects rarely stand still. Systems change. Teams rotate. And sooner or later, someone faces the nightmare scenario: a large-scale requirement rewrite.
The good news? You can prevent most of that pain by designing your documentation for scalability from the start.
Scalable requirements don’t break every time the architecture evolves, or the rollout expands to a new country. They survive redesigns, reorganizations, and multi-year delivery cycles because they’re anchored in stable business logic.
To get there, analysts need a set of techniques. Let’s look at them.
Note: All the following applies specifically to maintaining documentation in Confluence. These practices will not work in the same way in other tools.
When requirements are tied to how something looks or is built, even minor design or technical changes create unnecessary rework for business analysts.
In our team’s experience, requirements that avoid overly prescriptive instructions reduce rework by about 30%. For example, when you don’t specify exact interface actions like “the user must click the button,” you protect yourself twice: you save time now, and you prevent unnecessary requirements editing later.
There’s also a technical boundary to respect. Let architects and engineers handle the technical requirements management methodologies in their own space: dev wiki, API docs, Swagger, and architecture notes. Consequently, your primary focus should remain on the user behavior instead of the underlying widget.
Here’s a business analyst’s nightmare:
Developer: “We have three different rules for ‘Rewards’ in three specs. Which one is right?”
That confusion is on us.
The cure is reusability. Below are practical strategies tailored for BAs working in Confluence:
A glossary: Define each term (e.g., Reward, Customer Tier) clearly in a centralized glossary. Wherever these terms appear, link them back to the glossary to ensure consistency.
Templates: Use templates to streamline your work and ensure consistency. Keep a library of templates tailored to different needs (e.g., describing functional requirements, data objects, filter behaviors, reports, and more).
Excerpts (Confluence macro): Write a requirement once in one place. After that, do only two things: link to it, or reuse it with the “Insert excerpt” macro. When we change a rule in one place, the update flows everywhere.
Page properties (Confluence macro): Add page-level metadata (owner, status, related Jira issues, dependencies, mockup links) to every requirement page. Confluence’s Page Properties macro collects these attributes across hundreds of pages and builds auto-updating dashboards that show which specs are pending review or approval.
A shared repository: Keep a shared repository of error messages and email templates; reference them from specs, don’t inline them everywhere.
One of the most common documentation mistakes we see in projects is structuring specifications by sprints.
Teams often organize their wiki like this:
Sprint 1 specifications, Sprint 2 specifications, and so on.
At first glance, this feels logical, but only until the project moves forward. After a few sprints, several problems inevitably appear:
To solve this, requirements should be grouped by role or by functionality.
To keep specifications manageable, start with a simple rule:
One scenario — one specification.
Smaller documents are easier to review, validate, and change without unintended side effects.
But while this principle keeps individual specs clean, it naturally leads to a large number of them. This brings us to the next question:
How do you structure hundreds of specifications so people can actually find what they need?
A practical answer is to organize requirements around the lifecycle of a business object. This method helps ensure nothing important is missed. A widely used approach here is the CRUDL framework — Create, Read, Update, Delete, List.
Take an Order as an example. Requirements should map to each stage of its lifecycle:
Each of these is a distinct business scenario with its own rules, permissions, and risks.
Then, as flows grow more complex — many exceptions, many branches — move isolated parts of the logic (e.g., computation description) into child pages and link them from the main flow. The primary page stays readable; the details stay discoverable.
This is how to write requirements for a project that will still make sense when the team doubles or the product multiplies.
Traceability sounds like a heavy word, but in practice it answers two simple questions:
At minimum, each requirement should link to:
On a typical LeverX project, we:
When a business rule changes, we can ask: Which specs mention this rule? Which tests depend on it? Which Jira items delivered it? In other words, we don’t guess; we follow the trail.
Versioning matters just as much. Projects add features, drop others, and change their minds. Sooner or later, someone asks: “When did we change this? What was it before?”
Confluence gives you page history for free, but only if you use it well. Each time the requirements change, add a comment such as: “Updated validation logic after PAY-342 fix.”
As a result, history becomes a map.
All of these principles came not from a textbook, but from LeverX projects. Our BAs give the final advice:
“Write as if someone new will read your spec tomorrow, because they will.”
If you follow that, you will create documentation that outlives teams, supports every release, and gives everyone on the project a clear path forward.
Business requirements represent the high-level needs and objectives of the organization, such as strategic goals, business outcomes, or value to be achieved (e.g., “Increase customer satisfaction by 15% within a year”).
Approval authority for business requirements lies with business stakeholders, specifically those who own the business outcomes. Typically, approving roles include the product owner and the product sponsor.
Solution requirements (functional and non-functional) may be reviewed and verified by the delivery team (e.g., business analysts, QA specialists, and architects) to ensure technical correctness and feasibility. However, validation and formal approval of solution requirements must still be performed by the appropriate business stakeholders to confirm alignment with business requirements.
SMART requirements help teams avoid vague scope and keep delivery on track. A requirement is specific when it states the actor, action, and purpose. It becomes measurable when the team can verify completion using clear acceptance criteria or data points. It is achievable when delivery fits the project’s capacity, dependencies, and technical constraints, prioritizing the real scenario over the ideal one.
It stays relevant when it links directly to a validated business need rather than a stakeholder preference. And it is time-bound when it includes a concrete deadline or performance window that lets the team plan sprints.
If a user story feels too broad or difficult to test, consider breaking it down. Take a look at our guide on the 10 Robust User Story Splitting Techniques.
We rely on Confluence and Jira to maintain integrated traceability and keep specifications modular and easy to navigate. To keep every update transparent, version-control practices (whether through Git or Confluence history) allow us to track who changed what and why, ensuring accountability across the entire lifecycle.