Why asking about data quantities to be processed is the right interview question for requirements elicitation

Let me ask you something that often trips up projects more than any fancy tech: how much data does the system actually need to handle? It sounds simple, but in requirements elicitation, that question can unlock a lot of clarity. If you’re coming at this from the IREB Foundation Level perspective, you know the goal isn’t to pile up features but to understand needs so the team can design the right solution from day one. And yes, the question about data quantities is exactly the kind of targeted inquiry that keeps conversations productive and concrete.

Why the right interview question matters

In software and systems work, the devil is in the details. Stakeholders will happily describe the “what” of a system—what it should do, what it should look like, what it should integrate with. But requirements elicitation aims for the “how much,” the “how often,” the “how big.” Those data-centered questions help you translate vague aspirations into measurable needs. When you understand data volumes, you can begin to sketch the architecture, the storage strategy, and the performance expectations with real numbers behind them. That’s what separates a nice concept from something engineers can actually build and testers can verify.

Here’s the thing: a question about data quantities isn’t just about capacity. It’s about intent and reality. How big will the dataset be? How fast will it grow? Are we talking thousands of rows per day, or millions? Will data bursts occur during peak hours, or is there a steady stream? Answers to these questions ripple through choices about databases, data models, indexing strategies, and even how you measure success.

The right question in practice

Among common interview prompts, one stands out for its direct impact on how a system behaves under real load: What data quantities are to be processed?

• It’s specific without being prescriptive. It invites stakeholders to provide numbers or ranges instead of vague guarantees.

• It ties to both functional and non-functional requirements. Processing volume affects throughput, latency, reliability, and even operational costs.

• It acts as a gateway to architectural decisions. Knowing the data load helps you reason about storage, processing pipelines, and the kinds of data flows the system must support.

When you pose this question, you’re signaling that you care about the actual workload, not just the abstract feature list. You’re also inviting a dialogue about growth and future demands—areas where many projects stumble if they’re left implicit from the start.

But what about the other options? A quick look helps illustrate why the data-quantity question is more effective for elicitation.

• A: What are the most important technical terms for the application?

This tells you about vocabulary and domain knowledge, but it doesn’t reveal how the system will behave under real use. It’s a fine starter to ensure everyone shares language, but it doesn’t pin down requirements in a way that guides design or testing.

• B: If delivery deadlines are not met, is there a penalty?

Contractual or governance concerns are important, sure, but they don’t describe the system’s operational needs. This question focuses on risk and vendor terms rather than the product’s functioning.

• D: When is the installation of the system to be started?

Timing matters for project planning, deployment, and change management, but again, it doesn’t illuminate how the system will perform once up and running.

In short, C cuts straight to how the system must behave in realistic conditions. It’s a requirement-focused probe that grounds conversations in tangible constraints.

How to elicit data quantity requirements effectively

If you want to turn that single question into a fruitful discovery session, try these steps:

• Gather context first. Before you ask about data, understand what the system is supposed to achieve. What business problem does it solve? Who are the primary users? What data sources exist today? This background helps you interpret the numbers accurately.

• Ask for ranges, not absolutes. People often resist giving exact figures. A range is better: “We expect 100,000 to 500,000 records per day,” for example. If possible, include peak and average values, and note any seasonal fluctuations.

• Tie data to performance. Ask how much throughput the end users expect, what latency is acceptable, and how long data takes to be available for reporting or decision making. Translate data volumes into performance targets like transactions per second or batch windows.

• Look at data variety and velocity. Quantity is one piece; variety (structured, semi-structured, unstructured) and velocity (batch vs. streaming) change the design. A streaming feed of real-time events is a different beast than nightly batch processing, even if the daily data volume looks similar.

• Consider retention and growth. How long do we store data? What are legal or compliance constraints? Do we expect the dataset to double or triple in a couple of years? Scenarios like these push you to think about storage architecture, archiving strategies, and data lifecycle rules.

• Translate to concrete requirements. Move from numbers to statements you can test. For example: “The system shall process up to X records per day with a maximum latency of Y seconds for user-visible queries.” Or, “The data pipeline shall sustain Z MB per second during peak loads.”

• Validate with stakeholders. Revisit the numbers with data stewards, DBAs, and operations teams. Their eyes catch edge cases you might miss—like unusual data bursts caused by specific events or integrations that generate unexpected load.

A practical scenario to illuminate the point

Imagine you’re helping design a customer feedback portal for a mid-sized retailer. The stakeholders expect real-time dashboards, nightly analytics, and the ability to run historical reports. You pose the data-quantity question and hear: “We process roughly 200,000 feedback submissions per day, with peak days hitting 500,000 submissions around promotional events. We keep data for two years.” Those figures unlock a cascade of decisions:

• Data model: You’ll need a robust schema for feedback items, user metadata, and sentiment tags, with efficient indexing on timestamps and user IDs.

• Storage: A hybrid approach might suit best—hot storage for the most recent 30 days and a cost-effective cold store for older data, with clear retention policies.

• Processing: Real-time dashboards require a streaming path, while historical reports can be batch-processed. You’ll design a data pipeline that can ingest, transform, and load data with predictable throughput.

• Performance targets: You set latency bounds for the UI (e.g., sub-second page loads for recent data) and define batch windows for nightly analytics.

• Resource planning: You estimate CPU, memory, and storage needs, informing procurement and cloud or on-prem decisions.

By anchoring the conversation in data quantities, you gain a shared understanding that guides design decisions rather than leaving them to guesswork or heroic last-minute changes.

Techniques and tips that keep interviews human and effective

• Use plain language. Numbers are storytelling devices here, not showpieces. Encourage stakeholders to explain what the numbers mean for daily operations.

• Balance precision with practicality. If a stakeholder offers a rough estimate, note it, then help them refine with a range or a scenario.

• Do a quick sanity check. If a department claims “infinite capacity,” push back kindly to explore upper bounds and constraints. Real systems always have boundaries.

• Document the rationale. A short note about why a particular quantity matters helps future teams understand the decision context.

• Create a shared glossary. When everyone uses the same terms for data types, latency, and throughput, misinterpretation drops dramatically.

• Tie to verification. Once you’ve captured data volumes, draft testable acceptance criteria so testers can confirm the system meets the expected load.

A touch of realism and humility

No one expects to perfectly predict every data spike years in advance. The aim is to establish a credible baseline and a plan for evolution. You’ll learn more as the project progresses, and that’s entirely okay. The best interview questions aren’t oracle spells; they’re conversation starters that nudge stakeholders toward precise, verifiable requirements.

From talk to blueprint: turning discovery into design

When you connect the data-quantity question to concrete outcomes, you’re not just collecting information—you’re shaping the blueprint. The numbers become benchmarks that influence database choices (SQL versus NoSQL, indexing strategies), data governance (quality checks, lineage), and deployment considerations (scaling mechanisms, failover plans). This is where IREB Foundation Level concepts come alive: eliciting meaningful requirements, understanding stakeholders’ real needs, and translating them into a coherent set of functional and non-functional specifications.

A few closing thoughts to keep in mind

• Ask with curiosity, not as a test. Your role is to understand, not to catch someone out. Curiosity leads to richer data and stronger buy-in from the team.

• Your questions set the tone. A well-framed question signals that you value outcomes and reliability as much as features. It invites collaboration.

• Be ready to adapt. If data volumes are unclear, pivot to ask about use cases, typical user journeys, or peak business events. The goal is clarity, not pressure.

If you’re studying topics around requirements engineering, you’ll find that questions like “What data quantities are to be processed?” sit at the heart of practical elicitation. They anchor discussions in real-world constraints, guide architectural thinking, and help ensure the final product performs where it matters most. And while numbers aren’t the entire story, they’re a trustworthy compass—pointing teams toward designs that work now and scale gracefully later.

So next time you’re in a requirements conversation, try this: start with data quantities, listen for the edges and exceptions, and then map those insights into clear, testable requirements. You’ll discover that a single, well-posed question can steer a project toward a solid, workable outcome—without the hype, just solid, grounded engineering.

If you’re curious, there are plenty of real-world examples and case studies out there that show how data-driven elicitation changes the course of a project. Tools like Jira or Confluence can help capture and organize the insights, while data modeling practices and architectural patterns provide the structure you need to turn those insights into a system that behaves as promised. And in the end, isn’t that the whole point: building systems that align with what people actually do, under real-world constraints, with room to grow?