Here’s the thing about designing products: creativity and intuition will only get you so far.
You can have years of experience—hell, I’ve been doing this for a while now—but that doesn’t make you the user. My instincts about what works? They’re valuable, sure. But they’re also coloured by my own habits, my own biases, my own workflow. I’m not a small business owner trying to approve holiday requests between customers. I’m not an HR manager juggling staff documents on a Monday morning when half the team’s calling in sick.
That gap? Between what designers think users need and what they actually need? That’s where data comes in.
What we actually mean by “data-driven design”
It’s become a bit of a buzzword, hasn’t it? Everyone’s “data-driven” now. But what does that actually mean for design work?
At BrightHR, it means we don’t guess. Every decision—what feature to prioritise, which workflow to simplify, where users are getting stuck—gets backed by evidence. Real behaviour. Real feedback. Real patterns.
Usability studies. Power BI dashboards. Analytics tracking. User interviews. All of it feeds into how we shape what we build.
A designer’s gut feeling is worth something—I’d be lying if I said otherwise. But when you pair that instinct with hard data? That’s when things get powerful. It’s the difference between “I think this might work” and “Here’s proof that it does.”
The two types of data we actually use
When we talk about design data, it falls into two camps: what users do (quantitative) and why they do it (qualitative). You need both. One without the other is just…incomplete.
Quantitative data
This is the measurable stuff. Numbers, metrics, events. It tells us what’s happening.
We get it from:
- Google Analytics 4, tracking how people move through BrightHR and Blip, where they drop off, what they engage with.
- Userpilot, showing which features actually get adopted, which ones get ignored, how far people get through onboarding flows.
- Power BI dashboards, pulling from our data warehouse to combine usage patterns with feedback trends and operational metrics.
- A/B tests, run during usability sessions to see which design performs better when we give people actual tasks.
So if Power BI shows engagement tanking on a particular feature, or GA highlights a massive exit rate on a specific page, we know exactly where to dig deeper.
Qualitative data
Numbers tell you what happened. People tell you why.
We gather this through:
- User interviews, where customers walk us through how they actually use the product, what frustrates them, what they wish worked differently.
- In-app surveys, usually triggered through Userpilot. Short, contextual, asking questions at the right moment.
- Usability tests, both moderated (where we watch people use the product in real-time) and unmoderated (where they record themselves completing tasks).
- Internal teams, especially Payroll, Sales, and Customer Service. They’re on the phone with users every day. They hear the complaints, the feature requests, the pain points we’d never spot from analytics alone.
The magic happens when you combine the two. The numbers point you to the problem. The conversations tell you how to fix it.
Making sense of what we find
Collecting data is the easy bit. The hard part? Turning those findings into something that actually informs what we build.
We use empathy maps a lot at BrightHR. They help us visualise what customers are thinking, feeling, and doing when they interact with our software. Gets the whole team out of their heads and into the users’ shoes. Less assumption, more context.
Then there’s affinity mapping for qualitative data. Whether it’s sticky notes scattered across a FigJam board or tagged feedback in Airtable, clustering observations helps patterns emerge. You start seeing what actually matters to users versus what we thought mattered.
For the quantitative side, Power BI is our go-to. Usage trends, engagement dips, adoption rates—all visualised in one place. A sudden spike in help center searches? Probably means we’ve confused people somewhere. Increased mobile usage? Maybe it’s time to prioritise responsive fixes.
How we present data (and why it matters)
Here’s a truth: data doesn’t matter if nobody listens to it.
The trick isn’t just having the numbers. It’s telling a story people can connect with. When we present research findings at BrightHR, we never just throw up spreadsheets and graphs. We:
- Pair metrics with actual user quotes and screenshots. Numbers say “75% of users abandoned this screen.” A frustrated user saying “I had no idea what to click next” makes it real.
- Connect findings to business outcomes. Not just “this flow confuses people,” but “fixing this could reduce support tickets by 20% and improve retention.”
- Keep visuals clean and focused. Whether it’s GA data, Power BI dashboards, or Figma prototypes, people need to grasp the insight quickly.
When you help stakeholders see what users are experiencing—not just what the data says—design recommendations stop being opinions and start being obvious solutions.
Why designers need to care about data
It’s tempting to think data is someone else’s job. The analysts. The product managers. The researchers.
But honestly? Every design decision benefits from evidence. Button label. Onboarding flow. The placement of a help tooltip. All of it.
At BrightHR, data doesn’t kill creativity. It protects it. We still experiment. We still prototype bold ideas. But we validate them. Test them. Watch real people use them. And that balance—between creative exploration and empirical validation—means we can move fast without breaking things (too badly).
The core principles we stick to
- Designers aren’t users. Data bridges that gap.
- Use both types of data. Quantitative and qualitative. One without the other is incomplete.
- Present findings like stories, not reports. Connect with people emotionally, not just intellectually.
- Link insights to outcomes. Real user needs. Measurable results.
- Keep it continuous. Observe, test, learn, refine. Repeat.
This approach has become muscle memory at BrightHR. Whether we’re improving holiday booking, enhancing Blip’s mobile experience, or exploring new AI features, we always start with the same question: what does the data tell us?
Bottom line:
Data doesn’t replace design instinct. It sharpens it.
When creativity and evidence work together, you don’t just build things that look good. You build things that actually work for the people who need them most.