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ARTICLE

Designing with Data: making digital tools work for people

July 7, 2025
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Virginia Rispoli


At fresk.digital, we strongly believe that digital tools should work for the people using them, not the other way around. That belief has guided our service design work for years. But today, as data becomes more central to how products and services operate, we’re evolving our practice. We call it Data Service Design.

What is Data Service Design?

At its core, Data Service Design is the practice of designing services that are powered by data. But more importantly, it’s about designing services that make data useful, usable, and human-centred.

It builds on the foundations of Service Design, which is already a multidisciplinary approach to creating seamless, intentional experiences across touchpoints. Traditional service design focuses on understanding users, orchestrating interactions, and aligning business and operational layers. It often maps journeys, identifies pain points, and prototypes better experiences.

But where Service Design primarily works with qualitative insights (like interviews, observations, and workshops), Data Service Design brings in the quantitative layer, using data not just to validate decisions, but as material to design with. It's where dashboards, algorithms, APIs, and real-time feedback loops become part of the service offering itself.

Bringing qualitative and quantitative data together

What sets Data Service Design apart is how it blends quantitative data (the numbers, patterns, and behaviours) with qualitative insights (the stories, motivations, and contexts behind them).

Traditional service design leans on qualitative methods, like interviews, shadowing, and co-creation sessions, to uncover what people need and feel. These methods give depth, empathy, and context. But they can be limited in scale or generalisability. Data Service Design introduces the power of scale through quantitative data: millions of interactions, performance metrics, and usage patterns. But on their own, these numbers can feel abstract, detached from the lived experiences of real users.

That’s where the magic happens, when the two come together. We use qualitative research to make sense of what the numbers mean, and we use quantitative signals to challenge assumptions and reveal opportunities we might have missed in interviews. The result is a richer, more actionable understanding of both the why and the how behind user behaviour.

Instead of seeing data as an afterthought, something used to measure success once a service is live, we use it as a design tool from the start. Data becomes part of the conversation during ideation, prototyping, and decision-making. To do this we ask ourselves and the client questions like:
What data do we need to make this service smart? What context does a user need to understand this number? Where does data guide, overwhelm, or inspire action?

Bringing qualitative and quantitative data together

Our approach: merging human centred design with data intelligence

At fresk.digital, we take a structured approach that puts people first.

Understanding the user journey

We start by mapping how professionals interact with tools, data sources, and touchpoints in their day-to-day work. This helps us spot where data currently supports their tasks, or where it creates friction. We identify not just what people do, but why they do it, and where better data or clearer insights could make a difference.

Key questions we normally ask:

  • Where do users currently interact with data in their workflow? What decisions depend on it?
  • What workarounds have users created to access or understand the data they need?
  • Which moments feel unclear, slow, or frustrating, and could better data visibility help?

Defining key insights

This is where qualitative and quantitative research meet. Interviews and qualitative research allow us to understand the why behind user behaviours, while data analysis helps us detect the what, where, and how often. This dual lens reveals patterns and opportunities that a single data stream alone might miss.

Key questions we normally ask:

  • What qualitative insights have we gathered, and what assumptions do they challenge?
  • What data can we use to validate or explore these assumptions further?
  • Where do the stories from the field and the numbers from the system align, or diverge?
  • What behaviours or trends are we currently not measuring, but should be?

Prototyping and testing

We create early-stage prototypes that embed data into workflows in intuitive, human ways, whether through dashboards, alerts, decision aids, or feedback loops. These are tested with users to validate not only functionality, but also usefulness and clarity in real-world scenarios.

Key questions we normally ask:

  • Does the data in this prototype help users make decisions more quickly or confidently?
  • Are we visualising or presenting the data in a way that matches users’ mental models?
  • How could we simplify, contextualise, or personalise this experience further?

Aligning with business objectives

Data on its own doesn’t drive value, it’s what you do with it. We ensure every design decision supports wider organisational goals, from operational efficiency to customer experience. This means collaborating closely with product owners, analysts, IT, and service teams to create alignment between experience and strategy.

Key questions we normally ask:

  • How do our data insights support the business’s top three strategic goals?
  • Who needs to see or act on this data internally, and in what format or rhythm?
  • Are there internal metrics (e.g. adoption, retention, upsell) we can support with design?
  • What would ‘success’ look like in 6 or 12 months from a business perspective?

Iterating based on feedback

Our approach is always iterative. We return to users frequently, refining both the data and the design as we learn more about what works in practice. In Data Service Design, feedback isn’t just about the interface, it’s about how insights perform in real-world decision-making.

Key questions we normally ask:

  • What surprised us during testing? What didn’t land as expected?
  • Which parts of the data experience felt most valuable or frustrating to users?
  • How can we design feedback loops that improve the data experience over time?

Merging human centred design with data intelligence


Applying our approach: the Hertek Safety case

Hertek Safety, a leader in fire detection and evacuation solutions, developed Hertek Connect, a digital platform that enables remote monitoring of fire safety installations. While well received, the platform's adoption and impact varied across user groups. Hertek saw the opportunity to better connect data to the everyday decisions and needs of fire safety professionals.

Instead of diving straight into building large-scale infrastructure, Hertek partnered with us to take a lean, insight-led approach. We began by mapping how different professionals, from installers to facility managers, interacted with the platform. Using qualitative research, we captured their expectations, frustrations, and moments of need. We then connected this with quantitative usage data to validate patterns and spot gaps in behaviour and adoption.

By combining both data types, we uncovered specific areas where the platform could become more useful and intuitive (Moments that Matter), for instance, by improving the visibility of system status or making alerts more actionable. Real-world feedback helped us refine not just what data was presented, but how and when it was shown.

Crucially, this process also shaped Hertek’s broader data strategy, shifting from reactive reporting to hypothesis-driven insight generation, ensuring that everyone, from developers to commercial teams, could use data more effectively in their roles.

Turning data into impact

At fresk.digital, we believe data should do more than just accumulate, it should empower people to act. By merging human-centred design with data intelligence, we help organisations turn complexity into clarity and create services that truly work for the people who use them.

A thoughtful Data Service Design approach ensures that businesses don’t just collect information, but actively use it to drive continuous improvement. When designed with intent, data can tell compelling stories, support better decisions, and fuel services that are not only smarter, but more human.

The Hertek Safety case shows what’s possible when data becomes part of the design process. By shifting from fragmented collection to structured, insight-led usage, they unlocked a scalable strategy that drives ongoing value. With improved reporting, smarter feedback loops, and clear alignment between users and the organisation, Hertek now has a stronger foundation for product adoption, customer satisfaction, and long-term growth.

This is the future of digital transformation: not just building digital tools, but building smarter services, where data is no longer a burden but a catalyst for progress.

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