When Critical Decisions Depend on Spreadsheets
Client: Måsøval
The challenge
Måsøval handled large amounts of operational data critical to fish welfare, veterinary approvals, and regulatory documentation. But most of that information lived across paper forms, Excel sheets, and disconnected manual workflows.
Delousing is a high-stress operation for salmon, and deciding when fish are healthy enough to undergo treatment requires careful assessment and approval from veterinarians. Every decision also needed to be documented for the Norwegian authorities.
Fish farmers manually entered data into spreadsheets, veterinarians copied information between systems, and historical assessments were hard to track across locations and over time.
The problem wasn't resistance to change. It was the lack of systems designed around how the work actually happened.
What we found
The more we studied the workflow, the clearer the structural problems became.
Critical information was being manually copied between spreadsheets, systems, and reports. Assessments varied between locations, historical context was difficult to access, and small human errors could affect both operational decisions and animal welfare.
One insight became especially important: the expertise already existed inside the organization. Fish farmers and veterinarians had deep operational knowledge and highly refined decision-making processes. What they lacked was infrastructure that supported that expertise consistently.
The project also challenged our assumptions about digital maturity in the aquaculture industry. Despite relying heavily on manual workflows, users were highly motivated to adopt digital tools — as long as those tools reflected the realities of their work.
What changed
We designed a shared digital workflow that replaced fragmented spreadsheets and manual data collection with structured, centralized data collection.
The new system automated large parts of the assessment process by retrieving existing sensor data where possible and reducing repetitive manual entry. Historical assessments became accessible across locations, making it easier to compare previous cases and identify long-term patterns.
Instead of information being scattered across disconnected files and individual workarounds, the organization now had a shared system that supported collaboration between fish farmers and veterinarians in real time.
That improved consistency across assessments, reduced manual errors, and increased confidence in the data being used to make high-stakes decisions about fish welfare and treatment.
Significantly reduced manual data collection by automating key inputs for fish farmers and veterinarians
Centralized previously fragmented Excel-based data into a shared, structured source with clear ownership
Enabled data-driven risk assessment and decision-making in a previously low-digitalized organization
How I contributed
I worked as lead designer on the project, collaborating closely with a multidisciplinary team of designers, developers, architects, and domain experts throughout the process.
My role included user research, workshop facilitation, workflow mapping, wireframing, and translating complex operational requirements into usable digital flows and structured data models.
A large part of the work involved understanding expert behavior. Fish farmers and veterinarians operated in highly specialized environments with established routines, terminology, and decision-making processes. Designing the system required balancing their expertise with the need to simplify workflows, reduce manual handling, and improve consistency across assessments.
One of the most important turning points came when we observed how frequently users manually transferred data between spreadsheets and systems. That exposed that the core problem was not individual tools, but the lack of a connected structure around the data itself.
What I learned
This project taught me that designing for operational systems requires a very different mindset than designing for customer-facing interfaces.
The challenge was not creating a visually polished experience. It was designing structures people could trust when making high-consequence decisions.
It also reinforced the importance of designing with experts rather than for them. Fish farmers and veterinarians already had deep domain knowledge; the role of design was to support that expertise with clearer systems, better workflows, and more reliable data.
Most importantly, I learned how much impact structured information can have inside low-digitalized industries. Sometimes the biggest UX improvement is not simplifying an interface, but creating systems that allow organizations to learn from their own data over time.