Department of Fisheries and Oceans
Designing for flexibility in a modern data management platform
Overview
DFO's new salmon data-management system is being developed for scientists to store, review, and compare field data. It will replace a 20-year-old legacy system that no longer meets operational needs, limits data access, and lacks effective tools for comparison and review.
I co-led a strategic pivot in product direction to address major risks in the current design and close critical informational gaps impacting its effectiveness.
Goals
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Identify and address key risks by uncovering missing information and unmet requirements.
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Define a high-level design direction that is flexible, scalable. and capable of supporting all use cases and unmet user needs.
Role
Design (1 of 2)
Responsibilities
End-to-End UX design
Team
Design, Business, Data, Development
Tools
Figma, Miro, Azure DevOps, Excel, Loop
The problem
The current design met the documented business requirements—but prior ethnographic research showed it fell short of user needs. The absence of a clear high-level vision led to incomplete requirements and overlooked information gaps until development was already in progress.
As a result, the solution was rigid, ineffective, and misaligned with how users actually collected data in the field.
Business needs: Efficient, accurate entry of standardized, high-quality data
User needs: A flexible, intuitive way to enter all collected data for easy review and comparison
How might we
How might we design a flexible, intuitive data entry solution that aligns with how users collect data in the field?
Risk analysis
What problems exist with the current solution?
I first identified and categorized the types of risks and issues with the existing system.
1. Unmet user needs
I reviewed business requirements and past user research to identify inconsistencies between real-world workflows and product functionality.
2. Informational gaps
I mapped data collection and entry flows, capturing key touch points, timelines, edge cases, and failure scenarios.
3. Misaligned business requirements
I worked with regional stakeholders to redefine task completion criteria and business rules to better reflect actual use cases.
Key insight
The current linear data entry process doesn't reflect how users collect data in the field. Users with partial data could be blocked early in the flow, forcing them to either submit incomplete records or enter placeholder data to bypass validation.
Main risks
Time lost to inefficient entry
The linear flow doesn't align with field practices, making data entry and reviewing cumbersome.
Critical blockers
Rigid validation and sequencing can prevent users from completing tasks when data is incomplete.
Inaccurate data
A mismatch between system expectations and actual user input could result in inaccurate or fabricated data
Ideation
Exploring flexible, feasible solutions
After identifying gaps and risks in the current design, I co-facilitated a design sprint to align the team, discuss issues, brainstrom ways to address unmet needs, and explore potential design directions.
Kickoff and brainstorming
On day one, I collaborated with business, data, and development teams to map key needs across three dimensions for a new solution: business goals, technical feasibility, and user workflows. Several key themes emerged:
Flexible data entry requirements
Letter users choose which data sections to complete would reduce validation errors and avoid workflow blockers.
Submission statuses
Tracking progress with clearer submission states could improve visibility and reduce incomplete or lost data.
Match system flow to real world
Removing constraints on the order users enter data would allow them to follow their natural workflow, entering data as it's recorded rather than constraining them to a specific path.
Research and exploration
Building on these themes, I conducted exploratory research to evaluate patterns from other products that could support our goals. As we shared ideas, the team narrowed in on three promising front-end solutions:
Dashboards
A high-level overview that supports data review and progress tracking. However, it may require multi-step navigation to edit individual sections.
List items
Grouping content into a single-page list improves flexibility and navigation, but could constrain space for related content.
Templates
Customizable data entry templates offer efficiency and better support for common workflows but come with higher development complexity.
Iteration
Design solutioning
With the team’s key themes and ideas for solving in mind, I created multiple low- to high-fidelity iterations to explore and refine the core information architecture of the new system.
I focused on design concepts that prioritized efficient and intuitive navigation, clear progress and status summaries, and flexible data entry.
Removing blockers with flexible entry
By setting validation criteria for each section instead of the entire flow, I designed an experience that allows partial and incomplete entries—ensuring all data is captured without forcing filler input.
Supporting review with easy navigation
Enabling users to quickly navigate between sections and providing status summaries makes data review easier, faster, and helps prevent errors.
Improving scalability with modular entry
Grouping conditional content and validation at the section level reduces interdependencies, allowing new features, content, and logic to be added without disrupting the whole flow—supporting better scalability.
Outcome
An intuitive and scalable data management solution
The final design is a modular system that enables efficient, customizable data entry with the flexibility to support a wide range of real-world scenarios.
Final design approach
We introduced a high-level structure that supports flexible, non-linear data entry—designed to accommodate all use cases, not just ideal paths.
Supporting diverse use cases
By supporting two new use cases allowing users to complete tasks with partial/incomplete data, we remove blockers and satisfy all known data collection workflows.
Fulfilling unmet needs
The modular design includes status summaries and easy access to all entered data, making it easier for users to review and compare data.
Defining product structure
The new architecture established a clear, high-level framework for how future features and content will be integrated—enhancing adaptability and reducing ambiguity in product planning.
Improving scalability
By removing rigid dependencies from the previous linear flow, the modular approach simplifies adding new features, content, and business rules—supporting long-term growth.
Next steps
The product is still in development. Work is ongoing to build out key features and conduct evaluative user research. All work shown is subject to change.
Learnings
Design for the future, not just the present
Overlooking future needs can lead to code constraints, critical issues, and costly rework down the line.
Be a catalyst for change
When the current process fails, back your observations with evidence and propose actionable solutions.
Transparency is key to building trust
Open collaboration and honest conversations help gain support for UX-led changes.
Design the structure to support the features.
If the foundation doesn’t support user needs, it doesn’t matter how well designed the features are.