Senior UX Designer · FM Global
Enterprise Data Visualization Redesign
At FM Global, I redesigned an enterprise risk assessment dashboard used by account managers and risk engineering teams to evaluate commercial property exposure, monitor portfolio risk, and support insurance-related decision-making.
The platform surfaced complex operational and engineering data that influenced how risk was interpreted, prioritized, and communicated to clients.
Inconsistent system behavior was creating ambiguity in how users interpreted account-level risk, increasing hesitation and reducing confidence in downstream decisions. I led the redesign of the dashboard's interaction model and information hierarchy to create a clearer, more trustworthy decision-making system.
| Project Type | Enterprise dashboard redesign for decision infrastructure |
|---|---|
| Role | Senior UX Designer |
| Impact | Reduced system-induced decision errors by 30% · improved user confidence by 40% |
| Company | FM Global, leading global commercial and industrial property insurer, engineering-driven approach to risk management and loss prevention |
From fragmented data views to a unified decision system: the redesigned FM Global risk assessment dashboard.
Business Context
The dashboard lived inside an engineering-driven insurance model
FM Global is a commercial property insurer specializing in engineering-driven risk assessment and loss prevention.
Its model uses on-site engineering analysis, operational data, and proprietary risk scoring systems to evaluate property exposure and identify vulnerabilities before losses occur.
The dashboard helped account managers and risk engineering teams monitor account-level risk, compare performance against industry benchmarks, prioritize mitigation efforts, support insurance-related decision-making, and communicate risk conditions to clients.
Problem
From fragmented data views → unified decision system
Project Goals
The goal was a full redesign of the dashboard. Specifically the workflows where chart data leads to a decision, and where bad UX has direct financial consequences.
Main Problem
The primary failure wasn't a lack of data. It was a lack of clarity.
The dashboard allowed inconsistent data states across charts, creating ambiguity in how users interpreted results. The same actions could produce different results in different contexts. This led to a breakdown of trust in the dashboard for users.
This turned a tool meant for decision-making into a source of hesitation and uncertainty.
Solution overview
Introduced a global filtering system and structured data hierarchy to align system behavior with user expectations.
Discovery
Why isn't the data visualization dashboard being used to make risk assessment decisions?
Before redesigning the interface, I needed to align stakeholders around a more fundamental question: Why isn't the data visualization dashboard being used to make decisions for risk assessment across the board?
I led a stakeholder discovery phase across product leadership, design, account managers, and risk engineering to uncover how each group defined the problem. While everyone agreed the dashboard needed improvement, their viewpoints and context differed.
To better understand the context of each stakeholder, I tailored my questions for each group.
VP of Data & Analytics
Decision confidence
Clarify where interpretation errors become financial or operational risk.
- What decisions are users expected to make using this dashboard?
- Where are users hesitating, second-guessing, or escalating decisions?
Principal and Design Leads
Cognitive load
Identify where the interface itself creates ambiguity or confusion.
- Where do users struggle to understand relationships between data points?
- How do we validate that users are interpreting data correctly?
Account Managers
Day-to-day workflow
Understand how dashboard interpretation carries into client-facing work.
- What decisions are you expected to make or support with this data?
- What parts of the dashboard require the most manual effort or explanation?
Risk Engineering
Real-world consequences
Trace how ambiguity affects trust, action, and downstream outcomes.
- When reviewing data, what makes you trust or distrust what you’re seeing?
- What part of the workflow feels the most ambiguous or open to interpretation?
These conversations revealed a critical pattern: The system was technically correct, but operationally ambiguous. Users weren't making mistakes because of bad data, they were misinterpreting correct data due to inconsistent system behavior.
Research
Aligning with stakeholders to identify the highest-friction areas
To validate the pattern uncovered in stakeholder interviews, I partnered with UX Research to run usability testing and user interviews, observing how users actually worked across dashboards, Excel, and their reporting workflows.
We mapped the end-to-end decision process and saw how small inconsistencies in filtering, data hierarchy, and system feedback compounded into real trust problems.
To keep everyone aligned as we moved into design, I set up ongoing check-ins with each team:
- Weekly or Bi-weekly reviews with the Produc Manager on system behavior
- Async Figma workflows with feedback requests with the design team
- Early engineering collaboration to validate edge cases
- Ongoing synthesis of research and documentation into product decisions
Once everyone understood what was actually breaking, we were able to start designing something that addressed the breakdowns.
Design Iterations
Each iteration exposed a different usability problem
Through collaboration with UX Research, I uncovered that the real issue was inconsistent data behavior across the system. Filtering, sorting, and chart settings didn't work in a cohesive way, forcing users to repeatedly reapply logic while introducing uncertainty about what data they were actually seeing.
Usability testing revealed that the deeper problem was a lack of trust: users couldn't confidently tell whether multiple charts were reflecting the same underlying data, turning what should be a decision-making tool into a source of ambiguity.
Legacy Design
Localized Filtering
System Behavior: Each chart could display an independent data context. For example, if the "Division" value was set differently on different charts, the account's risk level was displayed as different values depending on which chart the user was viewing.
Why this is dangerous:
- Users assumed filters applied globally
- System behaved locally
- This mismatch created false confidence in outputs
Outcome: 40% error rate. Errors weren't random, they were system-induced misunderstandings. Two charts on the same screen could represent different realities without users realizing it.
Legacy design: localized filters
Iteration 1
Centralized but Hidden Controls
System Behavior: Filters moved to a global location, but visibility decreased.
Why this failed:
- Users lost awareness of active filters
- Increased cognitive load
- Reduced system transparency
Hiding controls reduced visual noise, but increased decision ambiguity. Error rate increased to 20–35%. Users hesitated, second-guessed, or misinterpreted results.
Iteration 1: All filters live in filter modal
Iteration 2
Condensed Controls with Inline State Summary
System Behavior: Active chart configuration values were moved out of the dropdown buttons and into the chart subtitle as a plain-text summary. Dropdowns became label-only controls, showing only the axis name, not the selected value.
Why this was attempted: Charts with many configurable dimensions made the control row feel crowded, especially on smaller viewports. Separating the state readout from the controls was a way to reduce horizontal footprint without removing functionality.
Why this failed:
- Increased cognitive distance: users had to scan two separate areas to understand a control's current value before changing it
- The subtitle read as instructional text, not a live state indicator: users couldn't tell if the summary was dynamic or static
- Moved the complexity rather than resolving it
Iteration 2: condensed controls with inline state summary
Final System
Global, Persistent Filters
System Behavior:
- Single source of truth for filters
- Persistent and visible across all charts
- Applied consistently across the system
Final system: global persistent filters
Problem 2
From Dense Data to Clear Decision Context
The Account Snapshot wasn't failing because it lacked data. It was failing because it didn't clearly communicate what data users were looking at, and how to interpret it.
In its original state, the layout blended too many metrics into a single visual pane: account-level performance, industry benchmarks, and supporting metrics. Users could see the data, but struggled to understand the relationship between metrics, making it harder to confidently interpret performance.
Original Design
Shared visual hierarchy between internal and external data
The account snapshot displays a high-level overview of important account information. The original design had some key issues: industry comparisons lived directly next to account metrics, which resulted in:
- Account metrics and industry benchmarks sharing the same visual hierarchy
- No clear distinction between internal performance and external comparison
- Dense table structure requiring manual cross-referencing
- Visual weight that did not reflect importance
This resulted in high cognitive load, slower interpretation, and increased risk of misreading performance context. The interface required users to do the work the system should have been doing.
Legacy design: account snapshot
Iteration 1
Dual Data States
System Behavior: Unfiltered and filtered data displayed simultaneously.
Why this is dangerous:
- Introduced competing sources of truth
- Users couldn't tell which dataset to trust
Observed Behavior: Users tried clicking values, asked clarifying questions, and misinterpreted static vs. dynamic data.
Iteration 1: account snapshot
Adding more data increased perceived transparency, but actually introduced competing sources of truth.
I approached this as a decision clarity problem: How do we structure the interface so users can immediately understand performance without needing to interpret relationships manually?
Final System
Singular, Contextual Data Model
System Behavior:
- One clear data state
- Visual hierarchy communicates what is affected by filters
- Static vs. dynamic data separated spatially
This reduced cognitive load, misinterpretation risk, and decision hesitation.
Final system: account snapshot
Outcomes
Designing for Consequence, Not Convenience
This redesign reinforced a key principle: in enterprise systems, the role of design is to ensure data is interpreted correctly.
By restructuring both interaction patterns and information hierarchy, the dashboard evolved from a collection of visualizations into a reliable system for decision-making.
We removed ambiguity not by adding clarity, but by eliminating competing interpretations of the same data.