Improving data clarity, insight discovery, and decision-making for enterprise users through a more intuitive analytics dashboard.
Sterling Pro is an enterprise analytics platform used internally by Sterling Bank to monitor performance, onboarding activity, transactions, complaints, and feature usage across business functions. The Analytics Dashboard is a central hub for operational insight — but its design made it difficult for users to extract meaningful trends quickly.
As Lead UX Designer, I was responsible for identifying usability pain points, redefining the information architecture, and designing a scalable analytics dashboard that supports faster, more accurate decisions.
The redesign had three primary objectives:
Reduce cognitive load by establishing a clearer visual hierarchy.
Highlight high-value metrics such as feature usage, complaint trends, and transaction channels.
Enable faster decision-making through more intuitive navigation and scalable components.
The original analytics dashboard posed several user-experience challenges, including:
Poor Information Hierarchy — key insights were buried under low-value, cluttered widgets.
Visual Clutter — redundant charts and inconsistent layouts increased cognitive load.
Limited Channel Insight — teams struggled to compare transaction channels easily.
Low Data Comprehension — uniform colors and dense visuals made patterns hard to see.
View the problem below
The final redesign delivered a more structured and insight-focused dashboard, including:
Streamlined Layout & Hierarchy
Reorganized widgets into a coherent grid layout
Prioritized high-value analytics
Reduced visual noise to accelerate scanning and comprehension
Regulatory and compliance requirements
Legacy backend systems that limited flexibility
Time-sensitive delivery timelines
ROLE
Lead UX Designer
INDUSTRY
Fintech / Enterprise Banking
RESPONSIBILITIES
UX research, flow design, wireframing, UI design, stakeholder collaboration
I restructured key workflows to reduce unnecessary steps and surface critical information earlier in the experience. Wireframes were used to validate flow changes before moving into high-fidelity UI designs. Iterative feedback sessions with stakeholders helped refine usability and ensure alignment with operational needs.
I conducted interviews with 20 internal users and stakeholders to identify their goals, pain points, and preferred insights. Key research findings revealed that users:
Spent excessive time searching for frequent metrics
Missed important trends due to labeling and layout issues
Navigated complex charts without clear guidance
To reduce clutter and improve clarity, I reorganized analytics into logical, high-impact categories. The new IA prioritized:
Feature usage trends
Channel performance
Transfer volumes and values
Complaints analysis
I consolidated redundant widgets and freed space for strategic metrics. This created a cleaner grid that supports scalable chart expansion and future modules.
Design changes focused on:
Strong visual hierarchy to guide users through key insights
Meaningful color coding for quick pattern recognition
Scalable data-visualization patterns compatible with additional analytics modules.
The final redesign delivered a more structured and insight-focused dashboard, including
Reorganized widgets into a coherent grid layout
Prioritized high-value analytics
Reduced visual noise to accelerate scanning and comprehension
Added dedicated breakdowns for transactions by channel
Enabled teams to compare usage patterns more easily
Improved visibility into strategic growth areas
Applied distinct colors to each feature for visual separation
Replaced multiple complaint charts with one unified chart + toggle
Used stacked bar charts for easier trend comparison
The redesigned prototype was tested with 15 participants to evaluate:
Speed of identifying key metrics
Ease of navigation
Accuracy of comprehension of complex visualizations
From this project, key takeaways include:
Designing internal tools requires deep understanding of real operational contexts
Consistency across systems significantly reduces user errors
Early stakeholder alignment minimizes late-stage rework
Planned future enhancements include:
Follow-up testing using real usage data
Tracking which visualizations users interact with most
Adding predictive analytics dashboards for proactive insights
Expanding filters and advanced comparison tools
The redesign delivered measurable benefits:
Improved data comprehension — critical insights are now easier to scan and act upon.
Reduced visual clutter — the interface feels cleaner with a clear focus on strategic metrics.
Faster operational decisions — teams no longer spend excessive time hunting for patterns.
The final design delivered a more intuitive and efficient platform that enabled internal teams to complete tasks with greater confidence and speed, while maintaining compliance and scalability.
