Scalable Data Experience Ecosystem

Building a Scalable Data Experience Ecosystem

Abiomed, a Johnson & Johnson MedTech company, operates in a highly complex and regulated healthcare environment. The organization manages multiple data platforms that support commercial teams, therapy awareness managers, executives, and operational leaders across regions.

As a Senior Product Designer, I led the creation and evolution of a unified data experience across dashboards, analytics portals, and internal platforms—collaborating with a cross-functional team of BI developers, data engineers, and Product stakeholders to ensure clarity, consistency, and usability at scale.

At a Glance

DATA EXPERIENCE PLATFORM ENTERPRISE ANALYTICS DESIGN SYSTEM DATA VIZ A/B TESTING AI INTEGRATION FIGMA TABLEAU POWER BI SALESFORCE CONFLUENCE ACCESSIBILITY

Problem Statement

Abiomed’s analytics ecosystem was growing rapidly across multiple tools and teams. However:

+20 dashboards lacked visual and structural consistency across teams.
Unclear KPI hierarchies reduced interpretability, leading to inconsistent decision-making across regional teams.
Collaboration between design, BI, and data engineering required alignment.
No centralized design system, resulting in duplicated work and inconsistent user experiences.
Adoption depended heavily on clarity and trust in metrics.
Limited user research meant designs weren’t validated against actual user needs.
Documentation and governance needed formalization.

The challenge was not just visual redesign, it was building a scalable, coherent data experience framework across enterprise platforms.

Our Approach

To tackle these challenges, we implemented the following strategic solutions:

Data Experience Strategy
Defined a clear structure for KPI hierarchies and dashboard navigation, prioritizing actionable insights over raw data to improve decision-making clarity.
Data Visualization Design System
Built a comprehensive analytics design system with 50+ reusable visualization components including:
Standardized chart patterns (bar, line, scatter, heat maps, KPI cards)
Typography hierarchy and color logic optimized for accessibility (WCAG AA)
Reusable templates for Tableau and Power BI ensuring brand consistency
Design tokens and documentation for seamless handoff to BI developers
Cross-Team Collaboration
Worked closely with data engineers, BI developers, and stakeholders to validate interactive prototypes and ensure seamless design-to-implementation alignment.
User Research & Validation
Conducted stakeholder interviews with commercial teams, therapy managers, and data engineers to understand pain points and usage patterns. Performed usability testing sessions to validate design decisions and measure comprehension of complex data visualizations.

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Outcomes

1. Unified Data Experience

Established a scalable design framework adopted across multiple analytics initiatives.

2. Improved Interpretability

Clearer KPI structures improved executive-level decision support and reduced ambiguity in metric interpretation.

3. Reduced Design-to-Implementation Friction

Reusable components and structured documentation improved collaboration between design and BI teams.

4. Stronger Stakeholder Alignment

Design strategy was articulated and validated with leadership-level stakeholders, ensuring business alignment.

5. Increased User Adoption & Satisfaction

Dashboard adoption rates increased across commercial teams, with users reporting improved confidence in data interpretation and faster time-to-insight during stakeholder interviews.

Enhanced User Experience

The redesigned analytics ecosystem prioritized:

Clear visual hierarchy separating primary KPIs from supporting metrics
Consistent navigation patterns reducing cognitive load
Plain-language tooltips and data labels eliminating need for technical documentation
Color-coded indicators (red/yellow/green) for instant status recognition
Accessible contrast ratios (WCAG AA) ensuring readability for all users

Achievements

Built and governed analytics design system adopted across 20+ enterprise dashboards, reducing design-to-implementation time by 40%.
Standardized 50+ visualization components across Tableau and Power BI, ensuring consistency and scalability.
Conducted user research with commercial teams, therapy managers, and data engineers to validate design decisions and measure impact.
Introduced experimentation culture (A/B testing) and AI-assisted workflows to accelerate iteration cycles.
Ensured WCAG AA compliance across all dashboards, improving accessibility for diverse user profiles.

Significant Benefits of the Project

+20

Enterprise dashboards standardized

+50

Reusable visualization components

40%

Faster iteration cycles

Cross-functional teams aligned

100%

Data-driven design decisions

Faster iteration cycles is measured by reduction in average design-to-implementation time from, 3 weeks to 1.8 weeks through reusable components and streamlined handoff)

🤓 manuelgasparcalvo@gmail.com