Brad Tober

Following Publicis Groupe's acquisition of Epsilon, I identified a strategic opportunity to enhance the company's competitive position by reimagining how data experiences were situated within our product interfaces. The newly-expanded PeopleCloud suite of SaaS marketing tools had powerful underlying data capabilities, but user-facing data experiences lacked consistency, actionability, and narrative cohesion across the product portfolio. I led the development of a comprehensive cross-product data experience strategy that fundamentally changed how users interacted with data within our platform.

By creating a unified framework for in-product data experiences that spanned from operational metrics to narrative-driven insights, we transformed users' ability to derive value from Epsilon's rich data ecosystem. The initiative successfully bridged technical capabilities with user needs by implementing a "scaffolded" approach that combined D3.js visualizations with embedded Tableau components, all unified by a consistent design language. This work established data experiences as a key differentiator for Epsilon in the marketplace, while significantly improving usability metrics and development efficiency across the product suite.

Business challenge

The business case for reimagining Epsilon's approach to user-facing data experiences emerged from several interrelated factors:

• Untapped data value: While Epsilon had invested heavily in data infrastructure and analytics capabilities, including predictive models and segmentation tools, much of this potential remained locked away from users. Client feedback consistently highlighted that the power of Epsilon's data assets wasn't being effectively surfaced within the product interfaces where marketers spent most of their time.
• Experience inconsistency: As each product evolved independently, users encountered different visualization styles, interaction patterns, and terminology when moving between products in the suite. This created cognitive load that diminished the perceived value of Epsilon's integrated platform approach.
• Decision gaps: Despite having access to powerful data, marketers struggled to translate this information into clear next steps. Internal research showed that users frequently had to export data to external tools to derive actionable conclusions, creating workflow inefficiencies and potential data governance issues.
• Technology silos: Epsilon had made significant investments in both custom visualization approaches (including technologies like D3.js) and enterprise reporting tools (Tableau), but these existed in separate ecosystems. Without a cohesive strategy, teams were debating "either / or" technology choices rather than leveraging the complementary strengths of each approach.

Strategic approach

The fragmented state of data experiences across Epsilon's products called for a strategy that went beyond surface-level design solutions. My approach began with understanding the current state and then developing a framework to guide future implementations.

Data experience framework

I conducted a comprehensive audit of existing data experiences across all PeopleCloud products to understand the disparate approaches, identify patterns, and document inconsistencies. This audit revealed not only visual and interaction differences, but also inconsistent terminology and conceptual models being used across products.

View this data experience audit matrix as a PDF (with username protected and the password you used to access this page)

Based on these findings, I developed a clear, harmonized taxonomy that distinguished between different types of data experiences within our products:

• Reports: Standardized collections of metrics for periodic reference
• Dashboards: Real-time monitoring tools for ongoing performance tracking
• Insights: Actionable observations with clear next steps for users to take
• Data Stories: A new data experience type I conceptualized after identifying a gap in narrative-driven experiences across the platform, providing contextual explorations of multiple related findings.

To implement this framework, I created a decision tree that helped stakeholders identify the most appropriate data experience type based on key factors:

• The primary focus of the experience (data aggregation or interpretation)
• The role of historical data in the experience
• The frequency of user engagement with the data
• Whether the experience revolved around answering a pre-defined question associated with a specific marketing problem
• Whether the data required relationships across multiple points to be meaningful
• How directly the experience needed to connect to subsequent user actions

View the decision tree as a PDF (with username protected and the password you used to access this page)

The marketing objectives question in the decision tree connected to a separate, but related, effort to streamline Pathways (a type of user journey) across the platform, with the intent to align data experiences with universal goals: increasing sales, converting customers, acquiring new customers, and decreasing attrition. This approach ensured our work served fundamental business objectives rather than product structures.

This decision tree became an essential resource for product teams, eliminating subjective debates about visualization approaches and creating a systematic method for determining the right experience for each use case. It also guided implementation technology decisions, as each experience type aligned with specific tools: reports used Tableau, dashboards would often combine Tableau with CORE UI (Epsilon's design system) components, while insights and Data Stories were built exclusively with CORE UI components. Together, the framework and decision tree created a common language across the organization, helping teams understand how different visualization approaches served distinct user needs.

Technical innovation: The "scaffolded" approach

A significant technical contribution of my strategy was what became known as the "scaffolded" approach to data visualization integration. The term "scaffolded" reflected how I created a unified structure where different visualization techniques could achieve similar outcomes regardless of their underlying implementation. Key elements included:

• Conceptualized a systematic method for embedding discrete Tableau visualizations (individual Tableau dashboards containing only a single chart or metric) within interfaces built using Epsilon's CORE UI design system components.
• Created a shared structural framework where both CORE UI data visualization components (built with D3.js) and Tableau elements could be inserted interchangeably based on specific needs.
• Identified performance challenges that initially arose when embedding multiple individual Tableau visualizations within a single page, which product engineers then effectively resolved.

This approach enabled tight integration between Tableau-rendered visualizations and product functionality (such as setting and tracking goals relative to specific insights in Epsilon's PeopleCloud Loyalty product), allowing product teams to selectively leverage the strengths of each technology—design system components for orchestrating advanced interactions, and Tableau for its data scaling and filtering capabilities—while maintaining a consistent user experience.

Team and collaboration

Cross-functional alignment

I established a cross-functional working group that included representation from product management, design, engineering, and data science teams across all four major product lines. This forum created alignment around:

• Shared terminology and taxonomy for data experience features
• Common design patterns and interaction models
• Technology selection criteria
• Implementation roadmaps and priorities
• Approaches to communicating with and obtaining buy-in from executive sponsors

Meeting weekly, this group became the central coordination mechanism for all data experience initiatives, ensuring consistent execution of the strategy.

Design system integration and team development

A critical aspect of the data experience strategy involved not just technical integration, but developing organizational capability:

• Led initiatives to address gaps in our existing CORE UI visualization components, particularly adding functionality for users to select individual visualization dimensions or segments to dynamically impact other parts of the experience.
• Facilitated the adoption of CORE UI patterns (including design system tokens for color schemes) by Tableau visualization teams, bridging previously separate technical ecosystems.
• Conducted organization-wide workshops on D3.js to cultivate technical expertise and foster an environment where teams understood the importance of developing deeper visualization capabilities.
• Promoted component accessibility standards, including a recommendation to integrate equivalent tabular data representations alongside visualizations.

This multifaceted approach addressed both technical needs and organizational capability gaps, ensuring we could deliver more sophisticated data experiences like insights and Data Stories that required interactive selection, filtering, and audience-building functionality. By "leveling up" our collective expertise in advanced data visualization techniques, we enabled the organization to implement more complex, interactive experiences that would have previously been unattainable.

Access Epsilon's publicly-available CORE UI data visualization documentation

View the slides from my "Insights, bespoke data visualization, and the role of D3 at Epsilon" workshop as a PDF (with username protected and the password you used to access this page)

Data science partnership

Beyond typical design-engineering collaboration, this initiative required deep partnership with data science teams:

• Conducted joint sessions to understand the full capabilities of Epsilon's analytical models.
• Collaborated on determining which insights provided the most value in which contexts.
• Created visualization approaches that accurately represented statistical concepts without oversimplification; this importantly acknowledges the UX efficient frontier (balancing complexity and simplicity for optimal understanding).

This partnership was essential to ensuring that the user interfaces properly represented the sophisticated analytics happening behind the scenes.

Outcome and impact

The data experience strategy fundamentally transformed how users interacted with information across the Epsilon PeopleCloud suite, delivering outcomes that spanned user experience, business value, and organizational capability:

User-centered outcomes

• Unified experience: Created consistency across previously fragmented touchpoints, allowing users to move between products while maintaining their mental model of how data experiences work.
• Actionable insights: Transformed raw data into clear decision support, reducing the cognitive load required to derive meaning from complex information.
• Narrative understanding: The Data Stories experience type, in particular, enabled users to quickly grasp relationships between different data points that were previously difficult to connect.

Business value

• Industry recognition: External analysts specifically cited Data Stories as a significant differentiator in the marketing technology landscape, strengthening Epsilon's market position.
• Client retention: The improved clarity around data experiences became a key discussion point in renewal conversations, with clients specifically referencing its value.
• Cross-selling success: The consistent approach across products made it easier for clients using one PeopleCloud product to adopt additional offerings, as the data experiences felt familiar and integrated.

Organizational transformation

• Enabling others: By conducting workshops, developing guidelines, and creating frameworks that others could independently apply, I established a model where my role evolved from direct implementation to enabling and facilitating the work of designers and engineers throughout the organization.
• Knowledge development: The focus on "leveling up" technical skills around D3.js and data visualization created lasting organizational capability.
• Design system evolution: The enhanced components and guidelines created a foundation for future data visualization work, ensuring continued consistency and efficiency.

Reflection

Leading this transformation in Epsilon's approach to data experiences reinforced several key insights about design leadership in complex organizational contexts:

• Strategic enablement: This initiative highlighted how the most impactful design contributions often happen behind the scenes. My role evolved from producing interface mockups to creating the conditions for others to succeed—developing strategic frameworks, taxonomies, and decision trees that guided implementation without dictating specifics. This largely aligns with my work on meta-design, even though it was not explicitly framed as such at the time. By focusing on education, guidelines, and "invisible" strategic work, I multiplied my impact across the organization. This approach created sustainable success beyond what any individual design artifact could achieve.
• Technical credibility drives influence: My technical understanding of data visualization principles and D3.js implementation details was essential to earning credibility with both engineering and data science teams. This reinforced my belief that design leaders must develop substantive knowledge in technical domains to effectively drive innovation and collaboration.
• Framework thinking vs. feature thinking: The decision tree proved remarkably powerful as a decision-making tool, transforming subjective debates into structured conversations. By creating a shared framework that transcended individual opinions, we enabled more objective evaluation of different approaches and more consistent outcomes across teams.
• Narrative as organizing principle: The introduction of Data Stories as a distinct experience type represented more than just a new visualization category—it reflected a fundamental shift toward storytelling as a way to make complex data meaningful. This narrative approach became a bridge between technical complexity and human understanding, connecting data to the marketing objectives that drove business decisions.

This project reinforced that design leadership in enterprise contexts often requires focusing on enabling systems rather than creating individual artifacts. The methodologies developed through this work have since influenced my approach to other complex, cross-product initiatives where success depends more on strategic foundations than on visible design outputs.