MULTI-DIMENSIONAL DATA MODEL & SCHEMA GOVERNANCE
Company: Workiva
Project: Cross-Functional Data Model Redesign & UX Architecture
Role: Lead UX Designer (Systems & Workflow Modeling)
​
Strategic Challenge
The ESG platform’s original data model didn’t support the multi-dimensional reality of ESG reporting. Metric structures, dimensional attributes, and values were difficult to interpret and maintain, creating confusion for users and complexity for engineering.
Our cross-functional team set out to redesign the underlying data model to support dimensionality more cleanly. My role was to define the user experience, map the workflows, and help shape the mental model and governance patterns needed to make a more flexible architecture usable and understandable.
Staff Leadership & Cross-functional Influence
This was a deeply collaborative effort with engineering, product, data architecture, and CX. I facilitated alignment by:
​
-
Mapping user flows across the new dimensional model
-
Translating technical constraints into user-understandable structures
-
Creating diagrams that clarified the relationships between metrics, dimensions, and values
-
Helping the team converge on a shared mental model that informed both the UX and the backend schema
My contribution ensured that the system would scale while still being teachable and predictable for end users.
Systemic Architecture & Foundational Frameworks
While engineering defined much of the data structure, I contributed UX frameworks that clarified:
-
How dimensions appear and behave in the interface
-
How users interact with dimensional structures
-
How values map to dimensional combinations
-
How the system communicates context and constraints
This work made the underlying architecture understandable and actionable for users, and aligned UX and engineering around the same conceptual model.
Legacy vs. New Experience
Legacy Model
In the original system, dimensions were inconsistently represented across the platform. Users often couldn’t tell which dimensions applied to a metric, how values were structured, or why certain combinations behaved differently. Engineering workarounds created fragmentation, and UX patterns drifted because there was no shared mental model guiding how dimensional data should appear or behave.
​
New Model
In the redesigned approach, dimensions, values, and metric definitions follow a consistent, predictable structure. I worked with engineering and product partners to translate the new schema into clear user experiences — defining how dimensional attributes are surfaced, how values map to dimensional combinations, and how the UI reinforces system rules instead of obscuring them.
The result is an experience where users can immediately understand:
-
what dimensions apply,
-
why a value exists,
-
and how dimensional context affects reporting and workflows.
This clarity reduces confusion and builds alignment across teams internally.
The New View - Add values workflow
One of my core contributions was designing the user flows and interaction patterns for adding and managing values within the new dimensional model. This included:
-
Mapping how users select dimensions before entering values
-
Defining the step-by-step workflow for creating valid dimensional combinations
-
Ensuring the UI reflects the underlying rules of the schema
-
Helping users understand when a value is missing, invalid, or incomplete
-
Creating consistency across other workflows that depend on dimensionality (Tasks, importing, bulk actions)
The new workflow reduces ambiguity by making the dimensional context visible and actionable, so users know exactly where a value belongs and what it represents.
This interaction model became a reference pattern for other teams as dimensionality expanded across ESG.
The Add Values workflow showing how Time is represented as a permanent dimension in the new model.
Users can now select values for each dimension explicitly, making the dimensional structure clear and predictable.
Strategic Impact & Forward Planning
The redesigned data model created more than a cleaner interface — it established a shared foundation for how ESG data is structured, interpreted, and governed. With a consistent schema and clear UX patterns:
-
Engineering can build new features without reinventing data structures
-
Users experience fewer inconsistencies and have more confidence in the system
-
Future workflows, such as automation and advanced reporting, now have a stable foundation
-
Cross-functional teams finally operate from the same mental model
This project strengthened the platform’s ability to scale sustainably while reducing onboarding friction, UX inconsistencies, and long-standing ambiguity around ESG data.
It represents the core of Staff-level UX leadership:
translating complexity into clarity, partnering across disciplines, and shaping systems that endure.
