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Data UX Power BI Dashboard Design Information Architecture AI-assisted Workflow Under NDA

Mondelez International — DataMockups

Redesigning Power BI dashboards to make complex Sell-In and Sell-Out data clearer for business decisions.

I collaborated on the UX redesign of Power BI dashboards for Sell-In and Sell-Out data, helping structure information for executive, tactical, and operational users. My work focused on improving navigation, hierarchy, visual clarity, data accessibility, and dashboard comprehension in a short timeline supported by AI-assisted workflows.

Company

Mondelez International

Industry

Data UX / Consumer goods / Business intelligence

Role

UX Designer — Data UX

Product type

Power BI dashboards / Business intelligence tool

Timeline

May 2025 – Jun 2025

Team

UX, Data/BI, Client Stakeholders, Business Users, Product/Delivery

Methods

Stakeholder Needs Analysis, IA, Dashboard UX, Wireframes, Data Visualization, Accessibility, Client Validation

Tools

Power BI, Figma, FigJam, Claude

Status

Delivered dashboards / Under NDA

Some parts of this project are under NDA, so I'm showing a curated version of the process, artifacts, and outcomes. Sensitive business data, dashboard content, KPIs, and client information have been blurred or simplified while keeping the design decisions, methodology, and my role clear. I'm happy to walk through more detail privately in an interview or portfolio review.

Sensitive data and business details have been blurred or simplified to protect confidential information.

Context

Mondelez International needed to improve the UX of Power BI dashboards used to analyze Sell-In and Sell-Out data. The dashboards had to support different business users with different levels of decision-making: executive, tactical, and operational.

The project had a short timeline and involved a complex data context. The challenge was to quickly understand the information, identify what each user profile needed, and translate that into clearer dashboard structures that improved comprehension, navigation, and data accessibility.

Why it mattered

Making the dashboards look cleaner was the visible part. The real work was understanding how different business users actually read data, what decisions each of them needed to make, and how information architecture could take some of the cognitive load off a genuinely data-heavy environment.

My role

I worked as UX Designer with a Data UX focus, collaborating on the redesign of Power BI dashboards within a broader data and BI team. My role connected stakeholder needs, dashboard structure, information hierarchy, accessibility, and client feedback.

  • Collaborated on the UX redesign of Power BI dashboards for Sell-In and Sell-Out data.
  • Analyzed stakeholder needs based on previous research and client context.
  • Structured dashboard experiences for executive, tactical, and operational users.
  • Improved navigation, information hierarchy, visual clarity, and data accessibility.
  • Helped define an accessible color palette for better readability and comprehension.
  • Explored dashboard wireframes and layout alternatives.
  • Validated proposals with the client and adjusted based on feedback.
  • Used Claude to organize complex information, understand the business context faster, and explore wireframe ideas.

The challenge

The main challenge was transforming complex business data into dashboard experiences that could support different levels of decision-making without overwhelming users.

  • User challenge: executive, tactical, and operational users needed different levels of detail, but the dashboard experience had to remain clear, navigable, and easy to understand.
  • Business challenge: the client needed dashboards that made Sell-In and Sell-Out information easier to read and use for business decisions.
  • Design challenge: the team had to understand a complex data context quickly, improve structure and accessibility, and deliver useful dashboard proposals in a short timeline.

Problem statement

How might we help business users understand and navigate Sell-In and Sell-Out data more clearly, while supporting different decision-making needs across executive, tactical, and operational levels?

Goals & success criteria

User goals

  • Help users find the right level of information for their role.
  • Reduce cognitive load when reading complex dashboard data.
  • Improve clarity, navigation, and confidence in data interpretation.

Business goals

  • Support better access to Sell-In and Sell-Out business information.
  • Make dashboards more useful for decision-making across different user profiles.
  • Improve consistency and readability in Power BI reporting.

Design goals

  • Define clearer dashboard structures for executive, tactical, and operational needs.
  • Improve information hierarchy and navigation.
  • Support accessible visual decisions, including color usage.
  • Validate dashboard alternatives with the client and iterate based on feedback.

Process

Discover Define Develop Deliver

01 — Discover

Understanding users, data context, and dashboard complexity

I started by understanding the dashboard context, the type of data involved, and the different users who needed to interact with it. Because the timeline was short and the subject was complex, I used Claude as a project workspace to organize key information and accelerate my understanding of the business context.

  • Reviewed previous research and stakeholder information.
  • Identified the needs of executive, tactical, and operational users.
  • Analyzed the differences between Sell-In and Sell-Out data usage.
  • Mapped where dashboard structure, navigation, and hierarchy needed improvement.
  • Identified accessibility and readability considerations for data visualization.
  • Used AI to organize complex context and reduce the learning curve.

How I used AI

I used Claude as a project workspace to centralize relevant information, understand a complex data context faster, and identify what needed to be clarified before moving into dashboard structure. AI helped me accelerate comprehension, but final decisions stayed grounded in UX criteria, client feedback, accessibility considerations, and product constraints.

Discovery structure used to connect user profiles with different levels of data detail and decision-making needs.

02 — Define

Translating business needs into dashboard structure

After understanding the users and data context, I helped define dashboard structures that responded to different levels of business decision-making. The goal was to make information easier to scan, prioritize, and interpret depending on the user's role.

  • Synthesized stakeholder needs into three main user levels: executive, tactical, and operational.
  • Defined differentiated dashboard structures for each type of user.
  • Prioritized information hierarchy based on decision-making needs.
  • Identified navigation improvements to help users move through data more clearly.
  • Supported the definition of an accessible color approach for better readability.
  • Aligned the proposed structure with client expectations before moving into visual exploration.

How I used AI

I used Claude to help organize complex stakeholder inputs and explore possible ways to structure dashboard information. AI supported the first layer of organization, while the final architecture was shaped through UX judgment, accessibility criteria, and client validation.

Information architecture used to separate dashboard needs by user level and reduce cognitive load.

03 — Develop

Exploring wireframes for clearer data comprehension

Once the structure was defined, I explored dashboard wireframes and layout alternatives. The focus was to improve how users scan information, understand relationships between data points, and navigate through the dashboards without unnecessary friction.

  • Created dashboard wireframe explorations for different user levels.
  • Improved layout hierarchy to make key information easier to identify.
  • Explored navigation patterns for moving between dashboard views.
  • Considered accessible color usage to support readability.
  • Validated wireframe ideas with the client and adjusted based on feedback.
  • Balanced data density with clarity and usability.

How I used AI

I used Claude to generate wireframe ideas based on the project context and the dashboard goals. These ideas were not used as final designs directly; they helped accelerate exploration. I reviewed, adapted, and validated them with the client before moving forward.

Wireframe exploration used to test dashboard structure before moving into final Power BI implementation.

Color exploration used to support clearer reading and more accessible data interpretation.

04 — Deliver

Delivering clearer dashboards for different business decisions

The final work contributed to three tailored Power BI dashboards designed around executive, tactical, and operational needs. Each dashboard aimed to improve navigation, visual clarity, and data accessibility so users could understand and use information more effectively.

  • Delivered three dashboard structures tailored to different business needs.
  • Improved navigation and hierarchy across Sell-In and Sell-Out data views.
  • Supported clearer visual organization for business users.
  • Helped define accessible color usage for readability.
  • Validated proposals with the client and iterated based on feedback.
  • Contributed to final dashboard documentation and design decisions.

How I used AI

I used Claude to support final refinement and ensure the dashboard logic remained clear across different user levels. AI helped with speed and organization, while final decisions were validated through client feedback, UX criteria, and accessibility considerations.

Final dashboard overview simplified for portfolio purposes to show structure without exposing sensitive business data.

Before / after

Before

Dashboard information was difficult to structure for different user needs, making it harder to scan, navigate, and interpret complex Sell-In and Sell-Out data.

After

The redesign organized dashboard experiences around executive, tactical, and operational needs, improving navigation, hierarchy, visual clarity, and data accessibility.

Final solution

The final proposal focused on three main experience improvements:

01

Role-based dashboard structure

The dashboards were organized around executive, tactical, and operational needs, helping each user access the right level of information.

02

Clearer information hierarchy

The redesign improved how data was prioritized, grouped, and presented, reducing cognitive load in a data-heavy experience.

03

More accessible data visualization

Color and layout decisions supported readability, comprehension, and more inclusive access to business information.

Impact & results

User value

Helped business users access and understand Sell-In and Sell-Out data with clearer dashboard structures.

Business value

Delivered three tailored dashboards supporting executive, tactical, and operational decision-making.

Product value

Improved navigation, information hierarchy, visual clarity, and data accessibility in Power BI dashboards.

Team / process value

Used AI-assisted workflows to move faster through a complex data context while validating decisions with the client.

Obstacles & trade-offs

This project required constant trade-offs between data density, clarity, accessibility, and delivery speed. The challenge was not to show less information, but to structure it in a way that made sense for each user profile.

  • Understanding a complex data context in a short timeline.
  • Designing for multiple user levels with different decision-making needs.
  • Balancing dashboard density with clarity and readability.
  • Improving accessibility without losing business meaning in the data.
  • Validating ideas quickly with the client and adjusting based on feedback.
  • Using AI to move faster while keeping UX judgment and client validation at the center.

What I learned

This project reinforced that data UX is not about displaying more information; it is about helping people make sense of information faster and with more confidence.

  • I learned how important user roles are when structuring dashboards.
  • I strengthened my ability to translate complex data contexts into clearer information architecture.
  • I improved how I balance visual clarity, accessibility, and business needs.
  • I learned how AI can help me understand complex domains faster without replacing client validation.
  • I confirmed that dashboard UX should support decisions, not just reporting.
  • I strengthened my confidence working with Power BI and data visualization contexts.

This is a good snapshot of how I handle a dense, unfamiliar data context on a tight timeline: structure it around what real users need, lean on AI to get up to speed faster, and turn what started as a wall of numbers into something people can actually use.

Stephanie Cacheo — Senior UX Designer