A dashboard that transforms complex, user generated data into clear insights for event organizers.
Role & Context
I led this project as a solo designer for my senior capstone over 16 weeks in Spring 2025. The work was for Rendez, an app that was preparing to launch.
Brief
Rendez is a location based networking app that connects people at events using hashtags. Users tag themselves with things like skills, industries, hobbies, or personal interests.
The goal of this project was to design a dashboard that shows potential partners why that data is valuable. Since Rendez hadn’t launched yet, there wasn’t any real user activity. One of the main priorities was landing partnerships with event organizers in LA, especially in the tech space.
To keep the project grounded, one local event company was used as the sample partner. I designed the dashboard with their needs in mind to help support early partnership conversations and demonstrate what Rendez could eventually offer, even without active users.
Project Challenge
Problem
Rendez collects rich user generated data through hashtags, but had no way to present that information in a clear or meaningful way. At the start of this project, the data existed only as long, unstructured lists: one for each of the app’s 13 tagging categories. This format made it difficult to understand what the data meant or how to extract useful insights.
A major issue was the potential for overlap across categories. Since users can tag themselves in multiple ways, like listing “tech” as a skill, an industry, or a hobby, that ambiguity had to be considered from the start. Without a clear way to separate and label these inputs, the data could quickly become messy and hard to interpret. This created a challenge in designing a system that would eventually allow event organizers to search or filter by terms without confusion.
Volume was another challenge. Each user contributes a significant amount of data, making it easy for the dataset to grow overwhelming as attendance increases. At scale, meaningful trends can easily get buried. Without strong organization and hierarchy, the experience risked becoming overwhelming and hard to use.
Finally, because Rendez hadn’t launched yet, the dashboard had to show the value of the data without any real user activity, turning a theoretical dataset into something that still felt concrete, credible, and worth investing in.
Insights
People don’t want all the data, they want the right data.
Event organizers aren’t trying to browse everything. They’re looking for patterns and meaning that support decisions. This shaped the dashboard’s need for prioritization and focus, not just display.
Event organizers aren’t trying to browse everything. They’re looking for patterns and meaning that support decisions. This shaped the dashboard’s need for prioritization and focus, not just display.
Clarity starts with hierarchy.
Even small amounts of information can feel heavy if they’re not organized well. The order, spacing, and emphasis of elements directly affects how intuitive the experience feels.
Even small amounts of information can feel heavy if they’re not organized well. The order, spacing, and emphasis of elements directly affects how intuitive the experience feels.
Visual trust is emotional.
Inconsistent alignment, spacing, or grouping makes users hesitate, even if the data is accurate. Design choices influence whether people feel like they can trust what they’re seeing.
Inconsistent alignment, spacing, or grouping makes users hesitate, even if the data is accurate. Design choices influence whether people feel like they can trust what they’re seeing.
Tools need to meet users where they are.
Some users want quick takeaways. Others want to dig deep. A successful system doesn’t choose one or the other, it makes space for both without sacrificing clarity.
Some users want quick takeaways. Others want to dig deep. A successful system doesn’t choose one or the other, it makes space for both without sacrificing clarity.
Solution
Everything in the design came from the insights I gathered early on.
To help organizers focus on the right data, I made sure the dashboard highlighted key takeaways first. Instead of showing everything up front, it brings the most important trends to the surface so people don’t have to dig to find meaning.
To keep things clear, I used strong visual hierarchy. The layout guides users from the top down, with smart spacing and simple structure. Even when the content is light, a clear order helps it feel easy to move through.
To build trust, I focused on visual consistency. I paid close attention to alignment, spacing, and grouping so that nothing felt off. When things are clean and intentional, people are more likely to trust what they’re seeing.
Since not everyone uses data the same way, I created space for both quick scans and deeper exploration. Some users just want fast answers. Others want to explore the details. The dashboard supports both without making either experience feel too busy or too thin.
Every choice came back to the same goal: help people see the value of the data quickly, even before the product had real users.
Process
Secondary Research
To ground the project, I looked closely at how platforms like Google Analytics, Discord, and Gemini present complex data clearly. These tools balance density and simplicity in different ways, but they all prioritize user understanding over raw volume. From this, I learned that a successful data experience isn’t just clean, it anticipates user questions and surfaces answers early.
Main Takeaways and Feature Highlights
Initial Sketch of Dashboard
Sketches & Wireframes
I used sketching and low-fidelity wireframes to explore structure, pacing, and information hierarchy. Early layout experiments helped me identify where overwhelm might occur, and where interaction could be simplified. These sketches served as a quick way to test ideas, discard weak ones, and make more informed decisions when building mid-fidelity prototypes.
User Testing
Even informal testing gave me crucial insights. Observing how people navigated early versions helped me see what information actually stood out, what was missed, and where cognitive load was too high. It also highlighted users’ expectations. For example, where they instinctively looked for filters or how they expected to interact with summaries.
User Testing
User Flow: Hashtag Comparison
User Flows
Mapping out user flows helped me consider not just what users might click, but why. I created pathways for both quick scanning and deeper analysis, based on different levels of intent. This helped me prioritize which features needed to be immediately visible, and which could sit deeper in the interface for exploration.
Information Prioritization & Hierarchy Planning
As I shaped the dashboard, one of the biggest design challenges was deciding what information should appear first, and what could wait. I worked through different layouts and display patterns to prioritize what event organizers would need most, like top trends, demographics, or filtering controls. This step helped define the dashboard’s core structure and made the experience feel more intuitive from the first glance.
Early Wireframe
Results
The final prototype is a dashboard that helps event organizers understand who’s coming to their events and what those people care about. It’s built to be clear and easy to use, even for people who don’t work with data regularly.
At the top, there’s a brief AI generated summary that gives a quick overview of the most important trends. It shows what’s most interesting right away, without needing any clicks or input.
Right below that is a demographics section with basic info like age, gender, and location. This gives helpful context and makes the rest of the data feel more grounded.
The main part of the dashboard shows the top five hashtags from each of Rendez’s thirteen tagging categories. Users can click into any category to see the full list, so it works for both quick scanning and deeper exploration.
To help compare tags or look at specific themes, there’s a simple filtering and search system. For example, someone can search “design” and see how it shows up in different categories, like hobby versus industry, and spot any overlap.
There’s also a built-in chatbot that lets users type in natural questions, like “What’s trending with early career attendees,” and get quick, specific answers without needing to go through all the data manually.
The finished dashboard is designed to adapt to different needs, offering both a clear entry point and the tools to go deeper when needed.
AI Assistant
Search Flow
Reflection
This project pushed me to think about what it really takes to make complex, messy information feel clear and useful. Working with hashtag data meant creating structure from scratch. The dashboard helps Rendez show its value, even without a live product, which was the main goal from the start.
There’s still a lot of potential. Features like event comparisons, custom layouts, and saved views could make it even more powerful. I won’t be part of the next phase, but the foundation is there for the team to keep building.
Since the dashboard won’t launch right away, I also created fillable PDF templates so the team can still share reports with partners. It was important to make sure the insights could still be put to use.
People don’t open dashboards just to see data. They come to understand it. That idea shaped every design decision. The goal was never just to show information. It was to make it make sense.