Duplicate Management
Senior Product Designer
2025
Background
Impact of duplicates on data quality
Venture capital firms are always searching for their next breakout company. Just one right investment can transform a firm's reputation and help make raising future funds a lot easier.
For an individual investor, one right investment can define an entire career and establish tons of credibility, so it's no surprise investors gravitate towards tools that help give them a competitive edge.
A tool like Affinity can help investors do their jobs better by surfacing insights in their current network that uncover new opportunities they might have otherwise not seen.
Since these insights are at the foundation of the Affinity platform, clean and accurate data is essential. However, since data is primarily imported from multiple sources, such as emails, calendars, and CSVs, duplicates can often surface, which can create confusion and prevent users from seeing accurate insights.
At the time of this project, Affinity was in the midst of a company-wide initiative to Nail the Basics, a strategy focused on strengthening the platform's foundation before shipping new features. This initiative was driven by a lion's share of customer feedback where trust in the data was prioritized over adding shiny new features.
Reducing duplicates naturally became a key part of this initiative to establish a strong base for data quality before building toward more advanced capabilities.
👥 Team
I worked in a cross-functional squad that included 1 PM, 1 front-end engineer, and a team of back-end engineers. I led weekly syncs with engineering to stay aligned on progress.
💼 Role
I was the sole designer and led the end-to-end design process from problem definition through launch. Some UX research had been previously conducted prior to me joining.
PROBLEM
"There's not much confidence using it at all"
At the start of the project, Affinity already offered a duplicate manager that allowed users to merge duplicate people and companies on the platform. However, the experience was falling way short of customer expectations and was seen as untrustworthy.
Improving the duplicate manager had become one of the most requested features with more than 100+ customers submitting feedback citing duplicate-related frustrations.
At the core, the problem existed in two areas:
Lack of identifiers
The duplicate manager lacked identifiers users needed to feel confident in their decisions. The information displayed didn't align with what users needed to evaluate duplicates, making it difficult for them to determine if matches were true positives.
Without these identifiers, merging felt risky, which discouraged users from taking action.
Inaccurate matches
The algorithm often surfaced incorrect matches, which made it feel inconsistent and unreliable. As a result, users avoided using the system entirely and relied more on manual workarounds like merging records directly from profiles, which came at a cost of around 20 seconds per duplicate to resolve.
These issues all compounded into a deeper root problem, which was a lack of trust in the system. Users didn’t feel confident committing merges from the duplicate manager, which ultimately left data quality issues unresolved and resulted in larger downstream problems.
GOALS
Building trust in the system
We kicked off with problem definition sessions that involved cross-functional partners across product, design, and engineering. Led by design, these exercises leveraged frameworks like How Might We questions and the 5 Whys to uncover underlying user challenges.
These sessions helped us all align on the core problems we were looking to solve and helped define our goals for this work.
Our hypothesis was that by surfacing relevant identifiers and providing more transparency into how matches are determined, users would be able to build trust in the system and eventually resolve duplicates with more efficiency.
To measure success, we focused mainly on adoption and data quality. We wanted to increase usage through higher merge rates, which would ultimately lead to a reduction in duplicates.
Title
We began by doing sessions with users to understand what types of relevant information they needed to see to compare duplicates with each other. While parallel engineering efforts focused on improving match accuracy, our approach centered on enabling users to still make confident decisions even when the system wasn't perfect. So we also wanted to expose the logic and algorithm as to why duplicates were being surfaced.
I explored different types of layouts and experiences. One was an accordion which prioritized efficiency and quicker workflows. The other was a full page layout that emphasized more on trust.
I explored different types of layouts and experiences. One was an accordion which prioritized efficiency and quicker workflows. The other was a full page layout that emphasized more on trust.
We ran these layouts by users in sessions to get feedback on them. There were pros and cons to both. Although accordion was more efficient, the full page was undeniable that users seemed to have more trust it more since it included a live preview that could be seen before merging and more details. The full page aligned more with our goals.
After multiple sessions with customers also made changes incremental where we saw as fit.
Final Designs
Understanding data at a glance
Since this was an MVP dashboard, our goal was to keep this initial release lean and lightweight. We looked to launch quickly and provide risk teams with early access for feedback, which would allow us to iterate and expand based on their input.
Using data visualizations allowed analysts to quickly spot patterns, avoiding the need to manually sift through raw data. The column chart presents a combo view, showing both the count and rate of declines triggered by custom rules and those flagged by the ML risk scoring model.
We focused on keeping the visualizations simple and intuitive to enhance clarity and ease of understanding.
Impact
Delivering value quickly to customers
The Transaction Monitoring Dashboard was a key component of the RiskControl product suite that was launched in Q2 2022. It quickly became a valuable tool for customers and helped them understand their data better, while providing them with greater confidence in their decision-making process.
Takeaways
Simple solutions can be effective
During early explorations, we experimented with variations that included more data and metrics on the page. However, after multiple feedback sessions and iterations, the simplest design consistently received the most positive feedback. This experience reinforced the value of simplicity and served as a reminder that straightforward solutions shouldn't be overlooked.
Leveraging resources where you can for feedback
A challenge I faced at times in this project was coordinating customer calls for design feedback—due to busy schedules, it was often difficult to find suitable times for discussions. Additionally, since this was considered as a lighter project, many reviews only required brief feedback and didn’t justify long calls with customers.
To address this, I frequently consulted with internal risk analysts at Marqeta for feedback through quick Slack huddles and messages. Building a close rapport with them made it easy to reach out regularly and resulted in more candid feedback.
Through this experience, I’ve come to appreciate the importance in building strong relationships across teams and taking in feedback from all different types of users.