FINAL DESIGN
Edit Mode: The Shift Towards Partner Empowerment
Recognizing that Flagging wasn’t enough, we reframed our strategy around active collaboration instead of structured feedback. Instead of relying on data scientists to interpret and execute changes, we asked:
"How might we give partners more direct control while maintaining trust, security, and data integrity?"
This shift led to Edit Mode, a more robust approach that enabled partners to make real-time modifications within guardrails, reducing delays and ensuring smoother collaboration.
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Over the next few weeks, I explored and tested a wide range of flows with the data scientitsts on the team. I developed user-flow diagrams for both scientists and partners, detailing how they would navigate from data inspection to flag resolution. This ensured the interface naturally guided them through the review, flag, revise cycle without requiring heavy training.
FEATURES & DESIGN QUESTIONS
Edit Mode Toggle
Design Question: How can we give partners a simple way to switch between “view only” and “edit” states for their data plots—without overloading them with unnecessary functionality or revealing Ozette’s proprietary algorithms?
Initially, I considered keeping the editing tools visible at all times. However, users quickly became overwhelmed by extra buttons and panels when they only wanted to review results. This clutter also risked accidental edits that could corrupt gating thresholds.
Hence, I consolidated both experiences of viewing and editing into single dashboard with an “Enter Edit Mode” toggle. By default, partners see a clean, read-only layout for data review. When they click “Enter Edit Mode,” relevant tools appear contextually (e.g., gate adjustment sliders, annotation fields), while core proprietary menus remain hidden from external users.
Shared Access & Collaborative Editing Controls
Design Question: How do we handle simultaneous editing on complex immunological data plots so that two different users can make real-time changes without overwriting each other’s work—or requiring endless version management?
I explored Google Docs–style concurrency where all edits appear live. However, real-time concurrency on flow cytometry plots posed a significant data integrity challenge—gates might shift in ways that break subsequent gating logic.
Hence, i adopted a single user lock. With edit mode, a single user can enter “Edit Mode” to modify gating or thresholds. A banner clearly indicates who is editing. If another user attempts to edit, they see a message prompting them to wait or request access.
I also implemented time-based “locks” that automatically released if a user was inactive for a set period. System banners would notify collaborators who was editing and how long until the session times out.
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Quick Results Generation
Design Question: How do we empower partners to finalize and download results after making edits—without always needing a data scientist to confirm every step?
I first considered an “auto-compile” approach, but automatically re-running immunological calculations after every edit risked overwhelming compute resources and made gating changes harder to track.
Instead, when partners finish making gated edits in “Edit Mode,” they can click “Save All Edits” and then “Approve All Gates.” This triggers a streamlined, behind-the-scenes process to re-run the relevant calculations and generate updated data plots. Partners can then instantly download the revised results, freeing data scientists from repetitive final checks.
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