JPMC Bulk Upload (Maker–Checker Workflow Redesign)
Role
Senior UX Designer
Domain
Enterprise Systems · Operational Workflows · Data Governance
Skills
Workflow Design · Maker–Checker Model · Data Quality & Governance · Data-Heavy Interfaces · Operational UX · Table Design · Error Handling & Validation
Overview
JPMC’s Bulk Upload workflow enables operations teams to add or maintain hundreds to thousands of client entities at once. These uploads support two primary use cases:
Adding new entities into the official Party database
Correcting or maintaining data for existing records
The existing process relied heavily on spreadsheets, disconnected tools, and manual validation steps. My goal was to redesign the Bulk Upload experience into a cohesive, scalable, in-application workflow that reduced operational friction while preserving the firm’s strict Maker–Checker controls.
The Problem
Bulk Upload was one of the most operationally critical — and most painful — workflows in Party Central:
Makers and Checkers had to jump between multiple applications
Validation feedback was delayed and difficult to interpret
Errors were often discovered late in the process
Spreadsheet templates were rigid and error-prone
Large uploads were difficult to review safely at scale
Document linking and auditability were inconsistent
The Maker–Checker handoff lacked clarity and transparency
Ops users needed a way to see what was happening, understand what needed attention, and act confidently — without breaking governance rules.
My Role
I led UX design for the Bulk Upload redesign across discovery, design, and delivery. My responsibilities included:
Mapping the end-to-end Maker–Checker workflow across multiple systems
Interviewing Ops users to understand pain points at scale
Partnering with Product and Engineering to define realistic constraints
Designing a table-driven UI capable of handling large datasets
Defining validation, error states, and approval logic
Designing for strict data-quality and audit requirements
Creating prototypes that demonstrated complex flows clearly to stakeholders
Supporting demos and feedback sessions with Ops leadership
This work required deep attention to process integrity, role clarity, and edge cases.
Research & Insights
Several consistent themes emerged during discovery:
Users didn’t trust the system because errors surfaced too late
Spreadsheet uploads obscured what the system was actually validating
Reviewers struggled to safely assess large batches of records
Makers lacked visibility into what Checkers were seeing
Small issues could block entire uploads
Users wanted to fix problems in context, not start over
These insights made it clear the solution needed to be interactive, transparent, and forgiving, without compromising control.
Design Approach
1. Table-First Interaction Model
Instead of treating uploads as a “black box,” I designed the experience around a live, interactive data table that:
Displays uploaded records immediately
Surfaces validation status per row
Allows inline review and correction
Scales to hundreds or thousands of records
This shifted Bulk Upload from a static process into an understandable workflow.
2. Maker–Checker Workflow Embedded in the UI
The redesign made role boundaries explicit:
Makers upload, review, fix, and submit
Checkers review, approve, reject, or request changes
Status indicators clearly show ownership and progress
Actions are gated by role and state
The system communicates who can do what, and when — removing ambiguity.
3. Progressive Validation & Error Handling
Validation occurs early and continuously:
Errors appear inline at the row and field level
Warnings vs blocking errors are clearly differentiated
Users can filter by error type
Partial fixes are allowed without restarting the process
This reduced frustration and increased confidence.
4. Chunking & Safe Review at Scale
For large uploads, I introduced patterns that support safe review:
Batch segmentation for large datasets
Status summaries and counts
Filtering and sorting to isolate problem records
Visual cues to guide reviewer attention
These patterns make large uploads manageable without sacrificing rigor.
5. Auditability & Governance
Every design decision accounted for:
Traceability of changes
Clear approval history
Document linking where required
Compliance with internal governance standards
Nothing happens invisibly.
Key Design Decisions
Chose in-app tables over external spreadsheets
Designed validation to be transparent, not punitive
Embedded Maker–Checker logic directly into the workflow
Prioritized clarity over speed where risk was high
Used familiar enterprise table patterns to reduce learning curve
Designed the solution as a reusable template for future bulk workflows
Solution
Bulk Upload Entry
Clear explanation of use cases (Add vs Maintain)
Template guidance and validation rules
Data Review Table
Inline validation states
Error, warning, and success indicators
Filtering and sorting tools
Row-level actions
Maker Experience
Fix issues in context
Submit with confidence
Track review status
Checker Experience
Review at scale
Approve or reject with clarity
Request changes without ambiguity
End-to-End Transparency
Clear status at every stage
Reduced guesswork
Strong audit trail
Impact & Results
Reduced manual steps and tool-switching for Ops teams
Improved visibility into data quality issues
Enabled safer review of large uploads
Reduced rework caused by late-stage validation failures
Established a scalable pattern for future bulk workflows
Received strong feedback from Ops users and stakeholders
This work helped shift Bulk Upload from a fragile process into a reliable operational capability.
What I Learned
Designing for operations requires deep respect for risk and accountability
Tables are interfaces — not just containers
Clear status and ownership reduce anxiety and errors
Governance and usability can coexist when designed intentionally
Solving for edge cases early pays dividends at scale
Artifacts (Optional)
Workflow diagrams
Table state explorations
Validation logic flows
Maker–Checker role matrices
Before/after process comparisons
