I reconceptualized the entire application from a simple upload tool into a comprehensive data operations platform with four major feature areas:

1. Dashboard Transformation

Before: Two basic cards with no context or task visibility

After: A command center providing:

• Task Management - Pending validations, mapping approvals, workflow steps

• Dataset Access - Quick access to frequently used datasets with status indicators

• Collaboration - Share datasets with team members, manage permissions

• Status Visibility - Color-coded health indicators (green/yellow/red) for all datasets

• Contextual Actions - Direct access to relevant operations without deep navigation

The dashboard became the central hub where users could see what needed attention, access critical datasets, and collaborate with colleagues.

2. AI-Assisted Dataset Mapping

This was the flagship innovation solving the manual mapping problem:

First-Time Upload Flow

1. User uploads a new dataset structure

2. AI analyzes the data and suggests field mappings based on:

   • Field names and semantic meaning

   • Data types and formats

   • Historical patterns from similar datasets

   • PROS schema requirements

3. User reviews AI suggestions with confidence indicators

4. User confirms or adjusts mappings

5. System learns from corrections to improve future suggestions

Pattern Recognition Flow

1. User uploads data matching a previously mapped structure

2. System automatically recognizes the pattern

3. Stored mapping is applied instantly

4. User verifies rather than re-mapping from scratch

5. Massive time savings for recurring imports

Complex Scenarios Handled

• Partial matches - 80% matches known pattern, 20% new fields

• Confidence scoring - Visual indicators show AI certainty

• Conflict resolution - Multiple possible mappings for ambiguous fields

• User learning loop - Corrections improve future AI performance

• Validation rules - Ensures mapped data meets platform requirements

I collaborated closely with the PM to define the complete mapping logic, which was documented in Confluence, specifying system behavior, edge cases, and validation rules for the development team.

3. Upload & Validation Flow

I designed a comprehensive workflow handling:

• File upload initiation with drag-and-drop or file picker

• Format validation - Multiple checkpoints with clear error messages

• Data quality checks - Real-time validation as data is processed

• Error recovery paths - Users can fix issues without starting over

• Mapping interface integration - Seamless transition to AI-assisted mapping

• Success confirmation - Clear completion states with next actions

The flow included multiple decision points, branching logic, and recovery mechanisms documented in detailed flowcharts.

4. Data Tracking & Management

Data Tracking Page: Monitor ongoing operations

• Batch upload tracking - See all files uploaded in last 30 days

• Status visualization - Green (success), red (error), yellow (warning)

• File-level details - Drill down into individual files within batches

• Complete history - Full audit trail of data operations

• Download functionality - Retrieve processed files when needed

Datasets List Page: Organize and access data

• Comprehensive table view with filtering and sorting

• Status indicators showing dataset health

• Quick inspection panel for dataset details

• Bulk operations for efficient management

• Search and organization tools