Product Data Quality & Workflow Automation

Category: Product Management · Workflow Design · Operations
Context: E-commerce / Product Operations
Role: Product Manager

Background

In a large-scale e-commerce environment, product data accuracy directly impacts pricing, customer trust, and downstream operations.

As the product catalog grows, manual data entry and cross-system updates introduce a high risk of inconsistency — especially across fields such as weight, pricing logic, product variants, and inventory rules.

In this context, even small data errors can lead to:

Incorrect pricing or shipping calculations
Inaccurate product display
Operational inefficiencies across fulfillment and support teams

Problem

The core challenge was not technical limitations, but data reliability at scale.

Specifically:

Product attributes were manually maintained across multiple systems
Weight values used for pricing calculations could become inconsistent
Product URLs and metadata followed strict rules but were easy to miss
Variant-level requirements (images, inventory policies) were not always enforced
Errors were often discovered late, after products were already live

The root issue was volume and complexity, not tooling:
as SKU count increased, manual review became unreliable and unscalable

Product Role

Design a system that:

Detects data inconsistencies early
Reduces reliance on manual checking
Surfaces only actionable issues
Fits naturally into existing product and operations workflows

The goal was data reliability, not automation for its own sake.

Solution: Rule-Based Validation + Workflow Integration

Instead of attempting full automation, I worked on defining a structured validation framework that could scale with product volume while keeping humans in control.

Key Design Decisions

1. Rule-Based Validation

Defined clear validation logic for high-impact fields, including:

Weight consistency between systems
URL structure requirements
Variant-level asset completeness
Inventory policy alignment for made-to-order products

These rules reflected business logic, not technical constraints.

2. Event-Based Alerting (Slack Integration)

When a validation rule failed:

An automated alert was triggered
The alert included SKU, issue type, and context
The message was routed to the responsible product channel
Issues could be resolved before affecting pricing or customers

This ensured:

High visibility
Fast response
Minimal noise

3. Human-in-the-Loop by Design

The system was intentionally designed not to auto-correct data.

Instead:

Humans remained responsible for final decisions
The system acted as an early-warning layer
Product and ops teams retained controlThis reduced risk while improving efficiency

My Role

Defined validation rules based on business impact
Identified failure points affecting pricing and product accuracy
Partnered with engineering to translate business logic into system rules
Designed alert logic to balance visibility and signal quality
Evaluated operational impact and iteration priorities

Outcome & Impact

Reduced data-related pricing issues
Improved detection speed for product inconsistencies
Lowered reliance on manual review
Increased confidence in product data accuracy
Created a scalable foundation for future automation and AI-based validation

Why This Matters

This project reflects how I approach product work:

Focus on system reliability over surface-level fixes
Design workflows that scale with complexity
Use automation to support — not replace — human judgment
Bridge business logic and technical execution

It also established a foundation for future AI-assisted validation and content quality systems.