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Retail verified Verified Outcome TRL 9

Fashion Merchandising Data Marketplace

domain Client: Europe's leading online fashion platform handshake Provider: AWS / Databricks schedule Deploy: Q1 2021 (Scale)
94 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: Very High

Executive Summary

ANALYST: COI RESEARCH

The retailer transitioned from a centralized data lake to a distributed Data Mesh to manage its massive catalog and customer interaction data. They built a 'Data Intelligence Layer' where different teams (e.g., Logistics, Pricing, Personalization) publish datasets as products. This enabled the 'Size & Fit' team to consume 'Returns' data instantly to refine algorithms without central IT tickets.

rate_review Analyst Verdict

"A best-in-class example of reducing 'Data Friction' in e-commerce. By treating data as a product with defined ownership, Zalando solved the 'tragedy of the commons' where everyone used data but no one fixed it. It directly impacted their return-rate reduction strategies."

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Full Audit Report Available Includes Risk Register, Technical Specs & Compliance Data.

warning The Challenge

As the company scaled, the central data warehouse became a bottleneck. The 'Pricing' team couldn't get timely data from the 'Logistics' team because the central engineers were backlogged. Data quality issues were rampant as producers didn't know who was consuming their data.

psychology The Solution

The organization adopted Data Mesh principles. They built a central infrastructure-as-a-service for data (SAI), but pushed data ownership to the business units. Teams are incentivized to produce high-quality data products via internal chargeback models and gamified quality scores.

settings_suggest Technical & Deployment Specs

Integrations
Spark, AWS S3
Deployment Model
Public Cloud
Data Classification
Transactional / Behavioral
Estimated TCO / ROI
Medium
POC Summary (2019-01-01 to 2020-01-01)

"N/A"

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Data Duplication Medium Catalog visibility to encourage reuse over recreation.
Governance Overhead Medium Automated compliance checks in the deployment pipeline.

trending_up Impact Trajectory

Audited value realization curve

Hundreds of active internal data products Verified Outcome
Primary KPIReduction in data access ticket resolution time
Audit CycleImprovement in algorithmic fit prediction accuracy

policy Compliance & Gov

  • Standards: GDPR
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: Transactional / Behavioral

folder_shared Verified Assets

description
Verified Case Study
PDF • Version 1
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Technical Audit
PDF • Audited
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Security Architecture

The "Blind Verification" Protocol

How we verified these outcomes for Europe's leading online fashion platform without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-901
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-901

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