Picture running a 130-year-old luxury brand where every department has "their version of the truth."

At Swarovski, finance had one system. Marketing had another. Retail? Something else entirely. Data was duplicated, inconsistent, trapped in silos. Decisions stalled because nobody trusted the numbers.

Meanwhile, customers across 2,300 boutiques expected magic: personalized recommendations online, seamless in-store experiences, consistent service everywhere.

The catch? You can't deliver precision when your systems can't talk to each other.

Swarovski rebuilt from the ground up.

They consolidated 1,000+ data objects from 30+ sources—CRM, ERP, ecommerce, creative assets—into BigQuery. One place. One version. Everyone seeing the same numbers.

On top: a Customer Data Platform using Looker to unify every touchpoint. Then Génie, their internal gen AI portal built on Gemini and Vertex AI. Not just for data scientists—1,000+ employees use it daily for contract review, translation into 20+ languages, creative assets, and design cost estimates.

Why BigQuery? Real-time analytics over overnight batch processes. Why gen AI? Remove the coding barrier so anyone can create and analyze.

The genius move? Democratize AI access while maintaining strict governance.

The Stack:

• BigQuery Data Lakehouse: Single source of truth with real-time analytics replacing overnight reports
• Looker Customer Data Platform: Unified view across every channel—online browsing, in-store visits, abandoned carts
• Gemini + Vertex AI: Powers Génie for instant contract review, translation, creative generation, cost estimation
• Data Certification Program: Every critical data element verified, automated quality checks, cross-functional ownership

The Hard Parts:

Getting people to trust the data was harder than migrating it. They built an internal certification program and made every stakeholder responsible—a cultural shift disguised as a tech project.

Moving from month-end reports to real-time everything required rethinking the entire analytics approach. Store size became critical for profitability. Weather patterns started influencing demand forecasts.

Giving 1,000+ employees gen AI access needed guardrails. Every AI application gets evaluated through an ethics framework. Result? 77% approval rate in 2024—governance gave them confidence to move faster, not slower.

• 17% higher open rates on AI-personalized emails
• 7% higher click-through rates
• 10x faster campaign localization
• 77% of AI initiatives approved through responsible framework
• Customer service transformed with AI triaging tickets in real time

The deeper story:Génie became a creative partner across functions. Marketing tests visuals for different regions. Legal verifies contracts instantly. Merchandising estimates production costs in early design stages.

Financial reporting shifted from backward-looking to forward-thinking—integrating live marketing data, campaign performance, and weather patterns for scenario-based forecasts.

Data quality is a people problem. The best infrastructure is worthless if teams don't trust it. Swarovski invested in certification, cross-functional ownership, and automated quality checks. Trust came from culture, not technology.

Democratize AI, govern it hard.1,000+ employees with gen AI could have been chaos. Instead, they built ethics frameworks and literacy programs from day one. 77% approval rate because governance was built in, not bolted on.

Foundation first, fancy later. They spent years consolidating data before deploying AI at scale. When Génie launched, it delivered immediate impact because it was built on clean, trusted data.

Swarovski is piloting virtual try-on, predictive recommendations, and conversational interfaces. The goal: blend human creativity with AI-powered scale everywhere.

🐯 Tiger Takeaway

What execs should copy:

• Clean your data house first — Swarovski spent years consolidating before deploying AI. No shortcuts.
• Make governance your accelerant — Their 77% AI approval rate came from building ethics frameworks on day one, not retrofitting them later.
• Culture eats technology — Over 1,000 employees adopted gen AI because trust was built through certification programs and shared ownership, not mandates.

The lesson: AI amplifies whatever you feed it. Start with unified, trusted data—or you're automating chaos at scale.

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