From Zero to Millions in Savings: Ströer Transforms Advertising Success with Databricks

2025-12-30 · Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Marketing, Branding & Advertising · Depth: Intermediate, short

Summary

Ströer, a German leader in out-of-home and online advertising, adopted the Databricks Data Intelligence Platform to unify fragmented campaign data. This initiative addressed challenges like data inconsistency, manual reporting, and rising costs associated with their previous Amazon Redshift setup. By migrating ETL and BI reporting workloads to Databricks SQL, Ströer achieved a modern, elastic data warehouse with decoupled storage and compute, leading to faster, more predictable query performance and reduced operational overhead. The platform, leveraging data mesh principles, integrated AI and no/low-code tools, empowering over 500 users to access real-time, standardized insights, diagnose issues faster, and benchmark campaign performance across business units.

Key takeaway

For CTOs and VPs of Data struggling with fragmented data and rising infrastructure costs, migrating to a unified data intelligence platform like Databricks can yield substantial financial and operational benefits. Your teams can achieve €3.5 million in annual savings and 25% faster reporting by standardizing data access and empowering users with integrated AI and low-code tools, thereby enhancing campaign quality and fostering data-driven innovation across your organization.

Key insights

Unifying fragmented data on a modern data intelligence platform drives significant operational efficiency and cost savings.

Principles

• Decouple storage and compute for scalability.
• Standardize data access across the organization.
• Empower non-technical users with low-code tools.

Method

Migrate ETL and BI reporting from legacy data warehouses (e.g., Amazon Redshift) to a unified data intelligence platform following data mesh principles, integrating AI and no/low-code tools for broad user access.

In practice

• Implement a unified data platform for campaign analytics.
• Utilize AI assistants for data aggregation and reporting.
• Enable cross-departmental data sharing for granular analysis.

Topics

• Data Intelligence Platforms
• Advertising Analytics
• Data Fragmentation
• AI Integration
• Cloud Data Warehousing

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, AI Product Manager, Data Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.