5 Modernizing the marketing data platform: data lakehouses and composable CDPs

“Data silos are the enemy of effective marketing."

— Chris O’Neill, CEO of GrowthLoop and Board Member of Gap and Marketing Influencer

This chapter covers

How a data lakehouse unifies structured, unstructured, and semi-structured marketing data
Why composable customer data platforms (CDPs) serve as a real-time activation layer for targeted campaigns
How the medallion architecture (Bronze, Silver, Gold) refines data for analytics and personalization
A practical roadmap for modernizing your marketing data stack, with an eye on realistic constraints, costs, and risks

Data is at the heart of every marketing strategy. Yet with customers interacting across so many channels—websites, social platforms, email campaigns, call centers, and customer relationship management (CRMs) systems—your teams might feel like they’re trying to piece together a puzzle with missing pieces. Each platform, department, or vendor solution offers only a narrow slice of the data needed to fully understand customer behavior.

At the same time, today’s marketing leaders are under pressure to prove ROI more quickly and deliver highly personalized campaigns at scale. You need real-time insights and the flexibility to pivot fast, whether you’re retargeting abandoned carts or launching AI-powered recommendations.

That’s where two converging trends are transforming marketing data management:

5.1 Understanding the data lakehouse

5.1.1 Combining flexibility and performance

5.1.2 A single place for every customer touchpoint

5.1.3 Ensuring consistency and reliability

5.1.4 Fitting into your existing marketing ecosystem

5.2 Composable CDP

5.2.1 A more flexible approach to customer data

5.2.2 How composable CDPs evolved

5.2.3 The composable CDP advantage

5.2.4 Tying it back to the lakehouse

5.2.5 Streamlined and future-ready

5.3 Common architectural blueprints for a marketing data platform

5.3.1 Medallion architecture overview

5.3.2 Bronze layer

5.3.3 Silver layer

5.3.4 Gold layer

5.4 Key considerations for implementing a marketing data lakehouse

5.4.1 Laying the groundwork: scope, estimates, and planning

5.4.2 Designing a resilient architecture for marketing data

5.4.3 Implementation and testing in real environments

5.4.4 Ensuring long-term success and governance

5.4.5 Putting it all together: deliverables and reference architectures

5.4.6 Visualizing your project & measuring ROI

5.5 Summary