A retailer’s in-store sales, online orders, and loyalty purchases may sit in separate systems. A “high-value buyer” campaign built from only one system can miss customers or count them twice. A data lakehouse addresses that fragmentation by bringing diverse data into a shared foundation for analysis. section 1.5.1
A data lakehouse combines the flexible, cost-effective storage of a data lake with the fast structured-data queries of a data warehouse. It can bring CRM tables and transactions together with website logs, social feeds, and call-center transcripts, reducing the need to manage a separate analytical system for each format. The goal is a more complete customer view, not the replacement of every existing system. section 5.0 section 5.1.1
Three layers of the marketing approach have distinct jobs:
- The lakehouse stores and organizes customer data as a shared source for analytics and measurement. Reliable updates matter: transactional integrity helps keep segments and offers consistent as data changes. section 5.1.3 section 5.1.4
- A medallion architecture can guide data from raw bronze through silver to analytics-ready gold states. section 5.5
- A composable customer data platform (CDP) connects to the lakehouse to build segments and activate campaigns across channels, rather than hosting another large copy of the data. section 4.3.1 section 5.2.4
From customer activity to campaign measurement
A team can combine purchase records with audience membership and campaign data, then connect experiments to customer-level outcomes in the lakehouse. That makes it possible to examine revenue, unexpected effects on other products, and results beyond clicks or email opens. section 4.4.7 section 7.7.5
Adoption need not mean replacing a working warehouse or CRM. Add lakehouse capabilities when fragmented tools, data movement costs, or new data formats justify them; move historical data in phases and reconcile old and new outputs before retiring systems. Plan for tool training, governance, security, privacy compliance, and possible cost spikes during parallel migration. A lakehouse is most useful when those costs are outweighed by better access to trustworthy data and faster, measurable marketing decisions. section 5.1.4 section 5.5 section 5.1.2
