An open data model lets organizational data retain its natural complexity instead of forcing it into predetermined schemas or fields. This gives AI models richer information while supporting a single, consistent source of truth. section 9.3.2
Use the concept to assess composable architecture choices:
- Connect customer-centric data and channel-centric data through a marketing data lakehouse. section 9.3.2
- Treat openness as a spectrum rather than a binary feature, and make trade-offs intentionally. section 9.3.2
- Pair openness with zero-copy principles to reduce duplication, cost, and security risk. section 9.3.2
Marketing data example
A marketing system may optimize send times and click-through rates while lacking product-usage metrics. An open model can incorporate those metrics without requiring them to fit a fixed schema. section 9.3.2
