@BS_THE_ANALYST Thank you so much for your response.

The goal is to keep it flexible as a platform rather than a data product mindset. Keeping this in mind, essentially the customer data platform should enable contribution from different teams preventing the core data engineering team to act as a bottleneck to new data requirements. The end user should not be limited to business users, rather made available to different teams to use this data for varied use cases primarily as the single source of truth of customer single view -> Customer 360.

Thanks for the suggestion of Genie. I agree it adds value for the AI integration and other possible use cases.

Given there are already refined source team data sources, the idea of Gold in a different team specifically for Customer 360 data platform does not make sense to me. It conveys the data quality of it, though the context of bronze and silver is missing in the team scope.

Reviewing articles over the internet, I see most suggesting a unified view but may be there are internal abstractions which have not been shared explicitly.

https://aws.amazon.com/blogs/big-data/create-an-end-to-end-data-strategy-for-customer-360-on-aws/

If not a denormalized table, what would be your suggestion for data modelling to include contribution model from different stakeholders? Since in my opinion, data modelling is derived from business rules and Customer 360 as a team do not essentially have business processes with actors and entities but are more an integrating platform for a holistic customer 360 data view. What's your take on this? Would data modelling from the perspective of only performance and structure make sense?

Would also love to hear your experience of having such systems exposed as an A/B experiment and how they have helped in the process.