I am looking for insights and best practices from those who have successfully tackled data management challenges within their organizations. Our team is developing a comprehensive strategy for managing both structured and unstructured data assets across their lifecycle. I have two key questions I'd like to open for discussion: 1) How are you approaching the overall strategy for data archival and retention management for both structured and unstructured data? 2) How are you systematically implementing and enforcing defined retention schedules within your technical systems for both structured and unstructured data? 

I am working for the Dutch Tax department. All data is stored on arrival with metadata in our archive. Here is al retention conform law in place. When the data is correct it wil also be place on the Dataplatform (not analytics).
On the Dataplatform data is stored as facts and is active as long as processes need them. This could be another retention. Data in our applications are also placed as facts.
Data on the platform is based on our Vertical Data description Architectuur (VDA). It is based on the law and modeled down till the technical implementation models for our Model driven Engine.
For delivery we have our Horizontal Data logistic Architecture (HDA), this is model and contract based integration.
In the future: We don't copy data we only give the need data trough connection.
It is a big paradigm shift but brings us to a model driven data and AI ready organization