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? 

From my experience leading data, analytics, and marketing-technology platforms in highly regulated financial-services environments, a few practices have proven effective:
1) Archival and retention strategy (structured & unstructured)
We’ve found it critical to start with business value and regulatory intent, not storage mechanics. Our approach typically includes:
Data classification at ingestion, tagging datasets by sensitivity, regulatory obligation, business criticality, and analytical value.
Lifecycle tiering aligned to usage patterns hot, warm, cold, and archive - implemented consistently across structured platforms (data warehouses, CDPs) and unstructured stores (documents, logs, media, communications).
Retention tied to purpose, not system ownership. If a dataset no longer supports an active business, compliance, or analytical need, it is either archived with limited access or scheduled for defensible disposal.
Separation of archival from analytics environments to reduce cost, risk, and accidental reuse of expired data.
2) Implementing and enforcing retention systematically
Enforcement works best when it is automated and policy-driven rather than manual:
Centralized retention policies defined in partnership with Legal, Compliance, and Risk, then translated into technical rules rather than human processes.
Metadata-driven controls, where retention, purge eligibility, and legal-hold status are enforced through tags and attributes across platforms.
Platform-level automation (e.g., scheduled lifecycle jobs, policy engines, and event-based triggers) to apply retention consistently to both structured tables and unstructured objects.
Auditability and exception handling, ensuring every retention action is traceable and defensible, especially in regulated industries.
One consistent lesson: success depends less on tooling alone and more on strong governance alignment, shared definitions, and cross-functional ownership. When retention is treated as an enterprise data discipline, rather than a storage or IT task, it becomes far more sustainable.

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