Enterprise Storage-Architecture Selection

Context

Data volumes were growing across a mix of structured and unstructured datasets, and departments held conflicting views on storage. A one-size-fits-all decision was hard to justify, and getting it wrong carried both financial risk (unjustified spend) and operational risk (slow processing, reduced availability, inconsistent practice across teams).

Approach

Organised the problem around data categories and lifecycles rather than capacity, then translated those into storage requirements and constraints. Evaluated options (centralised improvement, independent storage, phased hybrid) by explicitly comparing advantages and disadvantages — focusing on controllability and fit rather than claiming a universally “best” choice — keeping every option explainable to the architectural committee.

Outcome

Enabled Large national company to make a defensible enterprise-storage decision — aligning storage choices to actual data lifecycles, avoiding unjustified cost, and reducing analytics teams’ dependency on a central warehouse.

• Three balanced options presented with explicit trade-offs, enabling a confident architectural decision.
• Storage choices aligned to data lifecycles (hot/warm/cold), avoiding over-provisioning and unjustified cost.
• A phased roadmap reduced analytics teams’ dependency on the central warehouse and improved expectations on reliability and availability.

Key result

Gave Large national company a defensible storage decision by presenting three lifecycle-aligned options with explicit trade-offs, avoiding over-provisioning while charting a phased path to independent analytics storage.