Building a Modern Enterprise Data Platform

Start with the decisions, not the tooling

Most data platform programmes begin with a technology selection. In practice, the platform's long-term success is decided by a smaller set of architectural choices: how data is ingested, where the contract between producer and consumer sits, how history is preserved, and who is accountable for a dataset once it is published.

A useful discipline is to write these down as architecture decision records before procurement. They outlive the tooling and give delivery teams something concrete to design against.

A workable reference shape

Regardless of vendor, most enterprise platforms settle into the same layers: source systems, ingestion, a raw landing zone, curated and conformed layers, published data products, and consumption through BI, analytics and AI workloads.

• Ingestion: batch, CDC and event-based paths with consistent metadata capture
• Storage: open table formats to avoid re-platforming later
• Modelling: a conformed layer with defined grain, keys and business definitions
• Data products: versioned, documented, owned and monitored
• Governance: catalogue, lineage, access control and quality checks as platform services, not project add-ons

Common failure modes

Platforms typically fail for organisational rather than technical reasons: no clear ownership of published data, modelling deferred until after ingestion is complete, and governance treated as a later phase. Each of these can be addressed in architecture rather than discovered in delivery.