Data platform

Where a data warehouse is one component, a data platform describes the whole chain: extracting data from the source systems, storing it, transforming it, securing it and making it available to whoever needs it. Well-known examples are Microsoft Fabric, Databricks and Snowflake. For AI, the starting point is not the platform but a single source of truth for the data that matters, the core of a data foundation for AI.

Under the bonnet, a data platform consists of the same building blocks: pipelines that load data (data engineering), storage in the form of a warehouse or data lakehouse, agreements on ownership and access (data governance) and a layer for reporting or AI.

Our advice: do not start with the choice of platform. Start with the questions you want answered and the processes you want to automate, then choose the lightest solution that does that reliably. For AI that works alongside your team every day, good integrations between your existing systems often matter more than a large central platform. How AI then works across those systems is covered under the AI Operating System.