Data Engineering & Analytics

Questions

Common questions

How do data foundations support AI and reporting?

Data foundations support both AI and reporting when information is modelled, trustworthy, accessible, and governed. Dashboards and models fail in the same way if source data is inconsistent or nobody owns the definitions. A shared foundation lets reporting and AI applications use the same picture of the business.

Where should a data platform project start?

Start with the decisions the business needs to make, not with a tool. From there, inventory the data that exists, who owns it, and which questions it can answer. The warehouse, pipelines, and reports should be shaped around those questions.

What belongs in a data pipeline?

A data pipeline moves information from operational systems into a warehouse or lakehouse, with quality checks and monitoring along the way. The model should reflect how the business actually works, so metrics stay consistent as sources change. Reliable movement of data matters more than another report built on disconnected exports.

How should data quality and governance be handled?

Assign ownership, shared definitions, and checks that catch bad data before it reaches a report or an AI feature. Keep governance light enough that people still use the platform. Tighten it when several teams share the same metrics, or when an AI system will depend on the data.