Many AI products depend on unstructured data such as documents, policies, emails, images, audio, video, and sensor logs. This information is often scattered across tools and teams, and it is rarely ready for reuse.
The aim of this step is to turn raw, inconsistent inputs into reusable data products that multiple models and teams can rely on.
Unstructured datasets are expensive to prepare, and the same preparation work is often repeated across projects. A product approach reduces duplication by creating one trusted, maintained version that can serve many use cases.
In practice, this also makes ownership clearer. Someone is accountable for the input data, how it is updated, and how others should use it.
The first job is deciding what data matters, then making it consistent enough to use.
Do not try to prepare every dataset. Start from the AI use cases on the roadmap and identify the unstructured sources they depend on.
This is easiest when data teams and AI teams review use cases together and look for reuse opportunities. For example, if several initiatives depend on policy documents, those documents are a strong candidate for a shared data product.
Avoid building real-time pipelines unless there is a clear need. If the use cases do not require real-time updates, batch refreshes are usually enough.
Each input type needs its own preparation steps.
The goal is consistency and usability, not perfection.