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Packaging Data Products

Once you have built analytical projections and engineered features, the next step is to make them accessible and reusable across the organization.

In a Data Mesh approach, this means packaging them as data products – high-quality, clearly defined datasets that other teams can easily discover, trust, and integrate into their work, including AI and machine learning projects.

From Features to Products

A feature dataset created for one AI use case often contains insights valuable in others. By packaging it as a data product, you ensure that:

  • The same high-quality, well-documented data is available to multiple teams
  • Redundant work is reduced
  • Consistency improves across the organization

For our example of the library, this means: A borrowing history dataset could power a recommendation engine, feed a demand forecasting model, and support statistical research – all from the same, trusted source.

This reuse speeds up delivery, reduces duplication, and ensures everyone is working from the same definitions.

Data Mesh Principles in Practice

In a Data Mesh, data is treated as a first-class product. For AI-ready datasets, this means:

  • Clear ownership by the domain team
  • Quality guarantees on freshness, completeness, and accuracy
  • Discoverability via documentation and metadata
  • Versioning so datasets can evolve without breaking existing consumers

These principles ensure AI models are trained and evaluated on trusted, stable data.

Example: Library Borrowing Patterns

Imagine a dataset that tracks weekly borrowing statistics per title, enriched with seasonal demand scores at the genre level.

As a packaged data product, it could be reused for:

  • Tailoring reading recommendations
  • Predicting demand for acquisitions
  • Analyzing long-term shifts in reading habits

The key is that this dataset is consistently defined, reproducible, and accessible to any authorized team.

AI/ML Benefits

Treating AI-ready datasets as products benefits the entire organization:

  • Models can be compared fairly over time because inputs remain stable
  • Features mean the same thing everywhere they're used
  • Every data point can be traced back to its source events, building trust and enabling explainability

Next up: Feeding Models and Generating Insights – connect your event-derived features to models and start delivering predictions.