For data to be truly usable, it must be delivered in a way that aligns with how users work. While data is often stored in lakes or platforms, expecting users to navigate complex systems creates unnecessary friction. Most users are not equipped to access or interpret data in raw formats. Instead, data should be placed in environments that meet users where they are—whether it’s downloading to Excel, visualizing in Tableau, querying in SQL, embedding in vector databases, or integrating directly into data science workflows.

Understanding user groups, their skill levels, and tool preferences is essential. Different types of data products may need different delivery formats depending on their relevance and user expectations. Prioritizing support for the most impactful use cases ensures effective resource allocation and high adoption.

Making Data Products Easy to Consume

Making Data Products Understandable

A major barrier to data usage is lack of clarity about what a data product contains, how it’s structured, and how it can be used. When data products lack proper descriptions and context, users are forced to seek answers through chat channels or help desks, draining time and creating bottlenecks.

Common questions include:

Providing clear documentation, metadata, and examples directly where users search for data reduces confusion and time-to-value. Ratings, user feedback, and usage examples can further increase confidence.

Conclusion

To maximize the value of data products, they must be accessible in user-friendly formats and described in business-relevant language. This empowers both data users and AI agents to get to a first win quickly.