Many organizations grapple with poor data quality, which can undermine everything from analytics to machine learning. The solution? Address the issue at its source by adopting a data product mindset.

What are data products?

At their core, data products are curated data sets designed for consumption, providing analytical or predictive insights. But they’re more than just data sets—they embody a product thinking philosophy.

By treating data sets as products, you introduce key principles such as clear ownership, thorough documentation, and quality control. This approach transforms data sets into reliable, well-maintained assets, ensuring higher data quality across the organization.

Think of data products like internal APIs in a service-oriented architecture.

A single team typically owns an API, managing its uptime, documentation, and availability while other teams consume it. Similarly, data products, when treated with the same rigor, become dependable building blocks for your data driven company.

What’s your experience with bad data quality?

#datamesh #dataorganization #datadrivencompany