Building High-Quality Data Products for Enterprise AI

Organizations aiming to be AI and data-driven often need to provide their internal teams with high-quality and trusted data products. [1] Building these data products ensures that organizations establish standards and a trustworthy foundation of business truth for their data and AI objectives. [1] While treating data as a product is a foundational pillar of the data mesh paradigm, applying product thinking resonates with customers even if they do not embrace data mesh. [1]

Modern data products deliver value when users and applications have the right data at the right time, with the correct quality and format. [1] These products are not restricted to tabular data and can also include machine learning models and dashboards. [1] To apply product thinking effectively, each data product should have a dedicated owner who manages its development and monitors its performance. [1]

Treating data assets like software products—with dedicated ownership and strict quality standards—is becoming a fundamental requirement for successful AI and machine learning initiatives. Organizations must look beyond basic tables and begin managing dashboards and ML models with the same rigorous lifecycle tracking. What the sources don't address: how organizations should measure the return on investment for migrating legacy data pipelines into dedicated, owner-managed data products.