How to Build Data Products

Evan Smith's article discusses the critical role of data products in AI workflows, emphasizing the necessity of balancing data access, governance, and compliance. Data products, described as packaged, reusable data assets with comprehensive metadata and clear lineage, apply product thinking to data management, enabling proactive data governance. The article highlights two key workflows: developing data products for AI by changing organizational approaches to data storage and governance, and developing data products with AI by using AI agents to streamline their creation and maintenance. Automating data governance, maintaining universal access while centralizing selectively, and enabling cross-team collaboration are identified as core principles in building data products for AI. The integration of AI in developing these products not only accelerates the process but also enhances data quality and accessibility, fostering a culture shift towards treating data as a product. The use of AI agents, particularly through natural language processing, simplifies data search and enhances discoverability, while platforms like Starburst are positioned to support this transition, offering scalable solutions for building and managing data products effectively.

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