Financial Aspects of Data Products

In this session, we’ll explore how to bridge the finance and accounting gap in Data Products and AI initiatives. We’ll uncover why traditional cost models fail in probabilistic, continuously learning systems and how to replace them with frameworks that reflect real-world operations. Together, we’ll examine how to treat Data Products as profit centers and account for compounding operational costs across the lifecycle. We’ll also discuss how governance and architectural design influence financial models and decision-making. The session introduces practical frameworks for capitalizing Data Product development, designing internal pricing mechanisms for Data Products, and managing financial accountability in regulated environments. This helps organizations turn AI and Data Products from experimental costs into measurable business assets.