Overview
Rethinking digital strategy by shifting from hoarding data to streaming insights
The Insight Economy: Stop Hoarding Data, Start Trading Value challenges the assumptions driving how organizations collect, manage, and extract value from data. Ruben Verborgh, a Professor of Computer Science, introduces a framework where value comes not from accumulating data but from streaming insights — paralleling the shift from physical media distribution to on-demand content delivery infrastructure. The book examines mismatches between technological, legal, and economic ideals in current data and AI practices.
Through diagrams, real-world use cases, and concrete examples, the book maps the intersection of technology, law, and economics to show where current data and AI strategies fail and how to redesign them. Rather than prescribing additional tools, Verborgh offers a structural rethinking of data approaches that aligns business incentives with societal values, equipping readers with a practical framework for building data strategies that are technically and economically sound.
Readers will also find:
- A reframing of data and AI through classical economics, replacing extraction with exchange and a zero-sum game with sustainable mutual benefit
- Analysis of how Big Data assumptions create conflicts between technological capability, legal compliance, and economic sustainability
- A detailed comparison of data hoarding versus insight streaming, modeled on the Netflix postal-to-digital business transition
- An insider's account of the open Web’s tense relationship with app stores and platforms, and what this historical pattern predicts for the AI era
- Use cases demonstrating how the Insight Economy model applies across varied business contexts
Written for business and technology leaders implementing data strategies, The Insight Economy provides a structural framework for moving beyond data accumulation toward insight-driven approaches. Decision-makers seeking to align their organizations' data practices with the economic reality, legal requirements, and societal values will find a concrete alternative to conventional data thinking.
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