Data Protection Economics

Data Minimization Collides with Growth Economics
Data minimization is clear in principle. Systems should collect only what is necessary, retain it only as long as needed, and avoid unnecessary exposure. These constraints are codified in policies and embedded in design expectations. On paper, they are unambiguous....
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Data Without Pricing Becomes Misused by Default
Data access is rarely treated as a decision. It is granted. Once a dataset exists, it is shared across teams, environments, and use cases with minimal friction. Denying access introduces delay and coordination overhead; granting access feels reversible and low-risk....
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Data Hoarding Is Incentive-Optimal
Data is rarely deleted. It persists in warehouses, backups, and replicated environments long after its purpose has faded. Teams hesitate to remove it—what if it’s needed later, what if deletion breaks something, what if it becomes valuable again. The safer...
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The Point Where Data Stops Paying for Itself
Data collection rarely presents itself as a decision. It accumulates. New fields are added to forms, logs are retained indefinitely, and integrations expand quietly across systems. Each addition is justified in isolation—future analytics, potential insight, optionality. The marginal cost appears...
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Welcome to the Work: What This Site Is, and What It Isn’t
This site began with a simple goal: to give shape to the kinds of conversations that often unfold outside formal channels—after the meeting has ended, between functions navigating ambiguity, or once the audit has concluded but the discomfort still lingers....
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