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Flipping the Model: From Data Control to Data Commoditization
Background
Traditional models: companies (telcos, ISPs, ad platforms, device OEMs, etc.) claim exclusive control over user/device/network telemetry. This control gives them power over pricing, contractual leverage, market advantage.
Problems with control: lack of transparency, incentives to overpromise/underdeliver, conflicts of interest, regulatory risk, consumer mistrust.
5.2 What is Commoditization of Data?
Treating data as a neutral public good / infrastructure rather than proprietary asset.
Data becomes standardized, verifiable, tradable, interoperable.
Incentive structures reward collection, quality, and honesty, rather than gatekeeping or obfuscating.
5.3 Why Flip?
Trust & Transparency: Enterprises, regulators, users demand verifiable metrics, not “vendor-trust us.”
Cost of Mistrust: Fines, reputational damage, regulatory scrutiny (GDPR, DMA, etc.).
Scaling & Network Effects: When data is open/commoditized, many more participants can build on top of it (analytics, AI, applications).
Competition & Consumer Benefit: Users and enterprises benefit from competition; commoditization lowers barriers for startups and improves service quality.
5.5 Risks / Challenges & Mitigations:
Data privacy: ensure anonymization / pseudonymization, minimal data collected.
Incentivizing quality vs volume: have fraud detection, signal coherence checks, Sybil defenses.
Legal / regulatory compliance: local data residency, auditability, etc.
Cost / scalability: efficient data collection, low overhead, lightweight agents.
5.5 Risks / Challenges & Mitigations:
Data privacy: ensure anonymization / pseudonymization, minimal data collected.
Incentivizing quality vs volume: have fraud detection, signal coherence checks, Sybil defenses.
Legal / regulatory compliance: local data residency, auditability, etc.
Cost / scalability: efficient data collection, low overhead, lightweight agents.
5.6 Impact / Outcome:
Data privacy: ensure anonymization / pseudonymization, minimal data collected.
Incentivizing quality vs volume: have fraud detection, signal coherence checks, Sybil defenses.
Legal / regulatory compliance: local data residency, auditability, etc.
Cost / scalability: efficient data collection, low overhead, lightweight agents.