"Dirty data damaged marketing - not because it was messy or incomplete, but because it trained systems to misunderstand people with confidence. Partial signals were treated as truth. Inference replaced intent. Surveillance masqueraded as insight. And an entire economy was built on the comforting lie that activity equals meaning. Clean data makes something different possible. It restores context. It requires consent. It reconnects signals to real human motivation. It replaces extraction with permission and replaces guesswork with verification."
"The work has been deeply practical: defining standards, codifying best practices and teaching organizations how to collect and use data responsibly across zero-, first-, second and third-party systems. The goal is clear - stop harming, reduce risk and restore integrity to the data supply chain."
Dirty data trained systems to misinterpret people by treating partial signals as truth, replacing intent with inference and turning activity into falsely meaningful insight. Clean data restores context, requires consent, reconnects signals to human motivation, and shifts from extraction to permission and verification. Despite improved accuracy, clean data alone does not create belonging, loyalty, or trust. Organizations can stop further harm and reduce risk through responsible data practices, but existing relational debt from past data-driven harm persists. Long-term growth requires mutual commitment and brand-owned community structures that generate genuine human connection.
Read at MarTech
Unable to calculate read time
Collection
[
|
...
]