With Data, Trust Comes First

Using John Carpenter’s The Thing as a metaphor, the piece describes how data teams face persistent uncertainty when large, complex datasets and opaque machine-learning models make it difficult to distinguish reliable insights from flawed ones. Errors in source data, statistical methods, or software can go unnoticed at terabyte or petabyte scale, potentially leading to harmful business decisions and undermining confidence in all analytics. It argues that trust must be the foundation of automated insight, requiring safeguards such as data observability, contracts, testing, clearer abstractions, and more mature data practices. As analytics and algorithmic automation become increasingly influential, preventing and detecting failures before they reach production is presented as a central industry challenge for 2023 and beyond.

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