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The Great Data Interregnum - Morbid Symptoms of an Industry Between Eras
The data industry is stalling in a 'great interregnum' where technical automation grows but fundamental operational and semantic issues persist.
What: Data teams are currently stuck between the commoditization of technical tasks (SQL, pipeline generation) and a lack of progress in core organizational areas like business definitions, data quality, and accountability.
Why it matters: AI is lowering the barrier to producing code, but it is not solving the human and semantic friction that makes data hard to trust and use effectively.
Original article
The data industry is caught between vendor consolidation, AI-driven automation, and unresolved operational problems. Generating SQL, pipelines, and documentation is becoming cheaper, but broken data, inconsistent business definitions, and unclear accountability remain. The lasting value of data teams lies in semantics, governance, and understanding rather than producing more technical artifacts.
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