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Close reading of the corpus at each pipeline stage: raw → clean → relevant → coded.

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Reading comments under one post — Emy Quevedo, J.D. · AI Ethics & Trust
You might be a data steward even if it’s not in your job title. You are a data steward if: • You double-check data before it’s shared with stakeholders. • You document definitions, assumptions, or q…
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Maarten Masschelein agreed this hits where most teams fail. Data stewardship isn’t a title, it’s discipline. Clean pipelines mean nothing if people don’t trust the data. The real test: can someone use your data without asking you? If not, it’s not a tech gap, it’s an ownership gap. Get this right, and AI delivers. Get it wrong, and you just scale confusion.
Data Strategy & Governance Advisor | He… AI Ethics & Trust value: transparency + accountability for: organisations demanding approval ⌕ thread → raw LLM
In the context of AI, informal data stewards are the people catching the problems that models will eventually amplify. The person who documents dataset quirks before they become training data assumptions is doing governance work that no formal review process will surface in time. That behaviour has always mattered, but even more now.
Co-Founder & CTO | Turning AI, Data & P… AI Ethics & Trust value: accountability for: individual_users demanding approval ⌕ thread → raw LLM
Recognizing an Organizational Data Steward is a sign of organizational maturity. For this to work, a specific mindset must exist: "Stop fixing bad data; start tuning the process!" The shift from liability to strategic asset only happens when we stop treating data stewardship as a solo role and start seeing it as a collective responsibility. The twist? Everyone who manages a process is a Contributing Data Steward, led by Organizational Data Stewards. Now, for AI to be credible, the accountability must lie with those who feed the model.
Business Intelligence | Data Modeling |… AI Ethics & Trust value: accountability for: organisations demanding approval ⌕ thread → raw LLM