Browse Comments — Clean (de-noised)
Close reading of the corpus at each pipeline stage: raw → clean → relevant → coded.
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Best stewards I've seen never had the title, they just gave a damn about getting it right.
totally, most stewards i meet don't have it in the title
Maarten totally right. The worst scenario is when everyone is doing it but inefficiently, or nobody is doing it and chaos reigns. In both cases, the results end up hidden all over the balance sheet don't they?
John Wernfeldt, they need to be appreciated more!
Reeves Smith What title did they generally have?
It part of the job description in one of my previous role as digital analyst. Would you consider data governance as part of data steward mandate?
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.
These are great points. "Data Steward" can be a confusing and intimidating title, but as you say, many people without the title are doing the job. The key is less about appointing data stewards than about identifying who is acting in that capacity.
Marcia Marinho Rayssa Santos Simone Gomes Padilha
The teams that get this right have people who care about downstream impact without being asked to.
Exactly 👏
Data Driven and AI First! 😂
John Wernfeldt nope, it just somehow makes its way for them to own it.
Dont you dare putting some bullshit title behind just having some common sense. Pls.
Yup! I do this every day!
Truly takes effort from more than just the data team with technical expertise to bring meaningful value to the business from “text written in tabular or whichever format” 💫💯 definitely related to this from my own team’s context, looking forward to the webinar 🙏🏾
But should you not know what Data Steward is even if not in your Title?????
I have been arguing for data stewards foe 20 years, but shortsighted people always shrug it off. Meanwhile, costs pile up!
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.
That was the conversation about Rolls Royce, right?