Browse Comments — Raw (as collected)
Close reading of the corpus at each pipeline stage: raw → clean → relevant → coded.
43
comments matched
· page 2 of 3
Hannah Cranford, PhD
So true....
80% of those working in data governance are grifters, chancers or fly-by-nights.
Of those, more than 60% don't even realise they don't know to what extent they are bullshitting the public.
Data Governance has become a zero-sum con game, where deceit, fraud and dishonest gaming of the criminally gullible has become the flavour of the day.
goodstrat . com
Good point, Maarten Masschelein. A lot of companies talk about being data driven, but the real test is simple, who owns the meaning, quality and use of the data once it moves across teams. In practice, stewardship starts when teams treat data as a shared business asset and build the habit of keeping it reliable, clear and usable.
A data steward isn’t just a buzzword, it’s a critical function focused on data governance, quality, consistency, and accountability. Without that foundation, “data-driven” decisions are often just well-dressed assumptions.
This reminds me of how casually titles like “Data Analyst” or “Business Intelligence” get handed out. Building spreadsheets and charts is part of the work, but it’s far from the whole picture.
These roles require a solid understanding of data modeling, statistics, context, and the discipline to ask the right questions, not just present numbers.
The data steward is such an important role for companies that want to get the most out of their data to scale effectively.
Amin Adatia Fair point no data, no mess.
But let’s be honest, no data is a bigger problem than dirty data. At least bad data can be fixed no data means no visibility, no decisions.
😂😂😂👍
Nicole Csintyan Goodwin Laura G. 😂
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.
Absolutely agree with this.
Data stewardship is what turns chaos into clarity — it cleans the “junk drawer” of data, removes the garbage, and makes everything actually usable and insightful.
Also not gonna lie, this meme made me laugh harder than it should have 😄
This is exactly how stewardship actually works. Most people think data stewardship is a role when in reality it shows up in small habits like validating numbers, clarifying definitions, and catching issues before they spread.
Where I’ve seen things break is when those habits exist but ownership doesn’t. People care, but no one is accountable. That’s when you still end up with two versions of the same number in the same meeting.
Stewardship starts as behavior, but it only scales when it becomes ownership.
Looking forward to the webinar.
I had to repost this. hear this every day, every meeting, every strategy conversation...
Jenny Oliver !
Akash Ankolia 😊 The Data is there but not in the "Source Systems". It has been improved and preserved on laptop local drives in expertly massaged Excel or csv files.
Amit Gandhi But surely, AI will soon know everthing about how the business works and fix the data. Who will be the prompt engineers -- "give me everything" crowd and "I know Excel -- I am a data modeler"
Why not a Decision Steward. Someone who helps find the data that will make a better decision, someone who understands the limitations of the data when trying to make the best decision possible, some who know how to restructure the data in a way that articulates the options more clearly. Data without decisions is as bad as decisions without data.
Yeah a lot of stewardship work quietly turns into “making sure people don’t accidentally interpret the same data in completely different ways.”
That trust layer usually matters more than the tooling itself.
lol, love this. But I think you could even go a step back and ask ‘define data’
Great meme usage ! I also think data stewardship also gives analysts a useful standard for looking at our own work. Ultimately, If we are not stewarding the data we use for analysis, are we really doing the job right?
The output matters, but so do the definitions, assumptions, quirks, and quality checks behind it. That care around the data is what makes the final analysis usable and trustworthy.