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Nicole Csintyan Goodwin Laura G. 😂

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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 !

How is this different from Mixture of Experts?

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.

👍

So you are claiming that your model beats Deepseek by a 15x on cost? Might want to be clear about this claim. For a listening model that understands emotions, it is okay to charge more because it is a very specialized model

Mike Pappas this is quite interesting! I’m surprised there can be such a large cost reduction - I’d expect the orchestration among “hundreds” of models to eat up any savings. How can I learn more? Also, on a related note: I sent you a connection request to address a UI issue with your site. LinkedIn wanted me to get Premium to message you, so apologies in advance for the public comment.

Mike Pappas this is very fascinating and congratulations on your achievement. As someone that leverages AI heavily in sales and GTM, I can attest to the inefficiency and sometimes lack of quality of current LLM output specifically around call transcripts. I can validate that key human components like sentiment and tone are often lost or mistreated. In the lens of sales and GTM, how do you see the ELM being most impactful?

I would love to see it applied to agentic coding. Is that in your plans?

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.

Mike Pappas attach this to specific agents and orchestrate them into more of generalist interface but with the power of specialists working together. This is really cool.

Literally not even close to a new concept dude this is standard

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