The article effectively highlights a persistent pattern: the enterprise world's shortsighted and ahistorical approach to metadata means it continuously fails to grasp the very principles that other disciplines solved decades ago.
Is the arc of this story of semantics and metadata infrastrctures a recurring theme on how enterprise leadership does not get it and has mostly left it forgotten in the past, and how library standards could be the salvific superhero to save us all from another round of data entropy?
The mission to incentivize change in the fortune 50/100 seems a bit daunting.
That sounds like a new article topic. Creating a synonym ring of terms which show how the new AI ways are mirroring age old processes... and how we can skillup by learning how LibSci/InfoSci built controlled vocabularies in past struggles. There are a plenty of messy mini histories on how these knoweldge architectures were invented and reinvented throughout history.
"More often than not, businesses search for metadata solutions as an afterthought, in response to findability challenges and mismanagement of messy data."
...and as a result, CIOs struggle to answer even simple questions about the data and systems they manage.
"Most enterprises don’t exchange rich descriptive metadata beyond a vendor or team boundary, so the incentives to model semantics are weaker."
How can we challenge the enterprise to make the next wave of job descriptions for the AI-Specialist role to elevate Metadata and Semantic thought work to align with data science practices?
Very cool quiz created by Stewart Alsop and Claude:
i built this quiz to help me learn the content:
https://claude.ai/public/artifacts/3c582cc6-d9c1-4c1c-89cb-035d370c269e
I'm reading thoroughly, and wondering...
The article effectively highlights a persistent pattern: the enterprise world's shortsighted and ahistorical approach to metadata means it continuously fails to grasp the very principles that other disciplines solved decades ago.
Is the arc of this story of semantics and metadata infrastrctures a recurring theme on how enterprise leadership does not get it and has mostly left it forgotten in the past, and how library standards could be the salvific superhero to save us all from another round of data entropy?
The mission to incentivize change in the fortune 50/100 seems a bit daunting.
It is daunting but can be done. In the next article, I propose Application Profiles and other methods to start the metadata as a data model journey.
A question for you: is MCP a metadata data model?
Thanks for reading, George!
That sounds like a new article topic. Creating a synonym ring of terms which show how the new AI ways are mirroring age old processes... and how we can skillup by learning how LibSci/InfoSci built controlled vocabularies in past struggles. There are a plenty of messy mini histories on how these knoweldge architectures were invented and reinvented throughout history.
So much packed in just first line of this blog.
"Metadata is the lowest common semantic denominator in most data ecosystems."
Thanks for sharing your perspective, it was a greqt learning experience for me.
Thanks you for reading, Aman, and for the appreciation.
"More often than not, businesses search for metadata solutions as an afterthought, in response to findability challenges and mismanagement of messy data."
...and as a result, CIOs struggle to answer even simple questions about the data and systems they manage.
"Most enterprises don’t exchange rich descriptive metadata beyond a vendor or team boundary, so the incentives to model semantics are weaker."
How can we challenge the enterprise to make the next wave of job descriptions for the AI-Specialist role to elevate Metadata and Semantic thought work to align with data science practices?