A journey to Data Mountains

Seems about time to revisit a famous Zen aphorism (more verbose translation here)
Morning mountains, only mountains
Noon mountains, more than mountains
Evening mountains, simply mountains
How does this apply to what we have been speaking about here? We've tried in the morning of our ignorance to pile up mountains of data, and had hard time making sense of them.
In the broad daylight of our powerful abstract thought, both intuition and logic, we found out that useful data were data about some thing(s). Making explicit the things data are about was really the way to follow to organize, understand, search, query, in a thousand ways make data more useful, more meaningful. So we went through those mountains with this "about-ness" in mind, and they looked indeed more than mountains, they looked like information about things. We called it classes, properties, relations, and started re-engineering data in all sorts of smart ways : metadata, RDF, topic maps, ontologies and the like. Happy to bring more and more meaning into data, we called it knowledge, and we thought we had found answers to some fundamental questions, discovered the information lost in data, and the knowledge lost in information. Captured the mountain's spirit.

Then came the time to harvest, to weave it all together. I had knowledge in my information system, and so did you. Or so we figured. But looking into your system, I found only data, and so did you when you looked into mine. Where are the things gone? Where is the knowledge hidden? Only data, which we had to figure again together how to weave.

So we'd not captured the mountain's spirit after all. But we did not travel in vain, because we've felt it blowing by. We know it's hidden somewhere beyond the data, beyond information, beyond what we have called knowledge, and that without this spirit our data would be completely meaningless and useless indeed. And what is more, we have at least found a piece of wisdom lost in knowledge : data are only data.

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