I saw this analogy and thought it was a good one - because of course you need to consume the information before it can become knowledge (and because cake - does anyone need another reason?)
And then, thinking about it a bit more, I developed the analogy further:
If we consider that the raw data, straight out of the instrument/wherever is the raw ingredients, then obviously there's a bit of processing to be done to turn it into something consumable, like this cake.
Sponge cake picture by nettle1234 from http://allrecipes.co.uk/recipe/12122/basic-plain-sponge-cake.aspx
This dataset/cake looks very nice. Someone's obviously taken care with it, it's nice and level and not burned or anything. But it still looks a bit dry, and would definitely need something to go with it, a nice cup of tea, perhaps.
Now, if we consider adding a layer of metadata/icing around the outside of the dataset/cake...
Victoria sponge from https://gollygoshgirl.wordpress.com/2013/06/05/a-little-twist-on-the-classic-victoria-sponge/
Doesn't that look so much more appealing? (Or it does to me anyway - you might be someone who doesn't like chocolate, or strawberries, or cream...but the analogy still works for your preferred cake topping!)
Metadata makes your dataset easier to consume, and makes it more appealing too.
Of course, you get good metadata, that adds to the dataset, makes it look gorgeous and yummy and delicious...
From Sweet Bakes
And then there's the bad metadata, which, er... doesn't.
From Cake Wrecks
And the moral of my analogy? Your dataset might be tasty enough for people to consume without metadata, but adding a bit of metadata can make it even yummier!
(mmmm....cake....)
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