I'm a big proponent of Big Data. I drink the Kool Aid.

I've noticed something interesting over the past year, though. The number of data-specific terms has skyrocketed. Don't believe me? Keep reading.

First up, we have Small Data, in my view a legitimate cousin of Big Data. The two compliment each other, and I'm not the only one who feels this way. Transactional, structured data still matters. You'll never hear me say anything different.

But it doesn't stop there. We also have Long Data. As Samuel Arbesman writes on Wired:

But no matter how big that data is or what insights we glean from it, it is still just a snapshot: a moment in time. That’s why I think we need to stop getting stuck only on Big Data and start thinking about Long Data.

And let's not forget Lean Data. As Matti Keltanen writes on The Guardian:

...we can use the term 'lean data' to describe an Occam's razor approach to data capture and analysis: the lightest, simplest way to achieve your data analysis goals is the best one.

Curious, I went to The Adjective Generator to, well, generate a random adjective and see if I could coin a new form of data. My first result: Flowery. Tough call. Flowery data seems like a stretch.

Next result: Abject. Abject data? Eh, I don't like it.

On my third try, though, the site presented me with "descriptive." Now we're talking. I can work with that. Descriptive data describes what's happening - as opposed to predictive data that attempts to, well, predict the future.

Simon Says

I'm all for adding new terms when appropriate, but I can't help but think that there's this rush to coin the next iconic phrase. We see this with some companies positing additional v's to big data: veracity, verifiable, etc.

Rather than waiting for this to play out, why not start playing with new forms and sources of data? There's big opportunity, irrespective of moniker.

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What say you?