Though the buzz around e-books and mobile is deafening right now, I'm hearing another word entering the buzz zone at rocket speed, and it's a word much more relevant to our businesses: curation.
Yes, it's become trendy and mainstream to acknowledge that with so much information so readily available these days, there is real value in plucking out the information that really matters. Malcolm Gladwell, speaking at the recent ALM LegalTech conference, reportedly summed up the problem beautifully, saying "Until search engines can filter as well as they can find, they only add to confusion."
Filtering is something of a geeky way to describe curation. Others may be more comfortable with an older term: editing. Yes, as I have said so many times before, there's not much on the Internet that's really new: it's mostly old ideas sporting flashy new names.
We've spent the last fifteen years on the Internet focused on aggregation. Everyone was trying to build huge pots of content, the most notable examples of this being the search engines. Now it seems that after this frenzy of aggregation, we're starting to stand back and say, "Well that's not very useful." Hence the race to curate.
Another interesting thing to note about this interest in curation is that the experts seem to agree that it's a task for humans. Only a few years ago, we would all have automatically assumed that "there's an app for that," or more precisely, some algorithm or technology that would solve the problem with point-and-click ease. Now, we're starting to appreciate how much nuance is involved.
Of course as data publishers, we have long been practicing a form of curation. We analyze, interpret and add value to information by normalizing it and fielding it. Further, we typically limit ourselves to standardized subsets of information that won't solve every need, but are amazingly powerfully for specific applications. Even better, our selected, normalized and fielded data is easily filtered, meaning that users can easily get to the nuggets most valuable to them. The lesson I take away from this is that it's easier to extract meaning and value from smaller, focused sets of information rather than trying to find small needles in large haystacks.