Peter Keane has a lengthy and worthwhile piece about the need for a "killer app" in data management. It's too meaty to relegate to a tidbits post; go read it and see what you think, then come back.
My reaction to the piece is complex, and I'm still rereading it to work through my own thoughts. Here's a beginning, however.
In at least some fields, data are their own killer app. I expect the number of fields to grow over time, especially as socio-structural carrots and sticks for data-sharing grow, which I expect will happen. We don't have to talk about the uses for data in the subjunctive mood; there are examples, real live present-day examples, of data and data-sharing assisting the progress of research, as well as examples of the lack of data retarding it. So I'm not at all sure we need to prove ab initio that keeping data is a good thing. What we do need to prove is a little more subtle: that putting effort and resources into keeping data is a good thing.
Believe me, those two propositions are absolutely not equivalent. Especially these days, there's any number of good things that nobody wants to put effort and resources into. The newer the good thing, the harder it is to win investment in it; old things have established, often powerful incumbencies to fight for them, and they have the innate advantages of tradition and custom as well.
For all the talk about speculative investment, risk-taking, and innovation—I generally don't bet on real investment in novelty in academia, and I'm even less likely to bet on it in a resource-constrained environment. Face-saving investment of nominal resources, yes; usually not enough resources to matter, because setting up a novelty to fail means that novelty can conveniently be done away with a little later—after all, it didn't work, did it?
I apologize for the cynicism inherent in this argument; I wouldn't be so cynical if I hadn't witnessed this very syndrome quite a few times in quite a few different contexts myself. (You'll forgive me for not offering concrete examples in a public context, I'm sure.) But the fact remains: those of us who advocate novelty in academia have to be terribly careful about how we do it. As the opening of this post may hint, I prefer evidence and example to speculation and futurology as advocacy tools.
Two kinds of nominally-attractive argument, both of which can be found in Keane's post, tend to actively scotch investment in new things. One is the very title of his post: "we need to find a killer app." The other is "it'll be effortless!" The latter especially strikes me as magical thinking, and I'm afraid I consider both counterproductive in the current organizational environment.
Let's pretend for a moment that we're administrators. Someone comes to us saying "I need to build a killer app for data curation." What's data curation? is the natural first question, and why do I care about it right here and now? is the natural sequel. You see the dilemma already: if what data curation needs is a "killer app," but nobody will invest in the building of said app until data curation itself is viewed as a strategic necessity, well…
In short, we need to justify data curation on its own merits, not because it's going to be great someday, really, promise! I think that's quite feasible, mind you. There's plenty of jam today; we don't need to rely on hypothetical jam tomorrow—and doing so may actively harm our cause.
On to the question of effortlessness, where the magical thinking comes thick and fast and from every direction. My cards on the table: data curation costs effort. Can we build tools to make it less effortful? Sure. Should we? Absolutely. Will that ever reduce the effort to zero? Absolutely not. TANSTAAFL, and when we try to imply that there is, we cut our own throats. If data curation is free, who needs data curators?
Right now, I see epic tons of magical thinking about data curation in academia generally and in the researcher community particularly. The idea that it can just be left to graduate students. The idea that information management can be taught in a week's intensive seminar. Metadata, who needs metadata in an age of search engines? Et cetera, and if you'd like concrete examples of some of this magical thinking on the part of researchers, try this JISC report or this Australian report, which are crawling with it.
Keane's "killer app," which will apparently serve every kind of research data in every discipline equally, bothers me a lot. Many a time, I've had poor hapless graduate students call me who have had a passel of research data dumped on them to manage with not the least idea what they should do with it. They assume, because the researchers they work for assume, that there is some kind of killer-app magic bullet that will take an unholy mess of undescribed, undifferentiated digital stuff and miraculously organize it.
There isn't. There is not. Not DSpace, not Fedora, not Drupal, not Vignette, not anything you name. Data curation costs effort. Data curation requires skill, time, process change (a tall order all by itself), and resources. TANSTAAFL.
If I still haven't convinced you, consider this. Around about 2003, libraries were promised that a cheap, easy software tool was going to provide universal open access with minimal ("five minutes per paper!") investment of time and effort. Sounds good, they thought, and many signed on.
The result was the institutional repository.
That's why I'm desperately leery of telling anyone that data curation is going to save effort in the short term, much less that it'll be cheap or easy. We went that route once, and it blew up in our face.