Meta Meta / by Dan Weingrod

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Leafing Swiping through The Economist's technology quarterly I ran across two articles in a row that demonstrate how tools that manage the massive amount of data that is just beginning to overwhelm us may signal a whole new opportunity for product innovation.

Both of these tools have been, oddly enough, developed by Israeli companies. The first, BriefCam, simplifies the viewing of CCTV footage by cleverly fixing the background imagery and just recording objects that pass through the frame or differ from the background. This leads to much shorter videos, since the only recording is only of anything that differs from the background, which makes it easier to review and saves on tape or memory. The second, TaKaDu, finds leaky water pipes by applying sophisticated statistical analysis to the data stream available from most typical, modern water companies. Since most lose 25-30% of the water they are delivering due to small, difficult to locate leaks this could be a boon in an age where water conservation is becoming much more important than we imagine.

As we rapidly move into the world of the "Internet of Things", with RFID, QR codes and NFC the question will become less and less what we can pull data from and more about how we can make this overwhelming amount of data useful. The examples above are interesting applications of metadata, or data about data, that manage to pull out the interesting bits from a background and put them to use in very helpful ways that save time and money.

Similarly we are already beyond overwhelmed by the amount of data we are trying to assimilate from social and frankly, all media sources. Attention has become one of the most cherished commodities and its value is only growing. So what aren't we able to do a better job of creating effective metadata. Part of the problem is possibly the whole aspect of curation, which somehow assumes human powered creation of metadata from social sources, a task that feels ultimately overwhelming. The alternative is using listening tools, filters, behavioral analysis and other approaches which feel too sterile or are sometimes just too creepy. But since we aren't planning to lower the amount of data we generate any time soon its likely that tools such as the ones above, focused on very tightly drawn boundries or use cases, may become a bigger part of our attention saving arsenal.