But we also need to look very closely with what we have and determine if the tools we used yesterday are able to manage horizontally scaled data correctly and with a large enough sample to do basic analysis as well as meet the demands of investigations where every element of certain criteria is require to be presented. If the tools of yesterday cannot do it and those being developed today will also fall short, it might be good to get involved as you have in trying to scope and define the tools of the future.
With 'Big Data', it isn't the quantity of data, it is the way you deal with it. After all, Nate Silver's spectacular predictions of the result of the US election were done on a spreadsheet. The first 'big data' applications I came across were in analyzing the test data for automobiles, in the days of Sybase and DECs. The trick is that, once you've extracted the 'juice' from the raw data, you archive it if you can/need, or else throw it away. You usually don't let it anywhere near the database doing the analysis. Think hierarchically. Nowdays we have Streaminsight and Hadoop to do the low-level drudgery for us. Sure it is easier, but these techniques were developed in the eighties when engineering industries were awash with test data and had to develop ways of dealing with it.
Best wishes,
Phil Factor