I use #BigData as a short-hand for the challenge - the problem space - in which data analysts try to provide useful #BusinessIntelligence from any volume of raw data, which delivers knowledge to decision makers on time, i.e.whilst it still counts (velocity), automatically combining any variety of data into a single version of the truth and creating a trusted source of actionable facts by cleaning up inconsistent data (veracity). In many ways this is a traditional #DataWarehouse challenge. The moniker 'Big Data', whilst it - in a technical sense - can be defined as data for which relational databases and patterns fail, seems the best phrase to connect around a shared problem space.

As to the efficacy and cost of non-relational database projects undertaken in-house, my reading of the case studies is that most IT projects disconnected from the outcomes that their users require fail.
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