Big data is a big topic in technology and business. With the rise of information volumes, together with storage and processing power, it has been suggested that time and information are taking over from land and labour as primary factors of production alongside capital (http://www.forbes.com/sites/ciocentral/2011/10/31/the-new-factors-of-production-and-the-rise-of-data-driven-applications/).
A more recent article in the Harvard Business Review clearly explains the key concepts, describes the challenges and provides some good examples, recommending specific steps: experimentation, measurement, sharing and replication. It reminds us of the importance of being able to say “what do we know?” as opposed to “what do we think?” (https://sites.google.com/site/nextprojectback/files/Big%20Data_%20The%20Management%20Revolution%20-%20Harvard%20Business%20Review.pdf?attredirects=0&d=1).
It is interesting how the earlier article defines big data as the four Vs: Volume, Velocity, Variety, Veracity, and the later as the three Vs: here Veracity is gone, perhaps because discipline in certifying data quality must be implied? Can it?
With data often comes error… does this mean the risk of big data, big error? This NY Times article provides some useful insights: http://www.nytimes.com/2012/12/30/technology/big-data-is-great-but-dont-forget-intuition.html?smid=li-share&_r=2&.
If you are looking for bold opinions around the topic of risk, and some sobering views on big data, this is a good starting point: http://www.wired.com/opinion/2013/02/big-data-means-big-errors-people/.
There is no doubting the power of big data today, but finding the value is less than straightforward.