However, the mere aggregation of masses of data is not enough. Big data does not identify opportunities by itself and harvesting data does not spontaneously create value. There is a risk that analytical results are assumed to be useful and correct simply because the analysis incorporates big data. This has been described as “automated arrogance” and is a pitfall that must be avoided amongst the ongoing hype - the value of a data warehouse is not the amount of data it holds but the new insights it delivers.
A solid understanding of the core concepts of statistics is fundamental to any big data analysis. The value of big data is optimised when it is combined with more proven forms of information collection and innovative statistical methods. This not only brings relevant focus to the analysis but militates against misleading results and unproductive decisions, such as confusing correlation with causation. The critical eyes of a statistician see past the data traps to the valuable information hidden amongst big data.