Everyone knows that the quality of any insights is dependent on the quality of the data. But here has never been completely accurate data and harvey Lewis from Deloitte makes the important point that you have to include messy data.
"Imperfections and errors must be an accepted part of the analysis" but these will be balanced by the fact you have more complete data spanning more of the universe impacting your business.
Raw data, unstructured data,, voice data... the range expands
All conclusions and predictions must be made with a stated confidence level and the lower the confidence level the wider the range of values. The latter gives you a better insight of the range of outcomes you may face and therefore should anticipate with potential problem analysis.
Ever was, ever is and ever will be.
Big data is no longer a novelty in corporate circles. It is a regular agenda item at boardroom meetings across the business landscape, and c-level executives are increasingly seeing the value of being at the helm of an insight-driven organisation, using data analytics and information to understand the market in which they operate. In short, big data is the new normal for organisations that want to understand their customers. However, there is a misconception among businesses that data analytics can be achieved only through precise, accurate data collection. On the contrary, being exact is not necessarily essential to achieving the desired results.
http://www.computerweekly.com/opinion/Why-it-pays-to-get-messy-with-big-data
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