Democratisation of Data Science: everyone needs to play their part

In the previous articles I looked at how data is being considered as currency today, and that to start to realise value from data it first needs to be sorted. I discussed how challenging this was not only because of the large quantities of data but because of something called'data bias' which can skew our perception of the results. We are creating more data than ever before in our history, with most ending up as'data exhaust' (a waste by-product that is not used or stored). Of the data we do store most is archived, never used and its value never realised. Alongside this we have a scarcity and lack of diversity in the people that sort our data and extract the business value and insights we need.