Rose Barnett says, “The boom in the B2B big data market (from a sub-$100m industry in 2009 to $130bn today) mirrors an enterprise-led scramble to invest in data mining, reminiscent of the California gold rush, accompanied by a similar media buzz.

Although much is still written about the near-magical potential of data analytics for business, this fervour is now giving way to a more serious debate on where the real business value can actually be found. It’s clear that data prospectors are diverging into two camps: the ‘haves’ and the ‘have-not-yets’.

A recent KPMG survey showed only 40% of executives have a high level of trust in the consumer insights from their analytics, and most said their C-suite didn’t fully support their current data analytics strategy. 58% of businesses told Teradata the impact of big data analytics on revenues was “3% or smaller”. The real bonanza appears confined to banking, supply chains, and technical performance optimisation – understandably some businesses feel left behind.

Guidance on using data analytics is aimed at companies with a massive pre-existing data hoard who wish to extract value from it – the equivalents of the gold rush’s “49ers” who arrived in California early in 1849 to stake a claim on a good piece of prospecting land”.

Big data: the golden prospect of machine learning on business analytics


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