AI & DATA ANALYTICS

Deep learning is about to give us access to more data than we ’ ve ever had , and possibly more than we can ever use . How can businesses manage this level of data wealth ?

WRITTEN BY : PADDY SMITH

But does it hold true in a new world of data mining and data lakes . It used to be the case that only structured data – and ‘ good ’ structured data , come to that – was of any practical use to industry . We now find ourselves at the edge of an abyss of unstructured data – emails , VoIP calls , video chat , instant messaging and documents , even handwritten ones . The emergence and further implementation of deep learning techniques to mine ever more data is pushing the curve towards data saturation . Surely we can ’ t need it all ?

Data troves According to a report from Mckinsey : “ Organisations now have troves of raw data combined with powerful and sophisticated analytics tools to gain insights that can improve operational performance and create new market opportunities . Most profoundly , their decisions no longer have to be made in the dark or based on gut instinct ; they can be based on evidence , experiments , and more accurate forecasts .”

But that isn ’ t the global view , with many concerned that core business strategies can be derailed by a glut of data that points one way , at the expense of traditional business assets such as understanding of customers and local markets , especially once unstructured data mined by deep learning processes is folded in alongside more traditional datasets .

MCKINSEY REPORT technologymagazine . com 125