Making machine learning fit for business
Machine Learning

Making machine learning fit for business

According to an O'Reilly survey of 11,400 data science experts, nearly half (49%) of organisations are at the early stages of adopting machine learning. Only 15% consider themselves sophisticated users of the statistical techniques expected to make such a big impact on business.

But even those with considerable experience in machine learning are still developing their understanding of how to apply it to real-world problems at a scale that benefits the business.

Lack of trust is a huge barrier to introducing machine learning in any business, according to Jagdish Mitra, chief strategy officer Tech Mahindra, a technology and business process outsourcing company.

He told a London conference hosted by machine learning software provider H2O.ai that the time it took to get return on investment from machine learning projects - on average more than one year - created scepticism among those expected to fund projects. As such, machine learning was more often applied to the problem of making existing processes more efficient, rather than transforming processes or helping the company find growth.

Walter Kok, director at ING, described how he had helped lead the introduction of machine learning to tackle the problem of money laundering at the global insurance firm. He said the key to gaining momentum was to be able to demonstrate benefits quickly, and directly relate that to customer experience or business performance.

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Lindsay Clark

Lindsay Clark is a freelance journalist specialising in business IT, supply chain management, procurement and business transformation. He has worked as news editor at Computer Weekly and several other leading trade magazines. He has also written for The Guardian, The Financial Times and supplements to The Times. 

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