Machine learning has become a buzzword in business technology but the implications of applying it are often overlooked.
"The major problem is that data science is science itself, and businesses aren't very well accustomed to using scientific methods of decision making," says Jane Zavalishina, CEO of machine learning and data analytics specialists Yandex Data Factory.
The company emerged as a spin-out from multinational technology corporation Yandex, the operator of the largest search engine in Russia. In December 2014, Yandex extended the capacity in data science it developed to support this core product into providing machine learning-based services for industry applications by launching the Yandex Data Factory.
The company emerged as a spin-out from multinational Yandex - the operator of the largest search engine in Russia - in late 2014. It provides machine learning and data science services to create predictive models for things such as targeted advertising campaigns and determining stock orders for shops.
The Yandex Data Factory team establishes its findings through a process of experimentation, and its success can only be judged once the experiment concludes.
"When you delegate some work to your employee, ideally you expect more or less a complete level of results," Zavalishina explains. "But it works differently with data scientists, because with data science you cannot expect guaranteed results."
Failure will be a legitimate outcome of any data science project and this is a prospect business managers must accept.
What makes a data scientist tick?
Working with data scientists requires an alternative approach to business in which logic overrules creativity and reality trumps belief. In other words, it depends on fact and logic rather than imagining what could be possible.
It'll be a struggle, then, to task data scientists with questions that they fundamentally consider meaningless.
"It sounds like division by zero, it doesn't make sense," says Zavalishina. "The problem is you can't make them do this; you cannot motivate people to divide by zero. They start thinking you're probably an idiot, which doesn't make your work with them better."
They need to understand the project and believe that it makes sense. If they are approached to use machine learning to improve systems, for example, they will need enough data to measure meaningful results.
"A lot of decisions in business are made by intuition, that's why there is no need to measure everything in regular business," says Yandex Data Factory COO Alexander Khaytin. "But then when it comes to a data science project or to communication with data scientists you can't just tell them, 'do this stuff, I feel it's going to be good.' It doesn't work."
Asking the right questions
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