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 data science conundrum


The AI, machine learning, and data science conundrum: Who will manage the algorithms? ZDNet

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Artificial intelligence and machine learning are being adopted into the enterprise at a rapid clip and adoption is likely to surge in 2019. What comes next is the real business challenge: How will we manage technology that we likely don't understand? The issue is likely to bubble up in the year ahead. For now, most of us are lulled into thinking more algorithms are better and even assuming we can outsource critical thought to models. Why hurt our brains when we can trust Einstein, Watson, Alexa, Google Assistant, and other software tools to think for us?


Solving the data science conundrum

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South Africa is facing a shortage of data scientists โ€“ a new breed of analytical data experts with the technical skills to solve complex problems. They're part mathematician, part computer scientist and part trend-spotter. And, because they straddle both the business and IT worlds, they're highly sought-after and well paid. The demand for data scientists is being driven by the emergence of big data โ€“ that unwieldy mass of unstructured information that can no longer be ignored and forgotten. It's a potential gold mine for companies โ€“ as long as there's someone who can dig in and unearth the business insights that no one thought to look for before.